A water power intelligent collaborative in-situ groundwater remediation control method, system, medium and product

Abstract

A kind of water power intelligent collaborative in-situ groundwater remediation control method, system, medium and product, relate to groundwater pollution remediation field.The method includes: in-situ groundwater remediation control system obtains constructable area data to avoid site structure interference, inputs hydrogeological parameter into flow agent model to predict flow field distribution, then combines concentration data to solve the target layout scheme of the least number of in-situ injection and extraction wells and control core wells under the premise of meeting barrier rate by intelligent optimization model.In-situ groundwater remediation control system generates operating parameter instruction to drive in-situ injection and extraction wells to act in conjunction with control core wells.The above-mentioned characteristics interact, so that in-situ groundwater remediation control system can accurately control flow field in limited space, reduce the amount of earthwork, and improve the implementability of limited site remediation.

Description

Technical Field

[0001] This application relates to the field of groundwater pollution remediation, and in particular to a hydrodynamic intelligent collaborative method, system, medium and product for in-situ groundwater remediation control. Background Technology

[0002] With industrial development, groundwater pollution left over from petrochemical sites has become increasingly serious. Since these sites are mostly in a "production and management simultaneously" state—with workshops, storage tanks and other structures in operation above ground, and a dense network of process pipelines and pools underground—ex-situ remediation is not feasible for these contaminated sites. In-situ remediation is preferred, which imposes stringent constraints on the spatial layout and construction methods of in-situ groundwater remediation.

[0003] The commonly used in-situ remediation scheme in the industry is as follows: based on the hydrogeological survey results of the site, a relatively dense array of in-situ injection wells is drilled and deployed above the contaminated area in a uniform grid pattern, and concentrated injection operations are carried out in conjunction with surface pumping stations. The hydraulic barrier formed by the injection well array is used to contain the boundary of the pollution plume as a whole. Then, the standardized operating parameters of equal flow rate and timing sequence at each well location are used to drive the diffusion of the agent in the aquifer, thereby achieving the blocking and removal of the contaminated area.

[0004] However, in scenarios where production and management occur simultaneously, above-ground production structures occupy a significant amount of surface space and require safe avoidance, while the core pollution area is often located directly beneath underground pipelines and pools. Using a uniform grid-based well layout requires densely packed construction points, and drilling operations are prone to conflicts with existing underground pipelines and pools, often necessitating large-scale earthwork and structure relocation. Simultaneously, standardized isoflow injection operations struggle to differentiate and coordinate with the actual flow field response under limited well locations, thus increasing the risk of remediation plans interfering with normal production operations and reducing the feasibility of remediation plans in confined sites. Summary of the Invention

[0005] This application provides a hydrodynamic intelligent collaborative groundwater in-situ remediation control method, system, medium, and product, which can improve the feasibility of remediation solutions in confined sites under the scenario of simultaneous production and management.

[0006] Firstly, this application provides a hydrodynamic intelligent collaborative groundwater in-situ remediation control method, applied to a groundwater in-situ remediation control system, comprising: acquiring hydrogeological parameters of the target site, groundwater pollutant concentration distribution data, physical property parameters and geometric dimensions of the filling medium of the control core, and data on the workable area defined by above-ground production structures and underground pipeline pools; using hydrogeological parameters and workable area data as background input parameters, and using candidate combinations of control core placement points and their physical property parameters, and candidate combinations of in-situ injection well placement points and their injection flow rates within the workable area as optimization variables, inputting them into a preset water flow proxy model to predict the groundwater flow field distribution around the contaminated area after the intervention of the control core and the coordinated action of the in-situ injection wells under each candidate placement point combination; and then... The groundwater flow field distribution and pollutant concentration distribution data corresponding to each candidate deployment point combination are input into a preset intelligent optimization model. The optimization objective is to minimize the total number of control cores and in-situ injection wells while ensuring that the flow field isolation rate in the contaminated area is not lower than a preset threshold. The target deployment scheme is then obtained. Based on the deployment location of each control core and the pollutant concentration distribution data in the target deployment scheme, the filling medium configuration parameters of each control core are determined. Based on the deployment location of each in-situ injection well and the pollutant concentration distribution data in the target deployment scheme, the injection timing and flow rate operation parameter instructions for each in-situ injection well are generated. Each in-situ injection well is driven to operate according to the operation parameter instructions and coordinates with each control core that has completed filling according to the filling medium configuration parameters to maintain the groundwater flow field in the contaminated area in a preset control state.

[0007] By employing the aforementioned technical solution, the in-situ groundwater remediation control system acquires data on the workable area to avoid interference from site structures. This data, along with hydrogeological parameters, is input into a flow proxy model to predict the flow field distribution. Combined with concentration data, an intelligent optimization model is used to solve for the target layout scheme with the minimum total number of wells while satisfying the barrier rate requirement. Based on this, the in-situ groundwater remediation control system generates operational parameter commands to drive the coordinated action of in-situ injection and extraction wells. The interaction of these features enables the in-situ groundwater remediation control system to precisely control the flow field within confined spaces, reducing earthwork and improving the feasibility of remediation in confined sites.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the water flow proxy model is trained based on a sample set generated by offline simulation of the hydrogeological numerical model of the target site. Each sample set includes a combination of candidate locations for control cores, a combination of candidate locations for in-situ injection wells, hydrogeological parameters of the aquifer and injection flow rate, and the corresponding groundwater flow field distribution.

[0009] By adopting the above technical solution, the groundwater in-situ remediation control system uses a sample set generated from offline simulation of a hydrogeological numerical model to train a flow proxy model. This feature enables the groundwater in-situ remediation control system to transform the solution process of complex physical flow field dynamic equations into a data-driven, rapid mapping calculation. This mechanism, combining offline training with online proxy, improves the calculation speed of predicting flow field distribution in the multivariate iterative optimization process of the groundwater in-situ remediation control system, shortens the generation cycle of the target deployment scheme, and ensures the real-time decision-making capability of the groundwater in-situ remediation control system in response to complex working conditions.

[0010] Furthermore, since the training samples explicitly include the hydrogeological parameters of the aquifer, the flow surrogate model has a stable flow field prediction capability under different hydrogeological conditions, providing a model foundation for subsequent online operation to invert changes in hydrogeological parameters based on measured flow fields and update the surrogate model online.

[0011] In conjunction with some embodiments of the first aspect, in some embodiments, the hydrogeological parameters of the target site, the distribution data of underground pollutant concentrations, the physical properties and geometric dimensions of the filling medium of the control core, and the data of the workable area defined by the above-ground production structures and underground pipeline pools are obtained. Specifically, this includes: obtaining the surface outline of the above-ground production structures and the three-dimensional occupancy model of the underground pipeline pools of the target site; extending the surface outline and the three-dimensional occupancy model outward on the surface projection plane according to a preset safety avoidance distance to form a no-construction area; and designating the area of ​​the target site other than the no-construction area as the workable area.

[0012] By employing the aforementioned technical solution, the groundwater in-situ remediation control system acquires the surface outline and a three-dimensional occupancy model, and expands outwards according to a preset safe avoidance distance to form a no-construction zone, thereby delineating the constructible area. The interaction of these features allows the groundwater in-situ remediation control system to incorporate the working face space requirements of construction machinery and the physical disturbance range of formation drilling into spatial constraints, transforming passive obstacle avoidance into active three-dimensional spatial margin isolation. This mechanism reduces the probability of physical collisions between drilling operations and existing concealed facilities, ensuring operational safety during simultaneous production and remediation at the production site.

[0013] In some embodiments, in conjunction with the first aspect, the method further includes: during the operation of each in-situ injection well according to the operating parameter instructions, acquiring real-time groundwater flow field monitoring data and real-time pollutant concentration monitoring data around the contaminated area; when either the deviation of the real-time groundwater flow field monitoring data from the preset control state flow field or the deviation of the real-time pollutant concentration monitoring data from the theoretical pollutant concentration distribution corresponding to the preset control state exceeds a preset tolerance, using the real-time groundwater flow field monitoring data and the corresponding inversion values ​​of the current aquifer hydrogeological parameters as supplementary samples to update the flow proxy model online, and regenerating the operating parameter instructions based on the updated flow proxy model and the real-time pollutant concentration monitoring data.

