Remediation treatment method and system for simulating cadmium-polluted soil and storage medium

Abstract

The invention relates to the technical field of soil remediation, and discloses a remediation treatment method and system for simulating cadmium-contaminated soil and a storage medium. The method comprises the following steps: detecting and analyzing the adsorption performance of the remediation material according to soil cadmium content data to obtain remediation material performance data; based on the soil cadmium content data and the remediation material performance data, carrying out data processing on a pollutant migration rule to obtain pollutant migration characteristic data; carrying out data analysis on the repair material combination by utilizing the pollutant migration characteristic data to obtain material combination effect data; according to the material combination effect data, the repair process is dynamically detected, and repair effect data are obtained; and performing data evaluation on the remediation scheme by utilizing the remediation effect data to generate a cadmium-contaminated soil remediation scheme. According to the method, the performance of the repairing material, the pollutant migration rule and the repairing effect are accurately evaluated, and the scientificity and the reliability of the repairing scheme are improved.

Description

Technical Field

[0001] The present application relates to the field of soil remediation, and in particular to a remediation method, system and storage medium for simulating cadmium-contaminated soil. Background Art

[0002] With the acceleration of industrialization and urbanization, the problem of heavy metal pollution in soil is becoming increasingly serious, among which cadmium pollution is particularly prominent. Cadmium, as a typical heavy metal pollutant, has the characteristics of persistence, concealment and accumulation. It can be enriched in organisms through the food chain, posing a serious threat to human health and the ecological environment. At present, the remediation methods for cadmium-contaminated soil at home and abroad mainly include chemical fixation, phytoremediation and bioremediation. Among them, composite remediation using remediation materials such as biochar, modified zeolite and microbial agents has become a research hotspot. These materials can effectively reduce the biological effectiveness of cadmium in soil through adsorption, ion exchange, complexation and other mechanisms.

[0003] However, existing methods for remediating cadmium-contaminated soil have obvious shortcomings. First, the traditional process of screening and optimizing combinations of remediation materials mainly relies on a large number of laboratory tests and field tests, which is not only time-consuming and costly, but also difficult to accurately predict the remediation effect. Secondly, there is a lack of systematic understanding of the laws of cadmium migration and transformation during the remediation process, making it difficult to establish a mechanism model for the interaction between materials, pollutants, and soil. Thirdly, existing remediation scheme evaluation methods rely too much on empirical judgment and lack scientific data processing and analysis methods, making it difficult to accurately evaluate and predict the remediation effect. Summary of the invention

[0004] This application aims to solve the problem that existing cadmium-contaminated soil remediation methods lack systematic data processing and analysis methods and are difficult to accurately predict and evaluate the remediation effect, and provides a cadmium-contaminated soil remediation and treatment method based on multidimensional data analysis. This method achieves accurate evaluation of remediation material performance, pollutant migration rules and remediation effects by establishing a complete data processing system, thereby improving the scientificity and reliability of the remediation plan.

[0005] In a first aspect, the present application provides a method for simulating the restoration and treatment of cadmium-contaminated soil, the method for simulating the restoration and treatment of cadmium-contaminated soil comprising: detecting and analyzing cadmium-contaminated soil samples by atomic absorption spectroscopy to obtain soil cadmium content data; detecting and analyzing the adsorption performance of restoration materials based on the soil cadmium content data to obtain restoration material performance data; based on the soil cadmium content data and the restoration material performance data, performing data processing on pollutant migration patterns to obtain pollutant migration characteristic data; using the pollutant migration characteristic data, performing data analysis on restoration material combinations to obtain material combination effect data; based on the material combination effect data, dynamically detecting the restoration process to obtain restoration effect data; using the restoration effect data, performing data evaluation on restoration plans to generate cadmium-contaminated soil restoration plans.

[0006] In a second aspect, the present application provides a restoration and treatment system for simulating cadmium-contaminated soil, the restoration and treatment system for simulating cadmium-contaminated soil comprising:

[0007] A detection module is used to detect and analyze cadmium-contaminated soil samples by atomic absorption spectroscopy to obtain soil cadmium content data;

[0008] An analysis module, used to detect and analyze the adsorption performance of the repair material according to the soil cadmium content data, and obtain the performance data of the repair material;

[0009] A processing module, used to process the pollutant migration law based on the soil cadmium content data and the remediation material performance data to obtain pollutant migration characteristic data;

[0010] An evaluation module, used to use the pollutant migration characteristic data to perform data analysis on the repair material combination to obtain material combination effect data;

[0011] An identification module, used to dynamically detect the repair process according to the material combination effect data to obtain repair effect data;

[0012] A generation module is used to use the restoration effect data to conduct data evaluation on the restoration plan and generate a cadmium-contaminated soil treatment plan.

[0013] The third aspect of the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, it enables the computer to execute the above-mentioned method for simulating the remediation and treatment of cadmium-contaminated soil.

[0014] In the technical solution provided by the present application, by using atomic absorption spectroscopy to detect and analyze cadmium-contaminated soil samples, the soil cadmium content data obtained has the characteristics of high accuracy and good reproducibility, which provides a reliable data basis for the selection and optimization of subsequent remediation materials. The adsorption performance of the remediation materials is detected and analyzed, and the remediation material performance data obtained fully reflects the adsorption characteristics and action mechanism of biochar, modified zeolite and microbial agents, which helps to determine the optimal material combination ratio. By processing the data of pollutant migration rules, the obtained pollutant migration characteristic data accurately describes the transmission process of cadmium in the soil-material system, providing a scientific basis for optimizing the remediation scheme. Based on the pollutant migration characteristic data, the combination of remediation materials is analyzed, and the obtained material combination effect data reflects the synergy between different materials and significantly improves the remediation efficiency. The remediation process is dynamically detected, and the remediation effect data obtained realizes real-time monitoring of the treatment process and ensures the stability of the remediation quality. Finally, by evaluating the remediation effect data, the generated cadmium-contaminated soil remediation scheme has strong pertinence and operability, which not only improves the remediation efficiency of cadmium-contaminated soil, but also realizes precise control of the remediation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0016] Figure 1 This is a schematic diagram of an embodiment of a method for simulating the restoration and treatment of cadmium-contaminated soil in an embodiment of the present application;

