An integrated method for environmental safety and sustainable utilization of groundwater resources

Abstract

The application belongs to the technical field of groundwater resource management, and discloses an integrated method of groundwater source environment safety and sustainable utilization technology, which comprises the following steps: first step, multi-source database construction and feature recognition; second step, technology system construction and applicability evaluation; third step, technology combination comparison and effect evaluation; fourth step, system integration and output. The beneficial effects are as follows: the technical problems of cross-disciplinary joint interpretation and mechanism identification caused by the format and scale conflicts of original data are solved completely. The repeated calculation contribution caused by the strong internal correlation of indexes is effectively weakened, so that the applicability score of the final calculation can truly and accurately reflect the physical matching relationship between the technology and the complex hydrogeological environment conditions. The globally optimal integrated technology path screening with high self-adaptation and cross-scenario robustness in the multi-dimensional conflict solution space is realized, and a scientific and operable tool for fine management is provided.

Description

Technical Field

[0001] This invention belongs to the field of groundwater resource management technology, specifically relating to an integrated method for groundwater source environmental safety and sustainable utilization technology. Background Technology

[0002] Currently, the comprehensive analysis and evaluation of groundwater source systems heavily relies on the integrity and consistency of basic data. However, in practical operations, multi-dimensional basic data, including geological, hydrogeological, hydrological, hydrochemical, meteorological, and ecological data, often originate from different sources and observation methods. These raw data exhibit significant multi-source heterogeneity, with severe inconsistencies in spatial benchmarks, temporal scales, and data formats, making direct interpretation and data fusion difficult. This results in a lack of high-precision alignment of multi-source data in space, time, and attributes, hindering the provision of a unified, reliable, and standardized data foundation for subsequent numerical simulations and risk screening.

[0003] Meanwhile, when evaluating the applicability of individual key technologies and constructing a multi-dimensional indicator system, there are often strong intrinsic correlations between evaluation indicators of different dimensions, such as water level depth and recharge intensity. In the traditional weighting and individual scoring process, if the independence between indicators is not quantitatively corrected and redundancy is eliminated, the information contribution carried by these strongly correlated indicators will be repeatedly calculated in the comprehensive evaluation results, resulting in redundant interference. This directly reduces the objectivity and scientific rigor of the technology applicability level determination, making it impossible to accurately identify the true matching relationship between various technologies and the complex geological environmental conditions of water source areas.

[0004] Furthermore, traditional groundwater environmental management and engineering technology selection often relies on the independent application effects of single methods, lacking systematic and collaborative methods for selection and effect evaluation. Groundwater source management naturally faces multiple conflicting and mutually exclusive decision objectives, such as ensuring environmental safety (e.g., water level regulation, water quality assurance, water quantity constraints) and controlling economic and social costs (e.g., life cycle costs, operation and maintenance costs). Traditional scheme selection typically employs conventional linear weighted scoring methods, which are prone to masking extreme vulnerabilities in certain core safety dimensions due to the average distribution of total scores. This fails to effectively reveal the multi-objective synergistic trade-offs between different technology combinations in resource utilization, risk prevention and control, and ecological maintenance, resulting in the final selected scheme lacking cross-scenario robustness and practical operability. Summary of the Invention

[0005] The main objective of this invention is to address the common problem in existing comprehensive analysis and evaluation methods for groundwater source systems that multi-source data cannot achieve high-precision alignment in space, time, and attributes, making it difficult to provide a unified, reliable, and standardized data foundation for subsequent numerical simulations and risk screening.

[0006] Another objective of this invention is to address the problem that when evaluating the applicability of a single key technology and constructing a multi-dimensional index system, redundant information often occurs, which reduces the objectivity and scientific rigor of the technology applicability level determination and makes it impossible to accurately identify the true matching relationship between various technologies and the complex geological environmental conditions of water source areas.

[0007] Another objective of this invention is to address the problem that traditional groundwater environmental management and engineering technology comparison methods often rely on the independent application of single methods and lack systematic and collaborative comparison and effect evaluation methods.

[0008] In order to achieve the above objectives and solve the above problems, the present invention provides an integrated method for the environmental safety and sustainable utilization of groundwater source areas.

[0009] The integrated method for groundwater source area environmental safety and sustainable utilization technology provided by this invention includes the following steps:

[0010] Step 1: Multi-source database construction and feature recognition;

[0011] The second step is the construction and applicability evaluation of the technical system.

[0012] Step 3: Comparison and evaluation of technology combinations;

[0013] Step 4: System integration and output.

[0014] The specific steps for multi-source database construction and feature recognition in the first step are as follows:

[0015] Step 1, Data Integration: Comprehensively collect and integrate multi-dimensional and cross-domain basic information related to typical groundwater sources, mainly including: geological and hydrogeological data obtained through borehole profiles and geological exploration; groundwater level, quantity, and quality obtained through hydrological monitoring; precipitation, evaporation, temperature, and wind speed from meteorological data; and ecological and land use data obtained through remote sensing interpretation and ground surveys.

