Groundwater health risk early warning method and system based on data analysis

Abstract

The application discloses a groundwater health risk early warning method and system based on data analysis, relates to the technical field of groundwater health analysis, and realizes quantitative analysis on the accumulation tendency of pollutants in potential slow-flow enrichment area data Pos by establishing a pollutant deposition potential function Fun oriented to the deposition mechanism. Compared with the coarse-grained evaluation mode of the traditional method which only relies on the average concentration value of pollutants for static judgment, the method can accurately identify the pollutant deposition amplification phenomenon existing in the local low-velocity area, then quantifies the compound enrichment degree of pollutants under the micro-scale hydrodynamic structure through the enrichment bias index Ebi, and constructs the groundwater pollution deposition risk grade standard and zoning early warning mechanism based on the same. The scheme effectively makes up for the identification loss and response lag problems of the existing groundwater pollution evaluation means for the slow-flow driven enrichment mechanism.

Description

Technical Field

[0001] This invention relates to the field of groundwater health analysis technology, specifically to a groundwater health risk early warning method and system based on data analysis. Background Technology

[0002] In the modern water environment science system, water resource security assessment and water quality early warning mechanisms have long been core research directions in the field of environmental monitoring. Particularly in underground environmental systems, the spatial concealment and slow-moving nature of processes make groundwater pollution identification, evolutionary process modeling, and health risk early warning particularly complex. Within this system, a specialized subfield focusing on groundwater health risk analysis has gradually emerged, emphasizing the identification of water quality fluctuations, long-term exposure to trace pollutants, and their migration characteristics that may pose health risks to the population.

[0003] Current mainstream groundwater risk monitoring and early warning methods still rely primarily on concentration threshold judgment and regional mean analysis. While these methods are simple in structure and mature in application, they exhibit significant limitations in identifying local risks when dealing with groundwater environments exhibiting marked spatial heterogeneity. For example, in scenarios where pollution accumulates in low-flow-velocity areas, these traditional methods often overlook the pollutant accumulation process caused by the slowing or near-stagnation of water flow within a localized area. Because the overall water quality parameters in such areas may not necessarily show a trend of exceeding standards, they are easily misclassified as "safe areas" under existing monitoring mechanisms.

[0004] More seriously, in real-world scenarios, monitoring well placement typically follows an average distribution or surface administrative demarcation standards, further leading to blind spots in the identification of microscale pollution points. For example, at industrial plant boundaries, in wastewater recharge zones, or in areas of old strata subsidence, the low flow of groundwater in localized spaces significantly slows down the natural dilution process of pollutants. This causes pollutants in these areas, such as heavy metals, fluorides, and nitrates, to remain in an enriched state for extended periods. However, such changes are unlikely to trigger conventional alarm mechanisms due to the slow concentration increase. This leads to a bias in the overall assessment of "groundwater health," consequently affecting downstream processes such as water resource allocation, safe water supply decisions, and public health management. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a groundwater health risk early warning method and system based on data analysis, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a groundwater health risk early warning method based on data analysis, comprising the following steps:

[0007] S1. Collect basic environmental dataset Env within the target monitoring area using a groundwater flow velocity sensor;

[0008] S2. Perform spatial interpolation and isosurface fitting on the basic environmental dataset Env to construct the water flow velocity map data Vmd and delineate the potential slow-flow enrichment area data Pos.

[0009] S3. Extract the basic environmental dataset Env from the identified potential slow-flow enrichment area data Pos, analyze the changing trend in the time dimension, and calculate the pollutant spatial offset rate index Dis.

[0010] S4. Combining the water flow velocity map data Vmd, the pollutant spatial offset rate index Dis, and the basic environmental dataset Env, a pollutant deposition potential function Fun is established. The deposition risk of pollutants under slow flow conditions is calculated for each slow flow enrichment micro-region in the potential slow flow enrichment region data Pos, and the enrichment bias index Ebi is obtained.

[0011] S5. Compare the enrichment bias index Ebi with the preset threshold area to construct a groundwater pollution deposition risk level standard for the target monitoring area, and issue an early warning based on the groundwater pollution deposition risk level standard.

[0012] Preferably, S1 includes S11 and S12;

[0013] S11. By deploying groundwater flow velocity sensors and hydrogeological observation equipment within the target monitoring area, collect groundwater hydrodynamic parameter data to form a groundwater dynamic dataset Dgw;

[0014] The groundwater dynamics dataset Dgw includes flow velocity distribution data Vel, hydraulic gradient distribution data Gra, groundwater permeability data Per, and aquifer thickness data Thk.

[0015] Among them, the groundwater flow velocity sensor and hydrogeological observation equipment include a groundwater miniature electromagnetic flow velocity sensor, a pressure sensor, a high-resolution automatic water level recorder, and a geological profile conductivity profiler.

[0016] S12. Collect concentration data of typical pollutants by deploying water quality testing equipment, obtain pollutant concentration monitoring data of the same spatial location in the target monitoring area, form a groundwater pollution index dataset Dpc, and then integrate it with the groundwater dynamic dataset Dgw to obtain the basic environmental dataset Env.

[0017] The groundwater pollution index dataset Dpc includes nitrate concentration data Con1, arsenic concentration data Con2, and lead concentration data Con3.

[0018] The water quality testing equipment includes an online ultraviolet absorption water quality analyzer, a groundwater arsenic-specific electrochemical analyzer, and a portable heavy metal ion detector.

[0019] Preferably, S2 includes S21;

[0020] S21. Extract the water flow velocity distribution data Vel from the basic environmental dataset Env. Using the spatial coordinate system of the target monitoring area as a reference, construct a two-dimensional regular spatial grid. Use each grid point in the two-dimensional regular spatial grid as an observation point. Then, using the water flow velocity distribution data Vel at the corresponding location of the observation point, execute the Kriging interpolation method to construct a continuous flow velocity prediction function to estimate the groundwater flow velocity value at any observation point.

[0021] The water velocity values ​​are mapped onto the entire two-dimensional regular space grid to form a continuously distributed two-dimensional velocity field, and a velocity isosurface model is generated accordingly. The velocity isosurface model divides the velocity change characteristics within the region through isovelocity lines, and generates water velocity map data Vmd covering the target area.

[0022] Preferably, S2 further includes S22;

[0023] S22. Statistically calculate the groundwater velocity values ​​of all observation points in the water velocity map data Vmd to obtain the regional average velocity parameter Vavg of the target monitoring area within the target monitoring period, which is used to reflect the benchmark level of groundwater velocity in the target monitoring area.