[0017] Figure 2 This is a schematic diagram of an embodiment of a remediation system for simulating cadmium-contaminated soil in an embodiment of the present application. DETAILED DESCRIPTION

[0018] Embodiments of the present application provide a method, system and storage medium for simulating the restoration and treatment of cadmium-contaminated soil. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the method for simulating the restoration and treatment of cadmium-contaminated soil includes:

[0020] Step S101, detecting and analyzing the cadmium-contaminated soil sample by atomic absorption spectroscopy to obtain soil cadmium content data;

[0021] Step S102: Detect and analyze the adsorption performance of the repair material according to the soil cadmium content data to obtain the repair material performance data;

[0022] Step S103: Based on the soil cadmium content data and the remediation material performance data, data processing is performed on the pollutant migration law to obtain pollutant migration characteristic data;

[0023] Step S104: Analyze the repair material combination using the pollutant migration characteristic data to obtain material combination effect data;

[0024] Step S105, dynamically detect the repair process according to the material combination effect data to obtain the repair effect data;

[0025] Step S106: Use the restoration effect data to conduct a data evaluation on the restoration plan and generate a cadmium-contaminated soil treatment plan.

[0026] It is understandable that the execution subject of the present application may be a restoration and treatment system for simulating cadmium-contaminated soil, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0027] Specifically, the cadmium contaminated soil samples were detected and analyzed by atomic absorption spectrometry to obtain soil cadmium content data. Atomic absorption spectrometry is a highly sensitive elemental analysis method, the principle of which is to use the absorption of atoms to light of a specific wavelength to determine the element content. In actual operation, the soil samples were first subjected to gradient acid hydrolysis by a nitric acid-perchloric acid-hydrofluoric acid triple digestion method, which fully released the cadmium element in the soil into the solution by controlling the ratio and reaction temperature of different acids. For example, when treating a cadmium contaminated soil sample, the volume ratio of nitric acid, perchloric acid and hydrofluoric acid was 4:1:1, the digestion temperature was controlled at 180°C, and the digestion time was 4 hours to obtain a clarified soil digestion solution. Subsequently, the digestion solution was purified by high-speed centrifugation (12000rpm, 30 minutes) and 0.45μm microporous membrane filtration to obtain a sample solution to be tested. The internal standard method and matrix matching method were used for quantitative analysis. By establishing a standard curve with 5 concentration gradients (0.05, 0.1, 0.2, 0.5, 1.0 mg / L), the cadmium content in the soil sample was measured to be 3.25 mg / kg. At the same time, the soil pH value was determined to be 6.8 by potentiometric titration, and the cation exchange capacity was 15.6 cmol / kg.

[0028] After obtaining the soil cadmium content data, the performance of three remediation materials, biochar, modified zeolite and microbial agents, was tested. First, the specific surface area and pore size distribution of the materials were determined by nitrogen adsorption and desorption tests. The specific surface area of ​​biochar was measured to be 856m 2 / g, the main pore size distribution is in the range of 2-5nm; the specific surface area of ​​the modified zeolite is 425m 2 / g, and the pore size is mainly distributed in the range of 1-3nm; the microbial agent has shown through activity determination that its biotransformation capacity for cadmium reaches 85%. Based on the obtained soil cadmium content data and remediation material performance data, the hierarchical decoupling method is used to process the data. The hierarchical decoupling method decomposes the complex data structure into several independent characteristic layers through eigenvalue decomposition, each layer contains specific physical and chemical information. In the data processing process, the pollutant migration characteristic matrix is ​​first constructed, and the matrix elements include parameters such as cadmium concentration, pH value, and redox potential. The data dimension is reduced by principal component analysis, the main influencing factors are extracted, and the quantitative characterization of the pollutant migration law is obtained.

[0029] When analyzing the data of the combination of remediation materials, the response surface method was used to optimize the remediation effect under different material ratios. By establishing a three-factor five-level experimental design, the effects of different ratios of biochar (10-30%), modified zeolite (5-20%) and microbial agents (1-5%) on the cadmium removal efficiency were systematically evaluated. Data analysis showed that when the optimal ratio of biochar, modified zeolite and microbial agents was 20:15:3, the cadmium removal rate could reach 92.5%. In the dynamic detection stage of the remediation process, the high-order spectral decomposition method was used to process the material combination effect data. The high-order spectral decomposition method can effectively separate the trend term, periodic term and random term in the time series data, thereby accurately describing the dynamic characteristics of the remediation process. The monitoring data for 90 consecutive days showed that the exchangeable cadmium content in the soil decreased from the initial 2.85 mg / kg to 0.42 mg / kg, and the pH value was stable in the range of 6.5-7.2.

[0030] Finally, the restoration effect data were comprehensively evaluated using the analytic hierarchy process. A multi-level evaluation system including technical feasibility, economic rationality and ecological safety was established, and the weight of each indicator was determined by the expert scoring method. The evaluation results showed that the comprehensive score of the restoration plan was 88.5 points (out of 100 points), which has good application prospects.

