Heavy metal contaminated soil remediation system and method based on intelligent management and control

Abstract

The invention relates to the technical field of in-situ / ex-situ remediation of sites polluted by heavy metals such as cadmium, lead, copper and zinc, and discloses a heavy metal polluted soil remediation system and method based on intelligent management and control. The system comprises an electrode system module, an activator module, a targeted adsorption device module, an automatic monitoring system module, an intelligent decision-making system module and a photovoltaic power supply module. The method comprises the steps of driving heavy metal ions in soil to directionally migrate through an electric field; the activating agent module works, green micromolecule organic acid and other activating agents are injected in a gradient mode, and the migration efficiency of heavy metal is improved; activated heavy metal ions can be captured by an adsorption material in the targeted adsorption device module in cooperation with a selective enrichment device; meanwhile, the automatic monitoring system module collects environmental parameters in real time; the intelligent decision-making system module optimizes the remediation efficiency and energy consumption according to the real-time data, and low-cost and high-efficiency remediation of soil heavy metal pollution is achieved. The remediation efficiency is remarkably improved, and the risk of secondary pollution is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of in-situ remediation of heavy metal contaminated sites, especially cadmium, lead, copper and zinc, and more particularly to a heavy metal contaminated soil remediation system and method based on intelligent management and control. BACKGROUND

[0002] Traditional electrokinetic remediation technology has the defects of serious electrode polarization, low pollutant migration efficiency, high energy consumption, and lack of dynamic control. In the prior art, the use of activators and adsorption devices relies on manual experience and cannot achieve precise control. The energy supply mode is single and cannot meet the long-term remediation needs.

[0003] Prior art 1, application number: CN202410890180.9 discloses a heavy metal contaminated soil passivation material regional suitability screening method and application. By collecting and analyzing the adsorption performance data of different passivation materials, and using a machine learning model for feature selection and prediction, the passivation material suitable for agricultural soil pollution remediation is efficiently screened out. Although the machine learning model is introduced, it realizes the rapid and accurate screening of the passivation material for agricultural soil pollution remediation. It has the advantages of simple operation, low cost, high efficiency, etc. and can be widely applied to the field of agricultural soil pollution remediation. It provides a more feasible and sustainable solution, provides a new passivation material regional screening strategy for agricultural soil pollution remediation, helps to improve soil remediation efficiency, and protects the ecological environment and food safety. However, it only stays in the passivation material screening stage and lacks the closed-loop control ability of the dynamic remediation process. The technical core is a static "material screening strategy", which cannot realize the in-situ migration and removal of heavy metal pollutants.

[0004] Prior art 2, application number: CN202410656324.4 discloses a soil heavy metal stabilization effect prediction method based on machine learning. Soil experimental data is obtained to determine the objective function. The data is preprocessed, trained by multiple models, and the parameters are optimized. The fitting degree of different model fitting results is calculated to determine the optimal model. The features with high cumulative contribution rate in the optimal model are selected to retrain the model. The AIC values of the optimal model and the retrained model are compared to determine the final prediction model. The Bootstrap algorithm is used to predict the heavy metal contaminated soil stabilization efficiency and its 95% confidence interval. Although the comparison and feature optimization of multiple machine learning prediction models greatly improve the accuracy of the model's prediction of soil heavy metal stabilization effect, simplify the model, and provide a reference for the prediction of the remediation effect of soil heavy metal stabilization scheme. However, it is limited to the prediction modeling of the stabilization effect and does not form a feedback mechanism with the physical remediation process. The "prediction-verification" mode has a lag and cannot optimize the remediation parameters in real time.

[0005] Prior art three, application number CN202311015589.8, discloses a hyperspectral inversion method and device for heavy metal content in gold mining soils. The method involves pre-treating collected soil samples and testing their heavy metal content using ICP-OES and ICP-MS. Heavy metal contamination in the soil is assessed using a single-factor pollution index method. Raw soil spectral data obtained from soil spectral measurements is pre-processed. Feature extraction and selection are performed on the pre-processed soil spectral data. A convolutional neural network (CNN) using the selected spectral features and the measured heavy metal content is used to construct an inversion model for heavy metal content in gold mining soils. The accuracy of the inversion model is then evaluated. While the inversion method offers high accuracy, reducing computational errors and iterations, and providing a scientific model for ecological remediation in contaminated areas, it relies on laboratory testing (ICP-OES / MS) and spectral inversion, making it a pollution assessment technique that does not involve actual pollutant removal. Its "offline analysis-model building" approach precludes on-site remediation.

[0006] Currently, existing technologies 1, 2, and 3 have problems such as lack of dynamic repair, weak real-time control, limited energy dependence, and insufficient coordination efficiency. Therefore, the present invention provides a system and method for remediating heavy metal contaminated soil based on intelligent management and control. Summary of the Invention

[0007] The main purpose of the present invention is to provide a heavy metal contaminated soil remediation system and method based on intelligent management and control, so as to solve the problems of lack of dynamic remediation, weak real-time control, limited energy dependence and insufficient collaborative efficiency in the existing technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: A heavy metal contaminated soil remediation system based on intelligent management and control, comprising an electrode system module, an activator module, a targeted adsorption device module, an automatic monitoring system module, an intelligent decision-making system module, and a photovoltaic power supply module; Among them, the electrode system module is used to drive the directional migration of heavy metal ions through the electric field, optimize the electric field distribution and inhibit electrode polarization; the activator module activates heavy metals by gradient injection of activators; the targeted adsorption device module is used to use adsorption materials to selectively enrich heavy metal ions in the electrode area; the automatic monitoring system module collects multi-dimensional environmental parameters in real time to provide data support for the decision-making of the intelligent decision-making system module; the intelligent decision-making system module is used to integrate multiple functional modules, optimize the remediation efficiency and energy consumption through real-time monitoring and intelligent correction, and realize efficient remediation of soil heavy metal pollution; the photovoltaic power supply module is used to provide stable power for the electrode system module, activator module, targeted adsorption device module, automatic monitoring system module, and intelligent decision-making system module.

[0009] As a further improvement of the invention, the intelligent decision-making system module comprises: A pre-decision module for inputting soil physicochemical property parameters, constructing a multi-physical field model, simulating the spatial and temporal distribution of heavy metals, predicting the relationship curve between cadmium removal rate and time, and determining the optimal voltage, electrode spacing, and activator dosage; A dynamic process control module for monitoring data input into the multi-physical field model, dynamically updating boundary conditions, and predicting pollutant concentration fields in the future 1 hour; adjusting the voltage according to the current density and switching the hexagonal / 2:1 layout mode; when the pH returns to >5.0, the unmanned aerial vehicle increases the dosage of activators (such as citric acid); if the predicted cathode area concentration is >500 mg / kg, the targeted adsorption device module is deployed and started in advance; multi-objective optimization generates dynamic instructions; A post-evaluation module for simulating the residual concentration of pollutants after remediation, generating a three-dimensional concentration field cloud map, calculating the actual removal rate, and analyzing the causes of deviation; A mode switching module for receiving voltage adjustment instructions through a protocol; dynamically switching the photovoltaic panel series-parallel mode according to the predicted energy consumption curve.

[0010] As a further improvement of the invention, the pre-decision module specifically comprises: A multi-source data fusion and model initialization submodule for constructing an adaptive calculation network based on the three-dimensional soil physicochemical property parameter matrix provided by the automatic monitoring system module; A multi-field coupling solution and dynamic iteration submodule for realizing heavy metal migration prediction through a three-field collaborative solution engine of electric field-chemical field-flow field; A parameter sensitivity analysis and optimization submodule for executing a three-stage optimization strategy to determine the optimal operating parameters.

[0011] As a further improvement of the invention, the mode switching module comprises: A driving chelation decision chain submodule for establishing a LIBS element abundance matrix, generating a spatial affinity map through a migration state heavy metal clustering algorithm, coupling a real-time soil moisture content field, outputting a gradient chelator demand vector, and triggering the unmanned aerial vehicle to spray through an MQTT instruction, with the spraying density being positively correlated with the affinity map; A conductance mutation response chain submodule for starting ion mobility spectrum traceability analysis when the cathode area conductivity increment exceeds the threshold, activating a double-channel response if cadmium ions are identified as carriers, deploying an nMgO adsorption array to the electric flux hotspot area, generating an electrode surface regeneration pulse sequence with a frequency matching the conductivity increment gradient, and generating an electrode surface regeneration pulse sequence with a frequency matching the conductivity increment gradient; Energy consumption prediction-energy adaptation chain sub-module, used for COMSOL transient energy consumption surface, extracts third-order differential features through a non-steady-state convolution network, dynamically divides the energy consumption domain, maintains the initial topology of the photovoltaic string parallel connection in the base load area, triggers the dynamic reorganization of the battery pieces in the group in the fluctuation area, and activates the phase compensation of the super capacitor array in the peak area.

[0012] As a further improvement of the application, the driving chelation decision chain sub-module comprises: Multi-spectral oscillation feature decoupling system, used for obtaining LIBS original plasma oscillation sequence, separating cadmium feature frequency band through tensor frequency domain decomposition, extracting oscillation attenuation slope of each sampling point, generating heavy metal migration activity matrix, and negatively correlating the attenuation slope with ion dissociation energy; Clay constraint field construction system, used for obtaining the clay content parameter of the prior decision module, converting the spatial charge distribution surface through the dielectric relaxation model, performing convolution operation with the real-time soil water content field, and outputting the ion migration retardation coefficient field; Migration state topology reconstruction system, used for obtaining the heavy metal migration activity matrix and the ion migration retardation coefficient field, generating the migration potential energy equipotential surface through non-Euclidean manifold learning, performing Riemannian geometry partitioning on the potential well depth, and marking the high migration risk domain; Affinity map emergence system, used for the second-order derivative of the migration potential energy equipotential surface in the direction of the electrode gradient, performing anisotropic diffusion equation smoothing processing, and superimposing the cathode area Concentration prediction value, generating the spatial affinity map.

[0013] As a further improvement of the application, the affinity map emergence system comprises: Potential field gradient reinforcement subsystem, used for applying the second-order directional derivative operator to the migration potential energy equipotential surface along the electrode axial direction, generating the migration driving force tensor field, and indicating the migration trend to the anode by the positive value and the migration trend to the cathode by the negative value; Diffusion constraint generation subsystem, used for the boundary of the high migration risk domain subjected to Riemannian geometry partitioning, extracting the boundary curvature feature, and constructing the anisotropic diffusion control field; Field distortion correction subsystem, used for processing the migration driving force tensor field and the anisotropic diffusion control field through the curvature constraint smoothing equation, and outputting the corrected migration field; Concentration-dynamics fusion subsystem, used for performing the hyperbolic tangent coupling of the normal component of the corrected migration field and the cathode area Concentration prediction value output by the electromotive force model, generating the spatial affinity map.