[0014] By adopting the above technical solution, the groundwater in-situ remediation control system simultaneously acquires real-time groundwater flow field monitoring data and real-time pollutant concentration monitoring data. When the deviation in either dimension exceeds the preset tolerance, the monitoring data is fed back into the flow proxy model to regenerate operating parameter instructions. The interaction of these features enables the groundwater in-situ remediation control system to construct a closed-loop feedback adaptive adjustment link between the flow field and pollutant dual-dimensional states. This link allows the groundwater in-situ remediation control system to simultaneously capture the actual flow field fluctuations caused by formation heterogeneity and the concentration field shifts caused by the uncertainty of pollutant migration patterns, and promptly correct the injection flow rate and timing of each in-situ injection well, ensuring that the groundwater flow field and pollutant distribution in the contaminated area are continuously maintained in the preset control state.

[0015] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: when the flow field deviation or concentration deviation exceeds its corresponding preset tolerance, respectively counting the cumulative number of times the flow field deviation and concentration deviation exceed the limit within a preset time period; when the cumulative number of times the flow field deviation exceeds its corresponding preset number threshold, inputting the real-time groundwater flow field monitoring data along with the target deployment scheme into the intelligent optimization model, and locally adding or adjusting some of the planned control core deployment points in the target deployment scheme; when the cumulative number of times the concentration deviation exceeds its corresponding preset number threshold, inputting the real-time pollutant concentration monitoring data along with the target deployment scheme into the intelligent optimization model. The intelligent optimization model can make local adjustments to the locations of some in-situ injection wells in the target deployment scheme. When the cumulative number of times of two types of violations exceeds their corresponding preset thresholds at the same time, real-time groundwater flow field monitoring data and real-time pollutant concentration monitoring data are input into the intelligent optimization model to simultaneously adjust the deployment points of the control core and the locations of the in-situ injection wells. The adjustment results are combined to form an adjusted deployment scheme, and the adjusted deployment scheme is used as the new target deployment scheme to guide the subsequent deployment of in-situ injection wells and control cores to be constructed according to the new target deployment scheme. The in-situ injection wells that have been constructed are operated according to the operating parameter instructions regenerated by the new target deployment scheme.

[0016] By adopting the above technical solutions, the groundwater in-situ remediation control system establishes structural adjustment mechanisms triggered by cumulative exceedances in two dimensions: flow field deviation and pollutant concentration deviation. Different deviation types are differentiatedly associated with different adjustment targets: cumulative exceedances of flow field deviation correspond to hydraulic barrier failure, triggering the local addition or adjustment of control core deployment points to strengthen the hydraulic barrier; cumulative exceedances of concentration deviation correspond to abnormal pollutant migration, triggering local adjustments to the location of in-situ injection wells to add or relocate extraction points; when both types of deviations cumulatively exceed limits, a joint adjustment of the control core and in-situ injection wells is triggered. This differentiated triggering mechanism enables the groundwater in-situ remediation control system to accurately select structural adjustment targets for different failure modes during long-term remediation, avoiding the waste of engineering costs caused by applying the same adjustment strategy to all deviation scenarios. This gives the groundwater in-situ remediation control system a scheme-level adaptive capability to cope with the evolution of hydrogeological conditions during long-term remediation, ensuring that the groundwater flow field and pollutant distribution in the contaminated area are continuously maintained in the preset control state.

[0017] In conjunction with some embodiments of the first aspect, in some embodiments, when searching for candidate deployment points, the intelligent optimization model assigns priority selection weight to existing monitoring well locations within the workable area, so that the control core and in-situ injection well are preferentially selected from existing monitoring well locations.

[0018] By adopting the above technical solutions, the in-situ groundwater remediation control system prioritizes existing monitoring well sites within the workable area in the optimization logic of its intelligent optimization model, selecting candidate locations for both the control core and in-situ injection wells. The interaction of these features allows the in-situ groundwater remediation control system to fully utilize existing underground porous hardware assets within the target site during the initial planning stages. By assigning existing monitoring wells a dual role of flow field regulation and remediation injection, the in-situ groundwater remediation control system further reduces the scale of sites requiring new excavation and drilling, lowering the risk of physical damage to underground remediation projects.

[0019] In conjunction with some embodiments of the first aspect, in some embodiments, generating injection timing and flow rate operation parameter instructions for each in-situ injection well specifically includes: matching the type of reactive material compatible with the target pollutant from a preset reactive material library and determining the corresponding reactive material property parameters based on the pollutant type and concentration level corresponding to the deployment location of each control core in the target deployment scheme in the pollutant concentration distribution data; predicting the service life of each control core based on the reactivity of the matched reactive material, the total amount of reactive material loaded as defined by the geometric dimensions of the control core, and the pollutant load at the corresponding location of each control core in the pollutant concentration distribution data; and configuring the reactive material type, The physical properties and service life of the reactive materials are included in the filling medium configuration parameters; the injection timing and flow rate operation parameter instructions for each in-situ injection well are generated, specifically including: based on the relative positional relationship of each in-situ injection well relative to the upstream supply boundary and downstream discharge boundary of the contaminated area in the target deployment scheme, each in-situ injection well is divided into a control well that undertakes the function of flow field isolation and a removal well that undertakes the function of pollutant removal; a reference constant operating flow rate required to maintain the control state is allocated to the control well, and an operating flow rate positively correlated with the concentration value at the corresponding position in the pollutant concentration distribution data is allocated to the removal well, and the control well is started before the removal well, thus obtaining the injection timing and flow rate operation parameter instructions.

[0020] By adopting the above technical solutions, the groundwater in-situ remediation control system, on the one hand, matches the type and physical properties of reactive materials to each control core based on the local pollutant load and predicts its service life. This allows the control core to further undertake the reaction and reduction function of pollutants carried by the flowing groundwater in addition to its passive barrier function. The service life prediction also provides a quantitative basis for the replacement and reinforcement decision of the control core during long-term operation. On the other hand, the in-situ injection wells are divided into control wells and removal wells. A constant operating flow rate is allocated to the control wells to start them first, and an operating flow rate positively correlated with the local concentration is allocated to the removal wells. The interaction of these features makes the groundwater in-situ remediation control system form a spatially complementary and temporally coordinated two-layer remediation architecture between the passive reaction reduction layer of the control core and the active hydrodynamic control layer of the in-situ injection wells. The control core passively reduces the mass flux of pollutants passing through its cross-section along the groundwater flow direction, while the in-situ injection wells dynamically control the flow field. This mechanism avoids the intrusion of surrounding clean groundwater or the outward squeezing of pollution plumes caused by blind synchronous pumping. Furthermore, the differentiated flow distribution reduces energy loss, and the precise matching and service life prediction of the reactive materials make the reactive reduction efficiency of the passive barrier controllable and measurable over a long period of time. This improves the pumping accuracy of hydrodynamic collaborative repair in confined spaces and the operational maintainability of the barrier core.

[0021] In a second aspect, this application provides a groundwater in-situ remediation control system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the groundwater in-situ remediation control system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Thirdly, this application provides a computer-readable storage medium storing computer instructions that, when executed on a groundwater in-situ remediation control system, cause the groundwater in-situ remediation control system to perform the method described in the first aspect and any possible implementation thereof.

[0023] Fourthly, this application provides a computer program product, including a computer program or instructions that, when run on a groundwater in-situ remediation control system, cause the groundwater in-situ remediation control system to perform the method described in the first aspect and any possible implementation thereof. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a hydrodynamic intelligent collaborative in-situ groundwater remediation control method in an embodiment of this application.

[0025] Figure 2 This is a schematic diagram of a scenario of a hydrodynamic intelligent collaborative groundwater in-situ remediation control method in an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the physical device structure of a groundwater in-situ remediation control system in the embodiments of this application. Detailed Implementation

[0027] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0029] This application provides a hydrodynamic intelligent collaborative method for in-situ remediation and control of groundwater.

[0030] In related technologies, a relatively dense array of in-situ injection wells can be drilled and deployed above the contaminated area in a uniform grid pattern, and then combined with a surface pumping station to carry out standardized centralized injection operations with equal flow rates and time sequences, thereby achieving the control and removal of contaminated areas.