[0016] Step 2, Homogeneous Alignment of Spatiotemporal Features: For multi-source heterogeneous raw data, a unified spatial reference and time scale are used for standardized alignment. On the time axis, moving average or spline interpolation is used to uniformly resample the multi-source data to the standard scheduling step size. On the spatial axis, projection transformation is used to dynamically unify the data to the CGCS2000 coordinate system. Kriging interpolation or bilinear interpolation is used to uniformly transform point source monitoring, vector boundaries, and remote sensing raster into homogeneous spatial standard grid cells, eliminating conflicts between different dimensions and spatiotemporal heterogeneity, and ensuring the consistency of data in space, time, and attributes.

[0017] Step 3, Spatial Mask Clipping and Feature Extraction: The shapefile vector file of the water source protection area boundary is introduced as a spatial mask. Batch spatial clipping and attribute feature matrix extraction are performed. Through statistical analysis and information extraction, the key change characteristics and driving relationships of the groundwater system are revealed, and a reliable data foundation is constructed.

[0018] Step 4, Interface Configuration and Dynamic Expansion: By configuring the real-time interface with the automated monitoring system and remote sensing data platform, the periodic and dynamic automatic updates of water level, water quality and meteorological data are realized, and a multi-source data access module is reserved to support the access of new types of data in the future and the long-term evolution of the database.

[0019] The specific steps for the technical system construction and applicability evaluation in the second step are as follows:

[0020] Step 1: Core Technology System Analysis and Encapsulation: The system constructs and encapsulates three types of core technology systems that are interconnected and complementary in terms of objectives and methods, as detailed below:

[0021] ① Numerical simulation technology system: Based on the medium characterization of pores, fissures and karst, the variable coefficient nonlinear flow equation and convection-dispersion-chemical reaction equation are derived. The multi-scale finite element method combined with the finite volume method or finite difference method is used for solution. Gated cyclic units are introduced for spatiotemporal fusion and ensemble Kalman filtering and Markov chain Monte Carlo method are used for parameter inversion. A multi-field coupled simulation model driven by mechanism and data is established.

[0022] ② Environmental risk screening and graded prevention and control technology system: Establish a structural equation model of risk source-migration path-receptor, combine isotope technology and positive definite matrix factorization method to analyze the source and contribution rate of pollutants, and use random forest or Bayesian network machine learning model to calculate risk index and determine graded threshold;

[0023] ③ Groundwater Sustainable Utilization Assessment Technology System: Based on the three-dimensional structure of "socio-economic-resource-environment-ecological security", the Delphi method and machine learning, namely random forest Gini index and K-means clustering algorithm, are used to screen indicators to construct a hierarchical system, establish a coupling coordination degree model to analyze the quantitative function relationship, and adopt a subjective and objective comprehensive weighting method, namely entropy weight-AHP-particle swarm optimization hybrid strategy, combined with GIS overlay to generate a five-level sustainable utilization zoning map.

[0024] Step 2, Construction and Standardization of Multidimensional Indicator Space, as follows:

[0025] A comprehensive, multi-dimensional indicator system is constructed from four dimensions: resource and environmental constraints, safety risk prevention and control, engineering applicability, and economic and social aspects. Data is standardized to eliminate the influence of unit dimensions. The formula used for positive indicators, i.e., benefit-type indicators, is as follows:

[0026] ;

[0027] The formula used for contrarian indicators, i.e., cost-based indicators, is as follows:

[0028] ;

[0029] The index values ​​are uniformly scaled to the [0,1] range in the above formula. The original data value represents the first... The sample at the th The actual value of each indicator; This represents the minimum value of the indicator across all samples. This represents the maximum value of the indicator across all samples; This is the new value after standardization;

[0030] Step 3, Initial weight independence correction and optimization, as follows:

[0031] The initial weights are formed by the linear superposition analytic hierarchy process and the entropy weight method. The Pearson correlation coefficient matrix is ​​introduced to calculate the correlation coefficient between any two indicators. Eliminate redundant interference caused by the inherent correlation between indicators, and use formulas to solve for the independence coefficients of each indicator. The formula is as follows:

[0032] ;

[0033] in, For the number of indicators, Characterizing the first The degree of independence of each indicator relative to other indicators is used to normalize the initial weights based on the independence coefficients to reduce redundant contributions, thus obtaining the final optimized weights. The formula is as follows:

[0034] ;

[0035] in, These are the initial weights; The coefficients are the independence coefficients.

[0036] Step 4, Applicability Score Calculation and Grading Determination, is as follows:

[0037] The final comprehensive applicability score for an individual technology is calculated using the weighted standardization method, as follows:

[0038] ;

[0039] in, For the first The applicability score of the technology As the indicator weight, These are the standardized indicator values;

[0040] Based on the score distribution characteristics, individual technologies are divided into four levels: Level I (highly applicable), Level II (relatively applicable), Level III (generally applicable), and Level IV (inapplicable). A unified framework for technology screening and comparison is established, and Level IV inapplicable technologies are automatically pruned and removed.

