Groundwater type water source monitoring method and device based on risk evolution and terminal

Abstract

The application is suitable for the technical field of data processing, and provides a groundwater source monitoring method and device based on risk evolution and a terminal. The method comprises the following steps: determining risk evolution information of a groundwater source in a target region according to historical monitoring data of the groundwater source; dividing the target region into multiple grid units, and determining a monitoring priority of each grid unit according to a spatial relationship between each grid unit and a risk factor; calculating a risk evolution index of each grid unit in the target region according to the risk evolution information; determining a monitoring well layout scheme according to a change direction of the risk factor, the risk evolution index and the monitoring priority of each grid unit; and monitoring the groundwater source based on the monitoring well layout scheme. The application can arrange the monitoring well in the direction of the change front of the risk factor, so that the monitoring well actively tracks the change front of the risk factor, and the monitoring result is more accurate.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, device, and terminal for monitoring groundwater-type water sources based on risk evolution. Background Technology

[0002] Groundwater sources are a major source of water for socio-economic development and daily life, making groundwater environmental safety assessments of these sources of significant practical importance. The data required for environmental risk monitoring and assessment of groundwater sources largely depends on monitoring wells, which are typically installed based on hydrogeological conditions, administrative boundaries, and the extent of the source protection zone to collect relevant data from the target area.

[0003] The inventors of this application discovered in their research on risk monitoring of groundwater-type water sources that once a monitoring well network is built, it remains fixed for a long period, making it difficult to capture the dynamic changes in groundwater risk factors. This may result in insufficient monitoring in high-risk areas and redundant monitoring in low-risk areas, making it difficult for monitoring data to accurately reflect the actual risk level of the water source. In addition, during decades of exploitation, water sources undergo an evolutionary trajectory from natural state to mild disturbance, strong impact, and then to regulation and recovery. Existing monitoring methods mainly rely on recent change rates to judge the degree of exploitation and on comparing current data with previous data to judge the trend of water quality changes, which can easily lead to assessment results that fail to reflect the actual state of the water source. Summary of the Invention

[0004] In view of this, the embodiments of this application provide a groundwater source monitoring method, device and terminal based on risk evolution, which can realize the dynamic optimization of the layout of monitoring well network, concentrate monitoring resources in the core risk area, enable monitoring wells to actively track the forefront of risk factor changes, and make the monitoring results more accurate.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, embodiments of this application provide a groundwater-type water source monitoring method based on risk evolution, comprising: determining risk evolution information of the groundwater-type water source based on historical monitoring data of the groundwater-type water source in the target area, wherein the historical monitoring data includes water quality monitoring data, water level monitoring data, hydrogeological data, and data on external potential pollution sources, and the risk evolution information includes the spatial distribution and direction of change of risk elements, wherein the risk elements include pollution plumes, groundwater drawdown cones, vulnerable zones, and external potential pollution sources, and the direction of change of risk elements includes the migration direction of pollution plumes, the expansion direction of groundwater drawdown cones, and the extension direction of vulnerable zones. The monitoring process involves: directing the monitoring wells towards potential external pollution sources; dividing the target area into multiple grid units; determining the monitoring priority of each grid unit based on its spatial relationship with the risk factors; calculating the risk evolution index of each grid unit in the target area based on risk evolution information, including the intensity of pollution plume migration, the intensity of fallout funnel expansion, the vulnerability intensity of weak areas, and the threat intensity of potential external pollution sources; determining the well deployment scheme based on the changing direction of risk factors, the risk evolution index, and the monitoring priority of each grid unit; and monitoring the groundwater source area based on the well deployment scheme.

[0007] Based on the first aspect, in some embodiments, determining the monitoring priority of each grid cell according to the spatial relationship between each grid cell and the risk element includes: determining the plume impact of each grid cell according to a first spatial relationship between the grid cell and the plume, the first spatial relationship including the grid cell's location within the plume's interior, external migration path, and external lateral diffusion zone; determining the groundwater drawdown impact of each grid cell according to a second spatial relationship between the grid cell and the groundwater drawdown cone, the second spatial relationship including the grid cell's location within the central region, edge region, and outer region of the groundwater drawdown cone; and determining the groundwater drawdown cone impact of each grid cell according to the grid cell's location within the groundwater drawdown cone's central region, edge region, and outer region. The type of weak zone in a grid cell determines the impact of the weak zone in each grid cell. The types of weak zones include the grid cell being located in a thinning aquifer, a window in the impermeable layer, or a fault zone. The impact of the migration channel of external potential pollution sources on each grid cell is determined based on the third spatial relationship between the grid cell and the migration channel of external potential pollution sources. The third spatial relationship includes the grid cell being located upstream, along, or downstream of the migration channel of external potential pollution sources. The monitoring priority of each grid cell is determined based on the impact of the pollution plume, the impact of the groundwater drawdown cone, the impact of the weak zone, and the impact of the migration channel of external potential pollution sources.

[0008] Based on the first aspect, in some embodiments, the water quality monitoring data includes the concentration of at least one characteristic pollutant among nitrates, chlorides, and heavy metals. The process of determining the migration direction of the pollution plume includes: determining the concentration of the characteristic pollutant from the water quality monitoring data, wherein each concentration value of the characteristic pollutant corresponds to a location coordinate and a sampling time; generating a continuous spatial concentration field for the concentration of the characteristic pollutant using the Kriging interpolation method; determining the pollution plume of the target area water source based on the areas in the spatial concentration field where the concentration is greater than the groundwater quality standard threshold; and weighting the coordinates of the grid cells using the concentration values ​​of the grid cells in the spatial concentration field. The process involves calculating the concentration-weighted centroid coordinates of the pollution plume; determining the migration direction of the pollution plume based on the trajectory of the concentration-weighted centroid changes over different times; and determining the expansion direction of the groundwater drawdown cone based on the water level monitoring data, which includes water level elevations. This process includes extracting the water level elevations of the monitoring wells for the target year from the water level monitoring data, generating groundwater isolevel lines for the target area based on these elevations, determining the lowest water level region defined by the closed isolevel lines based on the groundwater isolevel lines for the target area, thus obtaining the drawdown cone region. Finally, comparing the boundary positions of the drawdown cone regions in different years, the direction in which the drawdown cone region expands outward the fastest is determined as the expansion direction of the groundwater drawdown cone.

[0009] Based on the first aspect, in some embodiments, the hydrogeological data includes aquifer thickness and impermeable layer thickness. The process for determining the extension direction of the weak zone includes: determining the aquifer thickness variation and aquifer thickness variation rate of each grid cell based on the aquifer thickness of each grid cell; when the aquifer thickness variation indicates that the aquifer thickness of the grid cell is decreasing, and the aquifer thickness variation rate is greater than a threshold, and the degree of aquifer thickness variation of the grid cell is consistent with that of the surrounding adjacent grid cells, the grid cell is determined to be an aquifer thinning zone; for each grid cell, if the impermeable layer thickness of the grid cell is less than a critical threshold, the grid cell is designated as an impermeable layer window cell; traversing all grid cells, adjacent impermeable layer window cells are assigned the same label. Each label corresponds to an independent aquitard window. The geometric parameters of each aquitard window include area, boundary, and centroid coordinates. The spatial gradient of the aquitard thickness of each grid cell is calculated. Linear high-value zones are identified based on this spatial gradient. The lithology and water level of the boreholes located on or on both sides of the linear high-value zone are used to determine whether the rock strata corresponding to the linear high-value zone have fractured. If fractures have occurred, the grid cells corresponding to the linear high-value zone constitute a fracture zone. The areas corresponding to the aquifer thinning zone, the aquitard window, and the fracture zone are the weak zones. The covariance matrix of the boundary points of the weak zones is calculated based on the coordinates of the boundary points. The direction of the eigenvector corresponding to the largest eigenvalue of the covariance matrix is ​​determined as the geometric extension direction of the weak zones.

[0010] Based on the first aspect, in some embodiments, the step of calculating the risk evolution index of each grid cell in the target area according to the risk evolution information includes: the formula for calculating the risk evolution index of any grid cell i is as follows: ,in, The intensity of the pollution plume migration, To enhance the strength of the groundwater drawdown cone, The vulnerability intensity of the weak area, The intensity of the threat from potential external pollution sources; It is a time evolution factor, and The risk evolution index is used to generate a risk evolution index field and to determine the location of monitoring wells. The pollution plume migration intensity is determined based on the concentration of characteristic pollutants, the centroid coordinates of the pollution plume, and the angle between the migration direction of the pollution plume and the direction of the water source well. The groundwater drawdown funnel expansion intensity is determined based on the groundwater drawdown funnel expansion rate and the groundwater level drop rate. The vulnerability intensity of the weak zone is determined based on the aquifer thickness, the impermeable layer thickness, and the permeability coefficient of the fault zone. The external potential pollution source threat intensity is determined based on the toxicity of the pollution source, the probability of pollution source release, and the migration distance of the external potential pollution source along the groundwater streamline to the water source well. The time evolution factor is determined based on the risk evolution index of the current assessment period and the risk evolution index of the previous assessment period.

[0011] Based on the first aspect, in some embodiments, the intensity of the contamination plume migration The calculation formula is , The pollutant plume exceedance index. For the migration speed of the pollution plume, For the risk coefficient of migration direction, , , , The concentration of characteristic pollutants, The groundwater quality standard limit, The coordinates of the centroid of the pollution plume for the current assessment period. The coordinates of the centroid of the pollution plume from the previous assessment period. For time intervals, The angle between the direction of the pollution plume migration and the direction of the water source well; the intensity of the groundwater drawdown funnel expansion. The calculation formula is , The rate of expansion of the groundwater drawdown funnel. The rate of decline of the groundwater level. , , The area of ​​the groundwater drawdown cone in the current assessment period. This represents the area of ​​the groundwater drawdown cone from the previous assessment period. This refers to the water level elevation of the monitoring well during the current assessment cycle. The water level elevation of the monitoring well in the previous assessment cycle; vulnerability intensity of the weak area. The calculation formula is B i This is the aquifer thinning index. The skylight index of the waterproof layer, The fault zone influence index, , , , , This represents the initial aquifer thickness. This represents the decrease in aquifer thickness. Let i be the thickness of the waterproof layer. The maximum thickness of the waterproof layer in the target area. The permeability coefficient of the fracture zone is... The background permeability coefficient of the target area unaffected by fault zones; the threat intensity of potential external pollution sources. The calculation formula is E i The toxicity index of pollutants, , A represents the normal emission intensity of the external potential pollution source, B represents the probability of an accident occurring at the external potential pollution source, and C represents the historical leakage record of the external potential pollution source. The migration distance of potential external pollution sources along the groundwater flow line to the water well; time evolution factor. The calculation formula is , , , .

[0012] Based on the first aspect, in some embodiments, determining the monitoring well deployment scheme according to the changing direction of risk factors, the risk evolution index, and the monitoring priority of each grid unit includes: generating a risk evolution index field based on the risk evolution index of each grid unit, and calculating the spatial gradient field of the risk evolution index field; for grid units with high monitoring priority, where multiple risk factors are superimposed, deploying monitoring wells at the locations where multiple risk factors are superimposed in the grid unit; and if the gradient modulus of the grid unit is greater than a preset gradient threshold, deploying monitoring wells between the grid unit and the water source well along the gradient descent direction, and if the gradient modulus of the grid unit is less than or equal to the preset gradient threshold, deploying monitoring wells along the changing direction of the risk factors; for grid units with medium monitoring priority, where one risk factor exists in the grid unit, deploying a monitoring well at the center of the risk factor in the grid unit; and for grid units with low monitoring priority, not deploying monitoring wells in the grid unit.

