A GIS-based hydrogeological comprehensive investigation and dynamic monitoring system

Abstract

The application belongs to the technical field of hydrogeological exploration and environmental monitoring, and specifically relates to a hydrogeological comprehensive exploration and dynamic monitoring based on GIS, which comprises the following steps: a data acquisition module acquires multi-source hydrogeological data; a preprocessing and integration module performs cleaning, standardization and coordinate registration and establishes a GIS spatial database; a GIS analysis module performs spatial superposition, buffer zone, network analysis and Kriging interpolation parameter inversion; a numerical simulation module constructs a groundwater flow and pollution transport model for prediction; a dynamic monitoring module collects and transmits groundwater parameters in real time through sensors and the Internet of Things; a visualization module presents analysis and simulation results in two-dimensional and three-dimensional forms; and an early warning module performs anomaly detection, hierarchical early warning and tracing according to a threshold value. The application realizes integrated integration of multi-source data, deep fusion of GIS spatial analysis and numerical simulation and dynamic real-time monitoring and early warning, and significantly improves the intelligent level and response time of hydrogeological work.

Description

Technical Field

[0001] This invention relates to the field of hydrogeological exploration and environmental monitoring technology, and in particular to a GIS-based comprehensive hydrogeological exploration and dynamic monitoring system. Background Technology

[0002] Hydrogeological work is the foundation of water resource management, ecological environment protection and engineering construction. Its core lies in accurately obtaining hydrogeological parameters, understanding the occurrence patterns of groundwater, and predicting hydrogeological anomalies. Currently, traditional hydrogeological exploration and monitoring work suffers from several pain points: First, data sources are scattered. Hydrogeological borehole data, groundwater level monitoring data, water quality data, and geological structure data are collected by different departments, with inconsistent formats and scattered storage, making it difficult to achieve efficient data integration and collaborative analysis. Second, spatial correlation is poor. Traditional data are mostly presented in the form of tables and documents, which cannot intuitively reflect the spatial distribution characteristics and interaction relationships of hydrogeological elements, making it difficult for staff to quickly grasp the overall hydrogeological situation of the region. Third, the accuracy of simulation and prediction is insufficient. Existing hydrogeological simulations mostly rely on independent numerical models, which are disconnected from spatial geographic information. It is difficult to achieve accurate simulation by combining regional topography, geological structure and other spatial factors, and the simulation results have low visualization, which is not conducive to decision-making reference. Fourth, dynamic monitoring is lagging. Traditional monitoring methods are mostly manual periodic sampling or fixed-point monitoring, which cannot achieve real-time collection, transmission and analysis of parameters such as groundwater level and water quality, making it difficult to quickly respond to hydrogeological anomalies (such as the expansion of groundwater funnels and the spread of water pollution).

[0003] Geographic Information Systems (GIS), as a technology integrating spatial data acquisition, storage, analysis, and visualization, possesses powerful capabilities in spatial overlay analysis, data modeling, and visualization, and has been widely applied in fields such as geography and environment. While some existing technologies attempt to combine GIS with hydrogeology, such as the GIS-based groundwater environmental impact assessment prediction method (CN 115828508 A), these focus only on the single scenario of groundwater environmental impact assessment and lack coverage of the entire hydrogeological exploration process. Some technologies only achieve simple data visualization, failing to integrate data, numerical simulation, dynamic monitoring, and early warning, and do not fully utilize the spatial analysis advantages of GIS to achieve accurate inversion of hydrogeological parameters and anomaly tracing. Therefore, there is an urgent need for a GIS-based hydrogeological application system that can integrate multi-source hydrogeological data, achieve integrated exploration-simulation-monitoring-early warning, and possess high accuracy and high visualization capabilities, to address the pain points of traditional hydrogeological work and improve its efficiency and scientific rigor. Summary of the Invention

[0004] This invention proposes a GIS-based comprehensive hydrogeological survey and dynamic monitoring system, aiming to solve the technical problems in existing geological survey and monitoring work, such as difficulty in integrating multi-source data, weak spatial correlation analysis capabilities, insufficient accuracy due to the disconnect between numerical simulation and geographic information, and the lack of integrated fusion of dynamic monitoring and early warning response.

[0005] This invention provides a GIS-based comprehensive hydrogeological survey and dynamic monitoring system, including:

[0006] The data acquisition module is used to collect multi-source hydrogeological data within the study area;

[0007] The data preprocessing and integration module, connected to the data acquisition module, is used to preprocess the acquired multi-source data and integrate it into the GIS spatial database to establish a three-dimensional correlation based on spatial location, attribute parameters and time series.

[0008] The GIS core analysis module, connected to the data preprocessing and integration module, is used to perform spatial analysis on the integrated data using GIS spatial analysis functions and to invert the spatial distribution of hydrogeological parameters.

[0009] The numerical simulation module is connected to the GIS core analysis module and is used to construct groundwater flow numerical models and groundwater pollution transport numerical models based on GIS analysis results and inverted hydrogeological parameters for simulation and prediction.

[0010] The dynamic monitoring module, connected to the data preprocessing and integration module, includes a sensor network and an IoT communication unit, used to collect and transmit updates of groundwater dynamic parameters in real time; the visualization module, connected to the GIS spatial database, GIS core analysis module, numerical simulation module and dynamic monitoring module, is used to visualize the data integration results, analysis results, simulation results and monitoring data in two-dimensional and three-dimensional forms.

[0011] The early warning module, connected to the dynamic monitoring module and the numerical simulation module, is used to detect, issue early warnings, and perform source tracing analysis of abnormal events based on monitoring data and simulation results and according to preset thresholds.

[0012] The technical effects of this invention's GIS-based comprehensive hydrogeological survey and dynamic monitoring are as follows: By integrating data acquisition, preprocessing, GIS spatial analysis, numerical simulation, dynamic monitoring, visualization, and early warning modules into a modular architecture, it achieves automated cleaning, format unification, and spatial registration of multi-source heterogeneous hydrogeological data. Furthermore, it constructs a GIS spatial database with a three-dimensional association of "spatial location-attribute parameters-time series," fundamentally solving the problems of traditional data dispersion, isolation, and inconsistent formats. Through spatial overlay, buffering, network analysis, and interpolation-based parameter inversion functions of the core GIS analysis module, it achieves accurate characterization of the spatial distribution patterns of hydrogeological elements and in-depth mining of their correlations. Simultaneously, numerical simulation... The simulation module directly utilizes the continuous parameter field obtained from GIS analysis and inversion to drive the groundwater flow and pollution transport model, significantly improving the accuracy and spatial specificity of simulation predictions. The dynamic monitoring module relies on IoT technology to achieve real-time acquisition and transmission of dynamic groundwater parameters, overcoming the shortcomings of monitoring lag. The early warning module compares the monitoring and simulation results in real time based on preset thresholds, enabling it to quickly identify risk events such as abnormal water levels and water quality exceeding standards, and automatically issue early warning notifications and conduct anomaly source tracing analysis. Finally, the visualization module presents the analysis, simulation, and monitoring results intuitively in two-dimensional and three-dimensional forms, enabling the entire system to have a closed-loop processing capability from data acquisition to decision response, significantly improving the intelligence level, response timeliness, and management scientificity of hydrogeological work.