[0041] The specific steps for technology combination comparison and effect evaluation in the third step are as follows:

[0042] Step 1: Organic combination of cross-system technologies: Based on the candidate technology set selected in the second step, cross-system functional coupling is carried out in accordance with the principles of "technology complementarity, indicator constraint, and effect orientation". The dynamic parameter output of the numerical simulation system is used as the physical mechanism input of the risk screening system and transformed into spatial constraint information to be fed back to the sustainable assessment system. In the application of multi-media systems, a solution space of technology combination schemes between the sequence sets of "numerical simulation - risk prevention and control - sustainable assessment" is established.

[0043] Step 2, Performance Matrix Construction and Target Decomposition: Integrate the standardized index results of each technology combination under different target dimensions to form a comprehensive performance matrix. The standardized formula is as follows:

[0044] ;

[0045] in, For the first The technology combination in the The standardized values ​​under each indicator are used to represent the independent contributions of different target dimensions. The matrix is ​​then split into subsets X={X_{...}} based on the target category. a ,X b ,X c ,X d}, so that independent weighted aggregation can be performed according to the target dimension, X a ,X b ,X c ,X d Each represents a different submatrix;

[0046] Step 3, Dimension Score and Aggregate Weight Calculation: For any target dimension b, calculate the solution score for that target dimension using the formula, defined as follows:

[0047] ;

[0048] in, For the optimized indicator weights, Let b represent the set of indicators belonging to dimension b, thus forming the target matrix as follows:

[0049] ;

[0050] To comprehensively assess the importance of different dimensions, a dimension aggregation weight is introduced. It is determined by the following formula:

[0051] ;

[0052] Step 4: Multi-objective approximation and Pareto front pruning: A multi-objective approximation method combined with Pareto front analysis is used to quantitatively compare the various schemes, and the weighted distance of each scheme in the multi-objective space is calculated. as follows:

[0053] ;

[0054] in, Aggregate weights for dimensions; Score the target dimension solution;

[0055] The approximation score was then calculated. as follows:

[0056] ;

[0057] in, Weighted distance; Minimum distance; The maximum distance;

[0058] Based on approximation score and standardization costs or constraint strength The Pareto optimal solution set is constructed using the dominance criterion as follows:

[0059] ;

[0060] Automatically execute filtering rules to eliminate outdated solutions that are obscured by combinations of absolutely inferior technologies;

[0061] Step 5: Global Optimal Solution Reordering and Optimization: Standardize the objective values ​​of each solution in the Pareto optimal solution set to calculate the standardized cost index. Introducing a trade-off coefficient , The value is between 0.3 and 0.5, adjusting the balance between cost and approximation accuracy. The core function of the multi-objective approximation algorithm is used to solve for the final compromise distance from each scheme point to the absolute ideal reference point. The details are as follows:

[0062] ;

[0063] The globally optimal combination of technologies is determined based on the principle of minimum distance, and a serialized hierarchical rearrangement result is established.

[0064] Step 6: Multidimensional dynamic effect verification and evaluation: Establish a comprehensive evaluation framework with water quantity regulation, water quality protection and ecological maintenance as the core. Before final determination, quantitatively verify the synergistic efficiency and sustainability level of relevant indicators such as groundwater level change trend, pollutant concentration control effect, ecological water level and wetland maintenance capacity through back-generation simulation and scenario stress test.

[0065] The specific steps for system integration and output in step four are as follows:

[0066] Step 1: Building an integrated decision-making system: Following the construction approach of "unified standards, modular collaboration, and dynamic updates," and based on the aforementioned data, indicator system, and comparison algorithm results, an integrated decision support information system platform integrating "data-model-evaluation-decision" is established.

[0067] Step 2, Multi-database business data flow integration: Using the multi-source databases formed by the data support layer as the underlying core, the optimal rearranged solution set, indicator feature matrix and model bus output by the technology optimization and combination evaluation layer are integrated to achieve deep coupling and integration of data, technology and management;

[0068] Step 3: Output of refined management application results: The system will output the results in a systematic and information-based manner, automatically transforming complex scientific calculations and algorithmic convergence solutions into operational, updatable, and verifiable water source management application tools. The system will display the integrated results in a partitioned, graphical, and checklist format, specifically and dynamically outputting risk classification maps, technology combination recommendation tables, and water source safety management checklists, thus realizing the direct transformation from scientific calculations to refined management decisions.

[0069] The beneficial effects of this invention are:

[0070] (1) Breakthrough in the joint bottleneck of multi-source heterogeneous data: By performing dual-axis standardization homogeneous resampling and pruning of multi-dimensional raw data such as hydrology, geology, meteorology, and ecology through the data support layer, a standardized database with uniformity was constructed, which completely solved the technical problem that the previous cross-disciplinary joint interpretation and mechanism identification could not be achieved due to the conflict of raw data format and scale.

[0071] (2) Eliminates redundant interference of indicator information in single technology evaluation: In the evaluation of single technology applicability, the present invention innovatively introduces an independent coefficient solution mechanism based on the Pearson correlation coefficient matrix. The initial subjective and objective composite weights are corrected by product normalization through independent coefficients, which effectively weakens the contribution of repeated calculation caused by strong intrinsic correlation of indicators, so that the final applicability score can reflect the physical matching relationship between the technology and complex hydrogeological environmental conditions in a true and accurate manner.