[0024] Using 30% of the average flow velocity parameter Vavg in the region as the threshold for slow flow determination, each observation point in the flow velocity map data Vmd is traversed and judged to filter out all observation points whose predicted flow velocity value is lower than the threshold, and the observation points are marked as slow flow identification grid points.

[0025] Based on the spatial adjacency analysis algorithm, spatial clustering is performed on all slow-flow identification grid points to extract contiguous distribution areas, which are defined as potential slow-flow enrichment area data Pos.

[0026] Preferably, S3 includes S31;

[0027] S31. Based on the potential slow-flow enrichment area data Pos, extract the groundwater pollution index dataset Dpc from the basic environmental dataset Env, where all spatial coordinates are consistent with the slow-flow identification grid points in the potential slow-flow enrichment area data Pos. Then, perform interpolation completion and noise removal preprocessing on the groundwater pollution index dataset Dpc to fill in the missing data points in the monitoring period. Based on the sliding median filter, clean up abrupt values ​​and random noise.

[0028] After preprocessing, the groundwater pollution index dataset Dpc is structured according to the location of the slow-flow identification grid points to establish a pollutant concentration change trend dataset Dtc based on the slow-flow identification grid points in the slow-flow enrichment area data Pos.

[0029] Preferably, S3 further includes S32;

[0030] S32. By selecting from the basic environmental dataset Env the control grid points that are adjacent to the potential slow-flow enrichment region data Pos in the spatial coordinate system but are not marked as slow-flow identification grid points, a set of non-slow-flow control regions Nrg is constructed.

[0031] The groundwater pollution index dataset Dpc for each control grid point is extracted from the non-slow-flow control area set Nrg, and a time series dataset of control pollutant change trends DtcZ is constructed.

[0032] Then, calculate the rate of change of nitrate concentration data Con1, arsenic concentration data Con2, and lead concentration data Con3 in the pollutant concentration change trend dataset Dtc and the control pollutant change trend dataset DtcZ respectively, and obtain the difference in the rate of change of nitrate concentration Csl, the difference in the rate of change of arsenic concentration Cs2, and the difference in the rate of change of lead concentration Cs3.

[0033] The absolute values ​​of the change rates of the three types of pollutants are then calculated, and the average value is calculated within a preset monitoring period to obtain the spatial offset rate index Dis. The spatial offset rate index Dis is used to quantify the difference between the pollutant concentration change trend in the slow-flow area and the control area, reflecting whether there is a risk of pollutant migration slowdown and deposition.

[0034] The spatial offset index Dis includes the spatial offset Cd1 of nitrate concentration data Con1, the spatial offset Cd2 of arsenic concentration data Con2, and the spatial offset Cd3 of lead concentration data Con3.

[0035] Preferably, S4 includes S41;

[0036] S41. Extract the following parameters from the slow-flow identification grid points: nitrate concentration data Con1 spatial offset Cd1, arsenic concentration data Con2 spatial offset Cd2, lead concentration data Con3 spatial offset Cd3, water flow velocity map data Vmd, hydraulic gradient distribution data Gra, groundwater permeability data Per, and aquifer thickness data Thk. Then, normalize the parameters using the range normalization method to unify the parameter value range to [0,1] and eliminate the dimensional differences between different parameters.

[0037] Then, a linear weighted modeling method is used to establish a pollutant deposition potential function Fun for the normalized parameters, and the deposition potential of the three types of pollutants at the slow flow identification grid point is expressed quantitatively.

[0038] The pollutant deposition potential function Fun is established using the following formula:

[0039] ;

[0040] In the formula, Let Vmd(p) represent the deposition potential function of pollutant of type i at the slow-flow identification grid point p, and let Vmd(p) represent the groundwater velocity value at the slow-flow identification grid point p in the water velocity map data Vmd. This represents the spatial offset rate of the i-th type of pollutant at grid point p in the slow-flow identification grid. This represents the hydraulic gradient value of the hydraulic gradient distribution data Gra at the location p of the slow-flow identification grid point. This represents the permeability of groundwater permeability data Per at location p in the slow-flow identification grid. The water layer thickness data Thk represents the water layer thickness at the slow flow identification grid point p; w1, w2, w3, w4 and w5 represent the groundwater flow velocity, spatial offset rate index of the i-th type of pollutant, hydraulic gradient value, permeability and water layer thickness weight coefficients at the slow flow identification grid point p, respectively, and w1+w2+w3+w4+w5=1, the specific values ​​are set by the user;

[0041] Where i∈[1,2,3], 1 represents the spatial offset rate Cd1 of nitrate concentration data Con1, 2 represents the spatial offset rate Cd2 of arsenic concentration data Con2, and 3 represents the spatial offset rate Cd3 of lead concentration data Con3.

[0042] Preferably, S4 further includes S42;

[0043] S42. Based on the obtained pollutant deposition potential function representing the i-th type of pollutant at the slow-flow identification grid point p. The maximum value among the three types of pollutants is used to quantify the enrichment bias index Ebi(p) at the slow flow identification grid point p, which represents the joint enrichment trend.

[0044] The enrichment bias index Ebi(p) is obtained through... Obtain the calculation formula.

[0045] Preferably, S5 includes S51;

[0046] S51. Based on the enrichment bias index Ebi(p) at the location p of the slow-flow identification grid point, compare it with the preset threshold range to construct the groundwater pollution deposition risk level standard for the target monitoring area, and generate early warning and response according to the groundwater pollution deposition risk level standard.

[0047] The threshold range is preset to the range [0,1].

[0048] The groundwater contamination deposition risk level standard was obtained through the following comparison method:

[0049] When 0 ≤ enrichment bias index Ebi(p) < 0.3 at the location p of the slow flow identification grid point, it indicates a low risk of deposition bias. The location p of the slow flow identification grid point is marked as L1 level, and no warning or response is given. Monitoring continues.

[0050] When the enrichment bias index Ebi(p) at the location p of the slow flow identification grid point is less than 0.6, it indicates a medium risk of deposition bias. The location p of the slow flow identification grid point is marked as L2 level, and a prompt response operation to increase the sampling frequency and scheduling frequency is executed.

[0051] When the enrichment bias index Ebi(p) at the location p of the slow flow identification grid point is ≤1, it indicates a high risk of sedimentation bias. The location p of the slow flow identification grid point is marked as L3 level, and an early warning response and a groundwater use behavior restriction prompt response operation are performed at the location p of the slow flow identification grid point.

