Sewage intercepting and repairing system and method for underground water in industrial area

Abstract

The invention relates to the field of underground water pollution treatment, in particular to an industrial area underground water pollutant intercepting and repairing system and method. Comprising an intelligent sensing unit, a sewage interception and diversion unit, a purification unit, a central control platform, a distributed sensor group, an edge intelligent calculation module, a pollution flow dynamic engine, a diagnosis strategy module and the like. By adopting the method, the whole-course real-time tracking of the spatial distribution, the migration track and the dynamic change process of the pollution plume can be realized, and the problem of insufficient perception of the dynamic migration situation of the pollution plume caused by data lag in the traditional method is thoroughly solved.

Description

Technical Field

[0001] This invention relates to the field of groundwater pollution control, and in particular to a groundwater interception and remediation system and method for industrial areas. Background Technology

[0002] Industrial production activities often lead to the infiltration of pollutants such as heavy metals and volatile organic compounds (VOCs) into the ground, forming a continuously spreading pollution plume that seriously threatens the surrounding ecological environment and drinking water safety. Effective interception and remediation of groundwater pollution in industrial areas is a severe challenge currently facing environmental governance.

[0003] Traditional groundwater remediation relies heavily on manual sampling or single-point sensors, which have low data collection frequency and limited coverage, making it impossible to capture the dynamic migration of pollution plumes in real time and easily leading to delays in pollution spread warnings. Summary of the Invention

[0004] The present invention aims to provide a system and method for intercepting and remediating groundwater in industrial areas, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: An industrial zone groundwater interception and remediation system includes: The intelligent sensing unit is used to collect groundwater environmental data in real time, process, simulate and make decisions, and generate control commands; the intelligent sensing unit includes a distributed sensor group, an edge computing module, a pollution flow dynamic engine and a diagnostic strategy module. The pollution interception and diversion unit is used to actively divert, intercept, and precisely extract groundwater pollution plumes based on the control commands. The purification unit is used to purify, reuse, and dispose of polluted water from the interception and diversion unit. The central control platform is connected to the intelligent sensing unit, the sewage interception and diversion unit, and the purification unit.

[0006] Preferably, the edge computing module is communicatively connected to the distributed sensor group. The edge computing module is used to preprocess the collected data. The preprocessing includes the Z-Score algorithm and spatiotemporal kriging interpolation. The Z-Score algorithm and spatiotemporal kriging interpolation are used for outlier detection and missing data imputation, respectively.

[0007] Preferably, the pollution flow dynamic engine is communicatively connected to the edge computing module. The pollution flow dynamic engine includes a flow intensity calculation submodule and a pollution prediction submodule. The flow intensity calculation submodule is used to calculate the spatiotemporal distribution and transport flux of pollutants in real time. The pollution prediction submodule predicts the evolution trend of the pollution plume based on the output of the flow intensity calculation submodule.

[0008] Preferably, the diagnostic strategy module is communicatively connected to the pollution flow dynamic engine. The diagnostic strategy module includes an efficiency evaluation submodule and an adaptive strategy submodule. The efficiency evaluation submodule is used to diagnose the operating status and efficiency of the interception and diversion unit and the purification unit based on the output of the pollution flow dynamic engine. The adaptive strategy submodule is used to generate optimized control commands based on the diagnostic results of the efficiency evaluation submodule. The control commands include on / off and flow control commands for the extraction well group and process parameter adjustment commands for the purification treatment station.

[0009] Preferably, the intercepting and diverting unit includes an extraction well group and an intercepting wall; the extraction well group is configured to be controlled by the central control platform, actively changing the groundwater flow field by adjusting the pumping rate to divert the pollution plume and extract polluted water; the intercepting wall is arranged downstream of the extraction well group and includes three parallel permeable reactive walls, which, from upstream to downstream, are: an adsorption interception wall, a degradation wall, and a depth protection wall, respectively used to adsorb heavy metals, degrade organic pollutants, and deeply treat residual pollutants; the extraction well group is arranged upstream and downstream of the intercepting wall and between each of the permeable reactive walls.