[0013] Furthermore, the data preprocessing and integration module includes:

[0014] The data cleaning unit uses outlier detection algorithms to remove outliers from the monitored data and uses interpolation to fill in missing data.

[0015] The format standardization and registration unit is used to convert data of different formats into a unified spatial data format and attribute data format, and to unify them under the same geographic coordinate system to achieve spatial alignment; the data association unit is used to bind borehole coordinates with corresponding strata lithology and aquifer parameters, bind monitoring well locations with real-time water level and water quality data, and establish the three-dimensional association relationship.

[0016] The database construction unit uses a database system that supports spatial data storage to classify and store the integrated data, and at least constructs a basic geographic database, a hydrogeological exploration database, a groundwater monitoring database, an environmental database, and a geological structure database.

[0017] Furthermore, the GIS core analysis module includes:

[0018] The spatial overlay analysis unit is used to overlay the stratigraphic lithology layer, aquifer distribution layer, fault distribution layer, monitoring well layer, and pollution source layer to identify the aquifer distribution range and fault hydraulic connections.

[0019] The buffer analysis unit is used to set up buffer zones based on pollution sources, monitoring wells, or fault elements, and to analyze the scope of impact or monitoring coverage.

[0020] The network analysis unit is used to construct a groundwater flow network model, analyze groundwater flow direction and runoff path, and reveal the transformation mechanism of atmospheric precipitation, surface water and groundwater in combination with the distribution of surface water bodies;

[0021] The parameter inversion unit is used to perform spatial interpolation inversion of permeability coefficient and porosity using spatial interpolation algorithms to generate a continuous raster map of hydrogeological parameters.

[0022] Furthermore, the numerical simulation module includes:

[0023] The groundwater flow model construction unit uses the finite difference method to construct the groundwater flow equation, inputs the hydrogeological parameters inverted by the GIS core analysis module, and combines water level and flow monitoring data to simulate the distribution of groundwater flow field and dynamic changes in water level;

[0024] The pollution transport model construction unit constructs pollutant transport equations based on solute transport theory, and combines pollution source information and hydrogeological parameters to simulate pollutant diffusion paths and concentration distribution;

[0025] The model calibration unit uses actual monitoring data to calibrate the numerical model and adjusts the parameters until the simulation results and the monitoring data errors meet the preset accuracy requirements.

[0026] Furthermore, the groundwater flow model construction unit uses the finite difference method to construct two-dimensional or three-dimensional groundwater flow equations, with the governing equations being:

[0027] ;

[0028] ;

[0029] ;

[0030] In the formula, Aquifer permeability coefficient; Groundwater head; Source and sink items; Known water level boundary; Known flow boundaries; Study area.

[0031] Furthermore, the pollution transport model construction unit uses a two-dimensional point source Gaussian diffusion equation to solve for the pollutant concentration distribution. The equation expression is:

[0032] ;

[0033] In the formula, solute concentration; , Vertical and horizontal coordinates; time; The mass of solute released instantaneously from the pollution source; Aquifer porosity; Delay factor; Aquifer thickness; , Variance of Gaussian distribution in both longitudinal and transverse directions; Decay coefficient.

[0034] Furthermore, the dynamic monitoring module includes:

[0035] The real-time acquisition unit includes multi-parameter sensors deployed within the study area for real-time acquisition of groundwater level, water quality, and water temperature parameters;

[0036] The data transmission unit uses Internet of Things (IoT) communication technology to transmit the data collected by the sensor to the data preprocessing and integration module in real time via wireless communication.

[0037] The monitoring network optimization unit identifies monitoring gaps based on the analysis results of the GIS core analysis module and optimizes the deployment locations of monitoring wells and sensors.

[0038] Furthermore, the visualization module includes:

[0039] The two-dimensional display unit is used to display the spatial distribution map of hydrogeological elements in the study area with a map as the base map. It supports layer overlay and switching, and displays the real-time changes and historical trends of monitoring parameters in the form of curves and heat maps.

[0040] The three-dimensional display unit uses three-dimensional modeling technology to construct a three-dimensional solid model of groundwater, which is used to display the distribution of groundwater flow field and the diffusion path of pollutants;

[0041] The thematic map generation unit is used to automatically generate thematic maps of hydrogeological exploration, groundwater water-rich zoning maps, pollution impact range maps, and disaster early warning maps.

[0042] Furthermore, the early warning module includes:

[0043] The threshold setting unit is used to set the water level anomaly threshold and water quality exceedance threshold according to the hydrogeological conditions and relevant standards of the study area.

[0044] Anomaly detection unit is used to compare monitoring data with thresholds in real time and identify water level anomalies, water quality exceeding standards, and pollutant diffusion risks by combining numerical simulation results;

[0045] The early warning notification unit is used to automatically trigger an early warning when an anomaly is detected, and send early warning information containing the early warning type, anomaly location and risk level to the designated terminal;

[0046] The early warning and tracing unit is used to combine GIS spatial analysis functions to conduct source analysis of abnormal situations and trace the location of pollution sources or the causes of abnormalities.

[0047] Furthermore, the GIS core analysis module is built on the ArcGIS or MapGIS platform, and the data preprocessing and integration module uses the PostgreSQL+PostGIS spatial database. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of a GIS-based integrated hydrogeological survey and dynamic monitoring framework proposed in an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the data preprocessing and integration process provided in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the core GIS analysis process provided in an embodiment of the present invention;

[0051] Figure 4 This is a flowchart provided for an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] This paper addresses the technical problems mentioned in the background section regarding the difficulties in integrating multi-source data, weak spatial correlation analysis capabilities, insufficient accuracy due to the disconnect between numerical simulation and geographic information, and the lack of integrated fusion for dynamic monitoring and early warning response in geological exploration and monitoring.