[0052] The groundwater health risk early warning system based on data analysis includes a groundwater data acquisition module, a data flow velocity area identification module, a low flow velocity concentration change calculation module, a regional sedimentation risk calculation module, and a response decision module.

[0053] The groundwater data acquisition module collects basic environmental dataset Env within the target monitoring area through a groundwater flow velocity sensor.

[0054] The data velocity region identification module performs spatial interpolation and isosurface fitting on the basic environmental dataset Env to construct the water velocity map data Vmd and delineate the potential slow-flow enrichment region data Pos.

[0055] The low-velocity concentration change calculation module extracts the basic environmental dataset Env from the identified potential slow-flow enrichment area data Pos, analyzes the changing trend over time, and calculates the pollutant spatial offset rate index Dis.

[0056] The regional deposition risk calculation module combines the water flow velocity map data Vmd, the pollutant spatial offset rate index Dis, and the basic environmental dataset Env to establish the pollutant deposition potential function Fun. It calculates the deposition risk of pollutants under slow flow conditions for each slow flow enrichment micro-region in the potential slow flow enrichment area data Pos and obtains the enrichment bias index Ebi.

[0057] The response decision module compares the enrichment bias index Ebi with the preset threshold area to construct a groundwater pollution deposition risk level standard for the target monitoring area, and issues an early warning based on the groundwater pollution deposition risk level standard.

[0058] This invention provides a groundwater health risk early warning method and system based on data analysis, which has the following beneficial effects:

[0059] (1) By establishing a pollutant deposition potential function Fun guided by deposition mechanism, a quantitative analysis of pollutant accumulation tendency in the data Pos of potential slow-flow enrichment areas was achieved. Compared with the coarse-grained assessment method that relies solely on the average concentration of pollutants for static judgment, this method can accurately identify the pollutant deposition amplification phenomenon in local low-velocity areas. Then, the enrichment bias index Ebi is used to quantify the degree of composite enrichment of pollutants under micro-scale hydrodynamic structure, and based on this, a groundwater pollution deposition risk level standard and zoning early warning mechanism are constructed. This scheme effectively makes up for the lack of identification and response lag of the "slow-flow driven enrichment mechanism" in existing groundwater pollution assessment methods.

[0060] (2) Based on the slow-flow identification grid points in the potential slow-flow enrichment area data Pos, the corresponding groundwater pollution index dataset Dpc is extracted from the basic environmental dataset Env, and a control pollutant change trend dataset DtcZ is generated, forming the pollutant spatial offset rate index Dis. This effectively realizes the detection of minute dynamic differences in pollutant change trends between slow-flow areas and conventional areas, breaking through the monitoring bottleneck of traditional static concentration assessment that cannot identify the "slowing migration rate" precursor to enrichment. It provides a reliable dynamic behavior characterization basis for the subsequent construction of the pollutant deposition potential function Fun and the enrichment bias index Ebi, and is especially suitable for trend perception and latent risk judgment when early enrichment has not yet formed significant concentration accumulation.

[0061] (3) An enrichment bias index Ebi(p) is generated at the location p of the slow-flow identification grid point to express the enrichment bias trend of composite pollutants at that location. By comparing the enrichment bias index Ebi(p) at the location p of the slow-flow identification grid point with a preset threshold range, a groundwater pollution deposition risk level standard for the target monitoring area is constructed, and a segmented strategy of L1, L2, and L3 levels is formed to realize continuous monitoring from a low-risk state to a use restriction prompt response operation under a high-risk state. The above method significantly improves the quantitative identification capability of the deposition evolution process of groundwater pollutants, not only enhancing the response accuracy of microscale enrichment changes, but also providing an early warning and actionable response basis for risk management at the regional scale, possessing high practicality and regulatory integration value. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the steps of the groundwater health risk early warning method based on data analysis of the present invention.

[0063] Figure 2 This is a schematic diagram of the groundwater health risk early warning system based on data analysis according to the present invention.

[0064] Figure 3 A schematic diagram showing the enrichment bias index Ebi(p) and risk level for identifying grid point location p in slow flow. Detailed Implementation

[0065] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0066] Example 1: This invention provides a groundwater health risk early warning method based on data analysis. Please refer to [link / reference]. Figure 1 This includes the following steps:

[0067] S1. Collect basic environmental dataset Env within the target monitoring area using a groundwater flow velocity sensor;

[0068] S2. Perform spatial interpolation and isosurface fitting on the basic environmental dataset Env to construct the water flow velocity map data Vmd and delineate the potential slow-flow enrichment area data Pos.

[0069] S3. Extract the basic environmental dataset Env from the identified potential slow-flow enrichment area data Pos, analyze the changing trend in the time dimension, and calculate the pollutant spatial offset rate index Dis.

[0070] S4. Combining the water flow velocity map data Vmd, the pollutant spatial offset rate index Dis, and the basic environmental dataset Env, a pollutant deposition potential function Fun is established. The deposition risk of pollutants under slow flow conditions is calculated for each slow flow enrichment micro-region in the potential slow flow enrichment region data Pos, and the enrichment bias index Ebi is obtained.

[0071] S5. Compare the enrichment bias index Ebi with the preset threshold area to construct a groundwater pollution deposition risk level standard for the target monitoring area, and issue an early warning based on the groundwater pollution deposition risk level standard.

[0072] In this embodiment, a pollutant deposition potential function Fun, guided by deposition mechanism, is established to achieve quantitative analysis of pollutant accumulation tendency in the data Pos of potential slow-flow enrichment areas. Compared with the coarse-grained assessment method of traditional methods that rely solely on the average concentration of pollutants for static judgment, this method can accurately identify the amplification of pollutant deposition in local low-velocity areas. Furthermore, it quantifies the degree of composite enrichment of pollutants under microscale hydrodynamic structures using the enrichment bias index Ebi, and constructs groundwater pollution deposition risk level standards and zonal early warning mechanisms based on this. This scheme effectively compensates for the lack of identification and response lag of "slow-flow driven enrichment mechanisms" in existing groundwater pollution assessment methods. It is particularly suitable for early warning needs of depositional health risks in areas where concentration values ​​have not yet exceeded standards but where local dynamic anomalies exist, and has stronger foresight, spatial adaptability, and health response value.

[0073] Example 2: Specifically: S1 includes S11 and S12;

[0074] S11. By deploying groundwater flow velocity sensors and hydrogeological observation equipment within the target monitoring area, collect groundwater hydrodynamic parameter data to form a groundwater dynamic dataset Dgw;

[0075] The groundwater dynamics dataset Dgw includes flow velocity distribution data Vel, hydraulic gradient distribution data Gra, groundwater permeability data Per, and aquifer thickness data Thk.