[0010] Preferably, the purification unit includes a purification treatment station, a water quality buffer reuse tank, and a sludge treatment station. The purification treatment station is connected to the outlet of the intercepting and diverting unit and is used to receive and purify polluted water. The water quality buffer reuse tank is connected to the outlet of the purification treatment station and is used to store qualified water and realize water resource reuse. The sludge treatment station is connected to the sludge discharge port of the purification treatment station and is used to safely dispose of the generated sludge.

[0011] Preferably, the central control platform is configured to execute an optimization algorithm based on model predictive control (MPC) for rolling optimization of the control commands; the objective function of the optimization algorithm is: in, This indicates that the system is in a control cycle. Total operating costs within the facility; Indicates the system at time 10:00 The total energy consumption includes the energy consumption of the extraction well group and the purification treatment station; Indicates the system at time 10:00 The cost of pharmaceuticals and materials consumed; and These are the weighting coefficients for energy consumption and material consumption, used to balance operating costs and environmental risks; Indicates the current moment; This represents the prediction time domain of the model predictive control.

[0012] An industrial area groundwater pollution interception remediation method based on the industrial area groundwater pollution interception remediation system according to any one of claims 1 to 7, comprising the following steps: S1, data acquisition and transmission: collecting pollutant concentration data and hydrogeological parameter data of groundwater by a distributed sensor group, and transmitting to an edge intelligent calculation module for preprocessing; S2, data preprocessing and fusion: inputting the original environmental data set transmitted in S1 into the edge intelligent calculation module, applying Z-Score algorithm and space-time Kriging interpolation method to remove outliers and repair missing data of the data set, and generating standardized space-time fusion data; S3, pollution dynamic simulation and prediction: inputting the standardized space-time fusion data generated in S2 into the pollution flow dynamic engine, calculating the pollution flow intensity vector by using the flow intensity calculation submodule, and outputting the pollution plume space-time evolution prediction result by using the pollution prediction submodule to deduce the vector; S4, intelligent diagnosis and decision: inputting the pollution plume space-time evolution prediction result output in S3 into the diagnosis strategy module, generating a system health status report by the efficiency evaluation submodule, and generating an optimized control instruction set for controlling the pollution interception and flow guiding unit and the purification unit based on the report by the adaptive strategy submodule, and issuing through the central control platform; S5, precise flow guiding and collaborative interception: inputting the optimized control instruction set issued in S4 into the pollution interception and flow guiding unit, changing the groundwater flow field by adjusting the pumping rate through the extraction well group to realize active flow guiding of the pollution plume; at the same time, making the pollution plume pass through the adsorption interception wall, the degradation wall and the deep protection wall in turn to complete multi-stage in-situ reduction of pollutants; finally, transporting the high-concentration polluted water and residual low-concentration water to the purification unit; S6, deep purification and resource recycling: inputting the high-concentration polluted water and residual low-concentration water transported in S5 into the purification unit, performing deep treatment in the purification treatment station to form standard purified water and polluted sludge; the standard purified water enters the water quality buffer recycling pool to realize resource recycling; the polluted sludge is transported to the sludge treatment station for safe disposal to realize harmless terminal management.

[0013] Preferably, in step S3, the pollution flow intensity vector is calculated by the following formula: wherein, represents the pollution flow intensity at time , spatial coordinate , unit: kilogram per day (kg / d); represents the pollution flow intensity at time , spatial coordinate , the a measured concentration of a characteristic pollutant, in milligrams per liter (mg / L); represents the actual flow rate of groundwater at time , spatial coordinates , in meters per day (m / d); represents the toxicity weight coefficient of the th characteristic pollutant, which is a dimensionless constant; represents the effective water passing area of the calculation unit, in square meters (m²); represents the total number of monitored characteristic pollutants; in the formula, is a multiplication operator, ∑ is a summation operator, and [] is an operation binding symbol.