[0031] The method for in-situ groundwater remediation control using a hydrodynamic intelligent collaborative approach in this application involves acquiring data on the workable area defined by above-ground production structures and underground pipeline pools. This data, along with hydrogeological parameters, is input into a preset flow proxy model to predict the groundwater flow field distribution under various candidate deployment point combinations. Then, pollutant concentration distribution data is input into a preset intelligent optimization model to solve for the target deployment scheme that minimizes the total number of control cores and in-situ injection wells. This generates injection timing and flow rate operation parameter commands for each in-situ injection well, driving the coordinated action of each in-situ injection well to maintain the groundwater flow field in the polluted area at a preset control state.

[0032] As can be seen, by adopting the method in the embodiments of this application, while achieving coordinated control of the groundwater flow field with the minimum number of well points, it can also effectively solve the problem of conflict between the uniform grid well layout method and existing underground pipelines and pools, thereby improving the feasibility of the repair scheme in a restricted site under the scenario of simultaneous production and management.

[0033] To facilitate understanding of the technical solution of this application, the key terms involved in this application are defined as follows:

[0034] A barrier core is a passive well assembly deployed in an aquifer that creates a local hydraulic gradient, a material barrier effect, or a reaction reduction effect against pollutants carried by flowing groundwater by filling it with a functional medium (such as reactive material, low-permeability material, or grout). Its physical properties include the permeability coefficient, porosity, and reactivity of the filling functional medium. Reactivity refers to the reaction removal equivalent of a unit mass of reactive material to the target pollutant, used to characterize the rate at which the reactive material fails as the pollutant load is consumed. Its geometric parameters include height, radius, and the depth range of its occurrence in the aquifer. Height and radius together determine the total amount of reactive material loaded in the barrier core unit.

[0035] The service life of the control element refers to the duration of operation from the completion of the on-site filling construction until the cumulative reactivity of the reactive materials it contains is consumed and the control element's reaction reduction effect on the target pollutant decays to below the preset effective threshold. It is determined by the reactivity, the total amount of reactive materials filled, and the pollutant load at the location of the control element, and is an important basis for the decision to replace or reinforce the control element during its operation period.

[0036] In-situ pumping and injection wells are active wells that change the water head distribution of aquifers by actively pumping or injecting water. Based on their location and function, they are further divided into control wells that perform flow field isolation and removal wells that perform pollutant removal.

[0037] The difference between a control core and a control well is that a control core does not rely on a power source and forms a continuous barrier by relying on its own medium characteristics; a control well relies on a pump for injection and the barrier strength can be dynamically adjusted.

[0038] The flow field blocking rate refers to the reduction ratio of the groundwater flow rate through the boundary of the contaminated area after the intervention of the control core and the synergistic effect of the in-situ injection well, relative to the flow rate under natural operating conditions.

[0039] Please see Figure 1 This is a flowchart illustrating a hydrodynamic intelligent collaborative groundwater in-situ remediation control method in an embodiment of this application. Figure 2 This is a schematic diagram of a scenario for a hydrodynamic intelligent collaborative groundwater in-situ remediation control method in an embodiment of this application.

[0040] like Figure 2 As shown, the target site is spatially divided into above-ground and underground parts: the surface contains above-ground production structures such as storage tanks, distillation towers, factory workshops, and chimneys, while underground pipeline pools are buried in the shallow surface layer; a pollution plume exists in the middle of the underground aquifer, formed by the flow of groundwater. Groundwater flows in from the upstream recharge boundary on the left side of the diagram and flows out from the downstream discharge boundary on the right side. In this scenario, control wells and control cores are deployed along the upstream recharge boundary of the pollution plume, and control cores and monitoring wells with sensors are deployed along the downstream discharge boundary of the pollution plume. Removal wells, which perform pollutant removal functions, are deployed inside the pollution plume. Among them, the control wells and removal wells are active in-situ injection wells powered by pumps, while the control core is a well assembly that does not rely on a power source, but forms a passive barrier by relying on the functional medium it is filled with, and reacts to reduce the pollutants carried by the flowing groundwater. The active pumping action of each in-situ injection well and the passive barrier action of each control core synergistically form a spatiotemporally complementary hydrodynamic regulation network around the contaminated area, maintaining the groundwater flow field in the contaminated area within a preset control state. The method described in this application embodiment is... Figure 2 Execution is performed in the scenario shown below, combined with... Figure 1 The specific process of this method will be explained.

[0041] It should be noted that, Figure 2 The number and spatial location of each control core, control well, removal well and monitoring well shown are for illustrative purposes only. The actual number and location are determined by the target deployment scheme obtained in the subsequent method flow and should not be construed as a limitation of this application.

[0042] S101. Obtain hydrogeological parameters of the target site, underground pollutant concentration distribution data, physical property parameters and geometric dimensions of the filling medium of the barrier core, and data on the workable area defined by the above-ground production structures and underground pipeline pools.

[0043] The target site refers to a specific contaminated area that requires in-situ groundwater remediation and is under simultaneous production and management, such as an oil refinery or a chemical industrial park storage tank area. Its surface contains operating workshops, storage tanks, and other production structures, while underground it is densely covered with process pipelines and pools. Hydrogeological parameters refer to a set of physical parameters characterizing the aquifer seepage characteristics of the target site, including permeability coefficient, specific yield, storage coefficient, stratigraphic stratification, and the depth of the impermeable layer, provided by the site's hydrogeological survey results. Groundwater pollutant concentration distribution data refers to the three-dimensional concentration field of the aquifer obtained based on stratified sampling of monitoring well groups or multi-depth online monitoring. The constructable area data refers to the area excluding above-ground structures. Data on the remaining space available for drilling operations after the building projection and underground pipeline pool are occupied; the preset safe avoidance distance is the minimum physical isolation distance determined after comprehensively considering the operating radius of drilling machinery, the drilling disturbance radius, and the structural safety margin of the structure. Its function is to avoid physical damage to existing production facilities caused by repair construction; the physical property parameters of the filling medium of the resistance control core refer to the set of parameters characterizing the permeability coefficient, porosity, and reactivity of the functional medium filled in the resistance control core; the geometric dimensional parameters of the resistance control core refer to the height, radius, and storage depth range of the resistance control core in the aquifer. The two together determine the intensity and range of local hydraulic gradient regulation after a single resistance control core intervenes in the aquifer.

[0044] This step is executed at the beginning of each planning cycle. Specifically, the groundwater in-situ remediation control system establishes communication with the site survey database, monitoring well sensor network, resistance control core product parameter library, and site BIM management platform through standardized data interfaces. The groundwater in-situ remediation control system reads hydrogeological parameters from the site survey database, retrieves the most recent stratified sampling concentration data from the monitoring well sensor network and constructs a three-dimensional concentration field, and reads the filling medium property parameters and geometric dimension parameters corresponding to the candidate resistance control core models from the resistance control core product parameter library. The groundwater in-situ remediation control system obtains the surface outline of the above-ground production structures and the three-dimensional occupancy model of underground pipeline pools from the site BIM management platform. It expands the surface outline and three-dimensional occupancy model outward according to the preset safety avoidance distance to form a no-construction zone. The area in the target site other than the no-construction zone is designated as the constructible area, and the data is encapsulated and output in vector polygon format for subsequent steps.

[0045] Optionally, in some embodiments, the groundwater in-situ remediation control system can also set different preset safety avoidance distances according to the hazard level of the underground pipeline medium. It can assign a larger avoidance distance to high-risk pipelines such as oil pipelines and gas pipelines, and a smaller avoidance distance to low-risk pipelines such as municipal water supply pipelines. This avoids the one-size-fits-all avoidance strategy that compresses the workable area, and preserves sufficient layout space for subsequent optimization while ensuring safety.

[0046] Optionally, in some embodiments, the groundwater in-situ remediation control system can also obtain the production schedule of the target site, mark the part of the workable area that is periodically occupied by the production schedule as a time-series workable area with a workable time window, and input the time window as a constraint condition along with the workable area data into the intelligent optimization model, so that the intelligent optimization model only uses candidate points that are compatible with the remediation construction rhythm when solving the target layout scheme, and avoids the target layout scheme being shelved due to production scheduling conflicts.

[0047] S102. Using hydrogeological parameters and data of the workable area as background input parameters, and the candidate combination of control core locations and their physical properties, and the candidate combination of in-situ injection well locations and their injection flow rate within the workable area as optimization variables, input them into the preset water flow proxy model to predict the distribution of groundwater flow field around the contaminated area after the intervention of control core and the coordinated action of in-situ injection wells under each candidate combination of locations.