[0076] Among them, the groundwater flow velocity sensor and hydrogeological observation equipment include a groundwater miniature electromagnetic flow velocity sensor, a pressure sensor, a high-resolution automatic water level recorder, and a geological profile conductivity profiler.

[0077] The water flow velocity distribution data Vel is used to describe the spatial variation of groundwater flow velocity within different spatial units; the hydraulic gradient distribution data Gra is used to describe the trend of groundwater head variation in the vertical and horizontal directions; the groundwater permeability data Per is used to reflect the ease with which the formation allows water to permeate; and the aquifer thickness data Thk is used to reflect the effective water storage thickness of the aquifer.

[0078] S12. Collect concentration data of typical pollutants by deploying water quality testing equipment, obtain pollutant concentration monitoring data of the same spatial location in the target monitoring area, form a groundwater pollution index dataset Dpc, and then integrate it with the groundwater dynamic dataset Dgw to obtain the basic environmental dataset Env.

[0079] The groundwater pollution index dataset Dpc includes nitrate concentration data Con1, arsenic concentration data Con2, and lead concentration data Con3.

[0080] The water quality testing equipment includes an online ultraviolet absorption water quality analyzer, a groundwater arsenic-specific electrochemical analyzer, and a portable heavy metal ion detector.

[0081] The nitrate concentration data Con1 is used to assess the extent of agricultural non-point source pollution penetration; the arsenic concentration data Con2 is used to identify the impact of natural geochemical background or industrial emissions; and the lead concentration data Con3 is used to monitor the evolution of metallic pollutants in water bodies.

[0082] In this embodiment, a sensing system composed of a groundwater micro electromagnetic velocity sensor, a pressure sensor, a high-resolution automatic water level recorder, and a geological profile conductivity profiler is used to systematically collect water flow velocity distribution data Vel, hydraulic gradient distribution data Gra, groundwater permeability data Per, and aquifer thickness data Thk, forming a groundwater dynamic dataset Dgw. This dataset comprehensively characterizes the flow driving force and media response characteristics of groundwater in different spatial units. Simultaneously, using an online ultraviolet absorption water quality analyzer, a groundwater arsenic-specific electrochemical analyzer, and a portable heavy metal ion detector, nitrate concentration data Con1, arsenic concentration data Con2, and lead concentration data Con3 are simultaneously acquired at the same spatial location to construct a groundwater pollution index dataset Dpc. By integrating the groundwater dynamics dataset Dgw and the groundwater pollution index dataset Dpc into a unified structure, a basic environmental dataset Env with spatial consistency and index complementarity is formed. This provides a unified data benchmark platform for subsequent slow-flow identification, sediment modeling, and risk early warning, fundamentally improving data fusionability and modeling accuracy, and providing quantifiable and traceable real data support for the dynamic simulation of groundwater pollution processes.

[0083] Example 3: Specifically: S2 includes S21;

[0084] S21. Extract the water flow velocity distribution data Vel from the basic environmental dataset Env. Using the spatial coordinate system of the target monitoring area as a reference, construct a two-dimensional regular spatial grid. Use each grid point in the two-dimensional regular spatial grid as an observation point. Then, using the water flow velocity distribution data Vel at the corresponding location of the observation point, execute the Kriging interpolation method to construct a continuous flow velocity prediction function to estimate the groundwater flow velocity value at any observation point.

[0085] The water velocity values ​​are mapped onto the entire two-dimensional regular space grid to form a continuously distributed two-dimensional velocity field, and a velocity isosurface model is generated accordingly. The velocity isosurface model divides the velocity change characteristics within the region through isovelocity lines, and generates water velocity map data Vmd covering the target area.

[0086] The water flow velocity map data Vmd is used to express the spatial velocity difference distribution of groundwater throughout the region, and is a prerequisite input for subsequent identification of slow-flowing areas and establishment of pollutant enrichment models.

[0087] S2 further includes S22;

[0088] S22. Statistically calculate the groundwater velocity values ​​of all observation points in the water velocity map data Vmd to obtain the regional average velocity parameter Vavg of the target monitoring area within the target monitoring period, which is used to reflect the benchmark level of groundwater velocity in the target monitoring area.

[0089] Using 30% of the average flow velocity parameter Vavg in the region as the threshold for slow flow determination, each observation point in the flow velocity map data Vmd is traversed and judged to filter out all observation points whose predicted flow velocity value is lower than the threshold, and the observation points are marked as slow flow identification grid points.

[0090] Based on the spatial adjacency analysis algorithm, spatial clustering is performed on all slow-flow identification grid points to extract contiguous distribution areas, which are defined as potential slow-flow enrichment area data Pos.

[0091] The potential slow-flow enrichment area data Pos is used to represent a set of spatial regions that exhibit persistently low flow velocities and a tendency for pollutant deposition and enrichment under current groundwater dynamic conditions. It serves as the spatial input basis for subsequent pollutant spatial migration analysis and enrichment bias index calculation.

[0092] In this embodiment, Kriging interpolation is applied to the water velocity distribution data Vel in the basic environmental dataset Env to construct a continuous prediction function, generating a two-dimensional velocity isosurface model covering the target monitoring area. This leads to the establishment of a water velocity map data Vmd expressing the spatial velocity differences of groundwater at the regional scale. Based on the water velocity map data Vmd, a slow-flow judgment threshold is constructed using the regional average velocity parameter Vavg. A spatial adjacency analysis algorithm is then used to cluster the slow-flow identification grid points, ultimately constructing potential slow-flow enrichment area data Pos. This achieves spatial explicit expression and continuous area extraction of groundwater slow-flow risk. Compared to existing slow-flow area delineation methods based on single-point judgment or discrete sample inference, this method significantly improves the continuity of velocity identification and the objectivity of the judgment threshold, avoiding local misjudgments or human intervention in slow-flow identification. Simultaneously, it ensures that the potential slow-flow enrichment area data Pos possesses spatial traceability and boundary closure, providing a logically clear and data-consistent spatial input guarantee for the subsequent construction of the pollutant spatial offset rate index Dis and the execution of the pollutant deposition potential function Fun.

[0093] Example 4: Specifically: S3 includes S31;

[0094] S31. Based on the potential slow-flow enrichment area data Pos, extract the groundwater pollution index dataset Dpc from the basic environmental dataset Env, where all spatial coordinates are consistent with the slow-flow identification grid points in the potential slow-flow enrichment area data Pos. Then, perform interpolation completion and noise removal preprocessing on the groundwater pollution index dataset Dpc to fill in the missing data points in the monitoring period. Based on the sliding median filter, clean up abrupt values ​​and random noise.