[0014] Preferably, the specific process of generating the optimized control instruction set in step S4 includes: based on the spatiotemporal evolution prediction result of the pollution plume, determining whether the peak value of pollution flow intensity in a future specific period will exceed a preset treatment capacity threshold of the interception wall; If the result is yes, an instruction is generated to increase the pumping rate of the upstream extraction well in advance to strengthen hydraulic capture, and the processing unit corresponding to the pollutant in the purification treatment station is switched to a high-level operation mode. At the same time, based on the calculation of the cumulative pollutant flux of each permeable reactive wall, when the saturation reaches a preset warning value, a predictive maintenance instruction for replacing or regenerating the filler is generated.

[0015] The technical scheme compared with the prior art produces the beneficial effects: (1) The scheme realizes the synchronous monitoring and real-time data cleaning of pollutant concentration and hydrological parameters in the whole space and the whole period by deploying a high-density distributed sensor group and an edge intelligent algorithm module, calculates the pollution flow intensity vector with direction and intensity through a pollution flow dynamic engine, and dynamically deduces the pollution plume evolution trend by using a time series prediction model, thereby realizing the advanced prediction of the pollution plume migration path and concentration peak, and finally generating prospective control instructions through a diagnostic strategy module to drive the extraction well group to actively shape the hydraulic capture field and guide the pollution plume to accurately flow to the treatment area. The scheme completely changes the passive response mode caused by information lag in the traditional way, realizes the fundamental change from "postponed early warning" to "accurate prediction and active control in advance", and significantly improves the warning timeliness and reliability of interception control of pollution diffusion. Real-time fusion calculation of multi-source monitoring data is performed by the pollution flow dynamic engine to generate a pollution flow intensity vector with direction and intensity, which accurately represents the real-time migration state of pollutants in the aquifer, thereby realizing the whole-process real-time tracking of the spatial distribution, migration trajectory, and dynamic change process of the pollution plume, and completely solving the problem of insufficient situational awareness of the dynamic migration of the pollution plume caused by data lag in the traditional method.

[0016] (2) By setting the central control platform, the pollution prediction, system performance diagnosis and multi-unit control instruction generation are coupled as a closed-loop optimization process, realizing the dynamic cooperation of the extraction well group, the permeation reaction wall and the purification treatment station, and significantly improving the overall energy efficiency and stability of the system.

[0017] (3) By setting the three-dimensional layout of the extraction well group and the three parallel permeation reaction walls, the extraction well group is arranged on the upstream and downstream of the pollution interception wall and between the reaction walls, the groundwater flow field is changed by actively adjusting the pumping rate to realize the directional flow guiding of the pollution plume; the three permeation reaction walls are respectively used for the hierarchical treatment of heavy metals, organic pollutants and residual pollutants, and the pollutant removal rate is greatly improved compared with the traditional single pollution interception wall. BRIEF DESCRIPTION OF DRAWINGS

[0018] Fig. 1 The system framework diagram provided by the present application is shown in the figure; Fig. 2 The method flow chart provided by the present application is shown in the figure; DETAILED DESCRIPTION The present application will be further described in detail below in combination with the drawings and embodiments: As shown in the figure, an industrial area groundwater pollution interception and remediation system comprises: Figs. 1-2 An intelligent sensing unit is used for real-time collection of groundwater environment data, processing, simulation and decision-making, and generation of control instructions; the intelligent sensing unit comprises a distributed sensor group, an edge intelligent algorithm module, a pollution flow dynamic engine and a diagnosis strategy module; a pollution interception and flow guiding unit is used for active flow guiding, multi-stage interception and accurate extraction of the groundwater pollution plume based on the control instructions; a purification unit is used for purification treatment, resource recycling and sludge disposal of the polluted water from the pollution interception and flow guiding unit; a central control platform is in communication connection with the intelligent sensing unit, the pollution interception and flow guiding unit and the purification unit.