[0054] As attached Figure 1As shown, this invention provides a GIS-based comprehensive hydrogeological exploration and dynamic monitoring system, employing a modular architecture design. It includes a data acquisition module 1, a data preprocessing and integration module 2, a GIS core analysis module 3, a numerical simulation module 4, a dynamic monitoring module 5, a visualization module 6, and an early warning module 7. These modules are connected sequentially, work collaboratively, and interact with a GIS spatial database 8. Specifically, the data acquisition module 1 is responsible for the comprehensive acquisition of multi-source data; the data preprocessing and integration module 2 cleans, standardizes, and integrates the raw data to construct a unified spatial database; the GIS core analysis module 3 utilizes spatial analysis functions to uncover hydrogeological patterns and inverse parameters; the numerical simulation module 4 constructs professional models based on the analysis results for prediction; the dynamic monitoring module 5 realizes real-time perception and transmission of groundwater dynamics; the visualization module 6 presents various results intuitively in two-dimensional and three-dimensional forms; and the early warning module 7 is responsible for real-time detection, alarm, and source tracing of abnormal events. The specific composition and working method of each module are described in detail below.

[0055] Data acquisition module 1 is used to comprehensively collect multi-source hydrogeological data within the study area. This multi-source hydrogeological data specifically includes five categories: basic geographic data, hydrogeological exploration data, groundwater monitoring data, environmental data, and geological structure data.

[0056] The basic geographic data includes topographic data, administrative division data, transportation network data, and remote sensing imagery data of the study area. Topographic data, such as slope, aspect, and topographic relief, can be extracted using a digital elevation model (DEM). Remote sensing imagery data can utilize Landsat or Gaofen satellite imagery series, with spatial resolution selected according to the study scale, generally ranging from 10 to 30 meters, to extract surface information such as land use type and vegetation cover. Administrative division and transportation network data can be obtained from the National Geomatics Center of China.

[0057] The hydrogeological survey data includes borehole data, stratigraphic lithology data, aquifer parameter data, and groundwater flow direction data. Specifically, the aquifer parameter data includes permeability coefficient, porosity, specific yield, storage capacity, and aquifer thickness. These data are obtained through field borehole exploration, pumping tests, injection tests, pressure tests, and laboratory geotechnical tests. Borehole data should include borehole number, latitude and longitude coordinates, borehole elevation, final borehole depth, stratigraphic description, and lithological identification.

[0058] The groundwater monitoring data includes groundwater level, water quality, water temperature, and flow rate. Real-time data acquisition is achieved by scientifically deploying monitoring wells within the study area and installing multi-parameter integrated sensors. These sensors feature a low-power design, with built-in batteries capable of operating continuously for over a year. They support simultaneous measurement of multiple parameters, including water level, water temperature, pH value, dissolved oxygen, conductivity, chemical oxygen demand (COD), ammonia nitrogen, and heavy metal ions such as arsenic, lead, and chromium. The sensors support multiple wireless transmission protocols, including 4G, 5G, LoRa, and NB-IoT, allowing for flexible selection based on on-site network coverage conditions.

[0059] The environmental data includes precipitation, evaporation, surface water distribution, and surface water quality data. Precipitation and evaporation data are acquired through automatic rain gauges and evaporation pans at meteorological stations, typically once daily or hourly. Surface water distribution data is obtained through remote sensing interpretation or water resources survey data. Surface water quality data is obtained through regular sampling and testing at hydrological stations or environmental monitoring stations, with indicators including pH, dissolved oxygen, permanganate index, ammonia nitrogen, and total phosphorus.

[0060] The geological structural data includes fault distribution, fold morphology, and lithological contact relationship data. This data is obtained through regional geological surveys, geophysical exploration such as ground-penetrating radar, high-density electrical resistivity tomography, and seismic exploration, and is further digitized and supplemented using existing regional geological maps. For fault data, geometric parameters and mechanical properties such as fault strike, dip, dip angle, fault displacement, and fault zone width should be accurately recorded.

[0061] The data preprocessing and integration module 2 is connected to the data acquisition module 1 and is used to preprocess the acquired multi-source data and integrate it into the GIS spatial database 8, establishing a three-dimensional correlation based on spatial location, attribute parameters, and time series. The detailed workflow of this module is attached. Figure 2 As shown.

[0062] This module first performs data cleaning. The Z-score outlier detection algorithm is used to identify outliers in the monitoring data. Specifically, for time series data of water level or water quality indicators of a specific monitoring well, the standard deviation of each observation from the mean is calculated. When the absolute Z-score is greater than 3, the observation is determined to be an outlier and removed. For data gaps caused by sensor failure, transmission interruption, etc., linear interpolation is used to fill in the gaps. For continuous gaps exceeding a certain duration, such as more than 6 consecutive monitoring cycles, spatiotemporal kriging interpolation based on data from neighboring monitoring wells is used to fill in the gaps, ensuring the continuity and integrity of the data sequence.

[0063] After data cleaning, format standardization and coordinate registration are performed. Data from different sources and in different formats are uniformly converted: spatial vector data is uniformly converted to Shapefile format, attribute table data is uniformly converted to CSV format, and raster image data is uniformly converted to GeoTIFF format. The coordinate reference for all spatial data is uniformly adopted as the WGS84 geographic coordinate system. For scenarios requiring precise area or distance calculations, further projection conversion to a suitable local projection coordinate system, such as the CGCS2000 Gauss-Kruger projection, can be performed. Through the seven-parameter coordinate transformation method or the registration method using corresponding control points, it is ensured that all layers achieve accurate spatial overlay in the GIS environment, with registration errors controlled within half a pixel.

[0064] Following this, data association and fusion processing is performed. This is the core step in establishing the intrinsic connection between multi-source data. Through spatial location association, the latitude and longitude coordinates of borehole points are bound to the depths of various strata exposed by the borehole, lithological descriptions, permeability test values, and porosity test values, allowing users to query complete stratigraphic columns and parameter information by clicking on any borehole point. The latitude and longitude coordinates of monitoring wells are bound to real-time collected monitoring data sequences such as water level, water temperature, pH value, and COD concentration, achieving seamless integration of spatial location and dynamic monitoring data streams. Through attribute field association, using administrative division codes or watershed zoning codes as association keys, socio-economic data, water resource development and utilization data are connected to spatial units. Through these association operations, a three-dimensional association relationship of "spatial location—attribute parameters—time series" is established, enabling the system to quickly retrieve and collaboratively analyze any hydrogeological object from three dimensions: space, attribute, and time.