[0095] After preprocessing, the groundwater pollution index dataset Dpc is structured according to the location of the slow-flow identification grid points to establish a pollutant concentration change trend dataset Dtc based on the slow-flow identification grid points in the slow-flow enrichment area data Pos.

[0096] S3 further includes S32;

[0097] S32. By selecting from the basic environmental dataset Env the control grid points that are adjacent to the potential slow-flow enrichment region data Pos in the spatial coordinate system but are not marked as slow-flow identification grid points, a set of non-slow-flow control regions Nrg is constructed.

[0098] The groundwater pollution index dataset Dpc for each control grid point is extracted from the non-slow-flow control area set Nrg, and a time series dataset of control pollutant change trends DtcZ is constructed.

[0099] Then, calculate the rate of change of nitrate concentration data Con1, arsenic concentration data Con2, and lead concentration data Con3 in the pollutant concentration change trend dataset Dtc and the control pollutant change trend dataset DtcZ respectively, and obtain the difference in the rate of change of nitrate concentration Csl, the difference in the rate of change of arsenic concentration Cs2, and the difference in the rate of change of lead concentration Cs3.

[0100] The rate of change is obtained by using the first-order difference method;

[0101] The rate of change is the difference in the rate of change of pollutants between the slow-flow identification grid area and the control grid area at the same time point, expressed in mg / L·day. -1 ;

[0102] Examples of obtaining the rate of change differences in nitrate concentration (Csl), arsenic concentration (Cs2), and lead concentration (Cs3):

[0103] Set the following parameters:

[0104] The monitoring period is in days, and the concentration is in milligrams per liter (mg / L).

[0105] Nitrate concentration data Con1:

[0106] Time t1 = Day 1, Concentration (mg / L) at the slow flow identification grid point p: 19.5, Concentration (mg / L) at the control grid point: 21.0;

[0107] Time t2 = Day 2, Concentration (mg / L) at the slow flow identification grid point p: 20.1, Concentration (mg / L) at the control grid point: 21.8;

[0108] The rate of change of concentration at the slow-flow identification grid points is (20.1-19.5) / 1 = 0.6 mg / L;

[0109] The rate of change of concentration at the control grid point is (21.8-21.0) / 1 = 0.8 mg / L;

[0110] The rate of change of nitrate concentration, Csl = 0.6 - 0.8 = -0.2 mg / L;

[0111] Arsenic concentration data Con2:

[0112] Time t1 = Day 1, Concentration (mg / L) at the slow flow identification grid point p: 0.014, Concentration (mg / L) at the control grid point: 0.017;

[0113] Time t2 = Day 2, Concentration (mg / L) at grid point p in slow flow identification: 0.015, Concentration (mg / L) at control grid point: 0.021;

[0114] The rate of change of concentration at the slow-flow identification grid points is: (0.015-0.014) / 1 = 0.001 mg / L;

[0115] The rate of change of concentration at the control grid points: (0.021-0.017) / 1 = 0.004 mg / L;

[0116] The rate of change of arsenic concentration, Cs² = 0.001 - 0.004 = -0.003 mg / L;

[0117] Lead concentration data Con3:

[0118] Time t1 = Day 1, Concentration (mg / L) at the slow flow identification grid point p: 0.085, Concentration (mg / L) at the control grid point: 0.091;

[0119] Time t2 = Day 2, Concentration (mg / L) at the slow flow identification grid point p: 0.086, Concentration (mg / L) at the control grid point: 0.094;

[0120] The rate of change of concentration at the slow-flow identification grid points is: (0.086-0.085) / 1 = 0.001 mg / L;

[0121] The rate of change of concentration at the control grid point is (0.094-0.091) / 1 = 0.003 mg / L;

[0122] The difference in the rate of change of lead concentration, Cs3 = 0.001 - 0.003 = -0.002 mg / L;

[0123] As illustrated in the example above, within the same time period, the rate of change of pollutant concentration at grid point p in the slow-flow identification area is lower than the rate of change of pollutant concentration in the adjacent non-slow-flow control area.

[0124] The absolute values ​​of the change rates of the three types of pollutants are then calculated, and the average value is calculated within a preset monitoring period to obtain the spatial offset rate index Dis. The spatial offset rate index Dis is used to quantify the difference between the pollutant concentration change trend in the slow-flow area and the control area, reflecting whether there is a risk of pollutant migration slowdown and deposition.

[0125] The spatial offset index Dis includes the spatial offset Cd1 of nitrate concentration data Con1, the spatial offset Cd2 of arsenic concentration data Con2, and the spatial offset Cd3 of lead concentration data Con3.

[0126] In this embodiment, based on the slow-flow identification grid points in the potential slow-flow enrichment area data Pos, the corresponding groundwater pollution index dataset Dpc is extracted from the basic environmental dataset Env. Interpolation completion and moving median filtering are used to achieve structural integrity and stability control of the multi-pollutant data, ultimately forming a pollutant concentration change trend dataset Dtc. Simultaneously, a non-slow-flow control area set Nrg is constructed through spatial coordinate system proximity analysis, and a control pollutant change trend dataset DtcZ is generated. The first-order difference method is used to calculate the difference in nitrate concentration change rate Csl, arsenic concentration change rate Cs2, and lead concentration change rate Cs3 between the two types of areas. Furthermore, the spatial offset rate index Dis, composed of the spatial offset rate Cd1 of nitrate concentration data Con1, the spatial offset rate Cd2 of arsenic concentration data Con2, and the spatial offset rate Cd3 of lead concentration data Con3, is derived. This method effectively detects minute dynamic differences in pollutant change trends between slow-flowing and conventional areas, breaking through the monitoring bottleneck of traditional static concentration assessment that cannot identify the precursor of enrichment, "slowing migration rate". It provides a reliable dynamic behavior characterization basis for the subsequent construction of pollutant deposition potential function Fun and enrichment bias index Ebi, and is especially suitable for trend perception and latent risk judgment when early enrichment has not yet formed significant concentration accumulation.