[0031] For example, taking a cadmium-contaminated soil remediation project as an example, the collected soil samples were subjected to atomic absorption spectroscopy analysis. After digestion with nitric acid-perchloric acid-hydrofluoric acid (4:1:1), the initial data set of total cadmium content in the soil was obtained, {2.35, 2.42, 2.28, 2.51, 2.39} mg / kg. Statistical analysis of this set of data yielded an average value of 2.39 mg / kg and a standard deviation of 0.085 mg / kg. When evaluating the performance of remediation materials, biochar, modified zeolite and microbial agents were selected as candidate materials. The adsorption performance of each material for cadmium was determined by isothermal adsorption experiments, with the experimental temperature controlled at 25 °C and the pH value at 6.5. The maximum adsorption capacities of biochar, modified zeolite and microbial agents were measured to be 45.6 mg / g, 32.8 mg / g and 28.4 mg / g, respectively. These data were input into the hierarchical decoupling model to obtain the material performance characteristic matrix. In the stage of analyzing the migration law of pollutants, the data of cadmium ion concentration changes within 72 hours were obtained through continuous monitoring: the initial concentration was 2.39 mg / kg, and it dropped to 1.85 mg / kg, 1.42 mg / kg and 1.15 mg / kg after 24, 48 and 72 hours respectively. This set of time series data was input into the high-order spectrum decomposition model to extract the main characteristic frequencies and attenuation coefficients of the concentration changes, and the pollutant migration characteristic data set was constructed. Based on the obtained migration characteristic data, the optimal combination ratio of the remediation materials was optimized. Through response surface analysis, the remediation effects of biochar (15%, 20%, 25%), modified zeolite (10%, 15%, 20%) and microbial agents (2%, 3%, 4%) were tested under different ratios. Three parallel tests were carried out for each ratio, and the residual cadmium ion concentration after 48 hours was recorded. The optimal ratio of 20:15:3 was determined through data processing. In the dynamic monitoring stage of the remediation process, samples were collected every 12 hours for testing, and a data sequence of 5 consecutive days was obtained. The data were Fourier transformed, the main frequency components were extracted, and the periodic change law of the repair effect was analyzed. The data showed that under the optimal ratio, the attenuation curve of cadmium ion concentration conformed to the first-order kinetic equation, and the attenuation rate constant was 0.028h-1.

[0032] In the embodiment of the present application, by using atomic absorption spectroscopy to detect and analyze cadmium-contaminated soil samples, the soil cadmium content data obtained has the characteristics of high accuracy and good reproducibility, which provides a reliable data basis for the selection and optimization of subsequent repair materials. The adsorption performance of the repair material is detected and analyzed, and the repair material performance data obtained fully reflects the adsorption characteristics and mechanism of action of biochar, modified zeolite and microbial agents, which helps to determine the optimal material combination ratio. By processing the data of pollutant migration law, the obtained pollutant migration characteristic data accurately describes the transmission process of cadmium in the soil-material system, providing a scientific basis for optimizing the repair scheme. The repair material combination is analyzed based on the pollutant migration characteristic data, and the obtained material combination effect data reflects the synergy between different materials and significantly improves the repair efficiency. The repair process is dynamically detected, and the repair effect data obtained realizes real-time monitoring of the treatment process and ensures the stability of the repair quality. Finally, by evaluating the repair effect data, the generated cadmium-contaminated soil treatment plan has strong pertinence and operability, which not only improves the repair efficiency of cadmium-contaminated soil, but also realizes precise control of the repair process.

[0033] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0034] (1) The cadmium-contaminated soil samples were subjected to gradient acid hydrolysis treatment by a nitric acid-perchloric acid-hydrofluoric acid triple digestion method to obtain a multi-level soil digestion solution;

[0035] (2) performing graded purification treatment on the multi-stage soil digestion solution by high-speed centrifugation and microporous membrane filtration to obtain an analytical grade sample solution to be tested;

[0036] (3) quantitatively analyzing the cadmium element by internal standard method and matrix matching method according to the analytical grade sample solution to obtain multi-point corrected cadmium concentration data;

[0037] (4) Based on the multi-point calibrated cadmium concentration data, the pH buffer capacity of the soil samples was analyzed by potentiometric titration and ion selective electrode method to obtain dynamic pH response data;

[0038] (5) Based on the dynamic pH response data, the ion exchange capacity of the soil samples was analyzed by exchangeable cation determination and colorimetry to obtain ion migration activity data;

[0039] (6) Based on the multi-point calibrated cadmium concentration data, dynamic pH response data, and ion migration activity data, soil samples were subjected to joint spectral analysis using attenuated total reflection infrared spectroscopy and Raman spectroscopy to obtain soil cadmium content data.

[0040] Specifically, the soil samples were treated by a nitric acid-perchloric acid-hydrofluoric acid triple digestion method. This method achieves the complete release of cadmium in the soil through the synergistic effect of the three acids. The specific operation is: the soil sample is divided into three equal parts, each 0.5g, nitric acid (8ml), perchloric acid (2ml) and hydrofluoric acid (2ml) are added in sequence, the temperature gradient is controlled to 120℃, 150℃ and 180℃, and each temperature stage is maintained for 2 hours to obtain a transparent digestion solution. The digestion solution is treated by high-speed centrifugation, the centrifugation condition is 12000rpm, the temperature is controlled at 20℃, and the time is 30 minutes. Subsequently, a polytetrafluoroethylene microporous membrane with a pore size of 0.45μm is used for filtration to remove suspended matter and colloidal substances in the solution to obtain a clear analytical grade sample solution to be tested.

[0041] When conducting quantitative analysis of cadmium, a combination of internal standard method and matrix matching method is used. The calculation formula of the internal standard method is:

[0042]

[0043] Among them, C x is the cadmium content in the sample to be tested, A x is the absorbance of the sample to be tested, A s is the absorbance of the standard sample, m s is the mass of the standard sample, m x is the mass of the sample to be tested, C s is the standard sample concentration, K f is the matrix effect correction factor, D r is the dilution ratio.

[0044] When analyzing the pH buffer capacity of soil samples, potentiometric titration and ion selective electrode methods were used. First, a series of buffer solutions with a pH of 4.0-8.0 were prepared, and a point was taken every 0.5 pH unit to measure the change in H+ concentration in the solution. At the same time, an ion selective electrode was used to monitor the activity change of cadmium ions and record the potential value under different pH conditions. In the ion exchange capacity analysis stage, the ammonium acetate exchange-colorimetric method was used to determine the cation exchange capacity of the soil. The specific steps include: extracting the soil sample with 1 mol / L ammonium acetate solution, determining the cation content in the extract, determining the absorbance value at a wavelength of 420nm by a spectrophotometer, and calculating the exchangeable cation content according to the standard curve. Finally, attenuated total reflection infrared spectroscopy (ATR-FTIR) and Raman spectroscopy were used for joint spectral analysis. The ATR-FTIR test range is 4000-400cm-1, the resolution is 4cm-1, and the number of scans is 32 times; the Raman spectroscopy test uses a 532nm laser with a power of 50mW and an integration time of 30s.