[0013] Based on the first aspect, in some embodiments, the monitoring of groundwater source areas based on the monitoring well deployment scheme includes: determining the well location coordinates and monitoring well type of each monitoring well according to the monitoring well deployment scheme; the monitoring well type includes water level monitoring wells for collecting water level data and water quality monitoring wells for collecting water quality data; setting monitoring parameters for each monitoring well, wherein the monitoring parameters for water level monitoring wells include water level depth or water level elevation, and the monitoring parameters for water quality monitoring wells include the concentration of at least one characteristic pollutant among nitrates, chlorides, and heavy metals; collecting monitoring data according to an initial sampling cycle and uploading the monitoring data to the terminal according to the data upload frequency; wherein, the initial sampling cycle of monitoring wells in grid units with high monitoring priority is shorter than the initial sampling cycle of monitoring wells in grid units with low monitoring priority, and the data upload frequency of monitoring wells in grid units with high monitoring priority is higher than the data upload frequency of monitoring wells in grid units with low monitoring priority; according to the above... The monitoring data transmitted to the terminal is used to assess the risk level of each grid unit in real time: For any grid unit, if the concentration of the characteristic pollutant in the current sampling period is greater than that in the previous sampling period, and the rate of water level decline in the current sampling period is greater than that in the previous sampling period, or if the concentration of the characteristic pollutant continues to increase in two consecutive sampling periods, or if the rate of water level decline continues to increase in two consecutive sampling periods, then the risk level of the grid unit increases; if the concentration of the characteristic pollutant in the current sampling period is less than that in the previous sampling period, and the rate of water level decline in the current sampling period is less than that in the previous sampling period, then the risk level of the grid unit decreases; if the risk level of a grid unit increases, then the sampling frequency and data upload frequency of the monitoring wells in that grid unit are increased; if the risk level of a grid unit decreases and remains stable for more than two sampling periods, then the sampling frequency and data upload frequency of the monitoring wells in that grid unit are decreased.

[0014] Secondly, embodiments of this application provide a groundwater source monitoring device based on risk evolution, comprising: a risk evolution determination module, used to determine the risk evolution information of a groundwater source based on historical monitoring data of a target area. The historical monitoring data includes water quality monitoring data, water level monitoring data, hydrogeological data, and data on potential external pollution sources. The risk evolution information includes the spatial distribution and direction of change of risk elements. Risk elements include pollution plumes, groundwater drawdown cones, vulnerable zones, and potential external pollution sources. The direction of change of risk elements includes the migration direction of the pollution plume, the expansion direction of the groundwater drawdown cone, the extension direction of the vulnerable zone, and the direction of change of potential external pollution sources. The system includes: a priority determination module for dividing the target area into multiple grid units and determining the monitoring priority of each grid unit based on the spatial relationship between each grid unit and the risk factors; a risk evolution index calculation module for calculating the risk evolution index of each grid unit in the target area based on risk evolution information, wherein the risk evolution index includes the intensity of pollution plume migration, the intensity of fallout funnel expansion, the vulnerability intensity of weak areas, and the threat intensity of external potential pollution sources; and a monitoring module for determining the well deployment scheme based on the changing direction of risk factors, the risk evolution index, and the monitoring priority of each grid unit, and monitoring the groundwater source area based on the well deployment scheme.

[0015] Thirdly, embodiments of this application provide a terminal, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the risk evolution-based groundwater source monitoring method as described in any of the first aspects above.

[0016] The beneficial effects of the embodiments of this application compared with the prior art include:

[0017] The aforementioned risk evolution-based groundwater source monitoring method determines the risk evolution information of groundwater sources in the target area based on historical monitoring data. It then determines the monitoring priority of each grid unit based on the spatial relationship between each grid unit and the risk factors. Based on the risk evolution information, it calculates the risk evolution index of each grid unit in the target area. Finally, it determines the well deployment scheme based on the direction of risk factor change, the risk evolution index, and the monitoring priority of each grid unit. Monitoring of the groundwater source is then conducted based on this well deployment scheme. This method ensures that wells are precisely positioned in areas with high monitoring priority. Furthermore, it determines the direction of the risk factor change frontier based on the risk evolution index and the direction of risk factor change, proactively deploying wells in the direction of the risk factor change frontier with high monitoring priority. This allows monitoring resources to be precisely allocated to the highest-risk areas, avoiding the problem of redundancy in low-risk areas and insufficient coverage in high-risk areas by fixed monitoring networks, thereby improving the accuracy and rationality of monitoring results.

[0018] In addition, this application divides the grid into three monitoring priorities—high, medium, and low—based on the comprehensive score of the grid units. High-priority grids are equipped with multiple monitoring wells, medium-priority grids are equipped with a small number of monitoring wells, and low-priority grids are equipped with no monitoring wells. This differentiated configuration of monitoring wells enables monitoring resources to be precisely deployed to areas with concentrated risks, thereby improving the monitoring coverage of high-risk areas.

[0019] Furthermore, when monitoring data reflects an increase in the risk level of a grid cell, the sampling frequency and data upload frequency are automatically increased to more intensively track risk changes; when the risk level decreases and remains stable, the sampling frequency and data upload frequency are reduced. This dynamic adjustment increases monitoring density to obtain sufficient data to support emergency decision-making when the risk increases, and releases monitoring resources when the risk decreases, achieving a dynamic match between monitoring intensity and risk status. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the risk evolution-based groundwater source monitoring method provided in this application embodiment;

[0022] Figure 2 This is a structural block diagram of the groundwater source monitoring device based on risk evolution provided in the embodiments of this application;

[0023] Figure 3This is a structural block diagram of the terminal provided in the embodiments of this application. Detailed Implementation

[0024] The present application will be described more clearly below with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the function of the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0026] See Figure 1 The risk evolution-based groundwater source monitoring method provided in this application embodiment may include the following steps:

[0027] Step 101: Determine the risk evolution information of groundwater source areas based on historical monitoring data of groundwater source areas in the target area.

[0028] Historical monitoring data may include water quality monitoring data, water level monitoring data, hydrogeological data, and data on potential external pollution sources. Risk evolution information may include the spatial distribution and direction of change of risk factors. Risk factors include pollution plumes, groundwater drawdown cones, vulnerable areas, and potential external pollution sources. The direction of change of risk factors includes the migration direction of pollution plumes, the expansion direction of groundwater drawdown cones, the extension direction of vulnerable areas, and the direction of potential external pollution sources pointing towards water wells.

[0029] The aforementioned water quality monitoring data, water level monitoring data, hydrogeological data, and data on potential external pollution sources can be obtained through preliminary investigations, historical data, and measurements from existing monitoring wells, and stored in the water source database. Water quality monitoring data may include the concentration of at least one characteristic pollutant among nitrates, chlorides, and heavy metals; water level monitoring data may include water level elevation; and hydrogeological data may include aquifer thickness, aquitard thickness, lithology on both sides of the borehole, and water level.

[0030] Water quality monitoring data can be obtained by analyzing historical water quality data from existing monitoring wells in and around the water source. These existing monitoring wells can be those installed during the exploration phase of the target area. For example, water samples can be collected from existing monitoring wells according to relevant standards, and the samples can be sent to a laboratory for analysis to determine the concentrations of characteristic pollutants such as nitrates, chlorides, and heavy metals, thus obtaining water quality monitoring data.

[0031] Water level monitoring data can be obtained through long-term observation records of existing monitoring wells. For example, water level gauges can be installed in existing monitoring wells or manual observations can be conducted to record the groundwater depth monthly, and the water level elevation can be obtained by subtracting the water level depth from the wellhead elevation. Water level monitoring data typically accumulates over a long period and can reflect the long-term trend of water level changes in water sources.

[0032] Hydrogeological data can be obtained through borehole exploration. For example, during the initial water source exploration phase, numerous exploratory boreholes are drilled. During drilling, the depths of the top and bottom plates of the aquifer and the impermeable layer, as well as geological information such as lithology and water levels on both sides of the borehole, can be recorded. The thickness of the aquifer and the impermeable layer can then be calculated based on the top and bottom plate depths. The water levels on both sides of the borehole can be obtained by querying historical water level monitoring records from the monitoring wells on both sides of the borehole.

[0033] Data on potential external pollution sources can be obtained through on-site investigations and data collection. For example, by conducting a pollution source census, information such as the location, types of pollutants, storage facilities, and anti-seepage measures of potential pollution sources such as industrial enterprises, gas stations, landfills, and large-scale farms in the vicinity and upstream areas of water sources can be recorded. At the same time, combined with data such as discharge permits and environmental impact assessment reports, an inventory of potential external pollution sources can be established.

[0034] The following explains the risk factors and the direction of their changes.

[0035] I. Pollution plume and its migration direction

[0036] A pollution plume is a continuous pollution zone formed by pollutants in groundwater. The centroid migration direction of the plume is the direction in which the centroid of the plume concentration moves over time, reflecting the overall movement trend of the plume. The centroid coordinates of plumes in different years can be calculated, and connecting the centroid points yields the migration trajectory; the direction of this line is the centroid migration direction. The plume centroid is the weighted average position of the concentrations of all pollutants within the plume's range, reflecting the overall distribution center of gravity of the plume. The plume centroid coordinates are calculated by weighting the concentration values ​​and coordinates of each grid cell. High-concentration areas have a greater impact on the centroid, while low-concentration areas have a smaller impact.

[0037] In some embodiments, the water quality monitoring data includes the concentration of at least one characteristic pollutant among nitrates, chlorides, and heavy metals. The process of determining the migration direction of the pollution plume may include: determining the concentration of the characteristic pollutant from the water quality monitoring data, wherein each concentration value of the characteristic pollutant corresponds to a location coordinate and a sampling time; generating a continuous spatial concentration field for the concentration of the characteristic pollutant using the Kriging interpolation method; determining the pollution plume of the target area water source based on the areas in the spatial concentration field where the concentration is greater than the groundwater quality standard threshold; calculating the concentration-weighted centroid coordinates of the pollution plume by weighting the coordinates of the grid cells using the concentration values ​​of the grid cells in the spatial concentration field; and determining the migration direction of the pollution plume based on the changing trajectory of the concentration-weighted centroid of the pollution plume at different times.

[0038] Specifically, the migration direction of the pollution plume is determined based on the movement trajectory of the centroid of the pollution plume in different years. By observing the spatial distribution changes of pollutant concentration, the overall movement trend of the pollution body can be identified.

[0039] First, concentration data of characteristic pollutants (such as nitrates) are extracted from water quality monitoring data. Each concentration value corresponds to the location coordinates of a monitoring well and the sampling time, forming a discrete spatiotemporal dataset. These data points are distributed within the target area, and the concentration values ​​change over time.

[0040] Secondly, a continuous spatial concentration field is generated using the Kriging interpolation method. Kriging interpolation is a geostatistical method based on spatial autocorrelation. By calculating the variogram between monitoring point pairs, it determines the spatial correlation range and variation structure of pollutant concentrations. The target area is divided into a regular grid. For each grid node, monitoring points within a certain range (usually 1.5 times the range) are searched. Based on the concentration values ​​of these monitoring points and their spatial relationships, the optimal weights for each monitoring point are solved using the Kriging equations, and the weighted sum is used to obtain the concentration estimate for that node. After interpolation of all grid cells is completed, a continuous spatial concentration field is generated.

[0041] Next, groundwater quality standard thresholds are set (e.g., a Class III standard limit of 20 mg / L for nitrates). Continuous regions with concentrations exceeding these thresholds are identified as pollution plumes in the concentration field. The spatial extent of the pollution plume reflects the boundary of pollutant diffusion at the current moment. For each pollution plume region, the planar coordinates of the nodes are weighted using the concentration value of each grid cell to calculate the concentration-weighted centroid coordinates of the pollution plume. Because high-concentration regions have a larger weight, the centroid position shifts towards the higher concentration portion of the pollution plume, reflecting the core area of ​​the pollution.

[0042] Finally, the above process is repeated for multiple years to obtain the centroid coordinates of the pollution plume for each year. Connecting the centroids in chronological order yields the migration trajectory of the pollution plume. The direction of this trajectory is the migration direction of the pollution plume, and the migration rate is obtained by dividing the distance between the centroids of adjacent years by the time interval. The migration direction reflects the overall trend of the pollution body's movement, providing a directional basis for monitoring well placement.

[0043] II. Groundwater drawdown cone and direction of its expansion

[0044] A groundwater drawdown cone is a region of groundwater level decline caused by over-extraction of groundwater. The direction of drawdown cone expansion is the main direction in which the area of ​​the drawdown cone increases or its boundary shifts outward, reflecting the path of intensified over-extraction impact. By comparing the boundary positions of drawdown cones in different years, the direction of the fastest outward expansion of the drawdown cone can be identified as the direction of drawdown cone expansion.

[0045] In some embodiments, the water level monitoring data includes water level elevations, and the process of determining the expansion direction of the groundwater drawdown cone may include: extracting the water level elevations of the monitoring wells for the target year from the water level monitoring data, generating groundwater isostatic contours for the target area based on the water level elevations; determining the lowest water level area defined by the closed isostatic contours for the target area based on the groundwater isostatic contours, thus obtaining the drawdown cone area; comparing the boundary positions of the drawdown cone areas in different years, and determining the direction in which the drawdown cone area expands outward the fastest as the expansion direction of the groundwater drawdown cone.