[0127] Example 5: Please refer to Figure 1 and Figure 3 Specifically: S4 includes S41;

[0128] S41. Extract the following parameters from the slow-flow identification grid points: nitrate concentration data Con1 spatial offset Cd1, arsenic concentration data Con2 spatial offset Cd2, lead concentration data Con3 spatial offset Cd3, water flow velocity map data Vmd, hydraulic gradient distribution data Gra, groundwater permeability data Per, and aquifer thickness data Thk. Then, normalize the parameters using the range normalization method to unify the parameter value range to [0,1] and eliminate the dimensional differences between different parameters.

[0129] Then, a linear weighted modeling method is used to establish a pollutant deposition potential function Fun for the normalized parameters, and the deposition potential of the three types of pollutants at the slow flow identification grid point is expressed quantitatively.

[0130] The pollutant deposition potential function Fun is established using the following formula:

[0131] ;

[0132] In the formula, Let Vmd(p) represent the deposition potential function of pollutant of type i at the slow-flow identification grid point p, and let Vmd(p) represent the groundwater velocity value at the slow-flow identification grid point p in the water velocity map data Vmd. This represents the spatial offset rate of the i-th type of pollutant at grid point p in the slow-flow identification grid. This represents the hydraulic gradient value of the hydraulic gradient distribution data Gra at the location p of the slow-flow identification grid point. This represents the permeability of groundwater permeability data Per at location p in the slow-flow identification grid. The water layer thickness data Thk represents the water layer thickness at the slow-flow identification grid point p; w1, w2, w3, w4, and w5 represent the groundwater velocity, spatial offset rate index of the i-th type of pollutant, hydraulic gradient value, permeability, and water layer thickness weighting coefficients at the slow-flow identification grid point p, respectively, and w1+w2+w3+w4+w5=1, with specific values ​​set by the user; the actual physical meaning of this formula is to quantitatively assess the relative potential for deposition or enrichment of specific pollutants in a local micro-region under slow-flow groundwater conditions, that is, to measure whether pollutants have the risk tendency to "remain continuously" and potentially accumulate and deposit in a micro-environment with weak hydrodynamics and complex geological conditions.

[0133] The physical meaning of the groundwater velocity value Vmd(p) at the slow-flow identification grid point p in the water flow velocity map data Vmd is that: the lower the velocity, the more likely it is that the migration of pollutants will be slowed down → and the easier it is to deposit them; (therefore, the lower the value, the higher the risk).

[0134] The spatial offset rate index of the i-th type of pollutant at the slow-flow identification grid point p The physical meaning of this value is: the higher the value, the stronger the "retention" of the pollutant in the slow-flowing zone → indicating that migration is hindered and deposition is more likely;

[0135] The hydraulic gradient distribution data Gra at the slow-flow identification grid point p is the hydraulic gradient value. The physical meaning is: a large gradient → accelerated flow → less tendency to deposit; a small gradient → easier accumulation of sediment; it can also be expressed as hydraulic driving force.

[0136] The groundwater permeability data Per is the permeability at the slow-flow identification grid point p. The physical significance is that low-permeability strata (low Per) may cause pollutants to "remain and deposit" in the pores;

[0137] The water layer thickness data Thk refers to the water layer thickness at the slow-flow identification grid point p. The physical significance is that water exchange is slow in thin areas, making it easy to form stagnant zones, while the thicker the layer, the greater its "scouring" ability, which is not conducive to sedimentation.

[0138] Where i∈[1,2,3], 1 represents the spatial offset rate Cd1 of nitrate concentration data Con1, 2 represents the spatial offset rate Cd2 of arsenic concentration data Con2, and 3 represents the spatial offset rate Cd3 of lead concentration data Con3.

[0139] S4 also includes S42;

[0140] S42. Based on the obtained pollutant deposition potential function representing the i-th type of pollutant at the slow-flow identification grid point p. The maximum value among the three types of pollutants is used to quantify the enrichment bias index Ebi(p) at the slow flow identification grid point p, which represents the joint enrichment trend.

[0141] The enrichment bias index Ebi(p) is obtained through... The calculation formula is used to obtain the maximum value; where max represents the maximum value operation.

[0142] S5 includes S51;

[0143] S51. Based on the enrichment bias index Ebi(p) at the location p of the slow-flow identification grid point, compare it with the preset threshold range to construct the groundwater pollution deposition risk level standard for the target monitoring area, and generate early warning and response according to the groundwater pollution deposition risk level standard.

[0144] The threshold range is preset to the range [0,1].

[0145] The groundwater contamination deposition risk level standard was obtained through the following comparison method:

[0146] When 0 ≤ enrichment bias index Ebi(p) < 0.3 at the location p of the slow flow identification grid point, it indicates a low risk of deposition bias. The location p of the slow flow identification grid point is marked as L1 level, and no warning or response is given. Monitoring continues.

[0147] When the enrichment bias index Ebi(p) at the location p of the slow flow identification grid point is less than 0.6, it indicates a medium risk of deposition bias. The location p of the slow flow identification grid point is marked as L2 level, and a prompt response operation to increase the sampling frequency and scheduling frequency is executed.

[0148] When the enrichment bias index Ebi(p) at the location p of the slow flow identification grid point is ≤1, it indicates a high risk of sedimentation bias. The location p of the slow flow identification grid point is marked as L3 level, and an early warning response and a groundwater use behavior restriction prompt response operation are performed at the location p of the slow flow identification grid point.

[0149] In this embodiment, the enrichment bias index Ebi(p) is used to quantitatively express the joint enrichment trend of groundwater pollutants and spatially map it to the risk level standard. Water flow velocity map data Vmd, hydraulic gradient distribution data Gra, groundwater permeability data Per, aquifer thickness data Thk, and spatial offset indices of three types of pollutants (including spatial offset Cd1 for nitrate concentration data Con1, spatial offset Cd2 for arsenic concentration data Con2, and spatial offset Cd3 for lead concentration data Con3) are selected. After range normalization of each parameter, a linear weighted modeling method is introduced to construct the pollutant deposition potential function Fun. This enables the fusion analysis of driving factors from different sources and with different physical meanings within the same evaluation framework and accurately captures the spatially explicit characteristics of deposition trends. Based on this, the deposition potential function results of the three types of pollutants are weighted and integrated to form an enrichment bias index Ebi(p) for the location p of the slow-flow identification grid point. This index expresses the enrichment bias trend of the composite pollutants at that location. By comparing the enrichment bias index Ebi(p) with a preset threshold range, a groundwater pollution deposition risk level standard for the target monitoring area is constructed, and a segmented strategy of L1, L2, and L3 levels is formed. This enables continuous monitoring from a low-risk state to a use restriction warning response operation under a high-risk state. The above method significantly improves the quantitative identification capability of the deposition evolution process of groundwater pollutants, not only enhancing the response accuracy of microscale enrichment changes but also providing an early warning and actionable response basis for risk management at the regional scale, possessing high practicality and regulatory integration value.