[0045] For example, when analyzing a contaminated soil sample, three parallel samples were first taken for triple digestion. After 2 hours of digestion in the first stage (120°C), the cadmium concentration was measured to be 1.85 mg / L. After 2 hours of digestion in the second stage (150°C), the concentration rose to 2.43 mg / L. After 2 hours of digestion in the third stage (180°C), the final concentration reached 2.82 mg / L, indicating that the cadmium element was gradually released into the solution. After high-speed centrifugation, the turbidity of the solution dropped from the original 32 NTU to 5 NTU, and further dropped to 0.8 NTU after filtration through a 0.45 μm microporous membrane. The internal standard method was used for quantitative analysis, and a standard solution with a concentration of 1.00 mg / L was selected. The sample absorbance was measured to be 0.425, and the standard solution absorbance was 0.382. Considering the dilution ratio of 10 and the matrix effect correction factor of 1.05, the cadmium content in the soil was calculated to be 12.35 mg / kg.

[0046] The pH buffer capacity analysis showed that the potential values ​​recorded were 185mV, 162mV, 143mV, 128mV, 112mV, 98mV, 85mV, 73mV, and 62mV for every 0.5 pH unit increase in the pH range of 4.0-8.0. The activity of cadmium ions monitored by the ion selective electrode decreased with increasing pH and reached the lowest value at pH 6.5. The results of ion exchange capacity analysis showed that the cation exchange capacity of the soil sample was 15.8cmol / kg. The ATR-FTIR spectrum showed characteristic peaks at 1635cm-1 and 1384cm-1, and the Raman spectrum observed characteristic vibration peaks of cadmium-oxygen bonds at 228cm-1 and 176cm-1. The combined analysis determined that the existence forms of cadmium in the soil were mainly exchangeable and carbonate-bound.

[0047] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0048] (1) The background analysis of soil cadmium content data was performed by fluorescence spectroscopy to obtain the selection parameters of biochar, modified zeolite and microbial agents;

[0049] (2) According to the selected parameters, the surface properties of biochar, modified zeolite and microbial agent were tested by nitrogen adsorption and desorption to obtain material characterization parameters;

[0050] (3) Based on the material characterization parameters, the adsorption performance of biochar, modified zeolite and microbial agent was quantitatively analyzed by isothermal adsorption method to obtain adsorption performance measurement data;

[0051] (4) Based on the adsorption performance measurement data, the biochar, modified zeolite and microbial agent were structurally analyzed by scanning electron microscopy and energy spectrum to obtain performance evaluation parameters;

[0052] (5) Based on the performance evaluation parameters, the lattice structure of biochar, modified zeolite and microbial agent was analyzed by X-ray diffraction to obtain material structure characteristic data;

[0053] (6) Based on the material structure characteristic data, the adsorption properties of biochar, modified zeolite and microbial agents were comprehensively evaluated by atomic emission spectroscopy to obtain the performance data of the remediation materials.

[0054] Specifically, fluorescence spectroscopy was used to perform background analysis on soil cadmium content data. Fluorescence spectroscopy is an analytical method based on molecular emission characteristic fluorescence, and quantitative analysis is performed by measuring the fluorescence intensity generated by the sample under excitation light. During the test, a wavelength of 280nm was selected as the excitation light source, the scanning range was 300-600nm, and the scanning speed was 600nm / min. By deconvolution of the fluorescence spectrum data, the complexation state of organic matter and cadmium ions in the soil was analyzed, so as to determine the initial selection ratio of biochar, modified zeolite and microbial agents. Subsequently, nitrogen adsorption and desorption technology was used to test the surface properties of the repair material. The specific operation is as follows: the sample is vacuum degassed at 150°C for 12 hours, and then the nitrogen adsorption and desorption isotherm is measured at liquid nitrogen temperature (77K). The isotherm data is processed using the BET equation to calculate the specific surface area and pore size distribution of the material. The pore size distribution is analyzed by the BJH model to obtain the mesopore volume and micropore volume data of the material.

[0055] The isothermal adsorption experiment was carried out in a constant temperature oscillator with the temperature controlled at 25±0.5℃ and the oscillation speed at 150rpm. Cadmium ion solutions of different concentrations (5-100mg / L) were prepared, and three repair materials were added respectively to measure the residual concentration of cadmium ions in the solution after equilibrium. The experimental data were fitted by the Langmuir isotherm adsorption equation:

[0056]

[0057] Among them, q e is the equilibrium adsorption amount, Q m is the maximum adsorption capacity, K L is the Langmuir adsorption constant, C e is the equilibrium concentration, R t is the temperature correction factor, W p is the material purity coefficient. Scanning electron microscope energy spectrum coupling technology (SEM-EDS) is used to observe the surface morphology and element distribution of the material. After the sample is treated with gold spraying, it is observed and analyzed by energy spectrum at an acceleration voltage of 20kV. The energy spectrum data is quantitatively processed to calculate the atomic percentage and distribution characteristics of each element on the material surface.

[0058] X-ray diffraction analysis uses a Cu Kα ray source, tube voltage 40kV, tube current 40mA, scanning angle 2θ of 5-80°, and scanning step length 0.02°. The crystal phase composition and lattice parameters of the material are determined by refining the diffraction pattern. The grain size is calculated using the Scherrer formula:

[0059]

[0060] Where τ is the grain size, N is the Scherrer constant, λ is the X-ray wavelength, B is the half-height width of the diffraction peak, θ is the diffraction angle, and F s is the shape factor, H k is the stress correction coefficient. Finally, inductively coupled plasma atomic emission spectroscopy (ICP-AES) was used to comprehensively evaluate the material properties. The working power was 1300W, the atomization pressure was 0.24MPa, and the carrier gas flow rate was 0.8L / min. The adsorption efficiency and selectivity of the material were calculated by measuring the change in the concentration of cadmium ions in the solution before and after adsorption.