[0072] (3) Overcoming the problem of adaptive self-balancing optimization of technical solutions under multi-objective conflict constraints: This invention abandons the traditional linear weighted total score ranking method, which is prone to masking extreme vulnerability risks due to the total score distribution. Instead, it adopts an operations research decision-making mechanism that combines multi-objective approximation method with Pareto front analysis. It dynamically reveals and weighs the constraints and coordination relationships between resource utilization, environmental safety, risk prevention and control and socio-economic costs. It realizes the selection of globally optimal integrated technical paths with adaptive and highly robust cross-scenario capabilities in the multi-dimensional conflict solution space, providing a scientific and operable core tool for refined management. Attached Figure Description

[0073] Figure 1 This is a schematic diagram of the overall design of the integrated framework of the technical system described in this invention.

[0074] Figure 2 This is a schematic diagram of the technical route for each step described in this invention. Detailed Implementation

[0075] Please see Figures 1 to 2 As shown:

[0076] The integrated method for groundwater source area environmental safety and sustainable utilization technology provided by this invention includes the following steps:

[0077] Step 1: Multi-source database construction and feature recognition;

[0078] The second step is the construction and applicability evaluation of the technical system.

[0079] Step 3: Comparison and evaluation of technology combinations;

[0080] Step 4: System integration and output.

[0081] The specific process of multi-source database construction and feature recognition in the first step is as follows:

[0082] In this embodiment, the data support layer, serving as the foundation of the entire integrated framework, first performs the tasks of collecting, integrating, and identifying features from multi-source heterogeneous basic information. The specific execution process of this step in a typical groundwater source area scenario is as follows:

[0083] Step 1: Multidimensional Heterogeneous Data Integration: The system comprehensively integrates multidimensional and cross-domain basic information covering the boundary of the water source area. Geological and hydrogeological data are mainly obtained by retrieving historical borehole profiles and geological exploration results, used to characterize the aquifer structure and recharge boundaries; hydrological monitoring data include long-term continuous observation results of groundwater level, quantity, and quality; meteorological data cover time-series elements such as precipitation, evaporation, temperature, and wind speed; ecological and land use data are derived from remote sensing interpretation and ground surveys, used to reflect the characteristics of land cover type, vegetation response, and distribution of ecologically sensitive areas.

[0084] Step 2, Spatiotemporal Feature Homogeneous Alignment: To address the heterogeneous conflicts in the above-mentioned multi-source raw data, the system adopts standardized cleaning, as follows:

[0085] ① Time axis alignment: For water quality test data, daily meteorological monitoring data, and annual mining volume statistics collected irregularly, resampling is uniformly performed based on the time window moving average operator or cubic spline interpolation algorithm, and uniformly converted into a standard daily management step size.

[0086] ② Spatial axis alignment: Using the spatial projection transformation module, all non-homogeneous vector or raster data are dynamically and uniformly projected and transformed to the China Geodetic Coordinate System (CGCS2000); for spatially discrete monitoring well point source data and low-resolution remote sensing raster, spatial homogenization resampling is performed using the Kriging spatial interpolation algorithm based on the variogram function or the bilinear interpolation algorithm, and uniformly divided into a standard grid spatial matrix of 100m×100m.

[0087] Step 3: Spatial Mask Clipping and Feature Extraction: The system introduces a shapefile vector file of the water source protection area boundary as a spatial mask, and performs batch spatial clipping and attribute feature mapping on the standardized spatiotemporal matrix. Through statistical analysis and information extraction, the key change characteristics and driving relationships of the groundwater system are revealed, and the cleaned standardized spatiotemporal matrix data table is written into the standard spatial database of the storage layer.

[0088] Step 4, Interface Configuration and Dynamic Expansion: The system is equipped with an IoT automated monitoring real-time transmission interface and a remote sensing platform API to realize the periodic automatic addition and update of groundwater level, water quality and meteorological data; at the same time, the system reserves a multi-source data access module to enable the future access of new types of data such as ecological monitoring, socio-economic and engineering operation, so as to realize the long-term evolution and continuous support of the database.

[0089] The specific steps for the second step of technology system construction and applicability evaluation are as follows:

[0090] Based on the aforementioned data, the technology optimization layer systematically integrates and analyzes the applicability of key technologies related to the environmental safety and sustainable utilization of groundwater sources. The specific implementation process is as follows:

[0091] Step 1: Core Technology System Overview: This embodiment systematically constructs and encapsulates three types of core technology systems that are interconnected and provide closed-loop feedback in terms of objectives and methods:

[0092] Numerical simulation technology system: Through systematic characterization of the hydrogeological structure and key parameters (permeability coefficient, hydraulic gradient, dispersion coefficient, etc.) in pore, fracture, and karst media, variable-coefficient nonlinear groundwater flow equations and pollutant convection-dispersion-chemical reaction equations are derived, forming a mathematical model covering multi-media aquifer systems. The solution is obtained using the multi-scale finite element method (MSFEM) combined with the finite volume method (FVM) or the finite difference method (FDM). Transfer learning and gated recurrent unit (GRU) models are introduced to spatiotemporally fuse multi-source monitoring, remote sensing, and historical data to jointly correct the mechanism model. Ensemble Kalman filtering (EnKF) and Markov chain Monte Carlo (MCMC) methods are used for parameter inversion and uncertainty analysis to construct an efficient surrogate model, ensuring simulation accuracy is controlled within 10%.