[0065] Finally, the spatial database was constructed. A GIS spatial database was built using the PostgreSQL relational database management system combined with its spatial extension, PostGIS. Five logical sub-databases were constructed based on data categories: a basic geographic database storing topography, remote sensing imagery, administrative divisions, and transportation networks; a hydrogeological exploration database storing borehole locations, stratigraphic layers, pumping test results, and aquifer parameters; a groundwater monitoring database storing real-time monitoring data on monitoring well locations, sensor models, and water level, quality, and temperature; an environmental database storing meteorological and hydrological data, surface water distribution, and water quality data; and a geological structure database storing spatial geometry and attribute data of structural elements such as faults, folds, joints, and fissures. The combination of PostgreSQL and PostGIS supports efficient storage of massive spatial data, accelerated spatial indexing for queries, concurrent access control, and incremental update maintenance, providing stable and reliable data support for upper-level analysis and application modules.

[0066] The GIS core analysis module 3 is connected to the data preprocessing and integration module 2. It utilizes GIS spatial analysis functions to perform spatial analysis on the integrated data and invert the spatial distribution of hydrogeological parameters. This module is built on the ArcGIS or MapGIS platform and leverages its rich spatial analysis toolbox for in-depth data mining. The analysis process of this module is shown in the attached figure. Figure 3 As shown.

[0067] Spatial overlay analysis is one of the fundamental functions of GIS core analysis module 3. The system performs multiple overlay displays and spatial analyses of stratigraphic lithology distribution layers, aquifer thickness distribution layers, fault distribution layers, groundwater monitoring well location layers, and potential pollution source distribution layers on the same map view. Through visual overlay and spatial query operations, the control relationship between different lithological strata and aquifer water-bearing capacity can be clearly identified. For example, sand and gravel layers in Quaternary loose sediments constitute the main aquifer, while clay layers constitute a relatively impermeable layer. The relationship between the spatial distribution of faults and the direction of groundwater flow can be determined. When the fault strike is perpendicular to the groundwater flow direction and the fault zone has low permeability, the fault acts as a water barrier, forming groundwater backwater; when the fault zone is broken and has high permeability, the fault forms a water-conducting channel. Simultaneously, by overlaying the pollution source layer with the aquifer distribution and groundwater flow direction layers, the threat level and affected objects of potential pollution sources to the groundwater system can be assessed.

[0068] The buffer zone analysis function is used to assess the spatial impact range of specific elements. The system allows users to set buffer zones of different radii for groundwater pollution sources such as industrial parks, landfills, gas stations, key monitoring wells, and active faults. For example, a three-tiered buffer zone of 500 meters, 1000 meters, and 2000 meters can be set around the pollution source. The system analyzes whether there are centralized water supply wells, groundwater drinking water source protection areas, or ecologically sensitive targets within the buffer zone, thereby assessing the urgency of the pollution risk. Simultaneously, it analyzes whether there are spatial blind spots within the monitoring coverage of the monitoring wells. For example, if there is an uncovered area between the buffer zones of two monitoring wells, the system can indicate that this area is a monitoring blank zone, providing a basis for subsequent optimization of the monitoring network.

[0069] Network analysis is used to construct a groundwater flow network model. The system utilizes GIS hydrological analysis tools, combined with Digital Elevation Model (DEM) data, to extract the surface water network and catchment area boundaries, and couples this with groundwater flow field data. Groundwater isolevel maps are generated by extracting monitoring well water level data. The groundwater flow direction is determined based on the normal direction of the isolevel lines, thus constructing the groundwater flow network. The groundwater flow network is overlaid with the surface water network to analyze the recharge and discharge relationships between groundwater and surface water, quantitatively revealing the spatiotemporal mechanisms and transformation fluxes of the "atmospheric precipitation-surface water-groundwater" three-water transformation.

[0070] The parameter inversion function overcomes the shortcomings of insufficient representativeness of traditional single-point parameters, enabling accurate extension of hydrogeological parameters from discrete points to a continuous surface. The system uses the permeability coefficient K value obtained from borehole pumping tests and the effective porosity n value obtained from laboratory tests as known sample points, and employs the Kriging spatial interpolation algorithm for regional parameter inversion. Kriging interpolation is an optimal unbiased estimation method based on geostatistics, its core being the construction of a variogram model reflecting the spatial variability of parameters. Specific steps include: first, calculating the semi-variogram values ​​between all sample point pairs and plotting the experimental variogram scatter plot; second, selecting a suitable theoretical variogram model, such as a spherical model, exponential model, or Gaussian model, for fitting, determining the three key parameters: nugget value, sill value, and range; finally, based on the fitted variogram model, performing Kriging estimation on the parameter values ​​of any unknown point within the study area, and simultaneously providing the estimation variance as a measure of uncertainty. Kriging interpolation is used to generate high-resolution raster maps of key hydrogeological parameters such as permeability and porosity within the study area. The raster cell size is determined based on the study scale and sample density, typically ranging from 50 to 200 meters. This parameter raster map is directly used as the parameter field input for subsequent numerical simulation module 4, greatly improving the simulation accuracy and spatial specificity.

[0071] The numerical simulation module 4 is connected to the GIS core analysis module 3 and is used to construct a groundwater flow numerical model and a groundwater pollution transport numerical model based on the GIS analysis results and the inverted hydrogeological parameters, and to perform simulation and prediction.

[0072] The groundwater flow model is constructed using the finite difference method to establish two-dimensional or three-dimensional groundwater flow equations. For regional-scale problems, a two-dimensional planar flow model can be used; for scenarios with significant vertical stratification, involving cross-flow recharge, or deep mining, a three-dimensional groundwater flow model is constructed. The governing equations are as follows:

[0073] ;

[0074] ;

[0075] ;

[0076] In the formula, Aquifer permeability coefficient; Groundwater head; Source and sink items; Known water level boundary; Known flow boundaries; Study area.

[0077] The boundary condition types and geometric locations of the model are also extracted from the GIS layer. The first type of boundary, namely the known water level boundary, is usually the boundary of a surface water body hydraulically connected to the study area or the boundary of a regional groundwater watershed. The second type of boundary, namely the known flow boundary, is usually the boundary of an impermeable body or the boundary of a known lateral recharge and discharge. The source and sink terms W include various types such as atmospheric precipitation infiltration recharge, agricultural irrigation backflow, surface water seepage, and artificial extraction. The values ​​of each type of source and sink term are calculated and determined based on dynamic monitoring data and environmental data.

[0078] The groundwater pollution transport model is constructed based on solute transport theory to establish pollutant transport equations. For large-scale, long-term pollution assessment scenarios or situations where data is insufficient, a two-dimensional point-source Gaussian diffusion equation can be used for approximate solutions.