[0019] ​The distributed sensor group is composed of multi-parameter water quality sensors, specific pollutant sensors, water level meters, and seepage flow rate meters arranged in monitoring wells. The multi-parameter water quality sensors are used to monitor basic indicators such as pH, oxidation-reduction potential, conductivity, and dissolved oxygen in real time. The specific pollutant sensors are designed to target specific pollutants, such as heavy metal online analyzers and VOCs detection probes, to qualitatively and semi-quantitatively identify characteristic pollutants. The sensors are arranged in a "shallow-middle-deep" three-layer grid, with two dimensions of "vertical control" and "horizontal control", forming a three-dimensional monitoring network covering the possible migration path of the pollution plume. Vertical control refers to arranging sensors in the upper part (shallow layer), main runoff zone (middle layer), and bottom / roof of the aquifer (deep layer) in the vertical direction to accurately capture the vertical stratification caused by the density difference of pollutants and the different permeability of the stratum. Specifically, shallow layer sensors are mainly used to monitor the floating plume layer of light non-aqueous phase liquids, middle layer sensors track the core migration channel of the main pollution plume, and deep layer sensors capture the sinking of heavy non-aqueous phase liquids and define the vertical boundary of the pollution plume. Horizontal control refers to arranging monitoring points in a grid form in the upstream background area, pollution source core area, downstream migration path, and downstream boundary along the groundwater flow direction, with the pollution source as the core, to depict the spatial range and temporal and spatial evolution dynamics of the pollution plume. Among them, the monitoring point density is highest in the pollution source core area, and a continuous monitoring defense line is formed in the downstream boundary to ensure early warning of the front edge of the pollution plume.

[0020] The edge intelligence algorithm module is in communication connection with the distributed sensor group and is deployed in an industrial-grade gateway near the monitoring well, which has the ability to perform real-time calculation at the data source, and is used for pre-processing of the collected data, including Z-Score algorithm and spatio-temporal Kriging interpolation method. The Z-Score algorithm and spatio-temporal Kriging interpolation method are respectively used for anomaly value detection and missing data filling. The Z-Score algorithm performs real-time calculation on the received original data stream to generate the data mean and standard deviation of each monitoring point. For any new data point, calculate its Z-Score value, when the absolute value is greater than the preset threshold, determine that the point is an abnormal value, and mark or exclude it to prevent "dirty data" from affecting subsequent decision-making. The spatio-temporal Kriging interpolation method uses the spatio-temporal Kriging method to repair the missing data caused by sensor failure or communication interruption. This method not only considers the correlation of adjacent monitoring points in space (spatial variation function), but also considers the autocorrelation in time series (time variation function), through optimal unbiased estimation, to generate a continuous and complete data field in space and time, ensuring the spatio-temporal integrity of the data set. This process effectively reconstructs the pollutant concentration and hydrological parameters at the missing position and time, providing reliable input for subsequent accurate modeling.

[0021] The pollution flow dynamic engine is in communication connection with the edge intelligence calculation module, the pollution flow dynamic engine comprises a flow strength calculation submodule and a pollution prediction submodule, the flow strength calculation submodule is used for real-time calculation of the space-time distribution and migration flux of the pollutants; the flow strength calculation submodule fuses the pollutant concentration, the actual flow rate of the underground water, the toxicity weight of the pollutants and the water area, and outputs a pollution flow strength vector field with a spatial position, a direction and a value; the pollution prediction submodule predicts the evolution trend of the pollution plume based on the output of the flow strength calculation submodule. The pollution prediction submodule couples the numerical model of the underground water flow and the solute migration, or adopts a machine learning algorithm such as a long short-term memory network, takes the current pollution flow strength vector field as an initial condition, and simulates and predicts the form, concentration and flow strength change of the pollution plume in a future specific period.