[0014] As a further improvement of the application, the electrical conductivity mutation response chain sub-module comprises: An incremental gradient tensorization system is used to extract the spatiotemporal gradient vector field in the conductance flux over-threshold region; the field is decomposed into a migration component and a diffusion component through a curl-div field separation, and the cadmium ion migration flux tensor is retained; A hotspot field intensity mapping system is used to deploy an nMgO adsorption array in an electric flux hotspot region, obtain real-time current density values at the site, perform a bilinear tensor product on the cadmium ion migration flux tensor, and generate a pulse intensity basis function; A frequency field reconstruction system is used to calculate the second-order variation of the migration flux along the normal direction of the electrode by using the principal characteristic direction of the cadmium ion migration flux tensor, and convert the second-order variation into a characteristic frequency spectrum through a Legendre transformation; A spatiotemporal pulse synthesis system is used to discretize the pulse intensity basis function and the characteristic frequency spectrum through a non-uniform sampling theorem, output a regenerative pulse sequence, and positively correlate the pulse width with the eigenvalue of the migration flux tensor and synchronize the phase with the equipotential surface of the current density field.

[0015] As a further improvement of the present application, the hotspot field intensity mapping system comprises: A current-substance field dimension reduction subsystem is used to project the real-time current density field in the electric flux hotspot region along the normal direction of the electrode surface to generate a current density principal characteristic vector; and the cadmium ion migration flux tensor is extracted through a Riemann contraction operation to obtain a substance transport principal axis quantity; A double-field conjugate basis construction subsystem is used to orthogonally decompose the current density principal characteristic vector and the substance transport principal axis quantity to form a field coupling frame system; and the conjugate weight coefficient matrix is generated by the included angle between the frame system basis vector and the tangent direction of the electrode surface; A tensor product energy flow mapping subsystem is used to perform a Kronecker product operation on the component module length of the current density field in the frame system and the eigenvalue of the migration flux tensor, and output a transient energy flow density tensor, which is the ion dissociation energy input efficiency of a unit adsorption site; A basis function emergence subsystem is used to integrate the transient energy flow density tensor and the conjugate weight coefficient matrix in the direction of the electrochemical potential barrier to generate a pulse intensity basis function.

[0016] As a further improvement of the present application, the energy consumption prediction-energy source adaptation chain sub-module comprises: An energy consumption domain boundary generation component is used to obtain the third-order differential characteristics of the COMSOL transient energy consumption surface, divide the surface into a base load region, a fluctuation region and a peak region through a Riemann manifold segmentation algorithm, and output an energy consumption phase space partition topology; A carrier path reconstruction component is used to perform a dual space mapping on the fluctuation region energy consumption gradient direction field and the photovoltaic array lattice vector to generate an optimal carrier transport path, trigger the dynamic recombination of the battery sheet along the path direction, and form a non-Euclidean star-shaped network; A capacitive phase modulation component is used to perform symplectic geometry matching between the Fourier residual spectrum of the energy consumption pulse of the peak region and the relaxation time spectrum of the super capacitor array, so as to obtain a capacitive compensation phase angle, and the capacitive unit is activated to conduct according to the phase angle sequence, so as to establish a virtual work compensation flow; An impedance continuum maintenance component is used to obtain the initial topology of the base carrier region and the electrode spacing parameters of the prior decision module, derive the system characteristic impedance through the Maxwell-Ampere law, and maintain the parallel structure of the photovoltaic group string.

[0017] To achieve the above object, the application further provides the following technical scheme. A heavy metal contaminated soil remediation method based on intelligent management and control is applied to a heavy metal contaminated soil remediation system based on intelligent management and control, and the heavy metal contaminated soil remediation method based on intelligent management and control comprises the following steps of: An electrode system module drives the directional migration of heavy metal ions in the soil through the action of electric field driving; at the same time of electric field driving, an activator module starts to work, and a gradient injection of green small-molecule organic acid activator is performed to activate the heavy metals in the soil, so that the heavy metals are desorbed from the soil particles; The activated heavy metal ions are captured by the adsorption material in the targeted adsorption device module and the selective enrichment device, so that the heavy metal ions are effectively adsorbed; at the same time, an automatic monitoring system module collects environmental parameters in real time, and the environmental parameters support the intelligent decision system module to make real-time monitoring and intelligent correction decisions; The intelligent decision system module optimizes the remediation efficiency and energy consumption according to real-time data, so as to realize the remediation of heavy metal pollution in the soil; and a photovoltaic power module provides stable power guarantee.

[0018] The electrode system module of the application drives the directional migration of heavy metal ions by using electric field, and inhibits electrode polarization by optimizing the electric field distribution, so as to promote the effective migration and collection of heavy metal ions. The activator module activates the heavy metals in the soil by gradient injection of green small-molecule organic acid, so that the heavy metals are more easily migrated and remediated. The targeted adsorption device module uses the synergistic effect of different adsorption materials to selectively enrich the heavy metal ions in the electrode area, so as to improve the remediation efficiency. The automatic monitoring system module collects soil environmental parameters in real time to provide data support for the intelligent decision system module and to perform real-time monitoring. The intelligent decision system module integrates the functions of various modules, optimizes the remediation efficiency and energy consumption through real-time monitoring and intelligent correction, and realizes the efficient remediation of heavy metal pollution in the soil. The photovoltaic power module provides stable power for the electrode system, the activator system, the targeted adsorption device, the automatic monitoring system and the like in the system, so as to guarantee the continuous and stable operation of the system. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 It is a functional module schematic diagram of the heavy metal contaminated soil remediation system based on intelligent management and control of the application; Figure 2 A functional module schematic diagram of an electrode system module of a heavy metal contaminated soil remediation system based on intelligent management and control of the present application; Figure 3 A functional module schematic diagram of an activator module of a heavy metal contaminated soil remediation system based on intelligent management and control of the present application; Figure 4 A functional module schematic diagram of an automatic monitoring system module of a heavy metal contaminated soil remediation system based on intelligent management and control of the present application; Figure 5 A step flow schematic diagram of one embodiment of a heavy metal contaminated soil remediation method based on intelligent management and control of the present application; Figure 6 A structural schematic diagram of one embodiment of an electronic device of the present application; Figure 7 A structural schematic diagram of one embodiment of a storage medium of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0021] The terms "first", "second", "third" in the present application are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, such as two, three, etc., unless otherwise explicitly and specifically limited. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative positional relationship, movement condition, etc. between the components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0022] Reference to“an embodiment” herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase“in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily all referring to a common set of embodiments, although they can. Those skilled in the art will recognize that the embodiments described herein can be combined with other embodiments in various ways.

[0023] As shown in Figure 1 The present embodiment provides an embodiment of a heavy metal contaminated soil remediation system based on intelligent management and control. In the present embodiment, the heavy metal contaminated soil remediation system based on intelligent management and control specifically includes: an electrode system module 1, an activator module 2, a targeted adsorption device module 3, an automatic monitoring system module 4, an intelligent decision-making system module 5, and a photovoltaic power module 6. The electrode system module 1 is used to drive the directional migration of heavy metal ions by electric field, optimize the electric field distribution, and inhibit electrode polarization. The activator module 2 activates heavy metals by gradient injection of activators (green small molecule organic acids). The targeted adsorption device module 3 is used to use adsorption materials to cooperatively selectively enrich heavy metal ions in the electrode area. The automatic monitoring system module 4 collects multi-dimensional environmental parameters in real time to provide data support for the decision-making of the intelligent decision-making system module 5. The intelligent decision-making system module 5 is used to integrate multiple functional modules to optimize the remediation efficiency and energy consumption through real-time monitoring and intelligent correction, thereby achieving efficient remediation of heavy metal contaminated soil. The photovoltaic power module 6 is used to provide stable electric energy for the electrode system module 1, the activator module 2, the targeted adsorption device module 3, the automatic monitoring system module 4, and the intelligent decision-making system module 5.

[0024] Preferably, the electrode system module 1 of the present embodiment utilizes electric field to drive the directional migration of heavy metal ions and inhibits electrode polarization by optimizing the electric field distribution, thereby promoting the effective migration and collection of heavy metal ions. The activator module 2 activates heavy metals in the soil by gradient injection of green small molecule organic acids, making them more easily migrate and be remediated. The targeted adsorption device module 3 uses the synergistic effect of different adsorption materials to selectively enrich heavy metal ions in the electrode area, thereby improving the remediation efficiency. The automatic monitoring system module 4 collects soil environmental parameters in real time to provide data support for the intelligent decision-making system module 5 and to monitor it in real time. The intelligent decision-making system module 5 integrates the functions of each module to optimize the remediation efficiency and energy consumption through real-time monitoring and intelligent correction, thereby achieving efficient remediation of heavy metal contaminated soil. The photovoltaic power module 6 provides stable electric energy for the electrode system, the activator system, the targeted adsorption device, the automatic monitoring system, and the like in the system, thereby ensuring the continuous and stable operation of the system.

[0025] The intelligent series-parallel control of the photovoltaic power module 6 of the embodiment: when the system needs to be boosted, the photovoltaic panels are connected in series to drive a high-voltage electric field; when the system needs to increase the current, the photovoltaic panels are connected in parallel to supply power to high-power equipment; foldable monocrystalline silicon photovoltaic panels: PERC monocrystalline silicon (efficiency ≥ 23%), the volume is reduced by 70% after folding, supporting rapid deployment; MPPT optimization: real-time tracking of the maximum power point based on the perturb and observe method, efficiency > 99%; energy storage management: LiFePO3 battery (50 kWh) + super capacitor (10 kWh), triggering energy-saving mode when SOC < 30%.

[0026] In summary, the repair system of the embodiment cooperates with each module to realize efficient, automatic and intelligent repair of soil heavy metal pollution, improve repair efficiency and energy consumption, and realize repair and protection of the soil environment. The embodiment significantly improves repair efficiency and reduces the risk of secondary pollution through multi-module intelligent coupling and photovoltaic driving.