[0046] For example, the wellhead elevation and water level depth of each monitoring well can be extracted from the water level monitoring data. The water level elevation of the monitoring well can be calculated by subtracting the water level depth from the wellhead elevation. The water level elevation of each monitoring well corresponds to a coordinate location and a sampling time. The groundwater contour map of the target area is generated by using the Kriging interpolation method on the water level elevation data of all monitoring wells in the target area.

[0047] Next, the lowest water level region defined by closed isostatic contour lines is identified on the groundwater contour map. A closed isostatic contour line is a circular water level curve with the same starting and ending point, usually formed in a localized low-lying area. Multiple closed isostatic contour lines may exist on the groundwater contour map. In this case, an inclusion relationship judgment algorithm can be used to determine the nesting relationship of multiple closed isostatic contour lines: the innermost closed isostatic contour line corresponds to the central region of the drawdown cone, where the water level is lowest; the outermost closed isostatic contour line corresponds to the boundary of the drawdown cone. The area enclosed by the outermost closed isostatic contour line is defined as the drawdown cone region.

[0048] Finally, the boundary polygon of the area enclosed by the outermost closed isostatic contour lines is extracted from the groundwater contour maps of each year. The average value of the funnel center position for each year is taken as a unified reference point to ensure consistency in directional calculations. The funnel center position is the coordinate of the lowest point of water level elevation within the innermost closed isostatic contour line. Using this reference point as the pole, the 360° azimuth is divided into equal-angle intervals (e.g., each interval corresponds to 10° or 15°). For the funnel boundary of each year, rays are emitted from the reference point along each azimuth direction, and the distance from the intersection of the ray and the funnel boundary to the reference point's boundary is measured. This distance reflects the extension range of the funnel in that direction. For each azimuth direction, the average annual expansion rate in that direction is obtained by subtracting the boundary distance of the previous year from the boundary distance of the following year and dividing by the time interval. Finally, the average annual expansion rate in all azimuth directions is compared, and the azimuth direction with the largest value is the direction in which the funnel expands outward the fastest, thus determining it as the expansion direction of the groundwater drawdown funnel.

[0049] III. Weak Areas and Directions of Their Extension

[0050] The weak zone is the area in the aquifer system with weak protective capabilities, including aquifer thinning zones, impermeable windows, and fault zones. The extension direction of the weak zone is its geometric extension direction, which reflects the spatial distribution characteristics of the protective defects. The covariance matrix of the boundary points of the weak zone can be calculated based on their coordinates, and the direction of the eigenvector corresponding to the largest eigenvalue of the covariance matrix can be determined as the geometric extension direction of the weak zone.

[0051] In this embodiment, the hydrogeological data includes aquifer thickness and impermeable layer thickness. The process of determining the extension direction of the weak zone may include: determining the aquifer thickness variation and aquifer thickness variation rate of each grid cell based on the aquifer thickness of each grid cell within the target area; determining the grid cell as an aquifer thinning zone when the aquifer thickness variation indicates that the aquifer thickness of the grid cell is decreasing, the aquifer thickness variation rate is greater than the thickness variation rate threshold, and the degree of aquifer thickness variation of the grid cell is consistent with that of its surrounding adjacent grid cells; for each grid cell, if the impermeable layer thickness of the grid cell is less than the impermeable layer thickness threshold, then the grid cell is an impermeable layer window cell; traversing all grid cells, connecting adjacent impermeable layers... Each aquifer window cell is assigned the same label, and each label corresponds to an independent aquifer window. The geometric parameters of each aquifer window include area, boundary, and centroid coordinates. The spatial gradient of the aquifer thickness of each grid cell is calculated, and linear high-value zones are identified based on this spatial gradient. The lithology and water level of the boreholes located on or on both sides of the linear high-value zone are used to determine whether the rock strata corresponding to the linear high-value zone have fractured. If fractures have occurred, the grid cells corresponding to the linear high-value zone constitute a fracture zone. Among them, the areas corresponding to the aquifer thinning zone, aquifer windows, and fracture zones are weak zones. The covariance matrix of the boundary points is calculated based on the coordinates of the boundary points of the weak zones, and the direction of the eigenvector corresponding to the largest eigenvalue of the covariance matrix is ​​determined as the extension direction of the weak zone.

[0052] 1. Aquifer thinning zone

[0053] First, aquifer thickness distribution fields for multiple target years are established, with each grid cell containing a corresponding aquifer thickness value. For each grid cell, the difference in aquifer thickness values ​​between adjacent periods is calculated to obtain the aquifer thickness change. A negative change indicates a decrease in aquifer thickness, while a positive change indicates an increase. Dividing the aquifer thickness change by the time interval yields the aquifer thickness change rate.

[0054] Next, it is determined whether the aquifer thickness variation of this grid cell is consistent with that of its surrounding adjacent cells. For each grid cell i, the local Moran index of grid cell i is calculated. ΔTh i Let ΔTh be the change in aquifer thickness in grid cell i. j Let μ be the aquifer thickness variation of the surrounding grid cell j of grid cell i, μ be the mean aquifer thinning of all grid cells, σ² be the variance of aquifer thinning of all grid cells, and w be the mean aquifer thinning of all grid cells. ijLet be the spatial neighborhood weight between the i-th and j-th grid cells, and n be the total number of grid cells in the target region. The spatial neighborhood weight matrix is ​​constructed using the Queen's neighborhood principle: when two grid cells share a common boundary or a common vertex, they are considered adjacent grids, and their spatial weight is assigned a value of 1; when two grid cells do not share a common boundary or a common vertex, their spatial weight is assigned a value of 0. To avoid the influence of different neighborhood numbers on local spatial statistical results, the spatial weight matrix can be row-normalized so that the sum of the neighborhood weights corresponding to each grid cell equals 1, thereby improving the comparability of risk clustering analysis results between different regions. i >0 and ΔTh i When <0, the degree of aquifer thinning in grid cell i is consistent with the degree of aquifer thinning in the surrounding grid cells.

[0055] Finally, determine whether the grid cell represents a thinning aquifer region: if the aquifer thickness decreases in the grid cell, and the rate of change of aquifer thickness is greater than 0.1 m / year (the rate of change threshold), and I i If the value is greater than 0, the grid cell is marked as an aquifer thinning zone cell. Adjacent aquifer thinning zone cells are merged to obtain the complete aquifer thinning zone.

[0056] 2. Waterproof layer skylight

[0057] The thickness of the impermeable layer can be determined by the thickness of the cohesive soil layer between adjacent aquifers in the borehole data. Core samples are continuously taken during drilling to record the lithology, depth, and thickness of each layer. Cohesive soil layers (such as clay, silty clay, and sub-clay) between aquifers are identified. These cohesive soil layers have low permeability and can prevent hydraulic connection between upper and lower aquifers, thus forming an impermeable layer. For a single continuous cohesive soil layer, the thickness of that layer is determined as the impermeable layer thickness. If there are multiple cohesive soil layers between adjacent aquifers, the sum of the thicknesses of these multiple cohesive soil layers is determined as the impermeable layer thickness. Then, the impermeable layer thickness data from each borehole are kriging interpolated to generate an impermeable layer thickness distribution field for the target area, where each grid cell corresponds to one impermeable layer thickness.

[0058] For each grid cell, determine if the thickness of the impermeable layer is less than the critical threshold: if the thickness is less than the critical threshold, mark the grid cell as an impermeable window cell. Iterate through all grid cells, assigning the same label to adjacent impermeable window cells, with each label corresponding to an independent impermeable window, and determine the geometric parameters of each impermeable window: area, boundary, and centroid coordinates. The aforementioned critical threshold refers to the minimum thickness of the impermeable layer that can effectively block the hydraulic connection between the upper and lower aquifers. Specifically, for clay impermeable layers, the critical threshold can be 1-2 meters; for silty clay impermeable layers, the critical threshold can be 2-3 meters.

[0059] 3. Fault zone

[0060] The fracture zone can be determined based on the spatial gradient of the aquitard thickness. For each grid cell in the aquitard thickness distribution field, the spatial gradient of the aquitard thickness and the average spatial gradient of the target region are calculated. The modulus of the spatial gradient can reflect the degree of drastic change in the aquitard thickness.

[0061] Calculate the average spatial gradient modulus of all grid cells within the target area; traverse all grid cells and filter those with a spatial gradient modulus greater than a threshold (e.g., twice the average); perform connectivity analysis on the filtered grid cells; if adjacent grid cells are arranged linearly or in a continuous band in space, connect these grid cells to form a linear high-value band. Obtain existing borehole information located on or on both sides of the linear high-value band, compare whether the lithology of the same layer in the boreholes on or on both sides of the linear high-value band has changed (e.g., from clay to sand), and compare whether the water level difference between the two sides of the borehole exceeds the water level difference threshold; if the lithology has changed on both sides of the borehole and the water level difference exceeds the water level difference threshold, then the rock strata corresponding to the linear high-value band have fractured. Mark all grid cells covered by the linear high-value band where the rock strata have fractured as fractured grid cells, merge adjacent fractured grid cells through connected component marking to generate a fracture zone, and extract the geometric feature information such as the strike, length, and width of the fracture zone. The aforementioned water level difference threshold is a pre-set water level difference value used to determine whether there is an abnormal hydraulic connection on both sides of the borehole due to a fracture. This threshold is not a fixed value, but is dynamically determined based on the hydrogeological conditions of the target area: First, the average water level difference of the monitoring wells on both sides of the non-fracture zone in the target area is statistically analyzed as a background value; the measured water level difference on both sides of the fracture zone is compared with this background value. When the water level difference exceeds a specific multiple (e.g., 2 times) of the background value or exceeds a preset empirical value (e.g., 2 meters), it is judged as an abnormal water level. Combined with changes in borehole lithology, the existence of a fracture is determined.

[0062] The aquifer thinning zone, the impermeable window, and the fault zone are merged into a set of weak zones, each of which is an independent spatial object. For each weak zone, the planar coordinates of all points on its boundary are extracted, and the covariance matrix of these boundary points is calculated. This covariance matrix reflects the direction and degree of dispersion of the boundary points on the plane. The eigenvalues ​​and corresponding eigenvectors of this covariance matrix are calculated, and the direction of the eigenvector corresponding to the largest eigenvalue is the direction of the greatest dispersion of the boundary points, that is, the extension direction of this individual weak zone.

[0063] IV. External potential pollution sources and the direction in which they point towards the water source well

[0064] External potential pollution sources refer to potential pollution sources outside the water source area, which may include industrial enterprises, gas stations, landfills, etc. The direction from the external potential pollution source to the water source well is the direction from the exit of the migration path of the external potential pollution source towards the water source well. This direction is determined by the groundwater flow field and priority channels, and represents the possible migration path of pollutants. Monitoring wells can be deployed along this direction between the risk source and the water source well to form an early warning monitoring chain.

[0065] In this embodiment, the process of determining the migration channel of external potential pollution sources may include: extracting the spatial coordinates of each external potential pollution source from the external potential pollution source data, and selecting high-threat pollution sources as key analysis objects based on the comprehensive threat index; generating a groundwater flow field based on water level monitoring data, and tracing the streamlines along the groundwater flow direction from the pollution source location to simulate the potential migration path of pollutants; combining the priority channel information in the hydrogeological data, including the fault zone orientation, ancient river channel distribution, and abandoned well location, and superimposing the spatial distribution of priority channels with the streamline tracing results for analysis; if the risk source is located upstream of the fault zone or ancient river channel and the channel is continuous, then the channel is identified as the migration channel of the external potential pollution source.

[0066] In this embodiment, the determination of external potential pollution source migration channels is based on the comprehensive overlay analysis of pollution source investigation data, water level monitoring data, and hydrogeological data, aiming to identify the priority paths for pollutants to migrate from high-threat pollution sources to water wells.