[0150] Example 6: Groundwater Health Risk Early Warning System Based on Data Analysis (Please refer to...) Figure 2 Specifically, it includes a groundwater data acquisition module, a data flow velocity area identification module, a low flow velocity concentration change calculation module, a regional sedimentation risk calculation module, and a response decision module;

[0151] The groundwater data acquisition module collects basic environmental dataset Env within the target monitoring area through a groundwater flow velocity sensor.

[0152] The data velocity region identification module performs spatial interpolation and isosurface fitting on the basic environmental dataset Env to construct the water velocity map data Vmd and delineate the potential slow-flow enrichment region data Pos.

[0153] The low-velocity concentration change calculation module extracts the basic environmental dataset Env from the identified potential slow-flow enrichment area data Pos, analyzes the changing trend over time, and calculates the pollutant spatial offset rate index Dis.

[0154] The regional deposition risk calculation module combines the water flow velocity map data Vmd, the pollutant spatial offset rate index Dis, and the basic environmental dataset Env to establish the pollutant deposition potential function Fun. It calculates the deposition risk of pollutants under slow flow conditions for each slow flow enrichment micro-region in the potential slow flow enrichment area data Pos and obtains the enrichment bias index Ebi.

[0155] The response decision module compares the enrichment bias index Ebi with the preset threshold area to construct a groundwater pollution deposition risk level standard for the target monitoring area, and issues an early warning based on the groundwater pollution deposition risk level standard.

[0156] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A groundwater health risk early warning method based on data analysis, characterized in that: Includes the following steps: S1. Collect basic environmental dataset Env within the target monitoring area using a groundwater flow velocity sensor; S2. Perform spatial interpolation and isosurface fitting on the basic environmental dataset Env to construct the water flow velocity map data Vmd and delineate the potential slow-flow enrichment area data Pos. S3. Extract the basic environmental dataset Env from the identified potential slow-flow enrichment area data Pos, analyze the changing trend in the time dimension, and calculate the pollutant spatial offset rate index Dis. S4. Combining the water flow velocity map data Vmd, the pollutant spatial offset rate index Dis, and the basic environmental dataset Env, a pollutant deposition potential function Fun is established. The deposition risk of pollutants under slow flow conditions is calculated for each slow flow enrichment micro-region in the potential slow flow enrichment region data Pos, and the enrichment bias index Ebi is obtained. S5. Compare the enrichment bias index Ebi with the preset threshold area to construct a groundwater pollution deposition risk level standard for the target monitoring area, and issue an early warning based on the groundwater pollution deposition risk level standard.

2. The groundwater health risk early warning method based on data analysis according to claim 1, characterized in that: S1 includes S11 and S12; S11. By deploying groundwater flow velocity sensors and hydrogeological observation equipment within the target monitoring area, collect groundwater hydrodynamic parameter data to form a groundwater dynamic dataset Dgw; The groundwater dynamics dataset Dgw includes flow velocity distribution data Vel, hydraulic gradient distribution data Gra, groundwater permeability data Per, and aquifer thickness data Thk. Among them, the groundwater flow velocity sensor and hydrogeological observation equipment include a groundwater miniature electromagnetic flow velocity sensor, a pressure sensor, a high-resolution automatic water level recorder, and a geological profile conductivity profiler. S12. Collect concentration data of typical pollutants by deploying water quality testing equipment, obtain pollutant concentration monitoring data of the same spatial location in the target monitoring area, form a groundwater pollution index dataset Dpc, and then integrate it with the groundwater dynamic dataset Dgw to obtain the basic environmental dataset Env. The groundwater pollution index dataset Dpc includes nitrate concentration data Con1, arsenic concentration data Con2, and lead concentration data Con3. The water quality testing equipment includes an online ultraviolet absorption water quality analyzer, a groundwater arsenic-specific electrochemical analyzer, and a portable heavy metal ion detector.

3. The groundwater health risk early warning method based on data analysis according to claim 2, characterized in that: S2 includes S21; S21. Extract the water flow velocity distribution data Vel from the basic environmental dataset Env. Using the spatial coordinate system of the target monitoring area as a reference, construct a two-dimensional regular spatial grid. Use each grid point in the two-dimensional regular spatial grid as an observation point. Then, using the water flow velocity distribution data Vel at the corresponding location of the observation point, execute the Kriging interpolation method to construct a continuous flow velocity prediction function to estimate the groundwater flow velocity value at any observation point. The water velocity values ​​are mapped onto the entire two-dimensional regular space grid to form a continuously distributed two-dimensional velocity field, and a velocity isosurface model is generated accordingly. The velocity isosurface model divides the velocity change characteristics within the region through isovelocity lines, and generates water velocity map data Vmd covering the target area.

4. The groundwater health risk early warning method based on data analysis according to claim 3, characterized in that: S2 further includes S22; S22. Statistically calculate the groundwater velocity values ​​of all observation points in the water velocity map data Vmd to obtain the regional average velocity parameter Vavg of the target monitoring area within the target monitoring period, which is used to reflect the benchmark level of groundwater velocity in the target monitoring area. Using 30% of the average flow velocity parameter Vavg in the region as the threshold for slow flow determination, each observation point in the flow velocity map data Vmd is traversed and judged to filter out all observation points whose predicted flow velocity value is lower than the threshold, and the observation points are marked as slow flow identification grid points. Based on the spatial adjacency analysis algorithm, spatial clustering is performed on all slow-flow identification grid points to extract contiguous distribution areas, which are defined as potential slow-flow enrichment area data Pos.

5. The groundwater health risk early warning method based on data analysis according to claim 4, characterized in that: S3 includes S31; S31. Based on the potential slow-flow enrichment area data Pos, extract the groundwater pollution index dataset Dpc from the basic environmental dataset Env, where all spatial coordinates are consistent with the slow-flow identification grid points in the potential slow-flow enrichment area data Pos. Then, perform interpolation completion and noise removal preprocessing on the groundwater pollution index dataset Dpc to fill in the missing data points in the monitoring period. Based on the sliding median filter, clean up abrupt values ​​and random noise. After preprocessing, the groundwater pollution index dataset Dpc is structured according to the location of the slow-flow identification grid points to establish a pollutant concentration change trend dataset Dtc based on the slow-flow identification grid points in the slow-flow enrichment area data Pos.