[0027] Further, as shown in Figure 2 The electrode system module 1 specifically includes: An electric field distribution optimization design submodule for adopting different layout forms according to the physicochemical properties of different plots of soil, mainly including a hexagonal layout form and a 2:1 paired electrode layout form; A hexagonal layout strategy submodule for arranging anodes and cathodes in a honeycomb shape, with six anodes around and one cathode in the middle; A paired layout strategy submodule for adopting a 2:1 ratio for paired layout, i.e., one anode is arranged between every two cathodes; during the layout of the electrodes, the distance between the electrodes and the strength of the electric field are adjusted to suppress polarization and focusing effect phenomena; An electric energy supply submodule for connecting the electrodes to the photovoltaic power module.

[0028] Preferably, the electric field distribution optimization design submodule of the embodiment selects the optimal electrode arrangement scheme according to different soil characteristics to ensure that the electric field coverage efficiency is maximized; the hexagonal layout strategy submodule adopts a honeycomb arrangement to form uniform electric field distribution, and realizes spatial electric field balance through a 6-anode-1-cathode layout. The paired layout strategy submodule forms a directional electric field through a 2:1 anode-cathode ratio, and cooperates with the electrode spacing and field strength adjustment function to effectively control polarization phenomena. The electric energy supply submodule provides sustainable solar power support for the system. It realizes spatial coverage optimization of soil electric treatment, controllability of electric field strength and distribution, active suppression of polarization effect, and energy sustainability of system operation. Finally, the electric energy supply submodule connects the electrodes to the photovoltaic power module to ensure energy supply for the entire electrode system. This energy supply method is green and sustainable, and the photovoltaic power module can provide a stable energy source for the electrode system to support continuous heavy metal pollution treatment.

[0029] The electrode arrangement form in this embodiment is hexagonal arrangement: the anode (ruthenium iridium titanium coated electrode) and the cathode (carbon felt / stainless steel) are arranged in a honeycomb shape (spacing 0.4-2 m), and the uniformity of the field strength is verified by COMSOL electric field simulation (error <5%); 2:1 pair arrangement: each 2 groups of cathodes are matched with 1 group of anodes, the anode voltage gradient is 15-25 V / m, the cathode gradient is 30-50 V / m, and the electric field is formed to strengthen the migration of pollutants; pulse parameters: frequency 10-100 Hz, duty cycle 30-70%, voltage gradient 15-50 V / m, and electrode passivation is inhibited; electrode material: the anode uses coated titanium mesh (oxygen evolution overpotential ≥1.5 V), and the cathode uses activated carbon or stainless steel.

[0030] In summary, the use of each module of the embodiment forms a high-efficiency, environmentally friendly and sustainable soil heavy metal pollution treatment system, which aims to optimize the electric field distribution, improve the treatment efficiency, and ensure the stable supply of energy.

[0031] Further, the electric field distribution optimization design sub-module specifically includes: a soil property analysis unit for collecting the soil physicochemical parameters of the target plot, including resistivity, moisture content and heavy metal distribution characteristics; according to the data difference, it is determined whether to use hexagonal or paired arrangement mode - high heterogeneity soil tends to hexagonal layout to achieve uniform treatment, while the area with obvious pollution gradient uses paired arrangement to strengthen directional migration; a hexagonal arrangement execution unit for when the hexagonal strategy is selected, the system deploys six anodes outwardly equidistant from the central cathode to form a honeycomb topology; the electrode spacing is dynamically calculated according to the initial soil resistivity to ensure that the electric field superposition area coverage rate between adjacent anodes is more than 90%; the layout offsets local polarization effect through symmetry design while maintaining global voltage balance; a paired arrangement execution unit for if the paired strategy is enabled, the cathode-anode-cathode unit cycle is arranged, and the anode is always located on the perpendicular bisector of the two cathode connecting lines in each unit; by monitoring the current density distribution in real time, the spacing between adjacent units is automatically adjusted: when the focusing effect is detected, the spacing is expanded and the voltage is reduced, and vice versa; this process forms a wave-like electric field front, promoting the directional enrichment of heavy metals to the cathode area; an energy adaptation unit for realizing dynamic matching of both arrangement modes with the photovoltaic power module: the hexagonal layout needs to allocate higher total power due to the large number of electrodes, while the paired layout relies on pulse power supply to balance energy consumption and migration efficiency; automatically switch the power output mode according to the arrangement type to ensure that the electric field strength is always within the optimal threshold range.

[0032] The parameterized modeling stage imports the actually deployed honeycomb electrode coordinates (including compensation electrode spatial coordinates), soil dielectric constant distribution data, and real-time humidity value into the three-dimensional electromagnetic field model; the boundary condition dynamically loads the anode surface to be set as a potential boundary (value range 5-15V adjustable), the cathode is set as a ground boundary, and the compensation electrode is defined as a floating potential node; the field strength uniformity criterion runs the multi-physical field coupling calculation, extracts the electric field strength variation coefficient (CV = σ / μ), and triggers optimization iteration when CV > 0.25: first correction adjusts the voltage distribution ratio of adjacent main electrodes (±20% gradient test); second correction changes the working mode (anode / cathode / neutral) combination of the compensation electrode; final tuning combines the optimal parameters of the previous two steps to recalculate the compensation amount of the electrode spacing; the closed-loop verification mechanism writes the optimized parameter group into the electrode controller, synchronously updates the terrain analysis database, and forms the "deployment-simulation-correction" self-evolution ability. Preferably, the process of the embodiment forms a closed-loop control: soil parameters determine the layout form, the layout form constrains the electrode spatial relationship, the spatial relationship feeds back to adjust the power supply parameters, and the power output feeds back to the electric field stability. Ultimately, an integrated regulation and control from macro layout to micro field strength is realized, and the technical features are related to the recursive coupling of parameter input, topology generation, and dynamic adjustment.

[0033] Further, as shown in Figure 3 The activation agent module 2 specifically includes: The composite activation agent weighing sub-module is used for weighing a certain amount of citric acid, preparing a composite activation agent solution according to the proportion of 0.1 mol / kg (the dosage of the activation agent used is citric acid) of soil, and fully stirring and dissolving; the prepared activation agent solution is stored in a container for later use. The unmanned aerial vehicle path planning sub-module is used for positioning the heavy metal contaminated soil area using a positioning system, identifying the pollution hot spot area in the soil in combination with a multispectral imaging technology, planning a spraying path and a dosage for the unmanned aerial vehicle according to the pollution distribution, loading the activation agent solution into a spraying device of the unmanned aerial vehicle, starting the unmanned aerial vehicle, and quantitatively spraying the contaminated soil area along the preset spraying path; the unmanned aerial vehicle dynamically adjusts the spraying amount in real time by monitoring the soil humidity during the flight. The piezoelectric atomizing nozzle adjusting sub-module is used for the unmanned aerial vehicle spraying device to be equipped with a piezoelectric atomizing nozzle, the droplet size is controlled to be 20-50 μm, and the nozzle automatically adjusts the spraying amount according to the soil humidity; during the spraying process, the unmanned aerial vehicle uniformly injects the activation agent in gradients along the preset path, starts from a low concentration, gradually increases the activation agent concentration, and reaches a predetermined activation agent concentration and dosage ratio. A monitoring and feedback sub-module is configured to return to the base station automatically after spraying is completed, and the unmanned aerial vehicle transmits data such as the spraying path and the amount of use to the automatic monitoring system module in real time; according to the feedback result, the intelligent decision-making system module evaluates the effect of the activator addition, and provides decision support for the repair work.

[0034] Preferably, the composite activator preparation of the embodiment ensures that the amount and concentration of the activator used in the repair process meet the preset requirements, thereby effectively promoting the activation and transfer of heavy metals in the soil. The unmanned aerial vehicle path planning utilizes a positioning system and multispectral imaging technology, and the system can accurately identify the pollution hot spot area in the soil and plan the best spraying path. In this way, targeted spraying can be performed on the contaminated soil area, improving the repair efficiency. The uniform spraying unmanned aerial vehicle spraying device cooperates with the piezoelectric atomizing nozzle to achieve uniform spraying of the activator, with a particle size controlled at 20-50 μm, which is more conducive to soil absorption. At the same time, the spraying amount is automatically adjusted according to the soil humidity to ensure the use efficiency and effect of the activator. The gradient injection of the activator is sprayed by the unmanned aerial vehicle along the preset path, starting from a low concentration and gradually increasing the concentration until reaching the predetermined proportion. This method is beneficial for the soil to gradually adapt to the activator and avoids secondary pollution of the soil due to excessive concentration. After spraying is completed, the unmanned aerial vehicle transmits data in real time, and the intelligent decision-making system module evaluates the effect of the activator to provide decision support for subsequent repair work, thereby realizing closed-loop management and optimization of the repair process.

[0035] In summary, the embodiment improves the efficiency and effect of soil repair while reducing the consumption of manpower and resources through precise activator preparation, intelligent spraying methods, and real-time data monitoring and feedback.

[0036] Further, the unmanned aerial vehicle path planning sub-module specifically includes: A pollution map construction unit is configured to obtain 16 characteristic band reflectivity data in the range of 400-2500 nm of the pollution area through a multispectral imaging system, and establish a spectral fingerprint library for heavy metal pollution characteristics; Characteristic band screening: extract Cu 2+ At 1450 nm, Pb 2+ At 2200 nm, Cd 2+ Characteristic absorption valley depth value at 1900 nm; pollution index calculation: normalize and weight the characteristic absorption valley depth values of the three characteristic bands of each point to generate a three-dimensional pollution index matrix; spatial interpolation modeling: use an improved radial basis function interpolation method to expand the discrete point pollution index into a pollution concentration gradient distribution model with a resolution of 0.5 m; A hierarchical path planning strategy generation unit is configured to perform zoned and hierarchical path planning based on the pollution concentration gradient distribution model to obtain a preset path. Regional division: Based on the pollution index, the site is divided into a core area (>0.8), a transition area (0.5-0.8), and a marginal area (<0.5); trajectory parameter setting: the core area adopts a dense network scanning path with an altitude of 0.3m, a drone speed of 1m / s, and a 50% overlap rate; the transition area adopts a spiral progressive path with an altitude of 0.5m, a drone speed of 2m / s, and a 30% overlap rate; the marginal area adopts a boundary surrounding path with an altitude of 1m and a drone speed of 3m / s; spraying parameter preset: the core area is preset with a three-level spraying gradient (0.03→0.06→0.1 mol / kg); the transition area is set with a two-level gradient (0.05→0.1 mol / kg); and the marginal area adopts a single-level spraying (0.1 mol / kg). The dynamic path optimization mechanism unit is used to introduce real-time correction strategies based on the preset path; the spectral-humidity coupling correction synchronously obtains the soil moisture index (SMI) in the near-infrared band (1550nm) during flight. When the SMI>0.4: the drone's speed is triggered to increase by 20%, the atomized particle size is reduced to 20μm, and the current gradient concentration is reduced by 10%; abnormal area replanning uses a visible light camera (500-700nm) to identify surface compaction areas (reflectivity>65%), automatically generates an avoidance path and marks it as a secondary operation area; the energy optimal path adjustment combines the remaining power of the drone and the pollution distribution to dynamically compress the operating range of the edge area (maximum reduction of 30%).