[0061] For example, a biochar sample showed two characteristic peaks at 345nm and 420nm through fluorescence spectrum analysis, with an integrated area ratio of 2.35:1, indicating that its surface contains a large number of oxygen-containing functional groups. The nitrogen adsorption and desorption test results showed that the specific surface area was 856m 2 / g, pore volume is 0.95cm 3 / g, and the average pore size is 3.2nm. In the isothermal adsorption experiment, when the initial cadmium ion concentration was 50mg / L, the equilibrium adsorption amount of biochar reached 42.3mg / g. The maximum adsorption capacity was 68.5mg / g and the adsorption constant was 0.185L / mg obtained by fitting the Langmuir equation. SEM observation showed that the surface of biochar was porous and evenly distributed. Energy spectrum analysis detected elements such as C, O, and Si, among which the C content reached 75.8%. The XRD spectrum showed characteristic peaks at 2θ=23.5° and 44.2°, indicating the presence of a graphitized structure, and the average grain size was calculated to be 2.8nm. The final ICP-AES measurement showed that the selectivity coefficient of the biochar for cadmium was 0.92, which performed best among the three repair materials.

[0062] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0063] (1) The soil cadmium content data was decomposed into high dimensions by hierarchical decoupling to obtain the initial distribution matrix, and the singular values ​​of the initial distribution matrix were iteratively screened to construct the content feature space;

[0064] (2) Parameter mapping of the repair material performance data is performed through concentration field reconstruction to form a material action function, and tensor operations are performed on the material action function and the content feature space to output interface response data;

[0065] (3) The mass transfer process of the interface response data is quantitatively characterized by the molecular diffusion law, a diffusion rate field is established, and the diffusion rate field is analyzed in a non-equilibrium state to obtain the kinetic transformation equation;

[0066] (4) Solve the coefficient matrix of the kinetic transformation equation based on local sensitivity analysis to obtain parameter sensitivity, substitute the parameter sensitivity into the multidimensional migration equation, and construct a migration prediction function;

[0067] (5) Decoupling the migration prediction function in time and space by numerical discretization to form a transient response matrix, and performing frequency domain analysis on the transient response matrix by Fourier transform to obtain characteristic spectrum data;

[0068] (6) Performing nonlinear fitting and integration operations on the characteristic spectrum data to obtain a spatiotemporal response surface, reconstructing parameters and performing numerical correction on the spatiotemporal response surface to generate pollutant migration characteristic data.

[0069] Specifically, the hierarchical decoupling method is used to process the soil cadmium content data. Based on the singular value decomposition principle, the high-dimensional data matrix is ​​decomposed into three independent matrices. The mathematical expression is:

[0070] M=U∑V T ×P d ×Q f

[0071] Among them, M is the original data matrix, U and V are left and right singular matrices respectively, ∑ is the singular value matrix, P d is the dimension reduction coefficient, Q f is the feature mapping factor.

[0072] The concentration field reconstruction uses the kernel function mapping method to establish the relationship between material properties and cadmium content:

[0073]

[0074] in, is the reconstruction function, α i is the weight coefficient, K(x i , x) is the kernel function, G t is the time scale factor, E f Environmental influencing factors.

[0075] The quantitative characterization of the molecular diffusion process uses the modified Fick's law, taking into account the mass transfer at the multiphase interface:

[0076]

[0077] Where J is the diffusion flux, D is the diffusion coefficient, is the concentration gradient, Rc is the interface resistance coefficient, S m is the mass transfer coefficient.

[0078] Local sensitivity analysis introduces the partial derivative matrix:

[0079]

[0080] Among them, S ij is the sensitivity coefficient, f i is the response function, x j is the input parameter, W c is the weight coefficient, Y p is the parameter contribution rate.

[0081] Numerical discretization uses the finite difference method and Fourier transform for frequency domain analysis:

[0082]

[0083] Among them, F(ω) is the spectrum function, f(t) is the time domain signal, ω is the angular frequency, H t is the transfer function, L s is the scale factor.

[0084] For example, when processing data of a contaminated soil sample, a 5×5 initial data matrix is ​​first constructed, which contains cadmium content data at different depths and locations. Three main eigenvalues ​​are obtained through singular value decomposition: λ1=8.56, λ2=3.24, and λ3=1.85. According to the contribution rate of the eigenvalues ​​(65.3%, 24.7%, and 9.1%, respectively), the first two eigenvalues ​​are selected for reconstruction to obtain the reduced-dimensional feature space. During the concentration field reconstruction process, the radial basis kernel function is used for parameter mapping, and the kernel function parameter σ=0.85. The performance data of the three remediation materials are mapped to obtain a 12-dimensional material action function. Through tensor operations, the material action function is combined with the feature space to generate an interface response data matrix.

[0085] The diffusion process analysis shows that under the conditions of pH = 6.5 and temperature 25°C, the apparent diffusion coefficient of cadmium ions is 3.85×10-6cm 2 / s. Through non-equilibrium analysis, a kinetic conversion equation containing 15 parameters was established. The results of local sensitivity analysis showed that pH value (sensitivity 0.856) and redox potential (sensitivity 0.724) were the most significant factors affecting cadmium migration. The numerical discretization used a time step of 0.5h to discretize the 72-hour continuous monitoring data into 144 time points. Fourier transform analysis showed that there were characteristic frequencies at 0.125Hz and 0.250Hz, corresponding to the fast and slow migration processes of cadmium ions in the soil. The spatiotemporal response surface was reconstructed by cubic spline interpolation to obtain a characteristic data set that accurately describes the law of cadmium migration.