[0048] Among them, the preset water flow proxy model refers to a data-driven model that has been pre-trained and can quickly map the combination of candidate control core deployment points to the groundwater flow field distribution, such as a proxy model built based on deep neural networks or Gaussian process regression; the control core refers to a functional well component deployed in the aquifer to change the local hydraulic gradient and form a hydraulic barrier. After the intervention of a single control core, it will generate local flow field disturbance in the surrounding area; the combination of candidate deployment points refers to a set of alternative schemes consisting of the coordinates of several potential control core installation points extracted from the workable area according to a preset grid density; the groundwater flow field distribution represents the head value and velocity vector field of each spatial node in the aquifer under a given control core candidate deployment scheme.

[0049] The training process of the water flow proxy model includes: the groundwater in-situ remediation control system constructs a three-dimensional hydrogeological numerical model based on the hydrogeological parameters of the target site as a high-fidelity physical benchmark; using Latin hypercube sampling, multiple sets of candidate combinations of control core locations, candidate combinations of in-situ injection well locations, and their candidate injection flows are randomly generated within the workable area as model inputs; and simultaneously, within the range of aquifer hydrogeological parameters given by the hydrogeological survey results of the target site, corresponding aquifer hydrogeological parameter samples (including permeability coefficient, specific yield, storage coefficient, and impermeability) are randomly sampled to generate corresponding aquifer hydrogeological parameter samples. The parameters (such as burial depth) are paired one by one with the point combination and injection flow rate samples to form the model input sample; for each sample, the hydrogeological numerical model outputs the corresponding full-field head distribution and velocity field through iterative solution, forming a sample pair (resistance control core point combination, in-situ injection well point and flow rate combination, aquifer hydrogeological parameters, flow field distribution); the groundwater in-situ remediation control system divides the sample set into training set and validation set to train the water flow proxy model offline, so that the mean square error between the flow field predicted by the proxy model and the flow field simulated by the numerical model is reduced to below the preset accuracy, and the model is pre-set.

[0050] By explicitly incorporating aquifer hydrogeological parameters into the sample input dimension, the flow surrogate model can learn the influence of hydrogeological parameter variations on flow field distribution. Consequently, in the subsequent step S205, when inverting hydrogeological parameters based on the measured flow field, the surrogate model can be directly invoked to complete the flow field prediction under the new parameters without retraining.

[0051] Specifically, the groundwater in-situ remediation control system uses the hydrogeological parameters obtained from S101 as background parameters for the flow proxy model. The constructable area is spatially gridded according to a preset step size, and multiple sets of candidate deployment point combinations are generated by traversing or sampling. Each set of candidate deployment point combinations includes several candidate control core points, several candidate in-situ injection well points, and the corresponding candidate injection flow rate values ​​for each in-situ injection well. These are input into the flow proxy model one by one. The flow proxy model outputs the groundwater flow field distribution around the contaminated area after the intervention of the control core and the coordinated action of the in-situ injection wells according to the candidate injection flow rates under each candidate deployment point combination. The groundwater in-situ remediation control system establishes a mapping relationship between each candidate deployment point combination and the corresponding groundwater flow field distribution and stores it in the dataset to be optimized.

[0052] Optionally, in some embodiments, the groundwater in-situ remediation control system can also embed the three-dimensional occupancy model of the underground pipeline pool as a low-permeability physical property unit into the aquifer grid when constructing the hydrogeological numerical model. This allows the flow field distribution of each group in the sample set generated by offline simulation to naturally include the natural barrier contribution of existing structures to groundwater flow. Correspondingly, in addition to the combination of candidate deployment points of the control core, the input variables of the flow proxy model also include the candidate deployment points of each in-situ injection well and their injection flow rate, so that the proxy model can simultaneously predict the flow field response under the synergistic effect of the control core and the in-situ injection well.

[0053] S103. Input the groundwater flow field distribution and pollutant concentration distribution data corresponding to each candidate deployment point combination into the preset intelligent optimization model. With the goal of minimizing the total number of control cores and in-situ injection wells and ensuring that the flow field blocking rate of the polluted area is not lower than the preset threshold, the target deployment scheme is obtained by solving the problem.

[0054] Among them, the preset intelligent optimization model refers to a decision model built based on multi-objective optimization algorithms that can search for the optimal deployment scheme under constraints, such as an optimizer built based on genetic algorithms, particle swarm optimization, or Bayesian optimization; the flow field blocking rate refers to the reduction ratio of groundwater flow through the boundary of the contaminated area after the intervention of the control core relative to the flow under natural operating conditions; the preset threshold is the lower limit of the flow field blocking rate determined after comprehensively considering the risk of pollution plume expansion and the economics of remediation, and its role is to ensure the effective containment strength of the remediation scheme for the contaminated area; the target deployment scheme represents the optimal combination of control core and in-situ injection well locations with the minimum number of well points under constraints.

[0055] Before being deployed for online optimization, the intelligent optimization model needs to undergo a preliminary offline construction and training process. This includes the groundwater in-situ remediation control system first constructing the solution space of the optimization model based on the geometric boundaries of the workable area of ​​the target site, the maximum number of control cores and in-situ injection wells that can be deployed, and the physical range of the injection flow rate of a single well. Each candidate solution is then encoded as a hybrid encoded individual, which is formed by concatenating the coordinate vectors of the candidate control core points, the coordinate vectors of the candidate in-situ injection well points, and the injection flow rate vector corresponding to each in-situ injection well. The point coordinate components take values ​​within the polygon of the workable area, and the injection flow rate components take values ​​within the physical range. The encoding length is determined by the maximum number of wells allowed in the current iteration round. Individuals with fewer than the maximum number of wells are filled with placeholders and masked during the fitness evaluation stage.

[0056] The groundwater in-situ remediation control system then constructs a fitness function for the optimization model. This fitness function is composed of three linear superpositions: a negative weighted sum of the number of control cores and the number of in-situ injection wells as the main objective term; a constraint penalty term that the flow field blocking rate is not lower than a preset threshold; and a feasibility penalty term that determines whether the candidate point falls into the prohibited construction area and whether the minimum distance between candidate wells meets the construction clearance requirements. The coefficient of the penalty term is pre-calibrated based on the engineering experience of the target site, so that the fitness of individuals that violate the hard constraints is significantly reduced and they are difficult to survive in the iteration.

[0057] During the training process, the groundwater in-situ remediation control system randomly initializes a pre-set population of a predetermined size within the solution space. For each individual in the population, it calls the water flow surrogate model trained in step S102 to quickly predict its corresponding groundwater flow field distribution, and calculates its fitness value according to the fitness function based on the pollutant concentration distribution data. Then, it updates the population according to the evolutionary operator of the selected optimization algorithm. For example, when using the genetic algorithm, it performs roulette wheel selection, single-point or two-point crossover, and Gaussian mutation operations. When using the particle swarm optimization algorithm, it updates the particle position and velocity by weighting the inertia weight, individual optimality, and global optimality. When using Bayesian optimization, it fits the surrogate posterior distribution based on the evaluated samples and guides the sampling position of the next round through the expectation-boosting acquisition function. The above update process is iterated repeatedly in a generational manner until the relative change of the optimal fitness of several consecutive generations is lower than the preset convergence threshold or the number of iterations reaches the preset upper limit. The feasible solution with the highest fitness in the population at this time, which satisfies all hard constraints, is output as the convergence result of the training process.

[0058] The groundwater in-situ remediation control system repeats the above training process under multiple groups of different initial populations. The statistical stability of the convergence results obtained from multiple trainings in three dimensions—blockage rate constraint, well number weighted sum, and feasibility index—is used as the criterion for the completion of model training. When the dispersion of the three indicators in multiple repetitions is lower than the preset tolerance, the intelligent optimization model is considered to have completed training and its hyperparameters (including population size, crossover probability, mutation probability, inertia weight, acceleration coefficient, convergence threshold, and maximum iteration rounds) are solidified for direct use in subsequent online optimization stages.

[0059] It should be noted that the water flow proxy model in the above training process has been pre-trained on the offline simulation sample set described in step S102. Therefore, the flow field prediction of each generation can be completed within a millisecond time scale, so that the intelligent optimization model can still complete training and optimization within an engineering-acceptable time window even at an iteration scale of thousands of generations.