[0067] The method for identifying high-threat pollution sources can be as follows: Extract spatial coordinates, pollutant inventory, release probability, and anti-seepage measures for each pollution source from external potential pollution source data; calculate the comprehensive threat index for each pollution source, which is a weighted sum of spatial proximity factor, inherent toxicity factor, and release probability factor. The three factors can be normalized separately before the weighted sum is calculated to obtain the comprehensive threat index. The weights of the three factors can be determined using the analytic hierarchy process (AHP). Experts compare the relative importance of the three factors pairwise to construct a judgment matrix, and the weight vector is obtained after consistency testing. The spatial proximity factor is calculated based on the three-dimensional distance between the pollution source and the water well; the closer the distance, the higher the factor value. The inherent toxicity factor is determined by a weighted average of the toxicity coefficients in the pollutant inventory. The release probability factor is obtained by fusing normal release rate, accident probability, and historical leakage records using a Bayesian network. Based on the comprehensive threat index, pollution sources are classified into high, medium, and low threat levels, and high-threat pollution sources are selected as key analysis targets.

[0068] Starting from the location of each high-threat pollution source, streamline tracing is performed along the groundwater flow direction to simulate the potential migration path of pollutants moving with the groundwater. Specifically, starting from the high-threat pollution source, the tracing proceeds point by point along the hydraulic gradient direction. The position at each step is determined by the previous position plus the product of the flow velocity vector and the time step, until the source well is reached or the target area boundary is exceeded. The streamline tracing result is a continuous curve starting from the high-threat pollution source and extending along the groundwater flow direction to the source well or boundary.

[0069] Subsequently, priority pathway information and streamline tracing results from hydrogeological data were overlaid for analysis. Priority pathways include fault zone orientation, paleochannel distribution, and abandoned well locations. These pathways have high permeability coefficients and can accelerate pollutant migration. The streamline tracing results were overlaid with the spatial distribution of priority pathways to determine whether streamlines extend along the priority pathways. If the streamline orientation is consistent with the orientation of the fault zone or paleochannel, then the priority pathway becomes an important candidate path for pollutant migration.

[0070] Finally, the effectiveness of the channel is comprehensively assessed. If a high-threat pollution source is located upstream of a fault zone or ancient riverbed (i.e., pollutants can flow from the pollution source to the water well along the channel), and the channel is spatially continuous and uninterrupted (i.e., the channel is not blocked or cut off by an impermeable layer), then the channel is identified as a migration channel for potential external pollution sources.

[0071] Step 102: Divide the target area into multiple grid units and determine the monitoring priority of each grid unit based on the spatial relationship between each grid unit and the risk elements.

[0072] In this step, when dividing the target area into grid cells, the area boundary is first determined based on the scope of the water source and the spatial distribution of risk factors, covering the water source and extending to areas potentially affected by external pollution sources. Then, a regular grid is generated at a preset scale (e.g., 200m × 200m), with each grid cell having a unique spatial identifier containing its center coordinates and spatial extent information. After the grid cells are divided, the comprehensive impact of each grid cell is calculated based on its spatial relationship with the pollution plume, groundwater drawdown cone, vulnerable areas, and external potential pollution sources, serving as the basis for subsequent monitoring priority allocation.

[0073] In some embodiments, step 102 may include: determining the plume impact of each grid cell based on a first spatial relationship between the grid cell and the plume, the first spatial relationship including the grid cell's location within the plume's interior, external migration path, and external lateral diffusion zone; determining the groundwater drawdown impact of each grid cell based on a second spatial relationship between the grid cell and the groundwater drawdown cone, the second spatial relationship including the grid cell's location in the central, edge, and outer regions of the groundwater drawdown cone; determining the weak zone impact of each grid cell based on the type of weak zone, the weak zone type including the grid cell's location in aquifer thinning zone, aquitard window, or fault zone; determining the external potential pollution source migration channel impact of each grid cell based on a third spatial relationship between the grid cell and the external potential pollution source migration channel, the third spatial relationship including the grid cell's location in the upstream, along-line, or downstream region of the external potential pollution source migration channel; and determining the monitoring priority of each grid cell based on the plume impact, groundwater drawdown cone impact, weak zone impact, and external potential pollution source migration channel impact.

[0074] Specifically, if a grid cell is located inside the plume, it indicates that the cell is already contaminated and requires monitoring of concentration changes and migration trends; in this case, the grid cell is assigned a first plume influence value. If a grid cell is located on the outer migration path of the plume (i.e., downstream of the plume's centroid migration direction), it indicates that contamination is about to reach the cell, requiring early warning monitoring; in this case, the grid cell is assigned a second plume influence value. If a grid cell is located in the lateral diffusion zone outside the plume (i.e., within the possible diffusion range on both sides of the plume's centroid migration direction), it indicates that the cell may be affected by diffusion; in this case, the grid cell is assigned a third plume influence value. The first to third plume influence values ​​decrease sequentially.

[0075] If a grid cell is located in the central region of a groundwater drawdown cone, it indicates that the water source corresponding to that grid cell is experiencing the most severe over-extraction and the fastest water level decline; therefore, the grid cell is assigned a first drawdown cone influence value. If a grid cell is located at the edge of a groundwater drawdown cone, it indicates that the grid cell is at the forefront of the drawdown cone's expansion and requires monitoring of boundary changes; therefore, the grid cell is assigned a second drawdown cone influence value. If a grid cell is located in the outer region of a groundwater drawdown cone, it indicates that the grid cell may be affected by future expansion; therefore, the grid cell is assigned a third drawdown cone influence value. The influence values ​​decrease sequentially from the first to the third drawdown cone.

[0076] If a grid cell is located in an area corresponding to any one of the following weak zone types: aquifer thinning zone, impermeable window, or fault zone, it indicates that the basic area has poor protection capabilities and a high risk of contamination. Therefore, the grid cell is assigned a first weak zone influence value. If a grid cell is located in an area corresponding to at least two of the following weak zone types: aquifer thinning zone, impermeable window, or fault zone, then the weak zone influence value of the grid cell is equal to... V0 represents the basic evolution intensity corresponding to a single weak zone type, m represents the number of weak zone types identified within the grid cell, and λ is the coupling enhancement coefficient, ranging from 0 to 1, determined based on the hydrogeological conditions of the target area. When the groundwater conditions in the target area are relatively homogeneous, λ can be preferably set to 0.30–0.50. When the groundwater system exhibits significant fracture control characteristics or karst development characteristics, the coupling enhancement coefficient λ can be appropriately increased to reflect the enhanced pollutant migration capacity after the formation of dominant groundwater seepage channels. If a grid cell is not located in an area corresponding to any of the weak zone types among aquifer thinning zones, impermeable windows, and fault zones, then a second weak zone influence value is assigned to that grid cell. The first weak zone influence value is greater than the second weak zone influence value, and the second weak zone influence value can be zero.

[0077] If a grid cell is located along the migration path of an external potential pollution source (i.e., the path between the external potential pollution source and the water well), it indicates that the grid cell is a pollutant migration path and pollution transfer needs to be tracked. Therefore, a first external potential pollution source migration path influence value is assigned to this grid cell. If a grid cell is located downstream of the external potential pollution source migration path (close to the water well), it indicates that the grid cell is the last warning barrier against external potential pollution sources. Therefore, a second external potential pollution source migration path influence value is assigned to this grid cell. If a grid cell is located upstream of the external potential pollution source migration path (i.e., near the external potential pollution source), it indicates that the grid cell is a threat source area and leakage needs to be monitored. Therefore, a third external potential pollution source migration path influence value is assigned to this grid cell. A fourth external potential pollution source migration path influence value is assigned to other grid cells. The influence values ​​from the first to the fourth external potential pollution source migration path decrease sequentially, with the fourth external potential pollution source migration path influence value being zero.

[0078] Finally, the impact of pollution plume, fall funnel, vulnerable area, and external potential pollution source migration channels in each grid unit are weighted and summed to obtain a comprehensive score for each grid unit. Based on this comprehensive score, each grid unit is then divided into multiple monitoring priorities. The weight of each impact factor can be determined according to the severity and urgency of the threat posed by each risk factor to water source safety. Specifically, the impact of pollution plume has the highest weight because it represents pollution that has already occurred, directly threatening drinking water safety and representing the most urgent monitoring need. The impact of fall funnel has the second highest weight because the water level drop and aquifer compression caused by over-extraction are irreversible processes that have a long-term impact on water source sustainability. The impact of vulnerable area has the third highest weight because it reflects inherent protection deficiencies and is a static risk factor with relatively stable importance. The impact of external potential pollution source migration channels has the lowest weight because the external threat has not yet actually occurred and represents a potential risk.

[0079] Step 103: Calculate the risk evolution index of each grid unit in the target area based on the risk evolution information.

[0080] To further characterize the spatiotemporal evolution of risk factors in groundwater-type water source areas, after determining the direction of change of risk factors, a risk evolution index can be constructed based on the spatial distribution of risk factors. This index is used to quantitatively characterize the comprehensive risk evolution degree of each grid unit within the target area. The risk evolution index can include the intensity of pollution plume migration, the intensity of fallout funnel expansion, the vulnerability intensity of weak areas, and the threat intensity of external potential pollution sources. The intensity of pollution plume migration characterizes the risk level of pollutants migrating to the water source area; the intensity of fallout funnel expansion characterizes the risk level of water level decline caused by groundwater over-extraction; the vulnerability intensity of weak areas characterizes the vulnerability of groundwater protection structures; and the threat intensity of external potential pollution sources characterizes the potential threat level of pollution sources to the water source wells.

[0081] In some embodiments, the risk evolution index calculation formula for any grid cell i can be REI. i =αP i +βD i +γV i +δS i +εT i , where P i D represents the intensity of the pollution plume migration. i V represents the expansion strength of the groundwater drawdown cone. i S represents the vulnerability strength of the weak area. i The intensity of the threat from potential external pollution sources; T iThe time evolution factor, with α+β+γ+δ+ε=1, is used to generate the risk evolution index field and determine the location of monitoring wells. Specifically, the plume migration intensity is determined based on the concentration of characteristic pollutants, the centroid coordinates of the plume, and the angle between the plume migration direction and the direction of the source well; the groundwater drawdown cone expansion intensity is determined based on the groundwater drawdown cone expansion rate and the groundwater level decline rate; the vulnerability intensity of the weak zone is determined based on the aquifer thickness, the impermeable layer thickness, and the permeability coefficient of the fault zone; the threat intensity of external potential pollution sources is determined based on the toxicity of the pollution source, the probability of pollution source release, and the migration distance of external potential pollution sources along the groundwater streamline to the source well; the time evolution factor is determined based on the risk evolution index of the current assessment period and the risk evolution index of the previous assessment period, and is used to describe the risk change trend.

[0082] The risk evolution index calculation formula follows the basic principle of "multi-dimensional risk coupling," quantifying five fundamentally different but interrelated risk factors faced by groundwater source areas into a comparable and calculable comprehensive index. Pollution plume migration intensity P i This represents the degree of threat posed to water wells by "already-occurring pollution." Pollutants already exist in the groundwater; the higher their concentration, the faster their migration rate, and the more directly they migrate towards the water well, the greater the threat to the well. The groundwater drawdown cone expansion intensity D... i The degree of threat posed to source wells by "water imbalance caused by over-extraction" is indicated by the following: the faster the funnel expands and the more drastic the drop in water level, the worse the sustainability of the aquifer system and the higher the risk of source well depletion. Vulnerability intensity V of the weak zone. i The threat level to source wells posed by "inherent deficiencies in the protective capabilities of aquifer systems" is categorized as follows: Thinning of the aquifer, the appearance of gaps in the impermeable layer, and the development of fault zones all weaken the natural protective capabilities of the groundwater system, making it easier for pollutants to enter the aquifer and migrate rapidly. The intensity of the external potential pollution source threat, S, is also considered. i The threat level of "potential but not yet occurring pollution" to water wells is determined by the toxicity of the external pollution source, its likelihood of release into groundwater, and its proximity to the water well. The time evolution factor T represents this. i This provides trend information indicating whether the risk is increasing or decreasing. If the current P of two grid cells... i D i V i and S i Completely identical, but one grid cell shows an accelerated deterioration trend (T i (T is positive), while another grid cell shows an improving trend (T). i If the value is negative, then the former requires a more urgent monitoring and response. The introduction of the time evolution factor enables the risk evolution index to reflect this difference.

[0083] The five components of the aforementioned risk evolution index cover all dimensions of water source environmental safety, namely water quality status, water quantity status, geological protection capacity, external threat pressure, and time evolution trend, which can comprehensively measure the overall risk of water source.