[0093] Environmental risk screening and tiered prevention and control technology system: Establish a structural equation model of risk source-migration path-receptor to quantitatively characterize the risk formation mechanism and interaction relationship. Combine with isotope technology ( The sources and contribution rates of pollutants were analyzed using the positive definite matrix factorization (PMF) method. A risk state variable system consisting of pollutant exceedance rate, migration rate, water level depth, and mining intensity was constructed, and machine learning models such as random forest and Bayesian network were used to calculate the groundwater environmental risk index to determine the classification threshold.

[0094] A technical system for assessing sustainable groundwater utilization is established. Based on a three-dimensional structure of "socio-economic, resource-environment, and ecological security," the system employs the Delphi method and machine learning (random forest Gini index, K-means clustering algorithm) to select evaluation indicators, constructing a hierarchical system of "indicator layer - criterion layer - target layer." A coupling coordination degree model is established to quantify the mutual constraints and coordination relationships between different subsystems. A comprehensive subjective and objective weighting method is used to determine weight coefficients and introduce time decay and spatial heterogeneity correction. GIS overlay is used to generate a sustainable utilization zoning map of water sources, classifying water sources into five levels: "strong sustainability - relatively strong - average - weak - unsustainable."

[0095] Step 2, Multidimensional Indicator Space Construction and Standardization: A multidimensional indicator system is constructed from four dimensions to quantitatively describe the adaptation characteristics of different technologies under different water source conditions. The specific process is as follows:

[0096] ① Resource and environmental constraints: Key indicators include groundwater recharge intensity, aquifer thickness, dynamic fluctuation of water level, and water quality compliance rate.

[0097] ② Safety risk prevention and control dimension: The main indicators include the compliance rate of groundwater quality at level 3, pollutant migration sensitivity (determined by combining permeability coefficient, porosity and burial depth), and ecological stress index.

[0098] ③ Engineering applicability dimension: The main indicators include model simulation accuracy, technology maturity, maintenance complexity and parameter interpretability.

[0099] ④ Economic and social dimensions: Key indicators include total life cycle cost, operation and maintenance cost, policy support, and public satisfaction.

[0100] All indicator data were standardized before calculation to eliminate the influence of dimensions and orders of magnitude:

[0101] ① For positive indicators (benefit-oriented) where a larger value represents better technological adaptability or superior system conditions, the standardized formula is:

[0102] ;

[0103] ② For inverse indicators (cost-type) where a larger value indicates poorer adaptability or more unfavorable environmental conditions, the standardized formula is:

[0104] ;

[0105] in the formula The original data value represents the first... The sample at the th The actual value of each indicator; This represents the minimum value of the indicator across all samples. This represents the maximum value of the indicator across all samples; These are the new values ​​after standardization. After standardization, all indicators fall within the range of [0,1], and the larger the value, the higher the technological adaptability.

[0106] Step 3: Weight Optimization Based on Indicator Independence Correction: The system combines the Analytic Hierarchy Process (AHP) and the entropy weight method to linearly superimpose initial weights. Since there are often strong causal correlations between different indicators in the groundwater system (such as water level depth and recharge intensity, permeability coefficient and groundwater renewal rate), without independence correction, the contributions of some indicators may be counted repeatedly. Therefore, this embodiment introduces the Pearson correlation coefficient matrix to quantitatively analyze indicator independence and calculate the correlation coefficient of each indicator pair. Construct a correlation matrix. To eliminate redundancy among indicators, calculate the independence coefficients:

[0107] ;

[0108] in, For the number of indicators, Characterizing the first The degree of independence of each indicator relative to other indicators is determined. Based on the independence coefficient, the initial weights are normalized and multiplied to reduce the contribution of redundant indicators, thus obtaining the final optimized weights. :

[0109] ;

[0110] in, These are the initial weights; The coefficients are the independence coefficients.

[0111] Step 4, Score Calculation and Grading: Using the weighted standardization method, the standardized values ​​of each technology under each indicator are calculated. Perform a weighted summation to calculate the final comprehensive applicability score. :

[0112] ;

[0113] in, For the first The applicability score of the technology As the indicator weight, These are the standardized indicator values.

[0114] Based on the overall score Based on the distribution characteristics, each technology is divided into four applicability levels: Level I (highly applicable, score ≥ 0.85), Level II (relatively applicable, score [0.70, 0.85)), Level III (generally applicable, score [0.55, 0.70)), and Level IV (not applicable, score < 0.55). The system automatically performs pruning operations to remove inapplicable technologies belonging to Level IV, thus providing a unified framework for technology screening and comparison.