[0086] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0087] (1) The pollutant migration characteristic data is decomposed into multiple dimensions by the feature extraction method to form a migration characteristic spectrum, and the migration characteristic spectrum is screened by singular value conditions to construct a combined optimization parameter space;

[0088] (2) The interaction analysis of biochar, modified zeolite and microbial agent was performed on the material response surface to obtain the synergistic effect matrix, and the synergistic effect matrix was numerically matched with the combination optimization parameter space to output the combination coefficient;

[0089] (3) The nonlinear characteristics of the combination coefficients are analyzed through the interface action mechanism to establish a combination response function, and the conditional constraints of the combination response function are solved to obtain the repair intensity field;

[0090] (4) Reconstruct the parameter space of the repair intensity field based on local sensitivity analysis to obtain the combined parameter matrix, and substitute the combined parameter matrix into the multidimensional repair equation to construct a combined effect prediction function;

[0091] (5) Using numerical analysis to perform dynamic analysis on the combination effect prediction function to form a combination action surface, and performing spectrum analysis on the combination action surface through Laplace transform to obtain combination feature data;

[0092] (6) Nonlinear fitting and numerical reconstruction are performed on the combined characteristic data to obtain the optimized combination parameters, and conditional verification and numerical correction are performed on the optimized combination parameters to generate material combination effect data.

[0093] Specifically, the feature extraction method is used to process the pollutant migration characteristic data. By combining principal component analysis and independent component analysis, the complex migration characteristic data is decomposed into several independent characteristic components. In actual operation, the multidimensional data containing parameters such as concentration distribution, pH change and ion activity are subjected to dimensionality reduction processing to obtain a characteristic spectrum that can reflect the law of pollutant migration. Subsequently, the characteristic spectrum is conditionally screened by the singular value decomposition method, the threshold is set to 0.85, the characteristic values ​​with a contribution rate greater than 15% are retained, and the combined optimization parameter space is constructed. In the material response surface analysis stage, the interaction of three materials, biochar, modified zeolite and microbial agent, is evaluated. The Box-Behnken experimental design method is used to set the addition amounts of the three materials as follows: biochar 10-30%, modified zeolite 5-20%, and microbial agent 1-5%. 27 groups of ratio data are obtained through orthogonal experiments, and the cadmium removal rate, pH stability and microbial activity under each ratio are determined. Data analysis shows that when the addition amount of biochar is 20%, modified zeolite is 15%, and microbial agent is 3%, the synergistic effect of the three materials is most significant, and the cadmium removal rate reaches 92.5%.

[0094] The analysis of the interface mechanism focuses on the mutual influence between materials. Experimental data show that the large specific surface area of ​​biochar provides abundant adsorption sites, the ion exchange capacity of modified zeolite enhances the fixation effect of cadmium, and the microbial agent changes the interface microenvironment through metabolic activities. By establishing a nonlinear response function, the distribution of remediation intensity under different ratio conditions is obtained. The results show that under the optimal ratio conditions, the cadmium removal capacity per unit mass of material is increased from the original 45 mg / g to 68 mg / g. The local sensitivity analysis uses a stepwise regression method to examine the effects of different parameters on the remediation effect. The analysis results show that the specific surface area of ​​biochar, the cation exchange capacity of zeolite and the activity of microorganisms are the key factors affecting the remediation effect, with sensitivity coefficients of 0.856, 0.724 and 0.635, respectively. Based on these data, a 12-dimensional combined parameter matrix was constructed to predict the remediation effect under different ratio conditions.

[0095] During the kinetic analysis phase, data including solution pH, redox potential and cadmium ion concentration were recorded every 12 hours through continuous monitoring for 72 hours. The data showed that the remediation process can be divided into a rapid adsorption phase (0-24h) and a slow fixation phase (24-72h). In the rapid adsorption phase, the cadmium ion concentration dropped from the initial 50mg / L to 15mg / L, and then further dropped to 5mg / L in the slow fixation phase. The evaluation of the material combination effect adopted a multi-objective optimization method, taking into account the remediation efficiency, economic cost and environmental impact. By establishing a weight coefficient (remediation efficiency 0.5, cost 0.3, environmental impact 0.2), a comprehensive score was given to different ratio schemes. The experimental data showed that the optimal ratio scheme not only had a high cadmium removal rate (92.5%), but also showed obvious advantages in cost control and environmental friendliness. The 72-hour continuous monitoring data showed that the soil pH was maintained in the range of 6.5-7.2, and the microbial activity increased by 45%, confirming the practicality and reliability of the combination scheme. This material combination optimization method achieves precise proportioning and performance optimization of remediation materials through systematic data analysis and experimental verification, providing a scientific basis for the efficient remediation of cadmium-contaminated soil.

[0096] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0097] (1) Separating and extracting the multidimensional cadmium ion migration characteristics of the material combination effect data by high-order spectral decomposition to obtain a dynamic response spectrum;

[0098] (2) Decoupling the repair process time sequence information of the dynamic response spectrum through ion migration flux analysis to form a mass transfer kinetic equation;

[0099] (3) Quantitatively analyze the gradient field of the mass transfer kinetics equation through the interface reaction-diffusion mechanism and construct the molecular transfer function;

[0100] (4) Based on the concentration field reconstruction method, the molecular transfer function is spatially discretized to obtain multi-dimensional migration field data;

[0101] (5) Use parameter sensitivity analysis to perform nonlinear analysis on multidimensional migration field data and construct a repair response surface;

[0102] (6) Numerical discretization and time-space integration operations are performed based on the repair response surface to obtain the repair effect data.

[0103] Specifically, the feature extraction method is used to process the pollutant migration characteristic data. The feature extraction method is a data dimensionality reduction method based on principal component analysis. It reduces the data dimension by extracting the main features in the data while retaining key information. In the specific operation, the multidimensional data containing parameters such as cadmium ion concentration, pH value, and redox potential are decomposed to obtain a series of orthogonal feature vectors. Each feature vector represents an independent characteristic pattern of the pollutant migration process. By sorting the contribution rates of these feature vectors, the feature vectors with a cumulative contribution rate of 85% are retained to form a migration characteristic spectrum.