[0084] The methods for determining α, β, γ, δ, and ε can be as follows: First, subjective weights are determined using the analytic hierarchy process (AHP): experts in hydrogeology, water resources management, environmental protection, and other related fields compare the relative importance of the five dimensions pairwise, construct a judgment matrix, calculate the eigenvectors, and obtain the subjective weight vectors after passing a consistency test; Second, objective weights are determined using the entropy weight method: P values ​​of all grid units in the target area are collected over multiple evaluation periods. i D i V i S i and T i The data is processed by calculating the entropy value of each component. A smaller entropy value indicates greater data variation and more information in that component, thus requiring a higher weight. This yields an objective weight vector. Next, the subjective and objective weights are multiplied to obtain a combined weight. Finally, adjustments are made based on the evolutionary stage: during periods of strong influence, D... i and S i Appropriately increase, and during the adjustment and recovery period, T i The appropriate increase of the weights, with the sum of all weights remaining at 1 after correction. Among them, if the water level shows a continuous downward trend and the rate of decline is accelerating, the expansion rate of the groundwater drawdown cone area is greater than the threshold, and the water quality shows a deteriorating trend, then it is a period of strong influence; if the water level changes from declining to stable or rising, the groundwater drawdown cone area tends to stabilize or shrink, and the water quality improves, then it is a period of regulation and recovery.

[0085] For example, the pollution plume migration intensity P i The calculation formula can be , The pollutant plume exceedance index. For the migration speed of the pollution plume, For the risk coefficient of migration direction, , , , The concentration of characteristic pollutants, The groundwater quality standard limit, The coordinates of the centroid of the pollution plume for the current assessment period. The coordinates of the centroid of the pollution plume from the previous assessment period. For time intervals, It is the angle between the direction of the pollution plume migration and the direction of the water source well.

[0086] Specifically, the concentration of characteristic pollutants (such as nitrates) can be extracted from water quality monitoring data. This concentration value corresponds to a location coordinate and a sampling time. The Class III standard limit for this characteristic pollutant is determined according to the groundwater quality standards. C i A value greater than 1 indicates that the concentration of the characteristic pollutant exceeds the standard, and C i The larger the value, the more severe the pollution caused by that characteristic pollutant. The centroid coordinates of the pollution plume are extracted from the plume identification results of two assessment periods. The centroid coordinates for the current assessment period are... The centroid coordinates for the previous evaluation period were , The larger the value, the faster the pollution plume migrates, and the more urgent the threat to the water well. The direction of the water well is the direction from the center of mass of the pollution plume to the water well. When θ=0°, F i =1, the pollution plume migrates directly towards the water source well, posing the greatest threat; when θ=180°, F i =0, the pollution plume has migrated away from the water source well and poses no direct threat.

[0087] For example, the expansion intensity D of the groundwater drawdown funnel i The calculation formula can be D i =A i ×H i A i H represents the rate of expansion of the groundwater drawdown funnel. i The rate of decline of the groundwater level. , , The area of ​​the groundwater drawdown cone in the current assessment period. This represents the area of ​​the groundwater drawdown cone from the previous assessment period. This refers to the water level elevation of the monitoring well during the current assessment cycle. This refers to the water level elevation of the monitoring well in the previous assessment cycle.

[0088] Specifically, the expansion intensity D of the groundwater drawdown funnel i Determined based on the expansion rate of the groundwater drawdown cone and the rate of groundwater level decline, these two parameters reflect the speed of cone expansion and the severity of water level drop, respectively. i A positive value indicates that the groundwater drawdown cone is expanding. i The larger the value, the faster the groundwater drawdown cone expands, and the more rapidly the impact of over-extraction is spreading; H i A positive value indicates that the water level is dropping. i The larger the value, the faster the water level drops and the faster the aquifer's storage capacity is depleted. Groundwater drawdown cone expansion rate A i and the rate of decline of groundwater level H iThese two factors respectively reflect the changes in the "area" of the groundwater drawdown cone's expansion and the changes in the "point" of the water level drop. The two factors are independent of each other. The groundwater drawdown cone may expand in area while the rate of water level drop at the center slows down, or the area may remain stable while the water level continues to drop rapidly. Only by multiplying the two factors can we fully reflect the actual threat level of the groundwater drawdown cone.

[0089] For example, the vulnerability strength V of the weak area i The calculation formula can be B i This is the aquifer thinning index. The skylight index of the waterproof layer, The fault zone influence index, , , , , This represents the initial aquifer thickness. This represents the decrease in aquifer thickness. Let i be the thickness of the waterproof layer. The maximum thickness of the waterproof layer in the target area. The permeability coefficient of the fracture zone is... The background permeability coefficient of the target area is the stratum unaffected by the fault zone.

[0090] Specifically, and The aquifer thickness distribution field can be obtained from multiple drilling data. The reduction in aquifer thickness can be obtained by subtracting the initial aquifer thickness from the current aquifer thickness in the same grid cell. B i The larger the value, the more severe the aquifer thinning, and the smaller the water storage space and pollutant dilution capacity. Aquifer thickness The smaller the value, the lower the water-resistant layer skylight index. The closer the value is to 1, the higher the degree of sunroof development and the worse the vertical protection capability; when... When it is 0 =1, the waterproof layer is completely missing; when M i =M max hour =0, the waterproof layer is intact. Fault zone influence index. A higher value indicates a higher multiple of the fracture zone's permeability coefficient relative to the background strata, a more significant water-conductivity enhancement effect of the fracture zone, a greater degree of damage to the protective capacity of the aquifer system, and a higher vulnerability strength Vi; fracture zone influence index. A smaller value indicates a weaker water-conductivity enhancement effect of the fault zone, resulting in less damage to the aquifer's protective capacity. When the grid cell is located on the fault zone... Determined through pumping tests or empirical values ​​of the fault zone; when the grid cells are not located on the fault zone, =0.

[0091] Weights ω1, ω2, and ω3 correspond to the aquifer thinning index B, respectively. i Waterproof layer skylight index and fault zone influence index Vulnerability strength V in the weak area i The relative importance of the three weights can be determined based on the geological conditions of the study area. Specifically: First, the subjective weights are determined by the analytic hierarchy process (AHP). Then, the subjective weights are adjusted based on the geological background of the target area. If the target area has widespread and active fault zones, ω3 should be appropriately increased. If the target area mainly faces the problem of aquifer thinning (e.g., in plains areas with severe over-extraction), ω1 should be appropriately increased. If the target area is mainly characterized by primary skylights, ω2 should be appropriately increased.

[0092] For example, the threat intensity S from potential external pollution sources i The calculation formula can be E i R is the toxicity index of pollutants. i For the possibility of pollution source release, , The migration distance index, A represents the normal emission intensity of the external potential pollution source, B represents the probability of an accident occurring at the external potential pollution source, and C represents the historical leakage record of the external potential pollution source. This refers to the migration distance of potential external pollution sources along the groundwater flow line to the water source well.

[0093] Specifically, the pollutant toxicity index E i Characterizing the inherent hazard level of pollutants emitted from pollution sources, this toxicity coefficient is determined based on the toxicity coefficients of major pollutants exceeding standards (such as nitrates and heavy metals) in groundwater-type water sources within the target area, reflecting the differences in the potential harm of different pollutants to human health. Pollution source release probability R i Given a known normal emission intensity A, accident probability B, and historical leakage records C, the conditional probability of a pollution source releasing pollutants into groundwater is used to characterize the likelihood of a pollution source transitioning from a "potential threat" to an "actual pollution source." Pollution source release probability R i R is obtained by comprehensively evaluating the three pieces of information A, B, and C through a Bayesian network fusion. i The value is between 0 and 1. Based on the groundwater flow field generated from water level monitoring data, the streamline length of the groundwater can be obtained by tracing the flow path from the location of potential external pollution sources to the water source well. . The larger the value, the closer the pollution source is to the water well, the shorter the migration time of the pollutants, and the more urgent the threat to the water well.

[0094] Ei R i and The three parameters reflect the toxicity of the pollution source itself, the likelihood of its release, and its spatial distance, respectively. An increase in any of these parameters will increase the threat intensity S from potential external pollution sources. i Increase. E i and R i It reflects the properties of the pollution source itself. This reflects the spatial relationship between the pollution source and the water well; all three are independent and indispensable. No matter how highly toxic or likely to release a pollutant, if the migration distance is extremely long and the time to reach the water well is considerable, the actual threat is reduced due to pollutant degradation and dilution. Conversely, even if the pollution source is nearby, if the pollutant is non-toxic or unlikely to be released, it does not pose a real threat. Only when all three are simultaneously high does an external pollution source pose an imminent threat to the water well. The external potential pollution source threat intensity S i The larger the value, the greater the threat posed by potential external pollution sources to the location of that grid cell.

[0095] For example, the time evolution factor T i The calculation formula can be , , , .

[0096] Specifically, the time evolution factor T i It is used to describe the trend of risk change of grid cells over time, whether it is accelerating deterioration, decelerating deterioration, accelerating improvement, or decelerating improvement, thus expanding risk assessment from static spatial distribution to dynamic time dimension. A positive value indicates that the risk is trending upward (worsening), while a negative value indicates that the risk is trending downward (improving). The absolute value of risk indicates how fast the risk changes: the larger the absolute value, the faster the change; the smaller the absolute value, the slower the change. A positive value indicates that the first-order rate of change is increasing. A negative value indicates that the first-order rate of change is decreasing.

[0097] like >0 and If the value is greater than 0, the risk faced by the grid cell continues to increase, and the rate of increase is accelerating; that is, the risk faced by the grid cell is worsening, and the worsening trend is accelerating. >0 and If the value is less than 0, the risk faced by the grid cell is still increasing, but the rate of increase is slowing down; that is, the risk faced by the grid cell is worsening, but the rate of worsening is weakening. <0 and If the value is greater than 0, the risk faced by the grid cell is still decreasing, but the rate of decrease is slowing down; that is, the risk faced by the grid cell is being improved, but the momentum of improvement is weakening. <0 and If the value is less than 0, the risk faced by the grid cell continues to decrease, and the rate of decrease is accelerating, meaning that the risk faced by the grid cell is being improved and the improvement trend is accelerating.

[0098] The importance of reflecting the current rate of change The importance of reflecting the acceleration of the trend of change, under normal circumstances ≥ (For example =0.7, =0.3), because the first-order rate of change directly reflects the direction and magnitude of risk changes and is the core basis for judging trends; the second-order rate of change serves as a supplement, used to identify trend turning points (e.g., when the rate of deterioration begins to slow down, when the rate of improvement begins to accelerate). When it is necessary to more sensitively capture trend turning points, the rate can be appropriately increased. .

[0099] Time evolution factor T i When the time evolution factor T is positive, the risk evolution index REI of the grid cell is increased. i When the value is negative, the risk evolution index (REI) of the grid cell is reduced. Time evolution factor (T) i This allows monitoring wells deployed within grid cells with low current risk but a clear trend of deterioration to be shifted in the direction of the deterioration trend, thus enabling proactive deployment. Please refer to step 104 for details.

[0100] Step 104: Determine the monitoring well layout scheme based on the direction of risk factor change, risk evolution index and monitoring priority of each grid unit, and monitor the groundwater source area based on the monitoring well layout scheme.

[0101] In this step, when determining the well placement scheme based on the monitoring priority and risk factor change direction of each grid unit, the monitoring priority determines the density and number of wells in the grid unit, while the risk evolution index and risk factor change direction determine the specific location of the wells in the grid unit. High-priority grid units have multiple overlapping risk factors and are considered high-risk areas. Monitoring wells need to be placed at the locations where multiple factors overlap, and in the direction of risk factor change (such as the migration direction of the pollution plume, the expansion direction of the fallout funnel, the extension direction of the weak zone, or the direction from the exit of the external potential pollution source migration channel towards the water source well) and / or the gradient descent direction, in order to track the dynamic changes of the risk factors. Medium-priority grid units typically have only a single risk factor, and monitoring wells can be placed at the center of that risk factor. Low-priority grid units do not have any risk factors and do not require monitoring wells.

[0102] In some embodiments, step 104 may include: generating a risk evolution index field based on the risk evolution index of each grid cell, and calculating the spatial gradient field of the risk evolution index field; for grid cells with high monitoring priority, where multiple risk factors are superimposed, a monitoring well is deployed at the location where multiple risk factors are superimposed in the grid cell; and if the gradient modulus of the grid cell is greater than a preset gradient threshold, a monitoring well is deployed between the grid cell and the water source well along the gradient descent direction; if the gradient modulus of the grid cell is less than or equal to the preset gradient threshold, a monitoring well is deployed along the direction of risk factor change; for grid cells with medium monitoring priority, where a single risk factor exists in the grid cell, a monitoring well is deployed at the center of the risk factor in the grid cell; for grid cells with low monitoring priority, no monitoring well is deployed in the grid cell.