[0115] The specific steps for technology combination comparison and effect evaluation in the third step are as follows:

[0116] Based on the results of technology optimization, the combined evaluation layer conducts combined analysis and effect evaluation of different key technologies. The specific execution process is as follows:

[0117] Step 1: Organic Integration of Cross-System Technologies: Representative methods from three technical systems—numerical simulation, environmental risk screening and graded prevention and control, and sustainable groundwater utilization assessment—are functionally coupled across systems. In this embodiment, the dynamic water level changes, solute migration concentration fields, and recharge / discharge process parameters output by the numerical simulation system serve as the physical mechanism input to the risk screening system; the pollution source apportionment and risk zoning results calculated by the risk screening system are transformed into spatial constraint information and fed back into the sustainable utilization assessment system; the sustainable assessment system comprehensively calculates the water balance rate, pollution control rate, and ecological water demand satisfaction rate to achieve multi-objective coordination analysis. This establishes an integrated set of technical combination schemes for "numerical simulation—risk prevention and control—sustainable assessment" (e.g., combination 1, combination 2, ... combination n).

[0118] Step 2: Construct and decompose the comprehensive performance matrix: Integrate the standardized index results of each candidate technology combination under different target dimensions to form a comprehensive performance matrix. The matrix rows represent different combination schemes, and the columns represent various evaluation indicators. To reflect the independent contributions of different objective dimensions, the matrix is ​​split into a subset X = {X_{i=1} ... a ,X b ,X c ,X d} (X a ,X b ,X c ,X d These correspond to the resource and environmental constraints, safety risk prevention and control, engineering applicability, and economic and social dimensions, respectively.

[0119] Step 3: Solving for dimensional scores and dimensional aggregation weights: For any target dimension b, its solution score... Defined as the weighted sum of indicators belonging to this dimension:

[0120] ;

[0121] in, For the optimized indicator weights, This represents the set of indicators belonging to dimension b. This forms the target matrix:

[0122] ;

[0123] To comprehensively assess the importance of different dimensions, a dimension aggregation weight is introduced. It is determined by the following formula:

[0124] ;

[0125] Step 4: Multi-objective approximation and Pareto front analysis: To comprehensively consider the balance and deviation of various dimensions, a multi-objective approximation method (MOA) combined with Pareto front analysis is used to quantitatively compare the various schemes. First, the weighted distance of each scheme in the multi-objective space is calculated. :

[0126] ;

[0127] in, Aggregate weights for dimensions; Score the target dimension solution. Distance The smaller the value, the smaller the difference between the proposed solution and the ideal solution in terms of multiple objective indicators, and the better the overall performance. The approximation score is then calculated. :

[0128] ;

[0129] in, A larger value indicates that the solution is closer to the ideal optimal state. This is based on the approximation score. and standardization costs or constraint strength Construct a Pareto optimal solution set using the dominance criterion:

[0130] ;

[0131] The system executes filtering and pruning rules to automatically eliminate all solutions that are obscured by combinations of absolutely inferior technologies, and extracts a set of non-inferior solutions that cannot be surpassed by other solutions at the same time, in order to reveal the multi-objective trade-offs between different solutions in terms of resources, risks and costs.

[0132] Step 5: Global Optimal Solution Reordering and Optimization: For each solution in the obtained Pareto optimal solution set, first calculate its standardized cost index. To eliminate dimensional differences between dimensions, an absolutely ideal reference point is defined and a tradeoff coefficient is introduced. (In this embodiment) The value is between 0.3 and 0.5, used to adjust the balance between cost and safety approximation. The core function of the multi-objective approximation algorithm solves for the final compromise distance from each scheme point to the absolute ideal reference point (0,1). :

[0133] ;

[0134] Ultimately based on distance The minimum principle is used to determine the globally optimal combination of technologies, and the serialized sorting results are output in a hierarchical manner.

[0135] Step 6: Multidimensional Dynamic Effect Verification and Evaluation: Before finalizing the plan, the system established a comprehensive evaluation framework centered on water quantity regulation, water quality assurance, and ecological maintenance. Through back-generation simulation and scenario stress testing, the system quantitatively verified the synergistic efficiency and sustainability of indicators such as groundwater level change trends, pollutant concentration control effectiveness, ecological water level, and wetland maintenance capacity, providing a scientific basis for the subsequent integrated application layer system implementation.

[0136] The specific steps for system integration and output in step four are as follows:

[0137] The core task of this step is to integrate the research results from the aforementioned layers into a digital system and realize them at the information platform level, forming a comprehensive application platform for the refined management of groundwater source areas.

[0138] Step 1: Integrated System Construction: Based on the integrated construction approach of "data-model-evaluation-decision," a decision support information system platform is built. The system's presentation layer (UI) includes a digital integrated dashboard, spatial GIS partitioning layers, a technology combination recommendation module, and a management list interactive interface; the business logic layer (API) encapsulates the data alignment engine, weight optimization components, and multi-objective approximation service.

[0139] Step 2: Multi-database business data flow integration: The system presentation layer and business logic layer, through the model bus and standard SQL interface, conduct high-concurrency data interaction and logical calls with the five-database architecture of the backend storage layer.

[0140] ① Basic Database: Stores standardized spatiotemporal matrices of geology, hydrology, meteorology, etc., formed in the first step.

[0141] ② Model Library: Encapsulates machine learning algorithms such as multi-field coupled numerical simulation models, GRU models, structural equation models, and random forests built in the second step.