[0079] ;

[0080] In the formula, solute concentration; , Vertical and horizontal coordinates; time; The mass of solute released instantaneously from the pollution source; Aquifer porosity; Delay factor; Aquifer thickness; , Variance of Gaussian distribution in both longitudinal and transverse directions; The decay coefficient, measured in units of 1 per day, reflects the biodegradation or radioactive decay process of pollutants. For scenarios requiring high precision or with complex boundary conditions, numerical models of solute transport based on the finite difference method or finite element method, such as MT3DMS or SEAWAT, can be used to obtain more accurate pollutant plume morphology and spatiotemporal distribution of concentration.

[0081] The model calibration unit uses real-time water level and water quality data acquired by the dynamic monitoring module 5 to calibrate and verify the parameters of the constructed numerical model. For the groundwater flow model, the simulated water level of each monitoring well is compared with the measured water level, and the mean absolute error (MAE) and root mean square error (RMSE) are calculated. By adjusting sensitive parameters such as the permeability coefficient zoning value and boundary condition assignment, the error between the simulated and measured water levels is made to meet the preset accuracy requirements, typically requiring an MAE of less than 0.5 meters or a relative error of no more than 5%. For the contaminant transport model, the simulated and measured contaminant concentrations in the monitoring wells are compared. By adjusting parameters such as dispersion, delay factor, and decay coefficient, the concentration fitting error is made to meet the accuracy requirements. The calibrated and verified model can then be used to predict the groundwater system response under different extraction schemes, different precipitation conditions, or different pollution control scenarios.

[0082] The dynamic monitoring module 5 is connected to the data preprocessing and integration module 2 and includes a sensor network and an Internet of Things communication unit, used to collect and transmit updates of groundwater dynamic parameters in real time.

[0083] The real-time acquisition unit includes multi-parameter sensors deployed in monitoring wells within the study area. These sensors integrate water level measurement elements such as pressure level gauges, multi-parameter water quality electrode arrays, and water temperature sensors. Water quality parameters include at least pH, dissolved oxygen, conductivity, chemical oxygen demand (COD), and ammonia nitrogen. The detection function for heavy metals such as hexavalent chromium, arsenic, lead, and mercury can be expanded according to the actual pollution characteristics of the study area. The sensor acquisition frequency can be set to once per hour, once every two hours, or once per day, depending on monitoring needs. For areas with drastic changes in hydrogeological conditions or a risk of sudden pollution, the frequency can be increased to once every 15 minutes. The sensors employ a low-power design, with built-in batteries supplemented by solar panels, ensuring continuous operation under long-term unattended field conditions.

[0084] The data transmission unit employs IoT communication technology to achieve remote real-time data transmission. Sensors in each monitoring well are connected to the data acquisition and transmission terminal (RTU) at the wellhead via data cables. The RTU has a built-in 4G or 5G communication module, or in remote areas with poor public network coverage, it uses LoRa low-power wide-area network technology, relayed through a LoRa gateway to access the internet. Data is encapsulated in JSON or XML format and periodically pushed to the central server's data receiving interface via MQTT or HTTP protocols. The data is then parsed, verified, and stored by the data preprocessing and integration module 2, enabling real-time updates of groundwater dynamic data.

[0085] The monitoring network optimization unit scientifically evaluates and optimizes the existing monitoring network based on the analysis results of the GIS core analysis module 3. Through buffer zone analysis, it identifies the monitoring coverage of existing monitoring wells. Spatial overlay analysis identifies monitoring gaps and key monitoring areas such as groundwater over-extraction funnel zones, water conservation areas, ecologically fragile areas, and downstream directions of potential pollution sources. Based on this, the system generates a monitoring network optimization suggestion report, marking recommended locations for new monitoring wells on the map and providing a priority ranking of the new wells to guide the subsequent improvement and densification of the monitoring network.

[0086] The visualization module 6 is connected to the GIS spatial database 8, the GIS core analysis module 3, the numerical simulation module 4, and the dynamic monitoring module 5, and is used to visualize the data integration results, analysis results, simulation results, and monitoring data in two-dimensional and three-dimensional forms.

[0087] The two-dimensional display unit uses high-resolution remote sensing imagery or digital topographic maps as a base map, overlaying and displaying the spatial distribution of various hydrogeological elements in the study area. Users can control the display or hiding of each map layer, adjust its transparency, and control the overlay order via the layer panel. For dynamic monitoring data, the two-dimensional display unit can generate multi-parameter real-time curves for a single monitoring well, with the horizontal axis representing time and the vertical axis representing water level or concentration values, intuitively reflecting the changing trends and fluctuation patterns of each parameter over time. It can also generate comparative curves for the same parameter from multiple monitoring wells, facilitating the analysis of the synchronicity or differences in groundwater dynamics at different locations. For regional-scale parameter distribution, kriging interpolation results can be used to generate contour maps or heat maps, such as groundwater isohyet maps, water level depth zoning maps, and pollutant concentration distribution heat maps, intuitively displaying the spatial pattern of the regional groundwater status.

[0088] The 3D display unit utilizes 3D modeling technology to construct a 3D solid model of groundwater. Specifically, it uses borehole stratigraphic data and 3D geological modeling algorithms to construct a 3D raster model or bedding model of the study area, showcasing the stratigraphic structure, lithological distribution, and geological morphology from the surface to the bedrock. The groundwater flow field results calculated by numerical simulation module 4 are overlaid and displayed as 3D streamlines or vector arrows, demonstrating the spatial flow direction and velocity of groundwater. The pollutant transport simulation results are displayed as 3D isosurfaces or volumes, showing the 3D morphology, diffusion range, and concentration gradient of the pollutant plume. The 3D model supports interactive operations such as mouse drag-and-drop rotation, scroll wheel zoom, and arbitrary profile cutting, allowing staff to view the internal structure and dynamic evolution of the groundwater system from multiple angles and dimensions.

[0089] The thematic map generation unit is used to automatically generate thematic maps that conform to professional standards. The system has preset templates for various thematic maps, such as comprehensive hydrogeological survey results maps, groundwater water-bearing zoning maps, groundwater pollution impact range prediction maps, and hydrogeological disaster risk early warning maps. The templates include standard map frames, map titles, scale bars, north arrows, legends, and responsibility columns. After the user selects the output scope and thematic type, the system automatically calls up the relevant data layers, renders them according to the preset symbolization and color scheme, and generates editable thematic maps. These maps can be exported as high-resolution PNG, JPEG images, or PDF vector documents, making them convenient for direct use in report preparation, results presentation, and results archiving.

[0090] The early warning module 7 is connected to the dynamic monitoring module 5 and the numerical simulation module 4, and is used to detect, issue early warnings and conduct source analysis on abnormal events based on monitoring data and simulation results and according to preset thresholds.