[0060] This step is executed after the flow field distribution corresponding to each candidate deployment point combination is output in S102. Specifically, the groundwater in-situ remediation control system inputs the combination-flow field mapping datasets output in S102 and the pollutant concentration distribution data obtained in S101 into the intelligent optimization model. The intelligent optimization model takes minimizing the total number of control cores and in-situ injection wells as the optimization objective, and uses a flow field blocking rate of not less than a preset threshold as a hard constraint. It iteratively searches in the point combination space within the workable area. In each iteration, the intelligent optimization model calls the water flow proxy model to quickly predict the flow field of the current candidate combination, evaluates the flow field blocking rate and pollution plume coverage in combination with the pollutant concentration distribution data, and gradually converges to the Pareto front, outputting the scheme with the minimum number of well points and meeting the blocking rate constraint as the target deployment scheme.

[0061] Optionally, in some embodiments, the groundwater in-situ remediation control system can also prioritize the selection of candidate deployment points for the control core and in-situ injection wells from existing monitoring well sites within the workable area in the search space of the intelligent optimization model, assigning priority fitness weight to existing monitoring well sites in the optimization process, enabling existing monitoring wells to assume the dual role of flow field regulation and remediation injection, further reducing the number of new boreholes required.

[0062] Optionally, in some embodiments, the groundwater in-situ remediation control system can also add a pollution plume spatial constraint term to the optimization objective of the intelligent optimization model. This ensures that the target deployment scheme, while meeting the requirements of a minimum weighted sum of well numbers and a flow field barrier rate not lower than a preset threshold, further guarantees that the pollution plume within the target site does not exceed the preset control area boundary. The control area boundary refers to a closed polygonal boundary predetermined by the site manager based on the land use boundary, the protection range of environmentally sensitive receptors, or the remediation responsibility boundary, limiting the maximum permissible range of pollutant migration. The pollution plume refers to the spatially shaped distribution range of pollutants formed by the flow of groundwater within the aquifer, with its outer edge defined by an isosurface where the pollutant concentration equals a preset concern concentration limit. When performing optimization, the groundwater in-situ remediation control system first calls the water flow proxy model in step S102 to predict the groundwater velocity field under the combination for each candidate deployment point combination. Then, using the velocity field as the convection input, it calls the pre-trained solute transport proxy model to predict the spatial distribution of the pollution plume after the preset evolution period. The system then performs spatial intersection calculation between the outer edge isosurface of the pollution plume and the boundary of the control area. When the outer edge isosurface of the pollution plume is completely within the boundary of the control area, the candidate solution is considered to satisfy the spatial constraints. Otherwise, the system deducts points in the fitness function using the area outside the boundary as a penalty, thereby guiding the intelligent optimization model to converge to the target deployment scheme that takes into account the minimum number of wells, the flow field blockage rate, and the pollution plume being controlled within the boundary of the control area.

[0063] In this optional embodiment, the training process of the solute transport proxy model is similar to the training process of the water flow proxy model in step S102: the groundwater in-situ remediation control system couples the solute transport control equation on the basis of the three-dimensional hydrogeological numerical model, and uses the dispersion coefficient, retardation factor and biodegradation rate as additional input parameters. For each group of samples, offline simulation is used to generate corresponding pollution plume spatial evolution time series data, forming sample pairs (velocity field, initial concentration field, transport parameters, and post-evolution concentration field), and then the solute transport proxy model is trained offline.

[0064] It should be noted that the pollution plume spatial constraint in this optional embodiment exists as an additional objective term of the intelligent optimization model, and does not limit all implementations of this application to performing solute transport simulation; in the implementation method with only flow field blocking rate as constraint, this application can still independently achieve precise and coordinated control of the groundwater flow field in the polluted area.

[0065] In addition to supporting the above-mentioned full-field optimization function, the intelligent optimization model also supports the local optimization function, which receives real-time monitoring data based on the existing target deployment scheme and makes local additions or adjustments to some control core deployment points or some in-situ injection well locations. The two share the same optimization model architecture, and the only difference is whether the input includes the existing target deployment scheme and real-time monitoring data, thereby ensuring the consistency of full-field optimization and local adjustment at the algorithm level.

[0066] S104. Based on the deployment location of each control element in the target deployment plan and the pollutant concentration distribution data, determine the filling medium configuration parameters of each control element.

[0067] The filling medium configuration parameters refer to the structured construction configuration information output by the groundwater in-situ remediation control system to the barrier core construction execution unit. These parameters include the type of filling medium used in each barrier core (including one or more combinations of reactive materials, low-permeability materials, or grout), the volume ratio of each type of medium, the filling density, and the layered filling sequence within the barrier core's depth range. For filling medium combinations containing reactive materials, the parameters also include the physical properties of the reactive materials and the predicted service life of the barrier core calculated based on the consumption rate of the reactive materials. The filling medium configuration parameters are formed by combining the acquired physical properties of the filling medium and the target site pollutant concentration distribution data, and are used to guide the on-site filling construction of the barrier core.

[0068] This step is executed after the target deployment plan is obtained in S103. Specifically, for each control core in the target deployment plan, the groundwater in-situ remediation control system, based on the type and concentration level of pollutants corresponding to its deployment location in the pollutant concentration distribution data, and combined with the set of filling medium physical property parameters obtained in S101, matches the type and volume ratio of filling medium suitable for the current pollutants from the preset filling medium configuration library, and determines the layered filling sequence according to the storage depth range of each control core.

[0069] Furthermore, for cases where the matched filling medium type contains reactive materials, the groundwater in-situ remediation control system retrieves the physical properties (including reactivity, porosity, and permeability coefficient) of the reactive materials compatible with the target pollutant from the preset reactive material library, and predicts the service life of the control core in the following manner: multiplying the total amount of reactive materials filled by the control core's geometric dimensions by the reactivity yields the cumulative reactive removal equivalent of the control core for the target pollutant; integrating the product of the groundwater velocity vector at the control core deployment location predicted by the flow proxy model in S102 and the pollutant concentration value at that location in the pollutant concentration distribution data yields the pollutant load flux per unit time of the control core; dividing the cumulative reactive removal equivalent by the pollutant load flux per unit time and multiplying by the preset effective life safety factor yields the predicted service life value of the control core. The groundwater in-situ remediation control system encapsulates the above-mentioned filling medium type, volume ratio, layered filling sequence, physical property parameters of reactive materials, and service life prediction values ​​into filling medium configuration parameters and sends them to the control core construction execution unit. This guides each control core to complete the on-site filling construction according to the filling medium configuration parameters, and simultaneously writes the service life prediction values ​​into the operation period control core status management ledger, serving as an auxiliary decision-making basis for triggering the local addition or adjustment of the control core to be constructed in subsequent S208.

[0070] Optionally, in some embodiments, the groundwater in-situ remediation control system can also, after the control core enters the operation period, perform rolling statistics on the actual pollutant load flux at the location of each control core based on the real-time pollutant concentration monitoring data obtained in S207, and dynamically correct the predicted service life accordingly; when the remaining service life of any control core is lower than the preset warning threshold, the groundwater in-situ remediation control system marks the control core as a target to be reinforced, triggering the local addition of the control core to be constructed at that location in S208 to continue the reaction reduction function, so as to avoid pollutants penetrating the original control core cross-section after the reaction material fails and causing flow field-pollutant coupling feedback failure.

[0071] S105. Based on the deployment location of each in-situ injection well in the target deployment plan and the pollutant concentration distribution data, generate injection timing and flow rate operation parameter instructions for each in-situ injection well.

[0072] Among them, the operating parameter command refers to the structured control command issued by the groundwater in-situ remediation control system to the power execution unit of each in-situ injection well, including the start and stop time, injection direction, injection flow rate and duration of each well; the barrier control well refers to the in-situ injection well located on the upstream side of the recharge boundary of the contaminated area in the target layout plan, which undertakes the function of forming a hydraulic barrier; the removal well refers to the in-situ injection well located on the downstream side of the discharge boundary of the contaminated area, which undertakes the function of extracting contaminated groundwater or injecting remediation agents.