[0022] The diagnosis strategy module is in communication connection with the pollution flow dynamic engine, the diagnosis strategy module comprises an efficiency evaluation submodule and an adaptive strategy submodule, the efficiency evaluation submodule is used for diagnosing the running state and efficiency of the pollution interception and flow guiding unit and the purification unit based on the output of the pollution flow dynamic engine; the efficiency evaluation comprises calculation of the hydraulic capture efficiency of the extraction well group on the pollution plume, evaluation of the pollutant removal rate and saturation of each permeation reaction wall, and monitoring of the comprehensive removal rate and resource consumption index of the purification unit, and generation of a quantitative system health state report. The adaptive strategy submodule is used for generating optimized control instructions based on the diagnosis results of the efficiency evaluation submodule, the control instructions comprising switch and flow control instructions for the extraction well group and process parameter adjustment instructions for the purification treatment station. The adaptive strategy submodule dynamically adjusts the control strategy by analyzing the system health state report and the pollution prediction results.

[0023] The pollution interception and flow guiding unit comprises an extraction well group and a pollution interception wall; the extraction well group is configured to be controlled by the central control platform, actively changes the underground water flow field to guide the pollution plume, and extracts the polluted water by adjusting the pumping rate; the extraction well group is equipped with a variable frequency controlled submersible pump, which can realize stepless adjustment of the pumping flow according to the instructions; the pollution interception wall is arranged downstream of the extraction well group and comprises three parallel arranged permeation reaction walls, the three permeation reaction walls are sequentially arranged from upstream to downstream as follows: an adsorption interception wall, a degradation wall and a deep protection wall, the adsorption interception wall, the degradation wall and the deep protection wall are respectively used for adsorbing heavy metals, degrading organic pollutants and deeply treating residual pollutants; the adsorption interception wall is filled with activated carbon, the degradation wall is filled with a slow-release oxidant, and the deep protection wall is filled with activated carbon loaded with a catalytic component to cope with residual risks; the extraction well group is arranged upstream and downstream of the pollution interception wall and between the permeation reaction walls, forming a “flow guiding-interception-extraction-reinterception” coordinated arrangement pattern to ensure whole process control of the pollution plume.

[0024] The purification unit comprises a purification treatment station, a water quality buffer recycling pool and a sludge treatment station. The purification treatment station is in communication with the water outlet of the sewage interception and diversion unit and is used for receiving and purifying contaminated water. The purification treatment station adopts a modular design and can intelligently switch among high-level oxidation, coagulation sedimentation and membrane separation treatment processes according to the water quality of the inlet water. The water quality buffer recycling pool is connected with the water outlet of the purification treatment station and is used for storing qualified water bodies and realizing water resource recycling. The recycling approaches include industrial area greening, road sweeping and cooling tower water replenishment and the like. The sludge treatment station is connected with the sludge discharge port of the purification treatment station and is used for safely disposing of the generated sludge. The disposal modes include dewatering, stabilization and finally being handed over to qualified units for landfill or resource utilization.

[0025] The central control platform is configured to execute a model predictive control (MPC) based optimization algorithm for rolling optimization of control instructions. The objective function of the optimization algorithm is: wherein, represents the total operating cost of the system in a control cycle ; represents the total energy consumption of the system at time ; represents the total energy consumption of the system at time ; represents the total energy consumption of the system at time ; represents the total energy consumption of the system at time ; and and represent the weight coefficients of energy consumption and material consumption, respectively, for balancing operating cost and environmental risk. represents the current time; and represents the prediction time domain of the model predictive control. The model predictive control (MPC) algorithm solves the above objective function based on the current system state and the internal prediction model in each control cycle to obtain a series of optimal control sequences in the future time domain, and only executes the first control instruction in the sequence, and repeats the process in the next cycle to realize rolling optimization and feedback correction.