[0091] The threshold setting unit is used to establish a multi-level early warning threshold system. For groundwater levels, based on the hydrogeological conditions and historical water level change characteristics of the study area, excessively low and excessively high early warning thresholds are set. For example, for groundwater over-extraction areas, a blue warning line can be set at 1 meter below the historical lowest water level, a yellow warning line at 2 meters below the historical lowest water level, and a red severe warning line at 3 meters below the historical lowest water level or less than 5 meters above the aquifer top. For water quality indicators, based on the limits for various water quality indicators specified in the Groundwater Quality Standard GB / T 14848-2017, combined with regional background values, water quality exceedance warning thresholds are set. For general chemical indicators such as pH, total hardness, and total dissolved solids, the limits for Class III water in the standard can be set; for toxicological indicators such as heavy metals and organic matter, more stringent warning thresholds can be set. Based on pollutant transport simulation results, early warning thresholds can be set for pollution fronts reaching specific sensitive targets such as water source protection areas.

[0092] The anomaly detection unit runs continuously in the background, comparing the latest monitoring data pushed by the dynamic monitoring module 5 with preset thresholds item by item. When a monitored value exceeds any threshold level, the system immediately records the time, location, parameter name, measured value, and threshold of the anomaly event. Simultaneously, the anomaly detection unit also combines the prediction results from the numerical simulation module 4 for proactive anomaly identification. When the model predicts a risk of water level exceeding limits or pollution exceeding standards within a preset time period, such as the next 7 or 30 days, the system also triggers an early warning, achieving a shift from passive response to proactive early warning. Identifiable hydrogeological anomaly types include, but are not limited to: an abnormal drop in groundwater level leading to the expansion of the drawdown funnel; an abnormal rise in water level leading to groundwater inundation or soil salinization risks; pollution events caused by exceeding water quality standards; and the risk of sudden water inrush due to abnormally increased water pressure in fault zones.

[0093] The early warning notification unit is used to issue alarms immediately after an abnormal event is confirmed. Notification methods include system platform pop-up alerts, which display the warning level in a prominent red or orange color and are accompanied by an audible alert; SMS push notifications, sending a summary of the warning information to the pre-configured mobile phone numbers of on-duty personnel and management personnel; and email notifications with detailed warning reports. The warning information clearly includes the warning type (e.g., low water level warning, water quality exceeding standards warning), the precise spatial location of the anomaly (including latitude and longitude coordinates, monitoring well number and geographical description), the name of the parameter exceeding the standard and its measured or predicted value, and the corresponding risk level. Risk levels are divided into three levels according to preset rules: general, moderate, and severe, corresponding to blue, yellow, and red indicators respectively, allowing recipients to quickly assess the urgency of the event.

[0094] The early warning and tracing unit is used for in-depth causal analysis of complex anomalies such as water pollution. When an early warning of water quality exceeding standards is detected in a monitoring well, the early warning and tracing unit automatically invokes the hydraulic gradient calculation function and reverse particle tracing function of the GIS core analysis module 3. First, based on the regional isostatic map, the system calculates the groundwater flow direction and hydraulic gradient of the warning point and its surroundings. Then, starting from the early warning monitoring well, it performs reverse particle tracing in the opposite direction of groundwater flow to simulate the possible source paths of pollutants. Combining the potential pollution source distribution layers stored in the GIS spatial database, such as industrial enterprises, farms, and landfills, the system filters out suspected pollution sources that may cause the pollution event along the reverse tracing path and displays them in sorted order by distance and pollution source type. Simultaneously, the system invokes a groundwater pollution transport numerical model to perform a forward simulation with the suspected pollution source as the release point, verifying whether the pollutants can migrate to the location of the early warning monitoring well and reach the concentration exceeding the standard within a reasonable time. By combining reverse tracing and forward verification, the system provides a scientific basis for pollution liability identification and precise treatment.

[0095] As attached Figure 4 As shown, the overall workflow of the system of this invention is clear and well-defined, forming a complete closed loop from data acquisition to decision response. First, the data acquisition module 1 comprehensively collects multi-source data from the study area, including basic geographic, hydrogeological, groundwater monitoring, environmental, and geological structural data. Then, the data preprocessing and integration module 2 cleans, standardizes, registers, and integrates the raw data to construct a unified GIS spatial database 8. Based on this, the GIS core analysis module 3 performs spatial overlay analysis, buffer analysis, network analysis, and parameter inversion to reveal the spatial distribution patterns of hydrogeological elements and generate a continuous parameter field. The numerical simulation module 4 uses these parameter fields and boundary conditions to construct groundwater flow models and pollution transport models, performing current status simulations and future scenario predictions, and using monitoring data for model calibration. Simultaneously, the dynamic monitoring module 5 runs continuously, integrating groundwater dynamic data collected by sensors into the system in real time via the Internet of Things, achieving near real-time updates to the database. The visualization module 6 comprehensively presents the map data, monitoring curves, and simulation results generated in the above stages in two-dimensional and three-dimensional forms, generating various thematic maps. As the system's monitoring unit, the early warning module 7 compares the monitoring data with the preset thresholds in real time, combines the simulation prediction results, and promptly issues graded early warnings and conducts source tracing analysis for any abnormal events discovered, providing decision support for management personnel.

[0096] This embodiment applies the system of the present invention to a typical region in the middle reaches of the Yellow River Basin. This region covers approximately 500 square kilometers, with a terrain dominated by loess hills and gullies, interspersed with river valley plains. Surface water resources are relatively scarce in the region, and industrial and agricultural production and residential life mainly rely on groundwater, resulting in groundwater over-extraction and localized water pollution problems.

[0097] During the system deployment phase, data acquisition module 1 comprehensively collected multi-source data for the region. Basic geographic data utilized Gaofen-2 satellite remote sensing imagery with a spatial resolution of 10 meters, combined with 1:50,000 digital topographic maps to extract topographic information. Hydrogeological survey data came from 50 hydrogeological boreholes in the region, 30 of which were historical exploration boreholes and 20 were supplementary boreholes constructed for this work. Pumping or injection tests were conducted on all boreholes to obtain permeability coefficient values, ranging from 1.2 x 10⁻³ m³ / day to 3.5 x 10⁻² m³ / day; effective porosity was determined through indoor geotechnical tests, ranging from 25% to 35%; aquifer thickness ranged from 5 meters to 20 meters. Groundwater monitoring data was obtained by deploying 30 standard monitoring wells, primarily located in concentrated groundwater extraction areas and around potential pollution sources in the river valley plains. Each monitoring well is equipped with multi-parameter sensors to collect real-time data on water level (60-80 meters depth), water temperature, pH (7.0-8.5), dissolved oxygen (5-8 mg / L), COD, and other parameters. Data is collected every two hours. Environmental data is integrated from daily precipitation and evaporation data provided by three national meteorological stations in the region, as well as hydrological and water quality monitoring data from two major surface rivers. Geological data was obtained by digitizing a 1:50,000 regional geological map, and ground-penetrating radar was used to detect and verify major faults, obtaining their accurate locations and attitudes.