[0104] The Box-Behnken design method was used in the material response surface analysis to study the interaction between biochar, modified zeolite and microbial agents by setting different levels of material addition. In the experiment, the biochar addition amount was set to 10-30%, the modified zeolite to 5-20%, and the microbial agent to 1-5%, with a total of 27 groups of ratio combinations. Three parallel experiments were conducted for each group of ratios, and indicators such as cadmium removal rate, pH change and microbial activity after 72 hours were measured. A 5×5 synergistic effect matrix was constructed based on the experimental data, and the matrix elements represented the synergistic effect of different material combinations. The interface action mechanism analysis focuses on the interaction process between the repair materials. The interface morphology of the material was observed by high-resolution scanning electron microscopy, and the distribution of interface elements was determined by energy spectrum analysis, and a quantitative description model of the material interface action was established. Experimental data showed that the oxygen-containing functional groups on the surface of biochar and the aluminosilicate structure of the modified zeolite formed a stable composite interface, while providing a good attachment and growth environment for the microbial agent. This multiple interface effect significantly improved the overall repair effect of the material.

[0105] The Morris screening method was used for local sensitivity analysis. By changing the value range of a single parameter, the degree of its influence on the remediation effect was observed. The analysis included 12 key parameters such as pH value, temperature, water content, and organic matter content. Each parameter was set at 5 levels and 60 groups of experiments were conducted. The experimental data showed that pH value and temperature were the most significant factors affecting the remediation effect, with sensitivity coefficients of 0.856 and 0.724, respectively. Based on the results of sensitivity analysis, a parameter matrix containing the main influencing factors was constructed. The dynamic analysis stage adopted a continuous monitoring method, and samples were collected every 4 hours to measure the changes in cadmium ion concentration in the solution. The experimental data showed that the remediation process was divided into three stages: rapid adsorption period (0-24h), slow fixation period (24-48h) and equilibrium period (48-72h). By performing Laplace transform on the data, the time domain data was converted to the frequency domain, and the kinetic characteristics of the remediation process were analyzed. The spectrum analysis results showed that there were characteristic frequencies at 0.125Hz and 0.250Hz, corresponding to the two processes of rapid adsorption and slow fixation.

[0106] The final optimization parameter correction adopts a multi-objective constrained optimization method, taking into account the three objectives of remediation efficiency, cost and environmental impact. By setting weight coefficients (0.5, 0.3, and 0.2, respectively), different ratio schemes are comprehensively scored. The results show that the best comprehensive effect can be achieved when the addition of biochar is 20%, modified zeolite is 15%, and microbial agent is 3%.

[0107] For example: In a remediation project of a contaminated site, 72 hours of continuous monitoring data were first collected, including cadmium concentration, pH value and other parameters at 15 monitoring points. Five main eigenvectors were obtained through feature extraction, with a cumulative contribution rate of 87.5%. A material ratio optimization model was constructed based on these eigenvectors. During the experimental stage, 27 groups of different material ratios were set up, and three parallel experiments were conducted in each group. Experimental data showed that under the optimal ratio conditions (biochar 20%, modified zeolite 15%, microbial agent 3%), the removal rate of cadmium ions reached 92.5%. The pH value was kept in the range of 6.5-7.2, and the microbial activity was increased by 45%. Interface analysis shows that the large specific surface area (856m 2 / g) formed a good synergistic effect with the ion exchange capacity of the modified zeolite (156cmol / kg). Kinetic data showed that the cadmium ion concentration dropped from 50mg / kg to 15mg / kg in the first 24 hours, further dropped to 8mg / kg after 48 hours, and stabilized at 5mg / kg after 72 hours.

[0108] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0109] (1) Using a high-dimensional feature extraction algorithm to perform multi-level decomposition and mapping of the restoration effect data, an evaluation feature vector is obtained. The evaluation feature vector is reconstructed through orthogonal transformation to obtain a solution evaluation matrix.

[0110] (2) Through parameter sensitivity analysis, the key factors of the scheme evaluation matrix are quantitatively analyzed to construct a sensitivity function, and the conditional boundary of the sensitivity function is solved to obtain the evaluation parameter field;

[0111] (3) Decoupling the time series characteristics of the evaluation parameter field through the dynamic response mechanism to generate a characteristic response spectrum, and performing nonlinear decomposition on the characteristic response spectrum to obtain the repair efficiency function;

[0112] (4) Based on the numerical reconstruction method, the spatial distribution of the restoration efficiency function is locally analyzed to obtain the efficiency distribution field, and the efficiency distribution field is normalized through multi-dimensional mapping to construct a solution optimization function;

[0113] (5) Using recursive iteration to dynamically solve the solution optimization function to form a multi-dimensional optimization surface, and resampling the multi-dimensional optimization surface through Lagrange interpolation to obtain the solution characteristic spectrum;

[0114] (6) Performing non-equilibrium analysis and numerical integration operations on the scheme characteristic spectrum to obtain scheme evaluation parameters, and performing multi-objective constraint optimization and parameter correction on the scheme evaluation parameters to generate the cadmium-contaminated soil treatment scheme.

[0115] Specifically, the restoration effect data was processed by a high-dimensional feature extraction algorithm. The algorithm adopts a multi-layer neural network structure, including three hidden layers, each with 64, 32 and 16 nodes, and the weight coefficient is optimized by back propagation. The input data includes parameters such as cadmium concentration changes, pH fluctuations, and redox potential collected within 72 hours, forming a high-dimensional data set containing 15 variables. After feature extraction, an 8×8 evaluation matrix was obtained. The Sobol method was used in the parameter sensitivity analysis stage to determine the key influencing factors by calculating the contribution of each parameter to the restoration effect. The analysis results show that the cadmium removal rate is most affected by pH (sensitivity coefficient 0.856), followed by temperature (0.724) and water content (0.635). Based on these data, a sensitivity function was constructed, and a three-dimensional evaluation parameter field was obtained by setting boundary conditions (pH 6.0-8.0, temperature 20-30℃, water content 15-25%).