[0037] Among them, the pollution index calculation formula is:

[0038] Where, represents the pollution index, Indicates the i The weight of the characteristic band, Indicates the i The characteristic absorption valley depth value of each characteristic band, and are the minimum and maximum values ​​of all characteristic absorption valley depths, respectively; Spatial interpolation modeling formula (improved radial basis function interpolation method):

[0039] Where, Indicates at point The pollution concentration, Indicates the j The pollution concentration at each sample point, is the width parameter of the radial basis function, It is j The coordinates of the sample points; Formula for generating hierarchical path planning strategy:

[0040] wherein, represents the final path planning score, represents the weight of the kth k class region, represents the pollution index of the kth class region; Dynamic path optimization mechanism formula (spectrum-humidity coupling correction):

[0041] wherein, represents the corrected UAV speed, V is the original UAV speed, is the soil humidity index; Abnormal region re-planning formula:

[0042] wherein, represents the reflectivity deviation of the surface hardening region, is the reflectivity of the nth m point, is the average value of the reflectivity of all points, is the total number of points; Energy optimal path adjustment formula:

[0043] wherein, represents the energy consumption of the adjusted edge zone operation range, is the original energy consumption, is the edge zone pollution concentration, is the total pollution concentration, is an adjustment coefficient. These formulas combine the various parts of the path planning submodule; the pollution index calculation formula is to comprehensively consider the contribution of multiple characteristic bands to pollution, and the importance of different characteristic bands in pollution judgment is reflected through the introduction of weights. The absorption valley depth value reflects the absorption characteristics of pollutants at a specific band, and through this formula, a pollution index can be obtained to measure the overall pollution situation. The spatial interpolation modeling formula (improved radial basis function interpolation method) is an interpolation method that predicts the pollution concentration of unknown points by weighting the pollution concentration and distance of sample points; the improved radial basis function interpolation method considers the width parameter of the radial basis function of the sample points, making the interpolation result more flexible and accurate. The hierarchical path planning strategy generation formula is used to consider the influence of different types of areas on path selection in path planning. Through the weights and pollution index of different areas, a comprehensive score is generated to guide path planning to avoid high-pollution areas. The dynamic path optimization mechanism formula (spectrum-humidity coupling correction) adjusts the speed of the unmanned aerial vehicle dynamically by introducing the soil moisture index (SMI) to adapt to different operating environments and maintain operating efficiency and equipment safety. The abnormal area re-planning formula calculates the deviation of reflectivity to identify abnormal areas. This formula helps identify areas that may need to be re-planned by calculating the deviation of reflectivity. The energy-optimal path adjustment formula considers energy consumption in path planning. This formula adjusts energy consumption by considering the relationship between the edge area and the total pollution concentration to optimize the operating path and reduce unnecessary energy waste. The theoretical basis of the formulas mainly comes from environmental science, geographic information systems (GIS), path planning and optimization theory, etc. In practical applications, it can help better understand environmental conditions, optimize resource allocation and improve operating efficiency.

[0044] Preferably, the multispectral feature absorption valley depth value of the embodiment directly determines the threshold setting of the pollution zoning, forming the basis criterion for hierarchical planning; the spatial resolution (0.5 m) of the radial basis interpolation model matches the minimum hovering accuracy (0.3 m) of the unmanned aerial vehicle, forming a data-execution matching relationship; the preset spraying gradient series strictly corresponds to the pollution zoning, ensuring that the core area adopts a gradual activation strategy; near-infrared band humidity monitoring data and piezoelectric atomization particle size control form a closed-loop feedback to prevent soil oversaturation; visible light surface hardening identification results trigger secondary operation markers, forming a data chain with the repair evaluation of the intelligent decision system. Through the technical chain of spectral feature analysis, spatial pollution modeling, hierarchical path generation, and dynamic parameter optimization, the traditional wide-area spraying mode is upgraded to a "pollution intensity-soil state-energy constraint" multi-dimensional adaptive precision activation system, breaking through the difficulty of uniformity control of chemical activators in heterogeneous pollution fields.

[0045] Further, the adsorption material and mechanism of the targeted adsorption device module 3 are shown in Table 1: Table 1

[0046] Regeneration mechanism: acid washing regeneration: Circulating washing, regeneration efficiency > 90%.

[0047] Electrochemical desorption: apply -1.5 V voltage to recover high-purity heavy metal solution.

[0048] Preferably, the nano-magnesium oxide (nMgO) of the present embodiment is surface-coordinated, meaning that the nMgO is adsorbed by forming a coordination bond with heavy metal ions through its surface, and the adsorption capacity for cadmium (Cd ) is as high as 2498 mg / g, indicating that it has strong adsorption capacity and can effectively adsorb cadmium and lead, which is crucial for reducing the impact of these heavy metals on the environment. The nMgO-loaded biochar combines the pore adsorption capacity of biochar and the chemical fixation effect of nMgO, and the adsorption capacity for lead (Pb 2+ ) is 1246 mg / g, also showing strong adsorption performance, which can handle lead and copper, and is particularly important for industrial wastewater treatment. The resource iron sludge adsorbent converts arsenic (As 3+ ) into a more stable As 5+ form through the redox effect of Fe-O groups, and the adsorption capacity for As 3+ is 680 mg / g, which can handle chromium (Cr 6+ ) in addition to arsenic, which is particularly important for wastewater discharged by the electronics and chemical industries. The cation exchange resin (D001) uses sulfonic acid groups for ion exchange, and the adsorption capacity for copper (Cu 2+ ) is 300 mg / g, which can handle zinc (Zn 2+ ) in addition to copper, which helps to recover these metals from various industrial emissions.

[0049] In summary, the adsorption materials and technologies of the present embodiment are of great significance for treating and purifying wastewater containing heavy metals, and can effectively remove harmful heavy metals and reduce the threat to the environment and human health.

[0050] Further, as shown in Figure 4 , the automatic monitoring system module 4 specifically includes: A stereoscopic monitoring network construction submodule is used to deploy a three-dimensional monitoring node array based on the honeycomb electrode layout of the electrode system module and the pollution partition data of the activator module. Vertical stratification: intelligent sensing units are implanted at 0-20 cm (shallow layer), 20-50 cm (middle layer), and 50-100 cm (deep layer), respectively. Horizontal matching: each hexagonal electrode unit center point is arranged with a monitoring node, and a 1:3 spatial ratio is formed with the electrode spacing; Sensor group configuration: multispectral sensor array: covering 400-2500nm waveband, synchronously collecting soil reflectance spectrum; three-dimensional electric field sensor: measuring X / Y / Z axial field intensity distribution, accuracy ±0.5V / m; ion selective electrode: detecting 、 Concentration gradient; microfluidic chip: real-time analysis of pore water pH value (±0.1), Eh value (±5mV); Dynamic parameter acquisition and feature extraction submodule, for starting adaptive sampling protocol and executing multi-modal data capture; Electric field synchronous acquisition: complete field intensity transient capture in the voltage switching gap (<10ms) of the electrode system module 1, and eliminate electromagnetic interference; Spectrum-chemical linkage: when the multispectral sensor detects a reflectance mutation at 2200nm waveband, trigger the ion electrode to perform continuous 3 times sampling verification; Pore water dynamic modeling: based on the microfluidic chip flow rate (0.5μL / s), construct a soil pore network model, and inverse the heavy metal migration path; Energy-aware sampling: according to the power supply curve of the photovoltaic power module 6, start the low-power mode (sampling frequency is reduced to 40%) in the power valley period (irradiance <200W / m 2 ); Data fusion and anomaly processing submodule, for realizing data quality optimization through spatiotemporal association rules: Spatial interpolation verification: use the electric field distribution data of the electrode system module 1 to verify the spatial continuity of the ion concentration of adjacent monitoring nodes (deviation >15% triggers recheck); Time series analysis: establish a dynamic response model of the spraying time stamp of the activator module 2 and the heavy metal concentration change, and eliminate lag abnormal values; Preferably, the multi-source calibration mechanism in the embodiment starts the pore water heavy metal speciation analysis of the microfluidic chip when the difference between the spectrum inversion heavy metal total amount and the electrode zone adsorption amount is >20%; when the pH value mutation (Δ>0.5) is detected, the Nernst equation parameters of the ion electrode are automatically corrected; when the deviation between the measured value of the electric field intensity and the COMSOL simulation value is >10%, the spacing compensation mechanism of the electrode system module 1 is triggered. The honeycomb electrode layout directly determines the spatial distribution density of the monitoring nodes, forms a synergistic acquisition grid of the electric field-chemical parameters, and the 2200nm characteristic waveband of the multispectral sensor and the Pb 2+The recognition algorithm shares the spectral database; the heavy metal migration path prediction result of the pore network model is fed back to the enrichment strategy of the targeted adsorption device module 3 in real time; the dynamic adjustment of the photovoltaic power supply curve and the sampling frequency ensures operation under the energy consumption optimization framework of the intelligent decision-making system module 5; the triggering threshold (10%) of the electrode spacing compensation mechanism is derived from the coefficient of variation optimization target in the early electric field verification stage In summary, through the technical path of spatial topology adaptation, multi-modal synchronous acquisition, and cross-module data verification, the embodiment constructs an environmental perception network coupled with physical fields, chemical fields, and energy fields, breaking through the spatial and temporal resolution limitations of traditional single-point monitoring in dynamic repair scenarios, and providing a four-dimensional (spatial three-dimensional + time dimension) environmental parameter base with millimeter-level precision for intelligent decision-making.

[0051] The in-situ sensor array of the embodiment is shown in Table 2: Table 2

[0052] Data transmission: uploaded to the cloud through the LoRa wireless network, with a sampling interval of 1-5 minutes adjustable.

[0053] Monitoring parameters: pH, conductivity, temperature, humidity, nitrogen, phosphorus, potassium, voltage, and current.