[0098] The data preprocessing and integration module 2 systematically processed the aforementioned data. The Z-score algorithm was used to remove outliers from the monitoring data with a threshold of 3. For example, a COD monitoring value abnormally high, reaching 150 mg / L, significantly deviating from the background value, was identified as an outlier and removed after Z-score verification. For occasional missing data, linear interpolation was used to fill in the gaps. All spatial data was uniformly converted to Shapefile format in the WGS84 coordinate system, and attribute data was converted to CSV format. Through spatial location association, the coordinates of 50 boreholes were linked to stratigraphic lithology, permeability coefficient, porosity, and aquifer thickness; the coordinates of 30 monitoring wells were linked to real-time water level and water quality monitoring sequences. A spatial database was constructed using PostgreSQL+PostGIS, establishing five sub-databases: basic geography, hydrogeological exploration, groundwater monitoring, environment, and geological structure.

[0099] The GIS core analysis module 3 conducted in-depth analysis based on the ArcGIS platform. Spatial overlay analysis combined the Quaternary loose rock aquifer distribution layer with the underlying Neogene bedrock lithology layer and fault distribution layer. It was found that the Quaternary sand and gravel layer constitutes the main aquifer in the river valley plain area, with a relatively impermeable clay layer as its base. Near-east-west trending faults significantly impede groundwater flow, causing a water level difference of over 5 meters between the upper and lower plates of the fault. Buffer zone analysis established buffer zones of 500m, 1000m, and 2000m centered on two industrial parks. It revealed three centralized water supply wells within the 1000m buffer zone, indicating potential pollution risks. Parameter inversion employed ordinary kriging interpolation, using the permeability coefficient and porosity of 50 boreholes as sample points. A spherical model was selected through variogram fitting with a range of approximately 3500 meters, generating permeability and porosity raster maps with a raster cell size of 100m x 100m. The interpolation results clearly show the spatial differentiation pattern of high permeability in the valley area and low permeability in the hilly areas on both sides.

[0100] Numerical simulation module 4 uses the Visual MODFLOW software engine to construct a three-dimensional groundwater flow numerical model. The model's scope is consistent with the GIS analysis scope, vertically divided into five layers corresponding to unconfined aquifers, weakly permeable aquifers, and confined aquifers. The parameter fields are directly imported from the permeability and porosity raster maps generated by the GIS module. The model was calibrated for one year using historical water level observation sequences from 30 monitoring wells. Through repeated adjustments to boundary conditions and permeability zoning, the average absolute error between the simulated and measured water levels in all observation wells was finally 0.28 meters, with a relative error not exceeding three percent, meeting the accuracy requirements. For a COD pollution event caused by a storage tank leak at a chemical plant, the MT3DMS module was used to construct a solute transport model, simulating the transport path and concentration decay process of the COD pollution plume over the next 10 years. A two-dimensional point-source Gaussian diffusion equation was used for rapid verification and estimation.

[0101] The dynamic monitoring module 5 maintains a data update frequency of every 2 hours during system operation. Based on the monitoring blind spot discovered by GIS analysis—near a village on the eastern edge of the groundwater over-extraction funnel—the system recommended adding 5 monitoring wells, thus optimizing the spatial coverage integrity of the monitoring network.

[0102] The visualization module 6, based on ArcGIS and a 3D visualization plugin, provides rich visualization results. Real-time curves of water level and COD for each monitoring well can be viewed on a 2D map, with support for multi-well comparisons. The 3D solid model intuitively displays the three-dimensional characteristics of the groundwater flow field and the diffusion process of the pollution plume.

[0103] The early warning module 7 played a crucial role in this application. According to the Class III water standard in the "Groundwater Quality Standard" GB / T 14848-2017, the COD concentration early warning threshold was set at 50 mg / L. In one real-time monitoring session, the COD concentration in monitoring well J15, approximately 800 meters downstream of the pollution source, gradually increased from the background value of 20 mg / L to 55 mg / L. The system's backend, after real-time comparison, determined that a more severe warning had been triggered and immediately notified relevant personnel via SMS and pop-up notifications. The early warning tracing function automatically initiated reverse particle tracing, identifying the upstream chemical plant as the most likely pollution source on the GIS map and simulating the pollution diffusion path, providing precise data for emergency response.

[0104] Compared with the prior art, the present invention has the following beneficial effects:

[0105] 1) It achieves integrated integration of multi-source data, solving the problems of scattered, inconsistent formats and low correlation of traditional hydrogeological data. By constructing a GIS spatial database, it realizes centralized management and collaborative access to multi-source data such as basic geography, exploration, and monitoring, improving data utilization efficiency. At the same time, it combines data-driven and model-driven inversion technology to improve the accuracy of hydrogeological parameters.

[0106] 2) By integrating GIS spatial analysis and numerical simulation technologies, the simulation results are organically combined with the spatial distribution analysis of hydrogeological elements, groundwater dynamic simulation and pollution prediction. The simulation results are deeply correlated with spatial geographic information, with higher accuracy and stronger targeting. It can be adapted to different levels of hydrogeological evaluation scenarios. At the same time, through three-dimensional visualization technology, the simulation results are more intuitive and convenient for decision-making reference.

[0107] 3) Real-time monitoring and early warning of groundwater dynamics are achieved. Data is collected and transmitted in real time through Internet of Things technology, overcoming the problem of traditional monitoring lag. It can quickly identify risks such as abnormal groundwater levels and water pollution, issue early warnings in a timely manner and trace the causes, providing timely support for hydrogeological disaster prevention and control and pollution control.

[0108] 4) The system has comprehensive functions, covering the entire process of exploration, analysis, simulation, monitoring, visualization and early warning. It is suitable for various application scenarios such as regional groundwater resource assessment, groundwater pollution source tracing and hydrogeological disaster early warning. It can be widely used in geological survey, water resource management, ecological environment protection and engineering construction. It has strong practicality and promotion value. At the same time, it can rely on the domestic GIS platform to ensure the security of geospatial information.