[0142] ③ Method Library: Includes the standardized processing function described in this invention, the Pearson correlation coefficient method, the initial weight correction optimization method, and the multi-objective approximation and Pareto front selection algorithm.

[0143] ④ Knowledge Base: Solidify the safety constraint thresholds and risk classification and control judgment rules for groundwater level, water quality and water quantity.

[0144] ⑤ Solution Library: Stores the recommended table of globally optimal technology combinations and the list of historical preferred solutions generated after the third step of comparison and rearrangement.

[0145] Step 3: Output of Refined Management Application Results: When users input specific spatial queries or management target adjustment commands at the presentation layer, the business logic layer dynamically calls upon the resources of the five databases to complete the calculations. The system displays the integrated results in a partitioned, graphical, and checklist format. Specifically, it dynamically outputs a groundwater source area environmental risk classification result map, an adaptive matching output technology combination recommendation table, and a refined safety management checklist for water sources directly for review and implementation by management departments. This achieves a direct transformation from complex mechanism numerical simulation to a practical and verifiable refined decision support tool.

Claims

1. An integrated method for groundwater source area environmental safety and sustainable utilization technologies, characterized in that: The method includes the following steps: Step 1: Multi-source database construction and feature recognition; The specific steps are as follows: Step 1, Data Integration: Comprehensively collect and integrate multi-dimensional and cross-domain basic information related to typical groundwater sources, including: geological and hydrogeological data obtained through borehole profiles and geological exploration; groundwater level, quantity, and quality obtained through hydrological monitoring; precipitation, evaporation, temperature, and wind speed from meteorological data; and ecological and land use data obtained through remote sensing interpretation and ground surveys. Step 2, Homogeneous Alignment of Spatiotemporal Features: For multi-source heterogeneous raw data, a unified spatial reference and time scale are used for standardized alignment. On the time axis, moving average or spline interpolation is used to uniformly resample the multi-source data to the standard scheduling step size. On the spatial axis, projection transformation is used to dynamically unify the data to the CGCS2000 coordinate system. Kriging interpolation or bilinear interpolation is used to uniformly transform point source monitoring, vector boundaries, and remote sensing raster into homogeneous spatial standard grid cells, eliminating conflicts between different dimensions and spatiotemporal heterogeneity, and ensuring the consistency of data in space, time, and attributes. Step 3, Spatial Mask Clipping and Feature Extraction: The shapefile vector file of the water source protection area boundary is introduced as a spatial mask. Batch spatial clipping and attribute feature matrix extraction are performed. Through statistical analysis and information extraction, the key change characteristics and driving relationships of the groundwater system are revealed, and a reliable data foundation is constructed. Step 4, Interface Configuration and Dynamic Expansion: By configuring the real-time interface with the automated monitoring system and remote sensing data platform, the periodic and dynamic automatic updates of water level, water quality and meteorological data are realized, and a multi-source data access module is reserved to support the access of new types of data in the future and the long-term evolution of the database. The second step is the construction and applicability evaluation of the technical system; the specific steps are as follows: Step 1: Core Technology System Analysis and Encapsulation: The system constructs and encapsulates three types of core technology systems that are interconnected and complementary in terms of objectives and methods, as detailed below: ① Numerical simulation technology system: Based on the medium characterization of pores, fissures and karst, the variable coefficient nonlinear flow equation and convection-dispersion-chemical reaction equation are derived. The multi-scale finite element method combined with the finite volume method or finite difference method is used for solution. Gated cyclic units are introduced for spatiotemporal fusion and ensemble Kalman filtering and Markov chain Monte Carlo method are used for parameter inversion. A multi-field coupled simulation model driven by mechanism and data is established. ② Environmental risk screening and graded prevention and control technology system: Establish a structural equation model of risk source-migration path-receptor, combine isotope technology and positive definite matrix factorization method to analyze the source and contribution rate of pollutants, and use random forest or Bayesian network machine learning model to calculate risk index and determine graded threshold; ③ Groundwater Sustainable Utilization Assessment Technology System: Based on the three-dimensional structure of socio-economic, resource environment, and ecological security, the Delphi method, random forest Gini index, and K-means clustering algorithm are used to screen indicators and construct a hierarchical system. A coupling coordination degree model is established to analyze the quantitative functional relationship. An entropy weight-AHP-particle swarm optimization hybrid strategy is adopted and combined with GIS overlay to generate a five-level sustainable utilization zoning map. Step 2, Construction and Standardization of Multidimensional Indicator Space, as follows: A comprehensive, multi-dimensional indicator system is constructed from four dimensions: resource and environmental constraints, safety risk prevention and control, engineering applicability, and economic and social aspects. Data is standardized to eliminate the influence of unit dimensions. The formula used for positive indicators is as follows: ； The formula used for the contrarian indicator is as follows: ； The index values ​​are uniformly scaled to the [0,1] range in the above formula. The original data value represents the first... The sample at the th The actual value of each indicator; This represents the minimum value of the indicator across all samples. This represents the maximum value of the indicator across all samples; This is the new value after standardization; Step 3, Initial weight independence correction and optimization, as follows: The initial weights are formed by the linear superposition analytic hierarchy process and the entropy weight method. The Pearson correlation coefficient matrix is ​​introduced to