[0103] Specifically, based on the risk evolution index (REI) values ​​of each grid cell, a continuous risk evolution index field REI(x,y) covering the entire target area can be generated using the Kriging interpolation method. This field reflects the continuous spatial distribution characteristics of risk; areas with higher REI values ​​have greater risk, and areas with lower REI values ​​have less risk. Based on this, the spatial gradient field G = ∇REI = (∂REI / ∂x, ∂REI / ∂y) is calculated. The gradient direction points to the direction of the fastest increase in REI, i.e., the direction of increasing risk; the gradient magnitude |G| = [(∂REI / ∂x)]. 2 + (∂REI / ∂y) 2 ] 1 / 2 This reflects the rate of change of REI in space. The larger the gradient magnitude |G|, the more drastic the spatial change of risk at that location, indicating the location of the frontier of risk factor change.

[0104] When |G| > G crit When a preset gradient threshold is set, which can be 1.5 or 2 times the average gradient magnitude of the target area, the grid cell is designated as a rapid risk evolution zone. Rapid risk evolution zones typically correspond to the forefront of risk factor changes, such as the pollution plume front, the boundary of the fall funnel, and the edge of the fault zone; these are the areas where risk changes are most active.

[0105] In the rapid evolution zone of risk where the gradient modulus exceeds a preset gradient threshold, the risk changes rapidly in space, indicating that the grid cell is located at the forefront of risk factor change. The REI gradient direction points in the direction of increasing risk, while the gradient descent direction (i.e., the −∇REI direction) is the direction of the fastest decrease in REI. Deploying monitoring wells along the gradient descent direction between the monitoring well and the water source well is equivalent to setting up advance monitoring points on the path of risk factors to the water source well. As the risk factor change front advances, the monitoring wells deployed in this direction will detect signals of rising concentrations or accelerated water level drops before the water source well, buying time for emergency response.

[0106] Grid cells with high monitoring priority have multiple overlapping risk factors and belong to high-risk areas, where 2 to 3 monitoring wells can be deployed. The first monitoring well can be deployed in the area where multiple risk factors overlap, with the geometric center of this area chosen as the placement location for the first monitoring well. This allows for simultaneous monitoring of the current status changes of multiple risk factors. The placement direction of the remaining monitoring wells is determined by the gradient modulus of the risk evolution index field.

[0107] If the gradient modulus of the grid cell is greater than the preset gradient threshold (i.e., the grid cell is located within the rapid risk evolution zone), monitoring wells are deployed along the gradient descent direction between the grid cell and the water source well. Starting from the center of the grid cell, monitoring wells are deployed along the −∇REI direction to the water source well, selecting one or two points along this direction. The gradient descent direction integrates the contributions of all risk factor changes to the REI change, reflecting the comprehensive trend of risk change. When multiple risk factors change in different directions, deploying monitoring wells along this direction can capture the comprehensive risk change signal. If the gradient modulus of the grid cell is less than or equal to the preset gradient threshold (i.e., the grid cell is not located within the rapid risk evolution zone), monitoring wells are deployed along the risk factor change direction, specifically along the pollution plume migration direction, the fallout funnel expansion direction, the weak zone extension direction, and the direction from the external potential pollution source to the water source well. When multiple directions are consistent or close, one monitoring well is sufficient in the common direction; when multiple directions are inconsistent, one monitoring well is deployed in each of the two risk factors with the greatest impact.

[0108] For grid cells with a medium priority monitoring priority, if a single risk factor has a significant impact, a monitoring well can be deployed. This well can be placed at the geometric center of the risk factor's influence area. Specifically, based on the risk factor type corresponding to the grid cell, the boundary of the influence area of ​​that risk factor within the grid cell is extracted, and the geometric center coordinates of the boundary points are calculated as the well's placement location. In medium priority grid cells, after the placement location is determined, the well position can be appropriately adjusted based on the direction indicated by the spatial gradient field of the risk evolution index. If the gradient descent direction within the grid cell is inconsistent with the risk factor's center location, the monitoring well location is adjusted along the gradient descent direction to a compromise position between the risk factor's center location and the gradient descent direction. The adjustment distance should not exceed one-quarter of the grid's side length, thus balancing the monitoring needs of both the current risk center and the risk change trend.

[0109] No monitoring wells are deployed in low-priority grid cells where no risk factors are present.

[0110] In some embodiments, the above-mentioned monitoring of groundwater source areas based on the monitoring well deployment scheme may include: determining the well location coordinates and monitoring well type of each monitoring well according to the monitoring well deployment scheme; the monitoring well type includes water level monitoring wells for collecting water level data and water quality monitoring wells for collecting water quality data; setting monitoring parameters for each monitoring well, wherein the monitoring parameters for water level monitoring wells include water level depth or water level elevation, and the monitoring parameters for water quality monitoring wells include the concentration of at least one characteristic pollutant among nitrates, chlorides, and heavy metals; collecting monitoring data according to the initial sampling cycle and uploading the monitoring data to the terminal according to the data upload frequency; wherein the initial sampling cycle of monitoring wells in grid units with high monitoring priority is shorter than the initial sampling cycle of monitoring wells in grid units with low monitoring priority, and the data upload frequency of monitoring wells in grid units with high monitoring priority is higher than the data upload frequency of monitoring wells in grid units with low monitoring priority; and uploading the data to the terminal according to the data upload frequency. The monitoring data is used to assess the risk level of each grid unit in real time: For any grid unit, if the concentration of the characteristic pollutant in the current sampling period is greater than that in the previous sampling period, and the rate of water level decline in the current sampling period is greater than that in the previous sampling period, or if the concentration of the characteristic pollutant continues to increase in two consecutive sampling periods, or if the rate of water level decline continues to increase in two consecutive sampling periods, then the risk level of the grid unit increases; if the concentration of the characteristic pollutant in the current sampling period is less than that in the previous sampling period, and the rate of water level decline in the current sampling period is less than that in the previous sampling period, then the risk level of the grid unit decreases; if the risk level of a grid unit increases, the sampling frequency and data upload frequency of the monitoring wells in that grid unit are increased; if the risk level of a grid unit decreases and remains stable for more than two sampling periods, the sampling frequency and data upload frequency of the monitoring wells in that grid unit are decreased.

[0111] Specifically, water level data can be obtained through water level gauges installed in monitoring wells, and water quality data can be obtained by collecting water samples from the monitoring wells and sending them to the laboratory for analysis. Monitoring wells in high-priority grid units have shorter initial sampling cycles (e.g., monthly water sample collection for laboratory analysis to obtain water quality data, daily water level data collection) and higher data upload frequencies (e.g., daily water level data upload, monthly water quality data upload). Monitoring wells in low-priority grid units have longer initial sampling cycles (e.g., quarterly water sample collection for laboratory analysis to obtain water quality data, weekly water level data collection) and lower data upload frequencies (e.g., weekly water level data upload, quarterly water quality data upload). Monitoring wells in medium-priority grid units fall between these two categories (e.g., bi-monthly water sample collection for laboratory analysis to obtain water quality data and upload, daily water level data collection and upload). After the initial sampling cycle and data upload frequency are set according to the monitoring priority of each grid unit, monitoring data is collected according to the set initial sampling cycle and uploaded to the terminal according to the set data upload frequency.

[0112] After receiving monitoring data uploaded from each monitoring well, the terminal assesses the risk level of each grid unit in real time. For any grid unit, if the concentration of the characteristic pollutant in the current sampling period is greater than that in the previous sampling period, and the rate of water level decline in the current sampling period is greater than that in the previous sampling period, or if the concentration of the characteristic pollutant continues to increase in two consecutive sampling periods, or if the rate of water level decline continues to increase in two consecutive sampling periods, then the risk level of the grid unit increases, and both water quality and water level show a deteriorating trend. Alternatively, if either water quality or water level continues to deteriorate, it indicates that the risk faced by the grid unit is intensifying, and monitoring intensity needs to be increased. If the concentration of the characteristic pollutant in the current sampling period is less than that in the previous sampling period, and the rate of water level decline in the current sampling period is less than that in the previous sampling period, then the risk level of the grid unit decreases, and both water quality and water level show an improving trend, indicating that the risk faced by the grid unit is weakening, and monitoring resources can be appropriately released. If the changes in water quality and water level are inconsistent (e.g., concentration increases but water level decline slows down, or concentration decreases but water level decline accelerates), then the risk level of the grid unit remains unchanged, and subsequent trends need to be observed.

[0113] When the risk level of a grid cell increases, the sampling frequency and data upload frequency of monitoring wells within that grid cell can be increased. For example, the water sampling cycle can be shortened from once a month to once every two weeks, and the water quality data upload frequency can be increased from once a month to once every two weeks to more intensively track risk changes. When the risk level of a grid cell decreases and remains stable for more than two sampling cycles, the sampling frequency and data upload frequency of monitoring wells within that grid cell can be reduced, achieving dynamic release of monitoring resources. This dynamic adjustment mechanism enables real-time matching of monitoring intensity with risk status; that is, when the risk increases, monitoring is intensified to obtain sufficient data to support emergency decision-making, and when the risk decreases and stabilizes, monitoring resources are released to reduce operating costs.

[0114] In this embodiment, the monitoring well deployment scheme can be redefined after a certain period of time (e.g., multiple evaluation cycles), thereby achieving dynamic optimization of the monitoring well deployment. In this application, there may be a situation where multiple monitoring wells are deployed in a single grid cell, and whether each monitoring well is activated is determined based on the monitoring well deployment scheme.

[0115] The aforementioned risk evolution-based groundwater source monitoring method determines the risk evolution information of groundwater sources in the target area based on historical monitoring data. It then determines the monitoring priority of each grid unit based on the spatial relationship between each grid unit and the risk factors. Based on the risk evolution information, it calculates the risk evolution index of each grid unit in the target area. Finally, it determines the well deployment scheme based on the direction of risk factor change, the risk evolution index, and the monitoring priority of each grid unit. Monitoring of the groundwater source is then conducted based on this well deployment scheme. This method ensures that wells are precisely positioned in areas with high monitoring priority. Furthermore, it determines the direction of the risk factor change frontier based on the risk evolution index and the direction of risk factor change, proactively deploying wells in the direction of the risk factor change frontier with high monitoring priority. This allows monitoring resources to be precisely allocated to the highest-risk areas, avoiding the problem of redundancy in low-risk areas and insufficient coverage in high-risk areas by fixed monitoring networks, thereby improving the accuracy and rationality of monitoring results.

[0116] In addition, this application divides the grid into three monitoring priorities—high, medium, and low—based on the comprehensive score of the grid units. High-priority grids are equipped with multiple monitoring wells, medium-priority grids are equipped with a small number of monitoring wells, and low-priority grids are equipped with no monitoring wells. This differentiated configuration of monitoring wells enables monitoring resources to be precisely deployed to areas with concentrated risks, thereby improving the monitoring coverage of high-risk areas.

[0117] Furthermore, when monitoring data reflects an increase in the risk level of a grid cell, the sampling frequency and data upload frequency are automatically increased to more intensively track risk changes; when the risk level decreases and remains stable, the sampling frequency and data upload frequency are reduced. This dynamic adjustment increases monitoring density to obtain sufficient data to support emergency decision-making when the risk increases, and releases monitoring resources when the risk decreases, achieving a dynamic match between monitoring intensity and risk status.

[0118] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0119] Corresponding to the risk evolution-based groundwater source monitoring method described in the above embodiments, Figure 2 The diagram shows a structural block diagram of a risk evolution-based groundwater source monitoring device provided in an embodiment of this application. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0120] See Figure 2 This application provides a groundwater source monitoring device based on risk evolution, which may include a risk evolution determination module 201, a priority determination module 202, a risk evolution index calculation module 203, and a monitoring module 204.