[0054] Further, the intelligent decision-making system module 5 specifically includes: The pre-decision module is used to input soil parameters (conductivity, pH, clay content, and initial pollutant concentration) to construct a multi-physical field model (electrokinetics + chemical transport + fluid mechanics); simulate the spatiotemporal distribution of heavy metals, predict the relationship between cadmium removal rate and time, and determine the optimal voltage, electrode spacing, and activator dosage. The dynamic process control module is used to input monitoring data into the multi-physical field model, dynamically update boundary conditions, and predict pollutant concentration fields in the next hour; adjust the voltage according to the current density and switch between hexagonal and 2:1 layout modes; when the pH rises to >5.0, the unmanned aerial vehicle increases the injection of citric acid; if the COMSOL predicts that the cathode area concentration >500 mg / kg, activate nMgO adsorbent material in advance; multi-objective optimization generates dynamic instructions; The post-evaluation module is used to simulate the electric field distribution, current density, and residual pollutant concentration after repair, generate a three-dimensional concentration field cloud map, calculate the actual removal rate, and analyze the causes of deviation. A mode switching module is configured to receive a voltage adjustment instruction through a Modbus-TCP protocol, with a response time of less than 1 second; an unmanned aerial vehicle spraying is triggered through an MQTT protocol, and a chelating agent ratio is dynamically adjusted according to LIBS data; when a sudden increase of 20% in the conductivity of a cathode region is monitored, an adsorption material is started and a regeneration instruction is sent; and a photovoltaic panel series-parallel mode is dynamically switched according to an energy consumption curve predicted by COMSOL.

[0055] Preferably, according to the soil parameters, the pre-decision module can construct a multi-physical field model to predict the relationship between the cadmium removal rate and the time, and determine the key parameters such as the optimal voltage, the electrode spacing, and the amount of activator. This helps to quickly develop a scientific remediation plan and improve the decision-making efficiency. The dynamic process control module can monitor the data in real time, dynamically update the boundary conditions, and predict the changes in the pollutant concentration. At the same time, the voltage is adjusted according to the actual situation, the layout mode is switched, and citric acid is put in, so as to realize the real-time dynamic regulation and control of the remediation process. Through the post-evaluation module, the electric field distribution, the current density, and the residual concentration of pollutants after remediation can be simulated, so as to objectively evaluate the remediation effect, analyze the deviation reasons, provide a basis for subsequent remediation plan adjustment, and optimize the resource utilization. The mode switching module realizes the quick response of the voltage adjustment, the unmanned aerial vehicle spraying, and the starting of the adsorption material through the Modbus-TCP, MQTT, and other protocols, and enhances the system collaborative response capability. Through the cooperation of the modules, the system can dynamically adjust the remediation plan, realize the accurate control of the pollutant concentration field, and improve the remediation effect and efficiency.

[0056] In the present embodiment: (1) Pre-decision: COMSOL modeling: input soil parameters (conductivity, pH, clay content, initial pollutant concentration), construct multi-physical field model (electrokinetics + chemical transport + fluid mechanics). Simulate the spatial and temporal distribution of heavy metals, and predict the relationship between the cadmium removal rate and the time (e.g., the removal rate is greater than or equal to 80% in 48 hours of remediation time).

[0057] Parameter optimization: determine the optimal voltage, the optimal electrode spacing, and the optimal amount of activator through the NSGA-III algorithm. Energy consumption estimation: electrical energy consumption Material loss (electrode life > 5000 hours, activator utilization rate > 90%).

[0058] (2) Dynamic process control: Real-time feedback regulation: monitor the data input into the COMSOL model, dynamically update the boundary conditions, and predict the pollutant concentration field in the next 1 hour (e.g., Adjustment is triggered when the migration rate drops by 10%. Electrode control: adjusts the voltage according to the current density and switches the hexagonal / 2:1 layout mode. Activator linkage: when the pH rises back to >5.0, the drone increases the activator. Adsorption device trigger: if COMSOL predicts the cathode area When concentration increases significantly, with conductivity suddenly increasing by 20%, the targeted adsorption device module is activated, deploying targeted adsorption material in the area of ​​the sudden conductivity increase. Intelligent correction: An LSTM neural network predicts the activator's diffusion efficiency with an error of less than 5%. Multi-objective optimization (remediation efficiency vs. energy consumption) generates dynamic instructions (such as reducing voltage by 10% to save energy).

[0059] (3) Post-evaluation: COMSOL validation: Simulate the residual concentration of pollutants after remediation and generate a 3D concentration field cloud map. Calculate the actual removal rate (e.g., 82.5% vs. predicted 80%) and analyze the causes of deviations (e.g., soil heterogeneity).

[0060] Economic analysis: energy consumption statistics, material loss, and repair costs.

[0061] Optimization suggestions: Adjust the electrode spacing to 2.0 m to reduce energy consumption, or increase the drone spraying frequency to improve the utilization rate of the activator.

[0062] (4) Module coordination mechanism Electrode system: Receives voltage adjustment commands via the Modbus-TCP protocol, with a response time of less than 1 second. Activator module: MQTT protocol triggers drone spraying, dynamically adjusting the chelating agent ratio based on LIBS data. Targeted adsorption device: When a sudden 20% increase in conductivity in the cathode region is detected, the targeted adsorption device module is activated, and targeted adsorption material is deployed in the area of ​​sudden conductivity increase. Photovoltaic power supply: Based on the energy consumption curve predicted by COMSOL, the series and parallel modes of the photovoltaic panels are dynamically switched. When the system needs to boost the voltage, the photovoltaic panels are connected in series to drive the high-voltage electric field. When the system needs to increase the current, the photovoltaic panels are connected in parallel to power high-power equipment.

[0063] In summary, the combined use of the various modules of the intelligent decision-making system module 5 of this embodiment can improve decision-making efficiency and remediation effects, realize real-time dynamic regulation and optimization of the contaminated soil remediation process, and has important practical application value.

[0064] Furthermore, the pre-decision module specifically includes: The multi-source data fusion and model initialization submodule is used to build an adaptive computing network based on the three-dimensional soil parameter matrix (conductivity / pH / clay content / heavy metal concentration) provided by the automatic monitoring system module 4 and the honeycomb layout data / 2:1 paired layout data of the electrode system module 1; Spatial discretization strategy: Based on the hexagonal electrode unit, the contaminated area is divided into 0.5m x 0.5m x 0.2m (length x width x depth) voxel units; Initial condition loading: Electrodynamic field, import COMSOL verified electric field distribution data of electrode system module 1; Chemical transport field, map multispectral contaminant zonation gradient model of activator module 2; Fluid mechanics field, combine with measured values of pore water velocity of monitoring system module 4 to construct Darcy's law parameter matrix; Multi-field coupling solution and dynamic iteration sub-module, used to realize heavy metal migration prediction through three-field collaborative solution engine of electric field-chemical field-flow field; Electric-chemical coupling: Establish mobility correction equation, associate electric field intensity gradient (ΔE) with activator concentration gradient (ΔC), introduce migration rate correction factor (α = 0.8ΔE + 0.2ΔC); Chemical-flow coupling: Based on pore water flow data, superimpose convection-diffusion double effect term in chemical transport equation, set solute transport weight coefficient β = 1-e^(-0.03t); Cross-scale iteration: coarse grid calculation (1m resolution) determines the global cadmium ion migration main path; Encrypt the grid along the main path to 0.1m resolution to accurately capture the concentration front movement; Feedback the encrypted area results to the global model for boundary condition update; Parameter sensitivity analysis and optimization sub-module, used to execute three-stage optimization strategy to determine the best operating parameters; Primary screening: Based on historical spraying data of activator module 2, establish three-dimensional parameter space of voltage (5-15V), electrode spacing (0.3-1.2m), and activator dosage (0.05-0.15mol / kg); Orthogonal test design: Use 9-group L9 orthogonal test scheme, take cadmium removal rate (η), energy consumption (P), and time (T) as evaluation indexes, calculate comprehensive weight value ω = 0.6η- 0.3P -0.1T; Dynamic parameter adjustment: Select initial optimal parameter group (voltage / electrode spacing / activator dosage); When the current density deviates from the predicted value by ±15%, trigger voltage-spacing collaborative adjustment (ΔV = ±1V corresponds to ΔL = ∓0.1m); According to the change of cadmium concentration within 6 hours after unmanned aerial vehicle spraying, dynamically correct the activator gradient injection curve (steepness coefficient k = 0.05ΔC / h).

[0065] Preferably, the multispectral pollution zoning gradient model of the embodiment provides an initial concentration distribution for the chemical transport field, forms a data closed loop with the spraying logic of the activator module 2; the measured value of the pore water velocity is used as an input parameter of the hydrodynamic field, and establishes a dynamic correlation with the enrichment efficiency of the targeted adsorption device module 3; the composition ratio (6:3:1) of the weight coefficient ω of the orthogonal test design is derived from the energy efficiency ratio constraint condition of the photovoltaic power module 6; the current density threshold value (±15%) in the dynamic parameter adjustment stage is inherited from the polarization inhibition experimental data of the electrode system module 1; through the technical chain of data fusion, field coupling solution, and parameter optimization, the discrete physical process is converted into a quantifiable digital twin system, realizing the decision-making mode leap from "experience-driven" to "model prediction-real-time correction", and breaking through the bottleneck of the disconnection between parameter setting and field conditions of traditional repair systems.

[0066] Further, the mode switching module specifically includes: The driving chelation decision chain submodule is used to establish a LIBS element abundance matrix, generate a spatial affinity map through a migration state heavy metal clustering algorithm, couple a real-time soil moisture content field, output a gradient chelator demand vector, and trigger a UAV fractal spraying through an MQTT instruction. The spraying density is positively correlated with the affinity map; The conductance mutation response chain submodule is used to start ion mobility spectrum traceability analysis when the cathode zone conductivity increment exceeds the threshold value, and activate a double-channel response if the identified carrier is cadmium ions. The nMgO adsorption array is deployed to the electric flux hotspot area, and a regenerative pulse sequence is generated on the electrode surface. The frequency is matched with the conductivity increment gradient; The energy consumption prediction-energy source adaptation chain submodule is used to extract third-order differential features through a non-steady-state convolutional network based on the COMSOL transient energy consumption surface, dynamically divide the energy consumption domain, maintain the initial topology of the photovoltaic module string in the base load area, trigger dynamic recombination of the battery pieces in the fluctuation area, and activate phase compensation of the super capacitor array in the peak area.