calculate the correlation coefficient between any two indicators. Eliminate redundant interference caused by the inherent correlation between indicators, and use formulas to solve for the independence coefficients of each indicator. The formula is as follows: ； in, For the number of indicators, Characterizing the first The degree of independence of each indicator relative to other indicators is used to normalize the initial weights based on the independence coefficients to reduce redundant contributions, thus obtaining the final optimized weights. The formula is as follows: ； in, These are the initial weights; The coefficients are the independence coefficients. Step 4, Applicability Score Calculation and Grading Determination, is as follows: The final comprehensive applicability score for an individual technology is calculated using the weighted standardization method, as follows: ； in, For the first The applicability score of the technology As the indicator weight, These are the standardized indicator values; Based on the score distribution characteristics, individual technologies are divided into four levels: Level I (highly applicable), Level II (relatively applicable), Level III (generally applicable), and Level IV (inapplicable). A unified framework for technology screening and comparison is established, and Level IV inapplicable technologies are automatically pruned and removed. The third step is to compare and select the appropriate technology combinations and evaluate their effectiveness; the specific steps are as follows: Step 1: Organic combination of cross-system technologies: Based on the candidate technology set selected in the second step, cross-system functional coupling is carried out in accordance with the principles of "technology complementarity, indicator constraint, and effect orientation". The dynamic parameter output of the numerical simulation system is used as the physical mechanism input of the risk screening system and transformed into spatial constraint information to be fed back to the sustainable assessment system. In the application of multi-media systems, a solution space of technology combination schemes between the sequence sets of "numerical simulation - risk prevention and control - sustainable assessment" is established. Step 2, Performance Matrix Construction and Target Decomposition: Integrate the standardized index results of each technology combination under different target dimensions to form a comprehensive performance matrix. The standardized formula is as follows: ； in, For the first The technology combination in the The standardized values ​​under each indicator are used to represent the independent contributions of different target dimensions. The matrix is ​​then split into subsets X={X_{...}} based on the target category. a ,X b ,X c ,X d }, so that independent weighted aggregation can be performed according to the target dimension, X a ,X b ,X c ,X d Each represents a different submatrix; Step 3, Dimension Score and Aggregate Weight Calculation: For any target dimension b, calculate the solution score for that target dimension using the formula, defined as follows: ； in, For the optimized indicator weights, Let b represent the set of indicators belonging to dimension b, thus forming the target matrix as follows: ； To comprehensively assess the importance of different dimensions, a dimension aggregation weight is introduced. It is determined by the following formula: ； Step 4: Multi-objective approximation and Pareto front pruning: A multi-objective approximation method combined with Pareto front analysis is used to quantitatively compare the various schemes, and the weighted distance of each scheme in the multi-objective space is calculated. as follows: ； in, Aggregate weights for dimensions; Score the target dimension solution; The approximation score was then calculated. as follows: ； in, Weighted distance; Minimum distance; The maximum distance; Based on approximation score and standardization costs or constraint strength The Pareto optimal solution set is constructed using the dominance criterion as follows: ； Automatically execute filtering rules to eliminate outdated solutions that are obscured by combinations of absolutely inferior technologies; Step 5: Global Optimal Solution Reordering and Optimization: Standardize the objective values ​​of each solution in the Pareto optimal solution set to calculate the standardized cost index. Introducing a trade-off coefficient , The value is between 0.3 and 0.5, adjusting the balance between cost and approximation accuracy. The core function of the multi-objective approximation algorithm is used to solve for the final compromise distance from each scheme point to the absolute ideal reference point. The details are as follows: ； The globally optimal combination of technologies is determined based on the principle of minimum distance, and a serialized hierarchical rearrangement result is established. Step 6: Multidimensional dynamic effect verification and evaluation: Establish a comprehensive evaluation framework with water quantity regulation, water quality protection and ecological maintenance as the core. Before final determination, quantitatively verify the synergistic efficiency and sustainability level of relevant indicators of groundwater level change trend, pollutant concentration control effect, ecological water level and wetland maintenance capacity through back-generation simulation and scenario stress test. Step 4: System integration and output, the specific steps are as follows: Step 1: Building an integrated decision-making system: Following the construction approach of "unified standards, modular collaboration, and dynamic updates," and based on the aforementioned data, indicator system, and comparison algorithm results, an integrated decision support information system platform integrating "data-model-evaluation-decision" is established. Step 2, Multi-database business data flow integration: Using the multi-source databases formed by the data support layer as the underlying core, the optimal rearranged solution set, indicator feature matrix and model bus output by the technology optimization and combination evaluation layer are integrated to achieve deep coupling and integration of data, technology and management; Step 3: Output of refined management application results: The system will output the results in a systematic and information-based manner, automatically transforming complex scientific calculations and algorithmic convergence solutions into operational, updatable, and verifiable water source management application tools. The system will display the integrated results in a partitioned, graphical, and checklist format, specifically and dynamically outputting risk classification maps, technology combination recommendation tables, and water source safety management checklists, thus realizing the direct transformation from scientific calculations to refined management decisions.

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