[0121] The risk evolution determination module 201 is used to determine the risk evolution information of groundwater-type water sources based on historical monitoring data of groundwater-type water sources in the target area. The historical monitoring data includes water quality monitoring data, water level monitoring data, hydrogeological data, and data on potential external pollution sources. The risk evolution information includes the spatial distribution and change direction of risk elements. Risk elements include pollution plumes, groundwater drawdown cones, weak zones, and potential external pollution sources. The change direction of risk elements includes the migration direction of pollution plumes, the expansion direction of groundwater drawdown cones, the extension direction of weak zones, and the direction of potential external pollution sources pointing towards the water source wells.

[0122] The priority determination module 202 is used to divide the target area into multiple grid units and determine the monitoring priority of each grid unit according to the spatial relationship between each grid unit and the risk element.

[0123] The risk evolution index calculation module 203 is used to calculate the risk evolution index of each grid unit in the target area based on the risk evolution information. The risk evolution index includes the pollution plume migration intensity, the fall funnel expansion intensity, the vulnerability intensity of the weak area, and the threat intensity of external potential pollution sources.

[0124] The monitoring module 204 is used to determine the layout scheme of monitoring wells based on the changing direction of risk factors, risk evolution index and monitoring priority of each grid unit, and to monitor groundwater source areas based on the monitoring well layout scheme.

[0125] Optionally, the priority determination module 202 is specifically used for: determining the plume impact of each grid unit based on a first spatial relationship between the grid unit and the plume, where the first spatial relationship includes the grid unit's location within the plume's internal and external migration paths and external lateral diffusion zones; determining the groundwater drawdown impact of each grid unit based on a second spatial relationship between the grid unit and the groundwater drawdown cone, where the second spatial relationship includes the grid unit's location in the central, edge, and external regions of the groundwater drawdown cone; determining the weak zone impact of each grid unit based on the type of weak zone, where the weak zone type includes the grid unit's location in aquifer thinning zones, aquitard windows, or fault zones; determining the external potential pollution source migration channel impact of each grid unit based on a third spatial relationship between the grid unit and the external potential pollution source migration channel, where the third spatial relationship includes the grid unit's location in the upstream, along-line, or downstream regions of the external potential pollution source migration channel; and determining the monitoring priority of each grid unit based on the plume impact, groundwater drawdown cone impact, weak zone impact, and external potential pollution source migration channel impact.

[0126] Optionally, the water quality monitoring data includes the concentration of at least one characteristic pollutant among nitrates, chlorides, and heavy metals. The process for determining the migration direction of the pollution plume includes: determining the concentration of the characteristic pollutant from the water quality monitoring data, wherein each concentration value of the characteristic pollutant corresponds to a location coordinate and a sampling time; generating a continuous spatial concentration field for the concentration of the characteristic pollutant using the Kriging interpolation method; determining the pollution plume of the target area water source based on the areas in the spatial concentration field where the concentration is greater than the groundwater quality standard threshold; calculating the concentration-weighted centroid coordinates of the pollution plume by weighting the coordinates of the grid cells using the concentration values ​​of the grid cells in the spatial concentration field; and determining the migration direction of the pollution plume based on the changing trajectory of the concentration-weighted centroid of the pollution plume at different times.

[0127] The water level monitoring data includes water level elevation. The process for determining the expansion direction of the groundwater drawdown cone includes: extracting the water level elevation of the monitoring wells for the target year from the water level monitoring data; generating groundwater isostatic lines for the target area based on the water level elevation; determining the lowest water level area defined by the closed isostatic lines for the target area to obtain the drawdown cone area; comparing the boundary positions of the drawdown cone areas in different years, and determining the direction in which the drawdown cone area expands outward the fastest as the expansion direction of the groundwater drawdown cone.

[0128] Optionally, the hydrogeological data includes aquifer thickness and impermeable layer thickness. The process of determining the extension direction of the weak zone includes: determining the aquifer thickness variation and aquifer thickness variation rate of each grid cell based on the aquifer thickness of each grid cell in the target area; when the aquifer thickness variation indicates that the aquifer thickness of the grid cell is decreasing, and the aquifer thickness variation rate is greater than a threshold, and the degree of aquifer thickness variation of the grid cell is consistent with that of the surrounding adjacent grid cells, the grid cell is determined to be an aquifer thinning zone.

[0129] For each grid cell, if the thickness of the impermeable layer in the grid cell is less than the critical threshold, then the grid cell is an impermeable layer skylight cell. Traverse all grid cells and assign the same label to adjacent impermeable layer skylight cells. Each label corresponds to an independent impermeable layer skylight. The geometric parameters of each impermeable layer skylight include area, boundary and centroid coordinates.

[0130] The spatial gradient of the aquifer thickness in each grid cell is calculated, and linear high-value zones are identified based on this spatial gradient. The lithology and water level of the boreholes located on or on both sides of the linear high-value zone are used to determine whether the rock strata corresponding to the linear high-value zone have fractured. If fractures have occurred, the grid cells corresponding to the linear high-value zone constitute a fracture zone. The areas corresponding to the aquifer thinning zone, the aquifer window, and the fracture zone are the weak zones.

[0131] The covariance matrix of the boundary points is calculated based on the coordinates of the boundary points of the weak zone, and the direction of the eigenvector corresponding to the largest eigenvalue of the covariance matrix is ​​determined as the geometric extension direction of the weak zone.

[0132] Optionally, the risk evolution index calculation module 203 is specifically used for: calculating the risk evolution index of any grid cell i using the formula REI. i =αP i +βD i +γV i +δS i +εT i , where P i D represents the intensity of the pollution plume migration. i V represents the expansion strength of the groundwater drawdown cone. i S represents the vulnerability strength of the weak area. i The intensity of the threat from potential external pollution sources; T iThe time evolution factor is α+β+γ+δ+ε=1. The risk evolution index is used to generate a risk evolution index field and to determine the location of monitoring wells. Among them, the pollution plume migration intensity is determined based on the concentration of characteristic pollutants, the centroid coordinates of the pollution plume, and the angle between the migration direction of the pollution plume and the direction of the water source well; the groundwater drawdown funnel expansion intensity is determined based on the groundwater drawdown funnel expansion rate and the groundwater level drop rate; the vulnerability intensity of the weak zone is determined based on the aquifer thickness, the impermeable layer thickness, and the permeability coefficient of the fault zone; the external potential pollution source threat intensity is determined based on the toxicity of the pollution source, the release probability of the pollution source, and the migration distance of the external potential pollution source along the groundwater streamline to the water source well; and the time evolution factor is determined based on the risk evolution index of the current assessment period and the risk evolution index of the previous assessment period.

[0133] Optional, pollution plume migration intensity The calculation formula is , The pollutant plume exceedance index. For the migration speed of the pollution plume, For the risk coefficient of migration direction, , , , The concentration of characteristic pollutants, The groundwater quality standard limit, The coordinates of the centroid of the pollution plume for the current assessment period. The coordinates of the centroid of the pollution plume from the previous assessment period. For time intervals, The angle between the direction of the pollution plume migration and the direction of the water source well;

[0134] Groundwater drawdown funnel expansion intensity The calculation formula is , The rate of expansion of the groundwater drawdown funnel. The rate of decline of the groundwater level. , , The area of ​​the groundwater drawdown cone in the current assessment period. This represents the area of ​​the groundwater drawdown cone from the previous assessment period. This refers to the water level elevation of the monitoring well during the current assessment cycle. This refers to the water level elevation of the monitoring well in the previous assessment cycle;

[0135] Vulnerability strength of weak areas The calculation formula is B i This is the aquifer thinning index. The skylight index of the waterproof layer, The fault zone influence index, , , , , This represents the initial aquifer thickness. This represents the decrease in aquifer thickness. Let i be the thickness of the waterproof layer. The maximum thickness of the waterproof layer in the target area. The permeability coefficient of the fracture zone is... The background permeability coefficient of the target area unaffected by the fault zone;

[0136] external potential pollution source threat intensity The calculation formula is E i The toxicity index of pollutants, , A represents the normal emission intensity of the external potential pollution source, B represents the probability of an accident occurring at the external potential pollution source, and C represents the historical leakage record of the external potential pollution source. This refers to the migration distance of potential external pollution sources along the groundwater flow line to the water source well;

[0137] Time evolution factor The calculation formula is , , , .

[0138] Optionally, the monitoring module 204 is specifically used for: generating a risk evolution index field based on the risk evolution index of each grid cell, and calculating the spatial gradient field of the risk evolution index field; for grid cells with high monitoring priority, where multiple risk factors are superimposed, monitoring wells are deployed at the locations where multiple risk factors are superimposed in the grid cell; and if the gradient modulus of the grid cell is greater than a preset gradient threshold, monitoring wells are deployed between the grid cell and the water source well along the gradient descent direction; if the gradient modulus of the grid cell is less than or equal to the preset gradient threshold, monitoring wells are deployed along the direction of risk factor change; for grid cells with medium monitoring priority, where a single risk factor exists in the grid cell, a monitoring well is deployed at the center of the risk factor in the grid cell; and for grid cells with low monitoring priority, no monitoring wells are deployed in the grid cell.

[0139] Optionally, the monitoring module 204 is further configured to: determine the well location coordinates and monitoring well type of each monitoring well according to the monitoring well layout plan; the monitoring well type includes water level monitoring wells for collecting water level data and water quality monitoring wells for collecting water quality data; set monitoring parameters for each monitoring well, the monitoring parameters for water level monitoring wells including water level depth or water level elevation, and the monitoring parameters for water quality monitoring wells including the concentration of at least one characteristic pollutant among nitrates, chlorides, and heavy metals; collect monitoring data according to the initial sampling cycle and upload the monitoring data to the terminal according to the data upload frequency; wherein, the initial sampling cycle of monitoring wells in grid units with high monitoring priority is shorter than the initial sampling cycle of monitoring wells in grid units with low monitoring priority, and the data upload frequency of monitoring wells in grid units with high monitoring priority is higher than the data upload frequency of monitoring wells in grid units with low monitoring priority; and evaluate each monitoring well in real time based on the monitoring data uploaded to the terminal. Risk level of a grid cell: For any grid cell, if the concentration of the characteristic pollutant in the current sampling period is greater than that in the previous sampling period, and the rate of water level decline in the current sampling period is greater than that in the previous sampling period, or if the concentration of the characteristic pollutant continues to increase for two consecutive sampling periods, or if the rate of water level decline continues to increase for two consecutive sampling periods, then the risk level of the grid cell increases; if the concentration of the characteristic pollutant in the current sampling period is less than that in the previous sampling period, and the rate of water level decline in the current sampling period is less than that in the previous sampling period, then the risk level of the grid cell decreases; if the risk level of a grid cell increases, then the sampling frequency and data upload frequency of the monitoring wells within that grid cell are increased; if the risk level of a grid cell decreases and remains stable for more than two sampling periods, then the sampling frequency and data upload frequency of the monitoring wells within that grid cell are decreased.

[0140] Figure 3 This is a schematic diagram of a terminal provided in an embodiment of the present invention. For example... Figure 3 As shown, the terminal 300 in this embodiment includes a processor 310 and a memory 320. The memory 320 stores a computer program that can run on the processor 310, such as a risk evolution-based groundwater source monitoring program. When the processor 310 executes the computer program, it implements the steps in the above-described risk evolution-based groundwater source monitoring method embodiment, for example... Figure 1 As shown in 101 to 104. Alternatively, when the processor 310 executes the computer program, it implements the functions of each module in the above-described device embodiments, for example... Figure 2 The functions of the risk evolution determination module 201 to the monitoring module 204 are shown.

[0141] The terminal 300 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The terminal may include, but is not limited to, a processor 310 and a memory 320. Those skilled in the art will understand that... Figure 3 This is merely an example of terminal 300 and does not constitute a limitation on terminal 300. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0142] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0143] The memory 320 can be an internal storage unit of the terminal 300, such as a hard disk or memory of the terminal 300. The memory 320 can also be an external storage device of the terminal 300, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal 300. Furthermore, the memory 320 can include both internal storage units and external storage devices of the terminal 300. The memory 320 is used to store the computer program and other programs and data required by the terminal. The memory 320 can also be used to temporarily store data that has been output or will be output.