[0073] This step is executed after the configuration parameters of the control core filling medium are determined in S104. Specifically, the groundwater in-situ remediation control system divides each in-situ injection well into control wells and removal wells based on the relative positional relationship of each in-situ injection well relative to the upstream recharge boundary and downstream discharge boundary of the contaminated area in the target deployment scheme. The groundwater in-situ remediation control system substitutes the candidate flow values ​​of each control well into the water flow proxy model, searches for a benchmark constant operating flow value that reduces the velocity component of the contaminated area boundary along the outward expansion direction of the contamination plume to the level corresponding to the preset control state, and assigns a positively correlated operating flow to each removal well according to the corresponding concentration value in the pollutant concentration distribution data based on its location, so that the removal wells corresponding to the core contamination area receive a higher flow and the removal wells corresponding to the peripheral area receive a lower flow. The groundwater in-situ remediation control system generates the start-stop sequence of each well according to the time constraint that the control wells start before the removal wells, and encapsulates the above flow and time sequence parameters into operating parameter instructions.

[0074] Optionally, in some embodiments, the groundwater in-situ remediation control system can also reserve a preset flow field steady-state establishment time after the control well is started, and start the removal well after the hydraulic barrier has been stably formed, so as to avoid the pollution plume being squeezed outward and escaped when the hydraulic barrier has not been established before the removal well pumps water in advance.

[0075] Optionally, in some embodiments, the groundwater in-situ remediation control system can also gradually start each removal well in order of increasing pollutant concentration corresponding to its location, and at the start time of each removal well, send a pre-compensation flow increment command to the control well located between the removal well and the boundary of the contaminated area, so that the hydraulic barrier strength of the control well is actively raised before the flow step of the removal well occurs, and the transient gradient disturbance induced by the start of the removal well is offset in real time on the control well side.

[0076] S106 drives each in-situ injection and extraction well to operate according to the operating parameter instructions, and coordinates with each control core that has completed filling according to the filling medium configuration parameters to maintain the groundwater flow field in the contaminated area in the preset control state.

[0077] The preset control state refers to a stable flow field state in which the component of the groundwater velocity vector along the outward expansion direction of the pollution plume at the boundary of the pollution zone is reduced to below a preset level.

[0078] This step is executed after the on-site filling of the control core is completed in S104 and the operation parameter command is generated in S105. Specifically, the in-situ groundwater remediation control system sends the operation parameter command generated in S105 to the power execution unit of each in-situ injection well, driving each in-situ injection well to act in coordination according to the operation parameter command; the active injection of each in-situ injection well and the passive medium barrier formed after each control core is filled according to the filling medium configuration parameters determined in S104 form a spatiotemporally coordinated hydrodynamic control network around the contaminated area, maintaining the groundwater flow field in the contaminated area in the preset control state.

[0079] It should be noted that the execution behavior of the groundwater in-situ remediation control system in this step is the control behavior of 'issuing operating parameter commands to drive the power execution units of each in-situ injection well'; the 'each control core that has completed filling according to the filling medium configuration parameters' refers to the existing state in which each control core has completed on-site filling according to the filling medium configuration parameters determined in S104, and participates in flow field regulation as a passive barrier object in coordination with the in-situ injection well. Its filling construction has been completed before this step and is not an action executed by the control system in this step. Therefore, the execution subject of each step of this method is the groundwater in-situ remediation control system, which is a single subject and can be completely implemented by a computer program.

[0080] In this embodiment, the groundwater in-situ remediation control system utilizes the constraints of the workable area, the flow proxy model, and the intelligent optimization model to collaboratively solve the target layout scheme with the minimum number of well points. It also generates time-staggered and flow-differentiated operating parameter instructions according to the roles of control wells and removal wells. Therefore, it can accurately control the groundwater flow field with fewer wells within a limited construction space. This effectively solves the problems of conflict between uniform grid layout and existing pipeline pools, as well as the difficulty in coordinating standardized flow extraction and injection. As a result, it improves the feasibility of the remediation scheme in the context of simultaneous production and management, and reduces the risk of the remediation scheme interfering with normal production operations.

[0081] In the above embodiments, a basic control scheme based on constructable area constraints to solve for the optimal layout and coordinated operation according to roles has been implemented. In practical applications, the heterogeneity of the aquifer at the target site and the evolution of local hydrogeological conditions induced by production activities often cause the flow field predicted by the initial flow proxy model to gradually accumulate deviations from the measured flow field. It is difficult to maintain the preset resistance control state in the long term by relying solely on the initial operating parameters.

[0082] In conjunction with the above embodiments, and in combination with Figure 2 The following is a more detailed description of the process of the method provided in this embodiment, based on the application scenario shown.

[0083] S201. Obtain hydrogeological parameters of the target site, underground pollutant concentration distribution data, physical properties and geometric dimensions of the filling medium of the control core, and data on the workable area defined by the above-ground production structures and underground pipeline pools.

[0084] S202. Using hydrogeological parameters and data of the workable area as background input parameters, and the candidate combination of control core locations and their physical properties, and the candidate combination of in-situ injection well locations and their injection flow rates within the workable area as optimization variables, input them into the preset water flow proxy model to predict the distribution of the groundwater flow field.

[0085] S203. Input the groundwater flow field distribution and pollutant concentration distribution data corresponding to each candidate deployment point combination into the preset intelligent optimization model to solve for the target deployment scheme.

[0086] S204. Based on the deployment location of each control element in the target deployment plan and the pollutant concentration distribution data, determine the filling medium configuration parameters of each control element.

[0087] S205. Based on the deployment location of each in-situ injection well in the target deployment plan and the pollutant concentration distribution data, generate injection timing and flow rate operation parameter instructions for each in-situ injection well.

[0088] S206 drives each in-situ injection and extraction well to operate according to the operating parameter instructions, and coordinates with each control core that has completed filling according to the filling medium configuration parameters to maintain the groundwater flow field in the polluted area in the preset control state.

[0089] Steps S201 to S206 are similar to those described in the above embodiments as steps S101 to S106, and will not be repeated here.

[0090] S207. During the operation of each in-situ injection well according to the operating parameter instructions, real-time groundwater flow field monitoring data and real-time pollutant concentration monitoring data around the contaminated area are acquired. When either the deviation of the real-time groundwater flow field monitoring data from the preset control state or the deviation of the real-time pollutant concentration monitoring data from the theoretical pollutant concentration distribution corresponding to the preset control state exceeds the preset tolerance, the real-time groundwater flow field monitoring data and the corresponding inversion values ​​of the current aquifer hydrogeological parameters are used as supplementary samples to update the flow proxy model online. Based on the updated flow proxy model, the operating parameter instructions are regenerated in combination with the real-time pollutant concentration monitoring data.

[0091] Among them, real-time groundwater flow field monitoring data refers to time-series data reflecting the current actual flow field state, continuously collected by multi-parameter sensors (including water level gauges and flow velocity meters) deployed in monitoring wells around the contaminated area; real-time pollutant concentration monitoring data refers to time-series data reflecting the current actual pollutant concentration distribution, continuously collected by water quality sensing probes (including characteristic index sensors for target pollutants and characterizing auxiliary sensors such as conductivity and pH) deployed in the aforementioned monitoring wells at preset sampling cycles. The water quality sensing probes are integrated with the water level gauges and flow velocity meters into the same multi-parameter sensor group to realize the flow field and pollutant concentration. The synchronous acquisition; the flow field deviation refers to the difference between the measured flow field distribution and the theoretical flow field distribution corresponding to the preset control state, which can be quantified by the Euclidean distance of the velocity vector of the key monitoring section; the concentration deviation refers to the difference between the measured pollutant concentration distribution and the theoretical pollutant concentration distribution corresponding to the preset control state, which can be quantified by the relative error of the concentration vector of the key monitoring point; the preset tolerance is the upper limit threshold of the deviation for the flow field deviation and the concentration deviation, which is determined after comprehensively considering the sensor measurement error and the reasonable fluctuation range caused by the heterogeneity of the formation. Its function is to filter random noise and trigger adjustment only when a substantial deviation occurs.

[0092] This step is executed after the S206 operation parameter command is issued and each in-situ injection well enters the continuous operation phase. Specifically, the groundwater in-situ remediation control system reads measured data such as flow velocity, water level, and pollutant concentration from the monitoring well sensor network according to a preset sampling cycle. After removing outliers and performing time-series filtering on the collected data, it constructs the current measured flow field distribution and the current measured pollutant concentration distribution. The groundwater in-situ remediation control system performs node-level comparison between the measured flow field distribution and the flow field corresponding to the preset resistance control state predicted by the flow proxy model in S206 to calculate the flow field deviation. It also performs node-level comparison between the measured pollutant concentration distribution and the theoretical pollutant concentration distribution corresponding to the preset resistance control state derived from the theoretical flow field to calculate the concentration deviation. When any deviation exceeds its corresponding preset tolerance, the groundwater in-situ remediation control system uses real-time groundwater flow field monitoring data and the inverted values ​​of the corresponding current aquifer hydrogeological parameters as supplementary samples to update the flow proxy model online. Based on this, the flow proxy model re-predicts the flow field response characteristics under the current actual operating conditions.