[0026] As shown in the industrial district groundwater sewage interception and repair method shown in Figs. 1-2 , based on the industrial district groundwater sewage interception and repair system according to any one of claims 1 to 7, comprising the following steps: S1, data acquisition and transmission: collecting pollutant concentration data and hydrogeological parameter data of groundwater by a distributed sensor group, and transmitting to an edge intelligent calculation module for preprocessing; S2, data preprocessing and fusion: inputting the original environmental data set transmitted in S1 into the edge intelligent calculation module, applying Z-Score algorithm and spatio-temporal Kriging interpolation method to remove outliers and repair missing data of the data set, generating standardized spatio-temporal fusion data, and forming a high-quality data set with spatio-temporal continuity and no missing data; ​​S3. Pollution Dynamics Simulation and Prediction: The standardized spatiotemporal fusion data generated in S2 is input into the pollution flow dynamics engine. Using its flow intensity calculation submodule, the pollution flow intensity vector is calculated. Using its pollution prediction submodule, the vector is extrapolated, and the spatiotemporal evolution prediction results of the pollution plume are output. The prediction results include the spatial distribution, concentration changes and migration direction of the pollution plume in a specific future period. S4. Intelligent Diagnosis and Decision-Making: The spatiotemporal evolution prediction results of the pollution plume output in S3 are input into the diagnosis strategy module. Its performance evaluation submodule diagnoses and generates a system health status report. Its adaptive strategy submodule generates an optimized control instruction set for controlling the interception and diversion unit and the purification unit based on the report, and distributes it through the central control platform. S5. Precise diversion and collaborative interception: The optimized control command set issued in S4 is input into the sewage interception and diversion unit. The extraction well group changes the groundwater flow field by adjusting the pumping rate to achieve active diversion of the pollution plume. At the same time, the pollution plume passes through the adsorption interception wall, degradation wall and depth protection wall in sequence to complete the multi-stage in-situ reduction of pollutants. Finally, the high-concentration polluted water and residual low-concentration water are transported to the purification unit. S6. Deep Purification and Resource Recycling: The high-concentration polluted water and residual low-concentration water from S5 are input into the purification unit for deep treatment in the purification treatment station to form compliant purified water and polluted sludge. The compliant purified water enters the water quality buffer reuse tank to achieve resource reuse. The polluted sludge is transported to the sludge treatment station for safe disposal to achieve harmless terminal management.

[0027] In step S3, the contamination flow intensity vector is calculated using the following formula: in, Indicates at time Spatial coordinates Pollution flow intensity at the location, expressed in kilograms per day (kg / d). Indicates at time Spatial coordinates First The measured concentrations of the characteristic pollutants, expressed in milligrams per liter (mg / L). Indicates at time Spatial coordinates The actual groundwater flow velocity at the location, expressed in meters per day (m / d). Indicates the first The toxicity weighting coefficient of a characteristic pollutant is a dimensionless constant. This represents the effective cross-sectional area of ​​the calculation unit, in square meters (m²). This represents the total number of characteristic pollutant types monitored; in the formula, is a multiplication operator, ∑ is a summation operator, and [] is an operator binding symbol. The formula integrates concentration, flow rate, toxicity, and hydrogeological parameters to upgrade traditional static concentration monitoring to comprehensive quantitative assessment of pollutant dynamic migration flux and potential environmental risk.

[0028] The specific process of generating the optimized control instruction set in step S4 includes: Based on the prediction results of the spatiotemporal evolution of the pollution plume, it is determined whether the peak of the pollution flow intensity in a specific period in the future will exceed the preset threshold of the treatment capacity of the pollution interception wall. If the determination result is yes, an instruction is generated to increase the pumping rate of the upstream extraction well in advance to strengthen hydraulic capture, and the processing unit for the corresponding pollutant in the purification treatment station is switched to an advanced operation mode, which realizes the high-level operation mode by strengthening its modular process units (such as increasing the dosage of the reagent for advanced oxidation and the reaction intensity, increasing the dosage of the reagent for coagulation and sedimentation, and prolonging the residence time) to cope with high-concentration pollution load. At the same time, based on the calculation of the cumulative pollutant flux of each permeation reaction wall, when the saturation reaches the preset warning value, a predictive maintenance instruction for replacing or regenerating the filler is generated.

[0029] The above is only an embodiment of the present application, and common technical solutions and / or characteristics in the scheme are not described in detail. It should be noted that for those skilled in the art, without departing from the technical solutions of the present application, a number of modifications and improvements can be made, which should also be considered as the protection scope of the present application, and these will not affect the effect and practicality of the present application. The protection scope of the present application should be subject to the content of its claims, and the specific implementation mode and the like in the specification can be used to explain the content of the claims.