[0144] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A groundwater source monitoring method based on risk evolution, characterized in that, include: Risk evolution information of groundwater source areas is determined based on historical monitoring data of groundwater source areas in the target area. The historical monitoring data includes water quality monitoring data, water level monitoring data, hydrogeological data, and data on potential external pollution sources. The risk evolution information includes the spatial distribution and direction of change of risk elements. Risk elements include pollution plumes, groundwater drawdown cones, weak zones, and potential external pollution sources. The direction of change of risk elements includes the migration direction of pollution plumes, the expansion direction of groundwater drawdown cones, the extension direction of weak zones, and the direction of potential external pollution sources pointing towards the water source well. The target area is divided into multiple grid units, and the monitoring priority of each grid unit is determined according to the spatial relationship between each grid unit and the risk element. The risk evolution index of each grid unit in the target area is calculated based on the risk evolution information. The risk evolution index includes the intensity of pollution plume migration, the intensity of fall funnel expansion, the vulnerability intensity of weak areas, and the threat intensity of potential external pollution sources. The monitoring well layout scheme is determined based on the changing direction of risk factors, risk evolution index, and monitoring priority of each grid unit, and the groundwater source is monitored based on the monitoring well layout scheme.

2. The groundwater source monitoring method based on risk evolution according to claim 1, characterized in that, The step of determining the monitoring priority of each grid cell based on the spatial relationship between each grid cell and the risk element includes: The plume impact of each grid cell is determined based on the first spatial relationship between the grid cell and the plume. The first spatial relationship includes the grid cell's location within the plume's interior, external migration path, and external lateral diffusion zone. The influence degree of the groundwater drawdown cone for each grid cell is determined based on the second spatial relationship between the grid cell and the groundwater drawdown cone. The second spatial relationship includes the central region, edge region and outer region of the grid cell in the groundwater drawdown cone. The influence degree of the weak zone of each grid cell is determined according to the type of weak zone of the grid cell. The types of weak zones include grid cells located in aquifer thinning areas, aquitard windows, or fault zones. The influence of the migration channel of the external potential pollution source on each grid cell is determined based on the third spatial relationship between the grid cell and the migration channel of the external potential pollution source. The third spatial relationship includes whether the grid cell is located in the upstream area, along the channel, or downstream area of ​​the migration channel of the external potential pollution source. The monitoring priority for each grid unit is determined based on the impact of the pollution plume, the impact of the groundwater drawdown funnel, the impact of the vulnerable area, and the impact of the migration channels of potential external pollution sources.

3. The groundwater source monitoring method based on risk evolution according to claim 1, characterized in that, Water quality monitoring data includes the concentration of at least one characteristic pollutant among nitrates, chlorides, and heavy metals. The process for determining the migration direction of the pollution plume includes: determining the concentration of the characteristic pollutant from the water quality monitoring data, where each concentration value of the characteristic pollutant corresponds to a location coordinate and a sampling time; generating a continuous spatial concentration field using the Kriging interpolation method for the concentration of the characteristic pollutant; determining the pollution plume of the target water source area based on the regions in the spatial concentration field where the concentration is greater than the groundwater quality standard threshold; calculating the concentration-weighted centroid coordinates of the pollution plume by weighting the coordinates of the grid cells using the concentration values ​​of the grid cells in the spatial concentration field; and determining the migration direction of the pollution plume based on the changing trajectory of the concentration-weighted centroid of the pollution plume at different times. The water level monitoring data includes water level elevation. The process for determining the expansion direction of the groundwater drawdown cone includes: extracting the water level elevation of the monitoring wells for the target year from the water level monitoring data; generating groundwater isostatic lines for the target area based on the water level elevation; determining the lowest water level area defined by the closed isostatic lines for the target area to obtain the drawdown cone area; comparing the boundary positions of the drawdown cone areas in different years, and determining the direction in which the drawdown cone area expands outward the fastest as the expansion direction of the groundwater drawdown cone.

4. The groundwater source monitoring method based on risk evolution according to claim 1, characterized in that, Hydrogeological data includes aquifer thickness and impermeable layer thickness. The process of determining the extension direction of the weak zone includes: The amount of aquifer thickness change and the rate of aquifer thickness change in each grid cell are determined based on the aquifer thickness of each grid cell. When the amount of aquifer thickness change indicates that the aquifer thickness of the grid cell is decreasing, and the rate of aquifer thickness change is greater than a threshold, and the degree of aquifer thickness change of the grid cell is consistent with that of the surrounding adjacent grid cells, the grid cell is determined to be an aquifer thinning zone. For each grid cell, if the thickness of the impermeable layer in the grid cell is less than the critical threshold, then the grid cell is an impermeable layer skylight cell. Traverse all grid cells and assign the same label to adjacent impermeable layer skylight cells. Each label corresponds to an independent impermeable layer skylight. The geometric parameters of each impermeable layer skylight include area, boundary and centroid coordinates. The spatial gradient of the aquifer thickness in each grid cell is calculated, and linear high-value zones are identified based on this spatial gradient. The lithology and water level of the boreholes located on or on both sides of the linear high-value zone are used to determine whether the rock strata corresponding to the linear high-value zone have fractured. If fractures have occurred, the grid cells corresponding to the linear high-value zone constitute a fracture zone. The areas corresponding to the aquifer thinning zone, the aquifer window, and the fracture zone are the weak zones. The covariance matrix of the boundary points is calculated based on the coordinates of the boundary points of the weak zone, and the direction of the eigenvector corresponding to the largest eigenvalue of the covariance matrix is ​​determined as the geometric extension direction of the weak zone.

5. The groundwater source monitoring method based on risk evolution according to claim 1, characterized in that, The calculation of the risk evolution index of each grid cell in the target area based on risk evolution information includes: The formula for calculating the risk evolution index of any grid cell i is REI. i =αP i +βD i +γV i +δS i +εT i , where P i D represents the intensity of the pollution plume migration. i V represents the expansion strength of the groundwater drawdown cone. i S represents the vulnerability strength of the weak area. i The intensity of the threat from potential external pollution sources; T i The risk evolution index is a time evolution factor, and α+β+γ+δ+ε=1. It is used to generate a risk evolution index field and to determine the location of monitoring wells. Among them, the pollution plume migration intensity is determined based on the concentration of characteristic pollutants, the centroid coordinates of the pollution plume, and the angle between the migration direction of the pollution plume and the direction of the water source well; the groundwater drawdown cone expansion intensity is determined based on the groundwater drawdown cone expansion rate and the groundwater level drop rate; the vulnerability intensity of the weak zone is determined based on the aquifer thickness, the impermeable layer thickness, and the permeability coefficient of the fault zone; the external potential pollution source threat intensity is determined based on the toxicity of the pollution source, the probability of pollution source release, and the migration distance of the external potential pollution source along the groundwater streamline to the water source well; and the time evolution factor is determined based on the risk evolution index of the current assessment period and the risk evolution index of the previous assessment period.

6. The groundwater source monitoring method based on risk evolution according to claim 5, characterized in that, Pollution plume migration intensity The calculation formula is , The pollutant plume exceedance index. For the migration speed of the pollution plume, For the risk coefficient of migration direction, , , , The concentration of characteristic pollutants, The groundwater quality standard limit, The coordinates of the centroid of the pollution plume for the current assessment period. The coordinates of the centroid of the pollution plume from the previous assessment period. For time intervals, The angle between the direction of the pollution plume migration and the direction of the water source well; Groundwater drawdown funnel expansion intensity The calculation formula is , The rate of expansion of the groundwater drawdown funnel. The rate of decline of the groundwater level. , , The area of ​​the groundwater drawdown cone in the current assessment period. This represents the area of ​​the groundwater drawdown cone from the previous assessment period. This refers to the water level elevation of the monitoring well during the current assessment cycle. This refers to the water level elevation of the monitoring well in the previous assessment cycle; Vulnerability strength of weak areas The calculation formula is B i This is the aquifer thinning index. The skylight index of the waterproof layer, The fault zone influence index, , , , , This represents the initial aquifer thickness. This represents the decrease in aquifer thickness. Let i be the thickness of the waterproof layer. The maximum thickness of the waterproof layer in the target area. The permeability coefficient of the fracture zone is... The background permeability coefficient of the target area unaffected by the fault zone; external potential pollution source threat intensity The calculation formula is E i The toxicity index of pollutants, , A represents the normal emission intensity of the external potential pollution source, B represents the probability of an accident occurring at the external potential pollution source, and C represents the historical leakage record of the external potential pollution source. This refers to the migration distance of potential external pollution sources along the groundwater flow line to the water source well; Time evolution factor The calculation formula is , , , .

7. The groundwater source monitoring method based on risk evolution according to claim 1, characterized in that, The method of determining the monitoring well deployment scheme based on the changing direction of risk factors, the risk evolution index, and the monitoring priority of each grid unit includes: A risk evolution index field is generated based on the risk evolution index of each grid cell, and the spatial gradient field of the risk evolution index field is calculated. For a grid cell with a high monitoring priority, where multiple risk factors overlap, monitoring wells are deployed at the locations where multiple risk factors overlap in the grid cell. If the gradient modulus of the grid cell is greater than a preset gradient threshold, monitoring wells are deployed between the grid cell and the water source well along the gradient descent direction. If the gradient modulus of the grid cell is less than or equal to the preset gradient threshold, monitoring wells are deployed along the direction of risk factor change. For grid cells with a medium monitoring priority, if there is a single risk element in the grid cell, a monitoring well should be installed at the center of the risk element in the grid cell. For grid cells with low monitoring priority, no monitoring wells will be installed in that grid cell.

8. The groundwater source monitoring method based on risk evolution according to claim 1, characterized in that, The monitoring of groundwater-type water sources based on the aforementioned monitoring well deployment scheme includes: The well location coordinates and well type of each monitoring well are determined according to the monitoring well layout plan; the monitoring well types include water level monitoring wells for collecting water level data and water quality monitoring wells for collecting water quality data. Set monitoring parameters for each monitoring well. The monitoring parameters for water level monitoring wells include water level depth or water level elevation, and the monitoring parameters for water quality monitoring wells include the concentration of at least one characteristic pollutant among nitrates, chlorides, and heavy metals. Monitoring data is collected according to the initial sampling period and uploaded to the terminal according to the data upload frequency; wherein, the initial sampling period of the monitoring wells in the grid unit with high monitoring priority is shorter than that of the monitoring wells in the grid unit with low monitoring priority, and the data upload frequency of the monitoring wells in the grid unit with high monitoring priority is higher than that of the monitoring wells in the grid unit with low monitoring priority. The risk level of each grid unit is assessed in real time based on the monitoring data uploaded to the terminal: For any grid unit, if the concentration of the characteristic pollutant in the current sampling period is greater than that in the previous sampling period, and the rate of water level decline in the current sampling period is greater than that in the previous sampling period, or if the concentration of the characteristic pollutant continues to increase in two consecutive sampling periods, or if the rate of water level decline continues to increase in two consecutive sampling periods, then the risk level of the grid unit increases; if the concentration of the characteristic pollutant in the current sampling period is less than that in the previous sampling period, and the rate of water level decline in the current sampling period is less than that in the previous sampling period, then the risk level of the grid unit decreases. If the risk level of a grid cell increases, the sampling frequency and data upload frequency of the monitoring wells within that grid cell will be increased. If the risk level of a grid cell decreases and remains stable for more than two sampling cycles, then the sampling frequency and data upload frequency of the monitoring wells within that grid cell should be reduced.

9. A groundwater source monitoring device based on risk evolution, characterized in that, include: The risk evolution determination module is used to determine the risk evolution information of groundwater-type water sources in the target area based on historical monitoring data. The historical monitoring data includes water quality monitoring data, water level monitoring data, hydrogeological data, and data on potential external pollution sources. The risk evolution information includes the spatial distribution and change direction of risk elements. Risk elements include pollution plumes, groundwater drawdown cones, weak zones, and potential external pollution sources. The change direction of risk elements includes the migration direction of pollution plumes, the expansion direction of groundwater drawdown cones, the extension direction of weak zones, and the direction of potential external pollution sources pointing towards the water source well. The priority determination module is used to divide the target area into multiple grid units and determine the monitoring priority of each grid unit based on the spatial relationship between each grid unit and the risk element. The risk evolution index calculation module is used to calculate the risk evolution index of each grid unit in the target area based on risk evolution information. The risk evolution index includes the pollution plume migration intensity, the fall funnel expansion intensity, the vulnerability intensity of the weak area, and the threat intensity of external potential pollution sources. The monitoring module is used to determine the deployment scheme of monitoring wells based on the changing direction of risk factors, risk evolution index and monitoring priority of each grid unit, and to monitor groundwater source areas based on the deployment scheme of the monitoring wells.

10. A terminal comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the risk evolution-based groundwater source monitoring method as described in any one of claims 1 to 8.

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