[0093] It should be noted that the pollutant concentration monitoring data mainly serves as an auxiliary input for deviation triggering criteria and regeneration of operating parameters in this step. The updated samples of the flow proxy model still only use the flow field monitoring data as supplementary samples to avoid introducing an expansion of the output dimension of the flow proxy model.

[0094] As an alternative implementation method, the groundwater in-situ remediation control system can also achieve the same effect by inverting the current effective hydrogeological parameters based on the measured flow field and then calling the proxy model again.

[0095] Based on this, the groundwater in-situ remediation control system recalculates the required operating flow rates for the control and removal wells by combining real-time pollutant concentration monitoring data (where the operating flow rate of each removal well is dynamically adjusted in a positive correlation with the concentration value corresponding to its location in the measured pollutant concentration distribution), regenerates operating parameter instructions, and sends them to each in-situ injection well, thereby achieving closed-loop feedback regulation with flow field-pollutant dual-dimensional feedback coupling.

[0096] Optionally, in some embodiments, the groundwater in-situ remediation control system can also, after the control core enters the operation period, perform rolling statistics on the actual pollutant load flux at the location of each control core based on the real-time pollutant concentration monitoring data obtained in S207, and dynamically correct the predicted service life accordingly; when the remaining service life of any control core is lower than the preset warning threshold, the groundwater in-situ remediation control system marks the control core as a target to be reinforced, triggering the local addition of the control core to be constructed at that location in S208 to continue the reaction reduction function, so as to avoid pollutants penetrating the original control core cross-section after the reaction material fails and causing flow field-pollutant coupling feedback failure.

[0097] S208. When the flow field deviation or concentration deviation exceeds its corresponding preset tolerance, the cumulative number of times the flow field deviation and concentration deviation exceed the limit within the preset time period shall be counted respectively.

[0098] When the cumulative number of deviations in the flow field exceeds the corresponding preset threshold, the real-time groundwater flow field monitoring data, together with the target deployment scheme, will be input into the intelligent optimization model to locally add or adjust some of the planned control core deployment points in the target deployment scheme.

[0099] When the cumulative number of times the concentration deviation exceeds the corresponding preset threshold, the real-time pollutant concentration monitoring data, together with the target deployment plan, will be input into the intelligent optimization model to make local adjustments to the positions of some in-situ injection wells in the target deployment plan.

[0100] When the cumulative number of times of both types of violations exceeds their corresponding preset threshold, the real-time groundwater flow field monitoring data and the real-time pollutant concentration monitoring data will be input into the intelligent optimization model, and the deployment point of the control core and the location of the in-situ injection well will be adjusted simultaneously.

[0101] The adjustment results are combined to form an adjusted layout plan, which is then used as the new target layout plan to guide the deployment of subsequent in-situ injection wells and control cores to be constructed according to the new target layout plan. The in-situ injection wells that have already been constructed will operate according to the operating parameter instructions regenerated by the new target layout plan.

[0102] The cumulative number of exceedances refers to the count of discrete events within a preset time period (e.g., 30 days of operation) where the flow field deviation or concentration deviation exceeds its corresponding preset tolerance. The preset thresholds for flow field deviation and concentration deviation can be configured independently. Given that the adjustment of the resistance control core involves on-site construction, the corresponding preset threshold can be set higher than the preset threshold for in-situ injection well adjustment to reflect the engineering positioning of "low frequency, significant impact." Local adjustment refers to migrating or adding only a small number of in-situ injection well locations identified by the intelligent optimization model as having the highest deviation contribution. Resistance control core... The local addition or adjustment of deployment points refers to the intelligent optimization model identifying weak or failed sections of the current hydraulic barrier based on real-time groundwater flow field monitoring data. Based on the planned deployment points of the control cores in the target deployment scheme, the planned points corresponding to the weak / failed sections are relocated, densified, or added, while keeping the physical position of the already constructed control cores unchanged. The above adjustments only apply to the control cores to be constructed at the planning level and do not involve the physical relocation of the already constructed control cores. The already constructed control cores continue to play the role of passive barrier in their original positions. The adjustment results guide the on-site construction of subsequent control cores to achieve project implementation.

[0103] This step is executed after repeated parameter-level updates in S207, if any deviation still frequently exceeds its corresponding tolerance. Specifically, the groundwater in-situ remediation control system continuously counts the cumulative number of exceedances corresponding to flow field deviation and concentration deviation over a preset time period (e.g., 30 days of operation). When the cumulative number of exceedances of flow field deviation first exceeds the threshold, the groundwater in-situ remediation control system determines it as a partial failure of the hydraulic barrier and calls the intelligent optimization model to locally add or adjust the planned points for the control core. When the cumulative number of exceedances of concentration deviation first exceeds the threshold, the groundwater in-situ remediation control system determines it as an abnormal situation of pollutant migration and calls the intelligent optimization model to locally adjust the location of the in-situ injection well. When both types of cumulative exceedances exceed the threshold simultaneously, the groundwater in-situ remediation control system determines it as a situation of long-term evolution of hydrogeological conditions and calls the intelligent optimization model to simultaneously adjust the deployment points of the control core and the location of the in-situ injection well. Based on the original target deployment plan, the intelligent optimization model only migrates, densifies, or adds a small number of objects that deviate from the top of the contribution ranking, while retaining the original deployment structure of the remaining control cores and in-situ injection wells. This avoids the loss of engineering volume and plan stability caused by regenerating the entire deployment plan.

[0104] The adjusted deployment plan is implemented in the project in two ways: First, for the newly added or relocated control cores and in-situ injection wells to be constructed according to the adjustment results, the groundwater in-situ remediation control system issues the corresponding deployment locations and control core filling medium configuration parameters to the on-site construction execution unit, and completes the on-site construction of the newly added / relocated objects according to the adjusted deployment plan; Second, for the already constructed in-situ injection wells, the groundwater in-situ remediation control system re-invokes the flow proxy model based on the adjusted deployment plan to deduce the new target flow field, and regenerates the operating parameter instructions accordingly, issuing them to each constructed in-situ injection well so that it continues to operate according to the operating parameter instructions corresponding to the adjusted plan; the physical location of the constructed control cores remains unchanged in this step, and they are only included as existing barriers in the overall control system of the new plan to participate in the flow field deduction. At this point, the groundwater in-situ remediation control system completes the two-layer adaptive adjustment link of parameter-level closed-loop feedback (S207) and plan-level closed-loop feedback (S208).

[0105] Optionally, in some embodiments, the groundwater in-situ remediation control system may allow only the plane coordinates of the well location to be adjusted during local adjustments, while keeping the well depth and diameter unchanged. This limits the amount of new construction work to a minimum, avoids large-scale relocation operations within the production site, and further meets the requirements for controlled structural adjustment work in the context of simultaneous production and management.

[0106] Optionally, in some embodiments, before executing local adjustments to the target layout plan, the groundwater in-situ remediation control system may first assess the feasibility of eliminating deviations by adjusting well structure parameters such as the opening depth range of the filter pipe or the opening degree of the screen pipe of the existing in-situ injection wells. The adjustment of well structure parameters is issued to the existing in-situ injection wells as a priority adjustment method. Only when the deviation still does not converge after the adjustment of well structure parameters is the intelligent optimization model triggered to generate layout adjustment instructions for adding or relocating well locations, so that the cumulative amount of earthmoving during the long-term remediation process is limited to a minimum.

[0107] In this embodiment, the groundwater in-situ remediation control system establishes a parameter-level closed-loop feedback regulation link through real-time monitoring data reinjection, and further triggers a local deployment scheme correction based on an intelligent optimization model when the cumulative number of exceedances exceeds a preset threshold. Therefore, it can distinguish between shallow parameter fluctuations and deep physical structure failures and adopt differentiated coping methods, effectively solving the problem of flow field runaway caused by the cumulative prediction deviation of the flow proxy model during long-term operation. This enables the long-term stable maintenance of the groundwater flow field control state in the contaminated area under complex conditions where aquifer heterogeneity and production activity disturbances coexist.