Claims

1. An industrial area groundwater interception and remediation system, characterized in that, The application relates to a pollution control system for groundwater environment, comprising: an intelligent sensing unit for collecting groundwater environment data in real time, processing, simulating and deciding, and generating control instructions; the intelligent sensing unit comprises a distributed sensor group, an edge intelligent calculation module, a pollution flow dynamic engine and a diagnosis strategy module; a pollution interception and flow guiding unit for actively guiding, multi-stage intercepting and accurately extracting a groundwater pollution plume based on the control instructions; a purification unit for purifying, recycling and sludge disposal of the polluted water from the pollution interception and flow guiding unit; a central control platform, which is in communication connection with the intelligent sensing unit, the pollution interception and flow guiding unit and the purification unit.

2. An industrial area groundwater interception and remediation system as claimed in claim 1, wherein: The edge intelligent calculation module is in communication connection with the distributed sensor group, and the edge intelligent calculation module is used for pre-processing the collected data, wherein the pre-processing comprises Z-Score algorithm and space-time Kriging interpolation method, and the Z-Score algorithm and the space-time Kriging interpolation method are respectively used for abnormal value detection and missing data filling.

3. An industrial area groundwater interception and remediation system as claimed in claim 1, wherein: The pollution flow dynamic engine is in communication connection with the edge intelligent calculation module, and the pollution flow dynamic engine comprises a flow strength calculation submodule and a pollution prediction submodule, wherein the flow strength calculation submodule is used for real-time calculation of the space-time distribution and migration flux of the pollutants; the pollution prediction submodule predicts the evolution trend of the pollution plume based on the output of the flow strength calculation submodule.

4. An industrial area groundwater interception and remediation system as claimed in claim 1, wherein: The diagnosis strategy module is in communication connection with the pollution flow dynamic engine, and the diagnosis strategy module comprises an efficiency evaluation submodule and an adaptive strategy submodule, wherein the efficiency evaluation submodule is used for diagnosing the running state and efficiency of the pollution interception and flow guiding unit and the purification unit based on the output of the pollution flow dynamic engine; and the adaptive strategy submodule is used for generating optimized control instructions based on the diagnosis result of the efficiency evaluation submodule, wherein the control instructions comprise switch and flow control instructions for the extraction well group and process parameter adjustment instructions for the purification treatment station.

5. An industrial area groundwater interception and remediation system as claimed in claim 1, wherein: The pollution interception and flow guiding unit comprises an extraction well group and a pollution interception wall; the extraction well group is configured to be controlled by the central control platform, actively changes the groundwater flow field to guide the pollution plume by adjusting the pumping rate, and extracts the polluted water; the pollution interception wall is arranged downstream of the extraction well group and comprises three parallel arranged permeable reactive walls, and the three permeable reactive walls are sequentially arranged from upstream to downstream as an adsorption interception wall, a degradation wall and a deep protection wall; the adsorption interception wall, the degradation wall and the deep protection wall are respectively used for adsorbing heavy metals, degrading organic pollutants and deeply treating residual pollutants; the extraction well group is arranged upstream and downstream of the pollution interception wall and between the permeable reactive walls.

6. An industrial area groundwater interception and remediation system as claimed in claim 1, wherein: The purification unit comprises a purification treatment station, a water quality buffer recycling pool and a sludge treatment station; the purification treatment station is connected with the water outlet of the pollution interception and flow guiding unit and is used for receiving and purifying the polluted water; the water quality buffer recycling pool is connected with the water outlet of the purification treatment station and is used for storing qualified water bodies and realizing water resource recycling; and the sludge treatment station is connected with the sludge discharge port of the purification treatment station and is used for safely disposing the generated sludge.