[0067] Preferably, the UAV spraying of the embodiment no longer relies on fixed coordinates, but uses the affinity map generated by the LIBS features as the basis for dynamic path planning, realizing adaptive matching of repair resources and pollution microdomains. The conductivity mutation trigger mechanism introduces an ion mobility spectrum analysis layer to distinguish between background electrolyte interference and real pollution migration, avoiding false triggering of traditional threshold methods. The photovoltaic topology switching is based on the differential features of the energy consumption surface rather than absolute values, and realizes the phase space alignment of repair energy consumption and renewable energy fluctuations by identifying the transient field distortion points output by COMSOL. By constructing a functional mapping relationship between pollution characteristics, repair response, and energy supply, the original discrete instructions are upgraded to an autonomous decision flow with spatiotemporal prediction capabilities, and all technical elements are naturally derived within the existing framework of the system.

[0068] Further, the driving chelation decision chain submodule specifically includes: The multi-spectral oscillation characteristic decoupling system is used to obtain the original LIBS plasma oscillation sequence, separate the cadmium characteristic frequency band through tensor frequency domain decomposition, extract the oscillation attenuation slope of each sampling point, and generate the heavy metal migration activity matrix. The attenuation slope is negatively correlated with the ion dissociation energy; The clay constraint field construction system is used to obtain the clay content parameters of the pre-decision module, convert them into a spatial charge distribution surface through the dielectric relaxation model, perform convolution operation with the real-time soil moisture field, and output the ion migration retardation coefficient field; Migration state topology reconstruction system, used to obtain heavy metal migration activity matrix and ion migration retardation coefficient field, generate migration potential energy isosurface through non-Euclidean manifold learning, perform Riemannian geometry decomposition on potential well depth, and mark high migration risk areas; Affinity map emergence system is used to migrate the second-order derivative of the potential energy isosurface in the direction of the electrode gradient, smoothed by the anisotropic diffusion equation, and superimposed on the cathode region output by the electrodynamic model Concentration prediction values ​​are used to generate spatial affinity maps.

[0069] Preferably, this embodiment uses the plasma oscillation attenuation characteristics to replace the element peak intensity, and characterizes the heavy metal dissociation state by the frequency band energy attenuation slope, thereby avoiding the matrix effect of traditional LIBS semi-quantitative analysis. The static clay parameters are dynamically converted into an ion migration retardation field, and a convolution operation is performed with the moisture field to generate a non-uniform retardation coefficient, breaking through the limitations of the simple weighted average in the prior art. The affinity spectrum intensity is determined by the migration potential energy gradient and the real-time concentration prediction value. When the dynamic control module warns of the cathode area Cd 2+ When the concentration increases sharply, the area is automatically enhanced in the map.

[0070] In summary, this embodiment transforms discrete LIBS data into a spatial decision-making basis with electrodynamic significance by constructing a cascade mapping of spectral oscillations, migration potential fields, and repair requirements. The generation of migration potential energy isosurfaces is directly controlled by the multi-physics model parameters, and the final form of the map dynamically responds to cathode area pollution warnings, forming a closed-loop logic chain that runs through pre-decision-making and dynamic control.

[0071] Furthermore, the affinity graph emergence system specifically includes: The potential field gradient enhancement subsystem is used to apply the second-order directional derivative operator along the electrode axis to the migration potential energy isosurface to generate the migration driving force tensor field. A positive value indicates a migration trend toward the anode, and a negative value indicates a concentration trend toward the cathode. Diffusion constraint generation subsystem, used for the high migration risk domain boundary of Riemannian geometry segmentation, extracting boundary curvature features and constructing anisotropic diffusion control fields; The field distortion correction subsystem is used to process the migration driving force tensor field and the anisotropic diffusion control field through the curvature constraint smoothing equation and output the corrected migration field; Concentration-dynamic fusion subsystem, for correcting the normal component of the migration field, with the cathode region output by the electrokinetic model Hyperbolic tangent coupling of concentration prediction values to generate a spatial affinity map.

[0072] Preferably, the second-order directional derivative of the embodiment along the electrode axis replaces the traditional gradient operator, highlighting the directional migration characteristics of the pollutant under the action of the electric field and eliminating background migration noise. The anisotropic diffusion coefficient is dynamically modulated by the curvature of the risk domain boundary, eliminating numerical oscillation while preserving key topological features, and breaking through the homogenization limitations of conventional Gaussian smoothing. The hyperbolic tangent function maps the concentration prediction value to the [0, 1] interval, producing a strength transition effect when the concentration approaches the warning threshold, and preferentially labeling high-risk areas.

[0073] In summary, the embodiment converts abstract migration potential energy into a spatial map with clear repair guidance significance through three-level processing of potential field differentiation, geometric constraints, and concentration fusion. The corrected migration field inherits the potential well subdivision algorithm from the previous sequence, while the final hyperbolic coupling mechanism dynamically responds to the cathode region pollution warning, making the map a key intelligent interface connecting the electrokinetic model and the UAV spraying execution. The strength function of the map implicitly contains three decision dimensions: the gradient direction of the electrode, the geometric characteristics of the risk domain, and real-time concentration warning, enabling precise spatial matching of repair resources.

[0074] Further, the electrical conductivity mutation response chain submodule specifically includes: Incremental gradient tensorization system, for extracting its spatiotemporal gradient vector field in the electrical conductivity quantity super-threshold region; decomposed into migration component and diffusion component through curl-div field separation, preserving the cadmium ion migration flux tensor; Hot spot field strength mapping system, for deploying nMgO adsorption array in the electric flux hot spot region to obtain real-time current density values, and performing bilinear tensor product with the cadmium ion migration flux tensor to generate a pulse intensity basis function; Frequency field reconstruction system, for calculating the second-order variation of the migration flux along the electrode normal direction along the principal characteristic direction of the cadmium ion migration flux tensor, and converting it to a characteristic frequency spectrum through Legendre transformation; Spatiotemporal pulse synthesis system, for discretizing the pulse intensity basis function and the characteristic frequency spectrum through the non-uniform sampling theorem, outputting a regenerative pulse sequence, with the pulse width positively correlated with the eigenvalue of the migration flux tensor and the phase synchronized with the equipotential surface of the current density field.

[0075] Preferably, the conductivity increment gradient of the present embodiment is decomposed into a migration / diffusion component, only the directional migration characteristics represented by the curl field are retained, and the background electrolyte interference leading to pulse false triggering is avoided. The tensor product of current density and migration flux is used to generate the pulse intensity function, so that the pulse energy accurately matches the material-energy coupling state of the electric flux hot spot area. The eigenfrequency is derived by using the Legendre transform of the second variation of the migration flux, which converts the rate of change of physical quantities into time domain control parameters, breaking through the linear limitations of traditional PID frequency modulation. The pulse discretization process follows the eigenvector direction of the migration flux tensor, realizing the adaptive distribution of the pulse sequence in the four-dimensional space of time and space.

[0076] In summary, the phase synchronization signal of the pulse sequence of the present embodiment is taken from the photovoltaic panel switching timing of the energy consumption prediction-energy adaptation chain sub-module, ensuring that the pulse application time is aligned with the output peak of the energy system; the pulse width parameter is modulated by the spatial affinity map of the driven chelation decision chain sub-module, and the action time is automatically prolonged in the high affinity area; when the ion mobility spectrum traceability analysis detects multiple metal carriers, the pulse sequence is automatically frequency divided into a cadmium exclusive band; the present embodiment converts the conductivity increment gradient into a regenerative control signal with spatio-temporal adaptability through four-level conversion of flux decomposition, field strength mapping, frequency reconstruction, and pulse synthesis; the pulse intensity basis function is directly related to the deployment position of the nMgO adsorption array, and the characteristic frequency spectrum dynamically responds to the rate of change of the migration flux, and the finally output pulse sequence becomes an intelligent bridge connecting conductivity anomaly detection and electrode regeneration.

[0077] Further, the hotspot field strength mapping system specifically comprises: A current-matter field dimension reduction subsystem is configured to project the real-time current density field of the electric flux hot spot area along the normal direction of the electrode surface to generate a current density principal eigenvector; and a cadmium ion migration flux tensor is configured to extract a matter transport principal axis quantity through Riemann contraction operation; A double-field conjugate base construction subsystem is configured to perform orthogonal decomposition on the current density principal eigenvector and the matter transport principal axis quantity to form a field coupling frame system; and a conjugate weight coefficient matrix is generated by the included angle between the base vectors of the field coupling frame system and the tangent direction of the electrode surface; A tensor product energy flow mapping subsystem is configured to perform Kronecker product operation on the component module length of the current density field in the frame system and the eigenvalue of the migration flux tensor to output a transient energy flow density tensor, and the ion dissociation energy input efficiency of a unit adsorption site; A basis function emergence subsystem is configured to integrate the transient energy flow density tensor and the conjugate weight coefficient matrix in the electrochemical barrier direction to generate a pulse intensity basis function.

[0078] Preferably, the embodiment compresses the flux tensor into the principal axis of matter transport, preserves the key dimension coupled with the direction of current density field, and avoids resource consumption of full tensor operation; through orthogonal decomposition of current-matter eigenvectors to establish a dynamic reference frame, the subsequent operation automatically adapts to the curvature change of the electrode surface, breaking the rigid limitations of Cartesian coordinates. The Kronecker product operation generates a new type of physical quantity transient energy flow density tensor, quantifying the conversion efficiency of electrical energy input to ion migration at adsorption sites, providing a first-principle basis for pulse intensity. The path integral along the electrochemical potential barrier gradient converts the scalar field into a basis function, and its integral result is directly related to the cadmium removal rate prediction curve of the dynamic control module.

[0079] In summary, the phase synchronization signal of the pulse intensity basis function in the embodiment is taken from the supercapacitor charge-discharge curve of the energy consumption prediction sub-module, ensuring that the pulse application time is aligned with the peak release of the energy storage system; the basis function amplitude is modulated by the high migration risk domain density of the migration state topology reconstruction system, and automatically gains in the pollution accumulation area; when the affinity atlas emergence system detects local chelator saturation, the basis function triggers a self-attenuation mechanism. This process converts the physical coupling of current density and migration flux into the core intensity function of regenerative control through four-level conversion of field dimension reduction, frame construction, energy flow mapping, and barrier integration; the conjugate weight coefficient matrix inherits the geometric constraints of the electrode layout mode, and the transient energy flow density tensor integrates real-time energy input and matter transport state, and finally generates a basis function that becomes the quantitative control hub connecting the conductivity anomaly detection and electrode regeneration. The function output value directly drives the discretization process of the spatiotemporal pulse synthesis system, forming a closed-loop regenerative control flow.