[0109] 5) Easy to operate and highly visualized; no professional GIS skills are required. Staff can quickly obtain hydrogeological information, view simulation results and monitoring data through the visual interface, reducing work difficulty and improving work efficiency. It also supports thematic studies. Figure 1Key generation and export meet practical work needs.

[0110] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A GIS-based integrated hydrogeological survey and dynamic monitoring system, characterized in that, include: The data acquisition module is used to collect multi-source hydrogeological data within the study area; The data preprocessing and integration module, connected to the data acquisition module, is used to preprocess the acquired multi-source data and integrate it into the GIS spatial database to establish a three-dimensional correlation based on spatial location, attribute parameters and time series. The GIS core analysis module, connected to the data preprocessing and integration module, is used to perform spatial analysis on the integrated data using GIS spatial analysis functions and to invert the spatial distribution of hydrogeological parameters. The numerical simulation module is connected to the GIS core analysis module and is used to construct groundwater flow numerical models and groundwater pollution transport numerical models based on GIS analysis results and inverted hydrogeological parameters for simulation and prediction. The dynamic monitoring module, connected to the data preprocessing and integration module, includes a sensor network and an Internet of Things communication unit, used to collect and transmit updates of groundwater dynamic parameters in real time. The visualization module is connected to the GIS spatial database, GIS core analysis module, numerical simulation module and dynamic monitoring module, and is used to visualize the data integration results, analysis results, simulation results and monitoring data in two-dimensional and three-dimensional form. The early warning module, connected to the dynamic monitoring module and the numerical simulation module, is used to detect, issue early warnings, and perform source tracing analysis of abnormal events based on monitoring data and simulation results and according to preset thresholds.

2. The system according to claim 1, characterized in that, The data preprocessing and integration module includes: The data cleaning unit uses outlier detection algorithms to remove outliers from the monitored data and uses interpolation to fill in missing data. The format standardization and registration unit is used to convert data of different formats into a unified spatial data format and attribute data format, and to unify them under the same geographic coordinate system to achieve spatial alignment; the data association unit is used to bind borehole coordinates with corresponding strata lithology and aquifer parameters, bind monitoring well locations with real-time water level and water quality data, and establish the three-dimensional association relationship. The database construction unit uses a database system that supports spatial data storage to classify and store the integrated data, and at least constructs a basic geographic database, a hydrogeological exploration database, a groundwater monitoring database, an environmental database, and a geological structure database.

3. The system according to claim 1, characterized in that, The core GIS analysis module includes: The spatial overlay analysis unit is used to overlay the stratigraphic lithology layer, aquifer distribution layer, fault distribution layer, monitoring well layer, and pollution source layer to identify the aquifer distribution range and fault hydraulic connections. The buffer analysis unit is used to set up buffer zones based on pollution sources, monitoring wells, or fault elements, and to analyze the scope of impact or monitoring coverage. The network analysis unit is used to construct a groundwater flow network model, analyze groundwater flow direction and runoff path, and reveal the transformation mechanism of atmospheric precipitation, surface water and groundwater in combination with the distribution of surface water bodies; The parameter inversion unit is used to perform spatial interpolation inversion of permeability coefficient and porosity using spatial interpolation algorithms to generate a continuous raster map of hydrogeological parameters.

4. The system according to claim 1, characterized in that, The numerical simulation module includes: The groundwater flow model construction unit uses the finite difference method to construct the groundwater flow equation, inputs the hydrogeological parameters inverted by the GIS core analysis module, and combines water level and flow monitoring data to simulate the distribution of groundwater flow field and dynamic changes in water level; The pollution transport model construction unit constructs pollutant transport equations based on solute transport theory, and combines pollution source information and hydrogeological parameters to simulate pollutant diffusion paths and concentration distribution; The model calibration unit uses actual monitoring data to calibrate the numerical model and adjusts the parameters until the simulation results and the monitoring data errors meet the preset accuracy requirements.

5. The system according to claim 4, characterized in that, The groundwater flow model construction unit uses the finite difference method to construct two-dimensional or three-dimensional groundwater flow equations, and the governing equations are: ； ； ； In the formula, Aquifer permeability coefficient; Groundwater head; Source and sink items; Known water level boundary; Known flow boundaries; Study area.

6. The system according to claim 4, characterized in that, The pollution transport model construction unit uses a two-dimensional point source Gaussian diffusion equation to solve for the pollutant concentration distribution. The equation expression is as follows: ； In the formula, solute concentration; , Vertical and horizontal coordinates; time; The mass of solute released instantaneously from the pollution source; Aquifer porosity; Delay factor; Aquifer thickness; , Variance of Gaussian distribution in both longitudinal and transverse directions; Decay coefficient.

7. The system according to claim 1, characterized in that, The dynamic monitoring module includes: The real-time acquisition unit includes multi-parameter sensors deployed within the study area for real-time acquisition of groundwater level, water quality, and water temperature parameters; The data transmission unit uses Internet of Things (IoT) communication technology to transmit the data collected by the sensor to the data preprocessing and integration module in real time via wireless communication. The monitoring network optimization unit identifies monitoring gaps based on the analysis results of the GIS core analysis module and optimizes the deployment locations of monitoring wells and sensors.

8. The system according to claim 1, characterized in that, The visualization module includes: The two-dimensional display unit is used to display the spatial distribution map of hydrogeological elements in the study area with a map as the base map. It supports layer overlay and switching, and displays the real-time changes and historical trends of monitoring parameters in the form of curves and heat maps. The three-dimensional display unit uses three-dimensional modeling technology to construct a three-dimensional solid model of groundwater, which is used to display the distribution of groundwater flow field and the diffusion path of pollutants; The thematic map generation unit is used to automatically generate thematic maps of hydrogeological exploration, groundwater water-rich zoning maps, pollution impact range maps, and disaster early warning maps.

9. The system according to claim 1, characterized in that, The early warning module includes: The threshold setting unit is used to set the water level anomaly threshold and water quality exceedance threshold according to the hydrogeological conditions and relevant standards of the study area. Anomaly detection unit is used to compare monitoring data with thresholds in real time and identify water level anomalies, water quality exceeding standards, and pollutant diffusion risks by combining numerical simulation results; The early warning notification unit is used to automatically trigger an early warning when an anomaly is detected, and send early warning information containing the early warning type, anomaly location and risk level to the designated terminal; The early warning and tracing unit is used to combine GIS spatial analysis functions to conduct source analysis of abnormal situations and trace the location of pollution sources or the causes of abnormalities.

10. The system according to claim 1, characterized in that, The core GIS analysis module is built on the ArcGIS or MapGIS platform, and the data preprocessing and integration module uses the PostgreSQL+PostGIS spatial database.

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