7. An industrial area groundwater interception and remediation system as claimed in claim 1, wherein: The central control platform is configured to execute a model predictive control (MPC) based optimization algorithm for rolling optimization of the control instructions; an objective function of the optimization algorithm is: wherein, represents the total operating cost of the system in a control period ; represents the total energy consumption of the system at time , including the energy consumption of the extraction well group and the purification treatment station; represents the cost of reagents and materials consumed by the system at time ; and are weight coefficients of energy consumption and material consumption, respectively, used to balance the operating cost and environmental risk; represents the current time; represents the prediction time domain of the model predictive control.

8. An industrial area groundwater interception remediation method based on the industrial area groundwater interception remediation system according to any one of claims 1 to 7, characterized by, It comprises the following steps: S1, data acquisition and transmission: collecting pollutant concentration data and hydrogeological parameter data of groundwater by a distributed sensor group, and transmitting to an edge intelligent algorithm module for preprocessing; S2, data preprocessing and fusion: inputting the original environmental data set transmitted in S1 into the edge intelligent algorithm module, applying Z-Score algorithm and space-time Kriging interpolation method to remove outliers and repair missing data of the data set, and generating standardized space-time fusion data; S3, pollution dynamic simulation and prediction: inputting the standardized space-time fusion data generated in S2 into a pollution flow dynamic engine, calculating the pollution flow intensity vector by using the flow intensity calculation submodule, and outputting the pollution plume space-time evolution prediction result by using the pollution prediction submodule; S4, intelligent diagnosis and decision: inputting the pollution plume space-time evolution prediction result output in S3 into a diagnosis strategy module, generating a system health status report by an efficiency evaluation submodule, and generating an optimized control instruction set for controlling the pollution interception and flow guiding unit and the purification unit based on the report by an adaptive strategy submodule, and issuing through a central control platform; S5, precise flow guiding and collaborative interception: inputting the optimized control instruction set issued in S4 into the pollution interception and flow guiding unit, changing the groundwater flow field by adjusting the pumping rate of the extraction well group to achieve active flow guiding of the pollution plume; at the same time, the pollution plume passes through the adsorption interception wall, the degradation wall and the deep protection wall in turn to achieve multi-stage in-situ reduction of pollutants; finally, the high-concentration pollution water and residual low-concentration water are transported to the purification unit; S6, deep purification and resource recycling: inputting the high-concentration pollution water and residual low-concentration water transported in S5 into the purification unit for deep treatment in the purification treatment station to form standard purified water and contaminated sludge; the standard purified water is recycled in the water quality buffer pool; the contaminated sludge is transported to the sludge treatment station for safe disposal to realize harmless terminal management.

9. An industrial area groundwater interception remediation method as claimed in claim 8, characterized by: In step S3, the pollution flow intensity vector is calculated by the following formula: wherein, represents the pollution flow intensity at time , spatial coordinate , in units of kilograms per day (kg / d); represents the measured concentration of the th characteristic pollutant at time , spatial coordinate , in units of milligrams per liter (mg / L); represents the actual flow rate of groundwater at time , spatial coordinate , in units of meters per day (m / d); represents the toxicity weight coefficient of the th characteristic pollutant, which is a dimensionless constant; represents the effective water passing cross-sectional area of the calculation unit, in units of square meters (m²); represents the total number of characteristic pollutants monitored; in the formula, is a multiplication operator, ∑ is a summation operator, and [] is an operation binding symbol.

10. The method of claim 8, wherein: The specific process of generating the optimized control instruction set in step S4 includes: Based on the pollution plume space-time evolution prediction result, it is judged whether the pollution flow intensity peak value in the future specific period will exceed the preset pollution interception wall treatment capacity threshold value; If the judgment result is yes, an instruction is generated to increase the pumping rate of the upstream extraction well in advance to strengthen hydraulic capture, and the treatment unit of the corresponding pollutant in the purification treatment station is switched to a high-level operation mode; At the same time, based on the calculation of the cumulative pollutant flux of each permeable reactive wall, when the saturation reaches the preset warning value, a predictive maintenance instruction for replacing or regenerating the filler is generated.

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