6. The groundwater health risk early warning method based on data analysis according to claim 5, characterized in that: S3 further includes S32; S32. By selecting from the basic environmental dataset Env the control grid points that are adjacent to the potential slow-flow enrichment region data Pos in the spatial coordinate system but are not marked as slow-flow identification grid points, a set of non-slow-flow control regions Nrg is constructed. The groundwater pollution index dataset Dpc for each control grid point is extracted from the non-slow-flow control area set Nrg, and a time series dataset of control pollutant change trends DtcZ is constructed. Then, calculate the rate of change of nitrate concentration data Con1, arsenic concentration data Con2, and lead concentration data Con3 in the pollutant concentration change trend dataset Dtc and the control pollutant change trend dataset DtcZ respectively, and obtain the difference in the rate of change of nitrate concentration Csl, the difference in the rate of change of arsenic concentration Cs2, and the difference in the rate of change of lead concentration Cs3. The absolute values ​​of the change rates of the three types of pollutants are then calculated, and the average value is calculated within a preset monitoring period to obtain the spatial offset rate index Dis. The spatial offset rate index Dis is used to quantify the difference between the pollutant concentration change trend in the slow-flow area and the control area, reflecting whether there is a risk of pollutant migration slowdown and deposition. The spatial offset index Dis includes the spatial offset Cd1 of nitrate concentration data Con1, the spatial offset Cd2 of arsenic concentration data Con2, and the spatial offset Cd3 of lead concentration data Con3.

7. The groundwater health risk early warning method based on data analysis according to claim 6, characterized in that: S4 includes S41; S41. Extract the following parameters from the slow-flow identification grid points: nitrate concentration data Con1 spatial offset Cd1, arsenic concentration data Con2 spatial offset Cd2, lead concentration data Con3 spatial offset Cd3, water flow velocity map data Vmd, hydraulic gradient distribution data Gra, groundwater permeability data Per, and aquifer thickness data Thk. Then, normalize the parameters using the range normalization method to unify the parameter value range to [0,1] and eliminate the dimensional differences between different parameters. Then, a linear weighted modeling method is used to establish a pollutant deposition potential function Fun for the normalized parameters, and the deposition potential of the three types of pollutants at the slow flow identification grid point is expressed quantitatively. The pollutant deposition potential function Fun is established using the following formula: ； In the formula, Let Vmd(p) represent the deposition potential function of pollutant of type i at the slow-flow identification grid point p, and let Vmd(p) represent the groundwater velocity value at the slow-flow identification grid point p in the water velocity map data Vmd. This represents the spatial offset rate of the i-th type of pollutant at grid point p in the slow-flow identification grid. This represents the hydraulic gradient value of the hydraulic gradient distribution data Gra at the location p of the slow-flow identification grid point. This represents the permeability of groundwater permeability data Per at location p in the slow-flow identification grid. This represents the water layer thickness data Thk at the location p of the slow-flow identification grid point; w1, w2, w3, w4 and w5 represent the groundwater flow velocity value, spatial offset rate index of pollutant of type i, hydraulic gradient value, permeability and water layer thickness weight coefficients at the slow flow identification grid point p, respectively, and w1+w2+w3+w4+w5=1, the specific values ​​are set by the user. Where i∈[1,2,3], 1 represents the spatial offset rate Cd1 of nitrate concentration data Con1, 2 represents the spatial offset rate Cd2 of arsenic concentration data Con2, and 3 represents the spatial offset rate Cd3 of lead concentration data Con3.

8. The groundwater health risk early warning method based on data analysis according to claim 7, characterized in that: S4 further includes S42; S42. Based on the obtained pollutant deposition potential function representing the i-th type of pollutant at the slow-flow identification grid point p. The maximum value among the three types of pollutants is used to quantify the enrichment bias index Ebi(p) at the slow-flow identification grid point p, representing the joint enrichment trend.

9. The groundwater health risk early warning method based on data analysis according to claim 8, characterized in that: S5 includes S51; S51. Based on the enrichment bias index Ebi(p) at the location p of the slow-flow identification grid point, compare it with the preset threshold range to construct the groundwater pollution deposition risk level standard for the target monitoring area, and generate early warning and response according to the groundwater pollution deposition risk level standard. The threshold range is preset to the range [0,1]. The groundwater contamination deposition risk level standard was obtained through the following comparison method: When 0 ≤ enrichment bias index Ebi(p) < 0.3 at the location p of the slow flow identification grid point, it indicates a low risk of deposition bias. The location p of the slow flow identification grid point is marked as L1 level, and no warning or response is given. Monitoring continues. When the enrichment bias index Ebi(p) at the location p of the slow flow identification grid point is less than 0.6, it indicates a medium risk of deposition bias. The location p of the slow flow identification grid point is marked as L2 level, and a prompt response operation to increase the sampling frequency and scheduling frequency is executed. When the enrichment bias index Ebi(p) at the location p of the slow flow identification grid point is ≤1, it indicates a high risk of sedimentation bias. The location p of the slow flow identification grid point is marked as L3 level, and an early warning response and a groundwater use behavior restriction prompt response operation are performed at the location p of the slow flow identification grid point.

10. A groundwater health risk early warning system based on data analysis, applied to the groundwater health risk early warning method based on data analysis as described in any one of claims 1 to 9, characterized in that: It includes a groundwater data acquisition module, a data flow velocity area identification module, a low flow velocity concentration change calculation module, a regional sedimentation risk calculation module, and a response decision module; The groundwater data acquisition module collects basic environmental dataset Env within the target monitoring area through a groundwater flow velocity sensor. The data velocity region identification module performs spatial interpolation and isosurface fitting on the basic environmental dataset Env to construct the water velocity map data Vmd and delineate the potential slow-flow enrichment region data Pos. The low-velocity concentration change calculation module extracts the basic environmental dataset Env from the identified potential slow-flow enrichment area data Pos, analyzes the changing trend over time, and calculates the pollutant spatial offset rate index Dis. The regional deposition risk calculation module combines the water flow velocity map data Vmd, the pollutant spatial offset rate index Dis, and the basic environmental dataset Env to establish the pollutant deposition potential function Fun. It calculates the deposition risk of pollutants under slow flow conditions for each slow flow enrichment micro-region in the potential slow flow enrichment area data Pos and obtains the enrichment bias index Ebi. The response decision module compares the enrichment bias index Ebi with the preset threshold area to construct a groundwater pollution deposition risk level standard for the target monitoring area, and issues an early warning based on the groundwater pollution deposition risk level standard.

Images