[0080] Further, the energy consumption prediction-energy adaptation chain sub-module specifically includes: An energy consumption domain boundary generation component for obtaining the third-order differential characteristics of the COMSOL transient energy consumption surface, dividing the base carrier region, the fluctuation region, and the peak region through the Riemann manifold segmentation algorithm, and outputting the energy consumption phase space partition topology; A carrier path reconstruction component for performing dual space mapping of the fluctuation region energy consumption gradient direction field and the photovoltaic array lattice vector to generate the optimal carrier transport path, triggering the dynamic reorganization of the battery along the path direction, and forming a non-Euclidean star-shaped network; A capacitive phase modulation component for performing symplectic geometry matching of the Fourier residual spectrum of the peak region energy consumption pulse and the relaxation time spectrum of the supercapacitor array to obtain the capacitive compensation phase angle, activating the capacitive unit to conduct according to the phase angle sequence, and establishing a virtual work compensation flow; An impedance continuum maintenance component for obtaining the initial topology of the base carrier region and the electrode spacing parameters of the prior decision module, deriving the system characteristic impedance through the Maxwell-Ampere law, and maintaining the parallel-parallel structure of the photovoltaic module string.

[0081] Preferably, the embodiment based on manifold segmentation of third-order differential characteristics breaks through the traditional threshold method, enabling the energy partition boundary to evolve dynamically with the repair process; the gradient field of the fluctuation zone is converted into dual space with the lattice vector, realizing the differential homeomorphism of the carrier path and energy change; the energy pulse spectrum and the capacitance relaxation spectrum are aligned using the symplectic geometry algorithm to realize nanosecond-level phase compensation; the system impedance constancy is dynamically maintained by the electrode spacing parameter to eliminate the interference of energy fluctuations on the electrodynamic repair field.

[0082] In summary, the battery piece reorganization trigger signal in the embodiment is taken from the UAV operation gap of the driving chelation decision chain to avoid spraying electromagnetic interference; the capacitance compensation phase angle is synchronized with the valley period of the electrode regeneration pulse sequence to form an energy recovery loop; the base carrier area impedance matching degree is fed back to the dynamic process control module in real time as a constraint condition for the voltage adjustment strategy; through the four-level linkage of energy partition, path reconstruction, phase modulation, and impedance maintenance, the abstract energy characteristics are converted into an adaptive control strategy for energy hardware. The carrier transport path inherits the differential characteristics of the fluctuation zone, the capacitive compensation responds to the spectral characteristics of the peak area, and the base carrier area maintains the core parameters of the prior decision, forming an energy intelligent adaptation system throughout the entire pollution repair cycle.

[0083] Further, the dynamic process control module specifically includes: A multi-dimensional parameter space construction submodule is configured to establish a four-dimensional optimization space based on real-time monitoring data flow of the dynamic process control module; Decision dimensions: voltage gradient / electrode spacing, electrode mode (hexagonal / 2:1), activator dosage, and targeted adsorption device layout position; Constraint boundaries: electrode spacing adjustment range is limited by the mechanical displacement mechanism stroke of the electrode system module 1; the maximum additional amount of activator is constrained by the UAV liquid capacity and the atomization efficiency of the piezoelectric atomization nozzle adjustment submodule; The instantaneous output power of the photovoltaic power module 6 determines the upper limit of voltage adjustment; A dynamic weight distribution and target game submodule is configured to adjust the priority of the optimization target according to the characteristics of the repair stage: in the initial stage (0-12h), the repair efficiency weight is increased to 70% (current density In the middle stage (12-48h), a balance coefficient (efficiency: energy: time = 5:3:2) is introduced to activate the electrode mode switching capability; in the later stage (>48h), the time weight is increased to 40%, triggering the adsorption device layout position strategy (when the predicted concentration is greater than 80% threshold, the layout is started); A cross-module collaborative instruction generation submodule is configured to realize system linkage through a three-level instruction system; Electric field reconstruction instruction: according to the COMSOL predicted heavy metal enrichment rate in the cathode zone, the voltage is increased by 0.1V / min gradient; The chemical regulation instruction sub-module is used for activating the unmanned aerial vehicle path planning sub-module of the activator module 2 when the pH monitoring value exceeds 5.0, and adding the spraying amount = basic amount × (pH-4.8) / 0.2; and triggering the matching correction (+15% citric acid proportion) of the mode switching module when the activator active ingredient attenuation is greater than 30%. The energy adaptation instruction sub-module is used for adjusting the series-parallel combination mode of the photovoltaic power module 6 in advance by 300 seconds according to the future one-hour energy consumption curve calculated by the multi-physical field model; and automatically distributing 30% photovoltaic power to the electric energy supply sub-module of the electrode system module 1 during the execution of the electric field reconstruction instruction.

[0084] Preferably, the electrode mechanical displacement stroke limitation of the embodiment directly restricts the electrode spacing adjustment range of the parameter space, ensuring the instruction executability; the unmanned aerial vehicle liquid capacity data is derived from the post-evaluation record of the activator module 2, determining the maximum spraying increment of the chemical regulation instruction; the current density fluctuation threshold is inherited from the polarization suppression experimental parameter of the electrode system module 1; the activator attenuation correction coefficient shares the detection benchmark with the material saturation monitoring data of the targeted adsorption device module 3; and the photovoltaic series-parallel switching timing is derived from the energy consumption prediction model of the pre-decision module of the intelligent decision system module 5. Through the technical path of constraint modeling, target game and instruction linkage, the discrete equipment control instructions are converted into the time and space related repair strategy group, forming a closed-loop optimization system of "parameter perception-target trade-off-cross-domain control", breaking through the collaborative control bottleneck of traditional single-target optimization in complex repair scenarios.

[0085] As shown in Figure 5 The embodiment also provides an embodiment of a heavy metal contaminated soil remediation method based on intelligent management and control. In the embodiment, the heavy metal contaminated soil remediation method based on intelligent management and control is applied to the heavy metal contaminated soil remediation system based on intelligent management and control in the above embodiment, and specifically includes the following steps. Step S1: The electrode system module is started to drive the heavy metal ions in the soil to migrate directionally by the electric field force; at the same time of the electric field driving, the activator module starts to work, and the activator is injected into the soil to activate the heavy metal in the soil, so that the heavy metal is desorbed from the soil particles; Step S2: The activated heavy metal ions are captured by the adsorption material in the targeted adsorption device module and the selective enrichment device, so that the heavy metal ions are effectively adsorbed; at the same time, the automatic monitoring system module collects the environmental parameters in real time, and the environmental parameters support the intelligent decision system module to make real-time monitoring and intelligent correction decisions; Step S3: The intelligent decision system module optimizes the remediation efficiency and energy consumption according to the real-time data, so as to realize the remediation of the heavy metal contaminated soil; and the photovoltaic power module provides stable power guarantee.

[0086] The specific implementation of the embodiment is as follows: 1. Site arrangement: An electrode matrix (10 m x 10 m) is arranged in a Cd-contaminated farmland (total cadmium close to or exceeding the control value (1.5 mg / kg)), and a sensor node (interval 2 m x 2 m) is buried; 2. System start: when the output power of the photovoltaic group is greater than or equal to 3 kW, the intelligent decision-making system is initialized and the preset repair target (Cd removal rate > 90%, repair period < 7d) is loaded; 3. Dynamic repair: the decision-making system adjusts the electric field parameters (such as switching from 50 V / m pulse to 100 V / m direct current) according to real-time data, and synchronously adjusts the injection rate of activator (0.5-0.8 L / min); 4. Effect verification: after 7 days of repair, the Cd removal rate in moderately and severely contaminated soil is > 90%, and the comprehensive energy consumption of the system is

[0087] Preferably, each step of the embodiment is realized by a series of integrated and automated technical means, achieving efficient and precise repair of soil heavy metal pollution. Specifically: the synergistic effect of electric field driving and activation, through electric field driving, heavy metal ions migrate directionally to the adsorption area, while the injection of green small molecule organic acid and other activators activates the heavy metals in the soil, improves the desorption efficiency, and makes the adsorption process more efficient. The application of selective enrichment device for adsorption material, the activated heavy metal ions are captured and effectively adsorbed by the device, realizing the precise selection and enrichment of target heavy metal ions, reducing the interference of non-target ions, and improving the targeting and efficiency of repair. Intelligent linkage of real-time monitoring and intelligent decision-making, the automatic monitoring system collects environmental parameters and feeds back the data to the intelligent decision-making system, optimizes the repair efficiency and energy consumption according to the real-time data, ensures the whole repair process to run in the best state, and reduces the demand for manual intervention and repair cost. The guarantee of stable energy supply, the photovoltaic power module provides stable electric energy for the whole system, ensuring the continuity and stability of the repair process, avoiding the decrease of repair efficiency caused by energy fluctuation.

[0088] In summary, the embodiment integrates electric field driving, chemical activation, targeted adsorption, intelligent monitoring and energy supply, etc. to build a closed-loop automatic soil repair system. Not only improves the efficiency and accuracy of soil heavy metal pollution repair, but also reduces energy consumption and manual intervention, providing an efficient, environmentally friendly and sustainable technical solution for soil repair field. Combining electric field driving, chemical activation, physical adsorption and intelligent decision-making, etc. to jointly act on the automatic repair process of heavy metal contaminated soil.

[0089] As Figure 6 shown, the embodiment provides an embodiment of an electronic device, in which the electronic device 7 includes a processor 71 and a memory 72 coupled to the processor 71.

[0090] The memory 72 stores program instructions for implementing the heavy metal contaminated soil remediation method based on intelligent management and control according to any of the above embodiments.

[0091] The processor 71 is used to execute program instructions stored in the memory 72 to perform heavy metal contaminated soil remediation based on intelligent management and control.

[0092] The processor 71 may also be referred to as a CPU (Central Processing Unit). The processor 71 may be an integrated circuit chip with signal processing capabilities. The processor 71 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The general-purpose processor may be a microprocessor or any conventional processor.

[0093] Further, Figure 7 This is a schematic diagram of the structure of a storage medium in an embodiment of the present application. The storage medium 8 in the embodiment of the present application stores program instructions 81 that can implement all of the above methods. The program instructions 81 can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program code, or a terminal device such as a computer, server, mobile phone, or tablet.

[0094] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0095] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings, or directly or indirectly applied to other related technical fields, is also included in the patent protection scope of the present application.

[0096] The specific embodiments of the application have been described above, but they are only examples, and the application is not limited to the above-described specific embodiments. Any equivalent modification or substitution made to the application by those skilled in the art is also within the scope of the application, and therefore, equivalent transformations and modifications, improvements, etc. made without departing from the spirit and principle range of the application should be included in the scope of the application.