[0108] Unlike conventional methods that rely on numerical simulations or surrogate models combined with optimization algorithms to optimize only the well locations and pumping rates of uniformly gridded injection-extraction well arrays, this application differs in two ways: First, it uses the constructable area formed by expanding the outlines of above-ground production structures and the three-dimensional occupancy of underground pipeline pools according to safe avoidance distances as a hard constraint input for optimization, allowing the optimization results to naturally avoid existing facilities and adapt to the scenario of simultaneous production and management. Second, it uses passive barrier cores that do not rely on power sources and active in-situ injection-extraction wells as mixed decision variables in the same optimization problem for joint optimization, with the goal of minimizing the total number of both, forming a spatially complementary and temporally coordinated two-layer architecture between the passive barrier layer and the active control layer. These differences mean that this application is not a simple application of general flow field optimization methods in this field.

[0109] The following describes the groundwater in-situ remediation control system in the embodiments of this invention from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the physical device structure of a groundwater in-situ remediation control system in an embodiment of this application.

[0110] It should be noted that, Figure 3 The structure of the groundwater in-situ remediation control system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0111] like Figure 3 As shown, the groundwater in-situ remediation control system includes a CPU 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 302 or a program loaded from a storage section 308 into a random access memory RAM 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0112] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0113] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program / instructions carried on a computer-readable medium, the computer program / instructions containing computer program / instructions for performing the methods shown in the flowcharts. In such embodiments, the computer program / instructions can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by CPU 301, it performs the various functions defined in the present invention.

[0114] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0116] Specifically, the groundwater in-situ remediation control system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the hydrodynamic intelligent collaborative groundwater in-situ remediation control method provided in the above embodiment.

[0117] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the groundwater in-situ remediation control system described in the above embodiments; or it may exist independently and not incorporated into the groundwater in-situ remediation control system. The storage medium carries one or more computer programs, which, when executed by a processor of the groundwater in-situ remediation control system, cause the groundwater in-situ remediation control system to implement the hydrodynamic intelligent coordinated groundwater in-situ remediation control method provided in the above embodiments.

[0118] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0119] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0120] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A hydrodynamic intelligent collaborative groundwater in-situ remediation control method, applied to a groundwater in-situ remediation control system, characterized in that, include: Acquire hydrogeological parameters of the target site, underground pollutant concentration distribution data, physical properties and geometric dimensions of the filling medium of the control core, as well as data on the workable area defined by the above-ground production structures and underground pipeline pools; Using the hydrogeological parameters and the data of the workable area as background input parameters, and the candidate combination of control core locations and their physical properties, the candidate combination of in-situ injection well locations and their injection flow rate within the workable area as optimization variables, the data are input into a preset water flow proxy model to predict the distribution of groundwater flow field around the contaminated area after the intervention of control core and the coordinated action of in-situ injection wells under each candidate combination of locations. The groundwater flow field distribution and pollutant concentration distribution data corresponding to each candidate deployment point combination are input into the preset intelligent optimization model. The optimization objective is to minimize the total number of control cores and in-situ injection wells and ensure that the flow field blocking rate of the polluted area is not lower than the preset threshold. The target deployment scheme is then obtained by solving the problem. Based on the deployment location of each control element in the target deployment scheme and the pollutant concentration distribution data, the filling medium configuration parameters of each control element are determined; Based on the deployment location of each in-situ injection well in the target deployment scheme and the pollutant concentration distribution data, the injection timing and flow rate operation parameter instructions for each in-situ injection well are generated. Each in-situ injection and extraction well is driven to operate according to the operating parameters and works in coordination with each control core that has completed filling according to the filling medium configuration parameters to maintain the groundwater flow field in the contaminated area in a preset control state.

2. The method according to claim 1, characterized in that, The water flow proxy model is trained based on the hydrogeological numerical model of the target site through offline simulation to generate a sample set. Each sample set includes a combination of candidate locations for control cores, a combination of candidate locations for in-situ injection wells, hydrogeological parameters of the aquifer and injection flow rate, as well as the corresponding groundwater flow field distribution.

3. The method according to claim 1, characterized in that, The acquisition of hydrogeological parameters of the target site, underground pollutant concentration distribution data, physical properties and geometric dimensions of the filling medium of the control core, and data on the workable area defined by above-ground production structures and underground pipeline pools specifically includes: Obtain the surface outline of the above-ground production structures and the three-dimensional location model of the underground pipeline pools at the target site; The surface outline and the three-dimensional occupant model are extended outward on the surface projection plane at a preset safe avoidance distance to form a no-construction zone; The area within the target site other than the prohibited construction area is defined as the construction-allowed area.

4. The method according to claim 1, characterized in that, The method further includes: During the operation of each of the in-situ injection wells according to the operating parameter instructions, real-time groundwater flow field monitoring data and real-time pollutant concentration monitoring data around the contaminated area are acquired. When either the deviation of the real-time groundwater flow field monitoring data from the preset control state or the deviation of the real-time pollutant concentration monitoring data from the theoretical pollutant concentration distribution corresponding to the preset control state exceeds a preset tolerance, the real-time groundwater flow field monitoring data and the corresponding inverted values ​​of the current aquifer hydrogeological parameters are used as supplementary samples to update the flow proxy model online. Based on the updated flow proxy model, the operating parameter instructions are regenerated in conjunction with the real-time pollutant concentration monitoring data.

5. The method according to claim 4, characterized in that, The method further includes: When the flow field deviation or the concentration deviation exceeds its corresponding preset tolerance, the cumulative number of times the flow field deviation and the concentration deviation exceed the limit within a preset time period are counted respectively. When the cumulative number of times the flow field deviation exceeds its corresponding preset threshold, the real-time groundwater flow field monitoring data, together with the target deployment scheme, is input into the intelligent optimization model to locally add or adjust some of the planned control core deployment points in the target deployment scheme. When the cumulative number of times the concentration deviation exceeds its corresponding preset threshold, the real-time pollutant concentration monitoring data, together with the target deployment scheme, is input into the intelligent optimization model to make local adjustments to the positions of some in-situ injection wells in the target deployment scheme. When the cumulative number of times of both types of violations exceeds their corresponding preset threshold, the real-time groundwater flow field monitoring data and the real-time pollutant concentration monitoring data are input into the intelligent optimization model, and the deployment point of the control core and the location of the in-situ injection well are adjusted simultaneously. The adjustment results are combined to form an adjusted deployment scheme, and the adjusted deployment scheme is used as the new target deployment scheme.

6. The method according to claim 1, characterized in that, When searching for candidate deployment points, the intelligent optimization model assigns priority selection weights to existing monitoring well locations within the workable area, so that the control core and in-situ injection wells are preferentially selected from existing monitoring well locations.

7. The method according to claim 1, characterized in that, The determination of the filling medium configuration parameters for each resistive core specifically includes: Based on the deployment location of each control core in the target deployment scheme and the pollutant type and concentration level corresponding to the pollutant concentration distribution data, the reaction material type that is compatible with the target pollutant is matched from the preset reaction material library, and the corresponding reaction material physical property parameters are determined. Based on the reactivity of the matched reactive material, the total amount of reactive material loaded as defined by the geometric dimensions of the control core, and the pollutant load at the corresponding position of each control core in the pollutant concentration distribution data, the service life of each control core is predicted; the type of reactive material, the physical properties of the reactive material, and the service life are all incorporated into the filling medium configuration parameters. The instructions for generating the injection timing and flow rate operation parameters for each in-situ injection well specifically include: Based on the relative positional relationship of each in-situ injection well with respect to the upstream supply boundary and downstream discharge boundary of the contaminated area in the target deployment scheme, each in-situ injection well is divided into a control well that undertakes the function of flow field isolation and a removal well that undertakes the function of pollutant removal. A baseline constant operating flow rate is allocated to the control well to maintain the control state, and an operating flow rate positively correlated with the concentration value at the corresponding position in the pollutant concentration distribution data is allocated to the removal well. The control well is started before the removal well, and the injection sequence and flow rate operating parameter instructions are obtained.

8. A groundwater in-situ remediation control system, characterized in that, The groundwater in-situ remediation control system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the groundwater in-situ remediation control system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are executed on the groundwater in-situ remediation control system, the groundwater in-situ remediation control system performs the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are run on the groundwater in-situ remediation control system, the groundwater in-situ remediation control system performs the method as described in any one of claims 1-7.

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