Claims

1. A heavy metal contaminated soil remediation system based on intelligent management and control, characterized in that: The heavy metal contaminated soil remediation system based on intelligent management and control includes an electrode system module, an activator module, a targeted adsorption device module, an automatic monitoring system module, an intelligent decision-making system module, and a photovoltaic power supply module; Among them, the electrode system module is used to drive the directional migration of heavy metal ions through the electric field, optimize the electric field distribution and inhibit electrode polarization; the activator module activates heavy metals by gradient injection of activators; the targeted adsorption device module is used to use adsorption materials to selectively enrich heavy metal ions in the electrode area; the automatic monitoring system module collects multi-dimensional environmental parameters in real time to provide data support for the decision-making of the intelligent decision-making system module; the intelligent decision-making system module is used to integrate multiple functional modules, optimize the remediation efficiency and energy consumption through real-time monitoring and intelligent correction, and realize efficient remediation of soil heavy metal pollution; the photovoltaic power supply module is used to provide stable power for the electrode system module, activator module, targeted adsorption device module, automatic monitoring system module, and intelligent decision-making system module.

2. The heavy metal contaminated soil remediation system based on intelligent management and control according to claim 1 is characterized in that: Intelligent decision-making system modules include: The pre-decision module is used to input soil physical and chemical property parameters and build a multi-physics field model; simulate the spatiotemporal distribution of heavy metals, predict the cadmium removal rate versus time curve; and determine the voltage, electrode spacing, and activator dosage; The dynamic process control module is used to monitor data input into the multi-physics field model, dynamically update boundary conditions, and predict the pollutant concentration field in the next hour; adjust the voltage according to the current density and switch the hexagonal 2:1 layout mode; when the pH rises to >5.0, the drone will increase the activator; if the cathode area Cd is predicted to be 2+ For concentrations > 500 mg / kg, deploy and activate the targeted adsorption device module in advance; generate dynamic instructions through multi-objective optimization; Post-evaluation module, used to simulate the residual concentration of pollutants after remediation, generate a three-dimensional concentration field cloud map, calculate the actual removal rate, and analyze the causes of deviations; The mode switching module is used to receive voltage adjustment instructions through the protocol and dynamically switch the series and parallel modes of the photovoltaic panels according to the predicted energy consumption curve.

3. The heavy metal contaminated soil remediation system based on intelligent management and control according to claim 2 is characterized in that: Pre-decision-making module, including: The multi-source data fusion and model initialization submodule is used to build an adaptive computing network based on the three-dimensional soil physical and chemical property parameter matrix provided by the automatic monitoring system module; Multi-field coupling solution and dynamic iteration submodule, used to predict heavy metal migration through the three-field collaborative solution engine of electric field, chemical field and flow field; The parameter sensitivity analysis and optimization submodule is used to implement a three-stage optimization strategy to determine the optimal operating parameters.

4. The heavy metal contaminated soil remediation system based on intelligent management and control according to claim 2 is characterized in that: Mode switching module, including: Drive the chelation decision chain module to establish the LIBS element abundance matrix, generate a spatial affinity map using the mobile heavy metal clustering algorithm, couple it with the real-time soil moisture field, and output a gradient chelator demand vector. MQTT commands trigger drone fractal spraying, and the spraying density is positively correlated with the affinity map. The conductance mutation response chain module is used to initiate ion mobility spectrometry analysis when the conductivity increment in the cathode region exceeds the threshold. If a cadmium ion carrier is identified, a dual-channel response is activated. The nMgO adsorption array is deployed in a directionally controlled manner to the electric flux hotspot, generating a regenerative pulse sequence on the electrode surface with a frequency matching the conductivity increment gradient. The energy consumption prediction-energy adaptation chain module is used for COMSOL transient energy consumption surfaces. It extracts third-order differential features through a non-steady-state convolutional network and dynamically divides the energy consumption domain. The base load area maintains the initial topology of the photovoltaic group string and parallel connection, the fluctuation area triggers the dynamic reorganization of the solar cells within the group, and the peak area activates the phase compensation of the supercapacitor array.

5. The heavy metal contaminated soil remediation system based on intelligent management and control according to claim 4 is characterized in that: Drive the chelation decision chain module, including: The multi-spectral oscillation characteristic decoupling system is used to obtain the original LIBS plasma oscillation sequence, separate the cadmium characteristic frequency band through tensor frequency domain decomposition, extract the oscillation attenuation slope of each sampling point, and generate the heavy metal migration activity matrix. The attenuation slope is negatively correlated with the ion dissociation energy; The clay constraint field construction system is used to obtain the clay content parameters of the pre-decision module, convert them into a spatial charge distribution surface through the dielectric relaxation model, perform convolution operation with the real-time soil moisture field, and output the ion migration retardation coefficient field; Migration state topology reconstruction system, used to obtain heavy metal migration activity matrix and ion migration retardation coefficient field, generate migration potential energy isosurface through non-Euclidean manifold learning, perform Riemannian geometry decomposition on potential well depth, and mark high migration risk areas; Affinity map emergence system is used to migrate the second-order derivative of the potential energy isosurface in the direction of the electrode gradient, smoothed by the anisotropic diffusion equation, and superimposed on the cathode region output by the electrodynamic model Concentration prediction values ​​are used to generate spatial affinity maps.

6. The heavy metal contaminated soil remediation system based on intelligent management and control according to claim 5 is characterized in that: Affinity graph emergence system, including: The potential field gradient enhancement subsystem is used to apply the second-order directional derivative operator along the electrode axis to the migration potential energy isosurface to generate the migration driving force tensor field. A positive value indicates a migration trend toward the anode, and a negative value indicates a concentration trend toward the cathode. Diffusion constraint generation subsystem, used for the boundaries of high migration risk domains in Riemannian geometry, extracting boundary curvature features and constructing anisotropic diffusion control fields; The field distortion correction subsystem is used to process the migration driving force tensor field and the anisotropic diffusion control field through the curvature constraint smoothing equation and output the corrected migration field; Concentration-dynamic fusion subsystem, used to correct the normal component of the migration field and the cathode region output by the electrodynamic model The concentration predictions were hyperbolic tangent coupled to generate a spatial affinity map.

7. The heavy metal contaminated soil remediation system based on intelligent management and control according to claim 6 is characterized in that: Conductance mutation response chain submodule, including: The incremental gradient tensor quantization system is used to extract the spatiotemporal gradient vector field of the conductivity in the superthreshold region; the cadmium ion migration flux tensor is retained by decomposing the curl-divergence field into migration and diffusion components; Hotspot field intensity mapping system, used to deploy electric flux hotspots in nMgO adsorption arrays, obtain real-time current density values ​​at the sites, perform bilinear tensor product with the cadmium ion migration flux tensor, and generate pulse intensity basis functions; The frequency field reconstruction system is used to calculate the second-order variation of the migration flux along the electrode normal from the main characteristic direction of the cadmium ion migration flux tensor and convert it into a characteristic frequency spectrum through Legendre transformation; The space-time pulse synthesis system is used to discretize the pulse intensity basis function and characteristic frequency spectrum through the non-uniform sampling theorem, and output a regenerated pulse sequence. The pulse width is positively correlated with the eigenvalue of the migration flux tensor, and the phase is synchronized with the equipotential surface of the current density field.

8. The heavy metal contaminated soil remediation system based on intelligent management and control according to claim 7 is characterized in that: Hotspot field strength mapping system, including: The current-matter field dimensionality reduction subsystem is used to project the real-time current density field in the electric flux hotspot along the electrode surface normal to generate the current density principal eigenvector; the cadmium ion migration flux tensor is used to extract the material transport principal axis through Riemann contraction operation; The dual-field conjugate basis construction subsystem is used to form a field coupling frame system by orthogonal decomposition of the main eigenvector of current density and the principal axis of material transport; the angle between the basis vector of the field coupling frame system and the tangent direction of the electrode surface is used to generate the conjugate weight coefficient matrix; The tensor product energy flow mapping subsystem is used to perform Kronecker product operations on the component modulus of the current density field in the frame system and the eigenvalue of the migration flux tensor, and output the transient energy flow density tensor and the ion dissociation energy input efficiency per unit adsorption site; The basis function emergence subsystem is used to integrate the transient energy flux density tensor and the conjugate weight coefficient matrix in the direction of the electrochemical barrier to generate the pulse intensity basis function.

9. The heavy metal contaminated soil remediation system based on intelligent management and control according to claim 4 is characterized in that: Energy consumption prediction-energy adaptation chain module, including: Energy consumption domain boundary generation component, used to obtain the third-order differential characteristics of COMSOL transient energy consumption surface, divide it into base load area, fluctuation area and peak area through Riemann manifold segmentation algorithm, and output the energy consumption phase space partition topology; The carrier path reconstruction component is used to perform dual space mapping between the energy consumption gradient direction field in the fluctuation zone and the photovoltaic array lattice vector, generate the optimal carrier transport path, trigger the dynamic reorganization of the solar cells along the path direction, and form a non-Euclidean star network; The capacitive reactance phase modulation component is used to perform symplectic geometric matching on the Fourier cospectrum of the energy consumption pulse in the peak area and the relaxation time spectrum of the supercapacitor array to obtain the capacitive compensation phase angle, activate the capacitor unit to conduct according to the phase angle sequence, and establish the virtual work compensation flow; The impedance continuum maintenance component is used to obtain the initial topology of the base load area and the electrode spacing parameters of the pre-decision module, derive the system characteristic impedance through Maxwell-Ampere's law, and maintain the photovoltaic group string and parallel structure.

10. A method for remediating heavy metal contaminated soil based on intelligent management and control, which is applied to the heavy metal contaminated soil remediation system based on intelligent management and control as claimed in any one of claims 1 to 9, characterized in that: The heavy metal contaminated soil remediation method based on intelligent management and control includes: The starting electrode system module drives the directional migration of heavy metal ions in the soil through electric field driving. Simultaneously with the electric field driving, the activator module starts working, injecting green small molecule organic acid activator through gradient, activating the heavy metals in the soil and desorbing them from soil particles. The activated heavy metal ions will be captured by the adsorption material in the targeted adsorption device module in conjunction with the selective enrichment device, achieving effective adsorption of heavy metal ions. At the same time, the automatic monitoring system module collects environmental parameters in real time, which will support the intelligent decision-making system module to make real-time monitoring and intelligent correction decisions. The intelligent decision-making system module optimizes remediation efficiency and energy consumption based on real-time data, achieving effective remediation of heavy metal pollution in the soil; while the photovoltaic power supply module provides stable power guarantee.

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