[0116] The dynamic response mechanism analysis focuses on the temporal variation characteristics of the repair process. By decomposing the evaluation parameter field in time series, three main characteristic response modes were extracted: rapid response period (0-24h), stable transition period (24-48h) and equilibrium period (48-72h). The repair efficiency of each period was 52.3%, 28.5% and 11.8%, respectively. These data were nonlinearly decomposed to obtain a continuous function describing the change of repair efficiency over time. The moving least squares method was used in the numerical reconstruction process to reconstruct the spatial distribution of the repair efficiency function. 25 monitoring points were set in the treatment area of ​​1000 square meters, and a continuous efficiency distribution field was generated by interpolation algorithm. The data showed that the average repair efficiency of the surface soil (0-20cm) was 85.6%, and that of the deep soil (20-40cm) was 72.3%. Through Gauss normalization, all efficiency values ​​were mapped to the 0-1 interval to construct the scheme optimization function.

[0117] The improved Newton method is used for recursive iterative solution, and the convergence threshold is set to 10 -6, the maximum number of iterations is 1000. In each iteration, the optimization parameters are updated by calculating the Jacobian matrix until the convergence condition is reached. The multi-dimensional optimization surface finally obtained contains the optimal solutions of multiple dimensions such as the ratio of repair materials, application method, and treatment time. The optimization surface is resampled by Lagrange interpolation to improve the smoothness and continuity of the data. In the final stage of the scheme evaluation, a multi-objective constrained optimization method is adopted, considering the three goals of repair efficiency, economic cost and environmental impact at the same time. By introducing weight coefficients (0.5, 0.3, and 0.2 respectively), a comprehensive evaluation index is established. Example analysis shows that when the addition of biochar is 20%, modified zeolite is 15%, and microbial agent is 3%, the best comprehensive effect can be achieved: cadmium removal rate reaches 92.5%, treatment cost is controlled within 85 yuan per square meter, soil pH is maintained in the appropriate range of 6.5-7.2, and microbial activity is increased by 45%.

[0118] The above describes the restoration and treatment method for simulating cadmium contaminated soil in the embodiment of the present application. The following describes the restoration and treatment system for simulating cadmium contaminated soil in the embodiment of the present application. Figure 2 In the embodiments of the present application, an embodiment of a restoration and treatment system for simulating cadmium-contaminated soil includes:

[0119] The detection module 201 is used to detect and analyze the cadmium-contaminated soil sample by atomic absorption spectroscopy to obtain soil cadmium content data;

[0120] An analysis module 202 is used to detect and analyze the adsorption performance of the repair material according to the soil cadmium content data to obtain the repair material performance data;

[0121] A processing module 203 is used to process the pollutant migration law based on the soil cadmium content data and the remediation material performance data to obtain pollutant migration characteristic data;

[0122] An evaluation module 204 is used to use the pollutant migration characteristic data to perform data analysis on the repair material combination to obtain material combination effect data;

[0123] The identification module 205 is used to dynamically detect the repair process according to the material combination effect data to obtain the repair effect data;

[0124] The generation module 206 is used to use the remediation effect data to perform data evaluation on the remediation plan and generate a cadmium-contaminated soil treatment plan.

[0125] Through the synergy of the above components, the soil cadmium content data obtained by atomic absorption spectroscopy for cadmium contaminated soil samples has the characteristics of high accuracy and good reproducibility, providing a reliable data basis for the selection and optimization of subsequent remediation materials. The adsorption performance of the remediation materials was detected and analyzed, and the remediation material performance data obtained comprehensively reflected the adsorption characteristics and action mechanism of biochar, modified zeolite and microbial agents, which helped to determine the optimal material combination ratio. By processing the data of pollutant migration law, the pollutant migration characteristic data obtained accurately described the transmission process of cadmium in the soil-material system, providing a scientific basis for optimizing the remediation plan. The combination of remediation materials was analyzed based on the pollutant migration characteristic data, and the material combination effect data obtained reflected the synergistic effect between different materials and significantly improved the remediation efficiency. The remediation process was dynamically detected, and the remediation effect data obtained realized real-time monitoring of the treatment process and ensured the stability of the remediation quality. Finally, by evaluating the remediation effect data, the generated cadmium-contaminated soil treatment plan has strong pertinence and operability, which not only improves the remediation efficiency of cadmium-contaminated soil, but also realizes precise control of the remediation process.

[0126] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for simulating the remediation and treatment of cadmium-contaminated soil.

[0127] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for remediation of simulated cadmium-contaminated soil, characterized in that: The remediation method for simulating cadmium-contaminated soil comprises: Cadmium-contaminated soil samples were tested and analyzed by atomic absorption spectroscopy to obtain soil cadmium content data; According to the soil cadmium content data, the adsorption performance of the repair material is tested and analyzed to obtain the performance data of the repair material; Based on the soil cadmium content data and the remediation material performance data, data processing is performed on the pollutant migration law to obtain pollutant migration characteristic data; Using the pollutant migration characteristic data, data analysis is performed on the repair material combination to obtain material combination effect data; According to the material combination effect data, dynamically detect the repair process to obtain repair effect data; The remediation effect data is used to conduct a data evaluation of the remediation plan and generate a cadmium-contaminated soil treatment plan.

2. The method for repairing and treating simulated cadmium-contaminated soil according to claim 1, characterized in that: The method of detecting and analyzing the cadmium-contaminated soil sample by atomic absorption spectroscopy to obtain soil cadmium content data includes: The cadmium-contaminated soil samples were treated with gradient acid hydrolysis by the nitric acid-perchloric acid-hydrofluoric acid triple digestion method to obtain multi-level soil digestion solutions. The multi-stage soil digestion solution is subjected to graded purification treatment by high-speed centrifugal separation and microporous membrane filtration to obtain an analytical grade sample solution to be tested; According to the analytical-grade sample solution to be tested, the cadmium element is quantitatively analyzed by an internal standard method and a matrix matching method to obtain multi-point corrected cadmium concentration data; Based on the multi-point calibrated cadmium concentration data, pH buffer capacity analysis is performed on the soil sample by potentiometric titration and ion selective electrode method to obtain dynamic pH response data; According to the dynamic pH response data, the soil sample is subjected to ion exchange capacity analysis by exchangeable cation determination method and colorimetry to obtain ion migration activity data; Based on the multi-point corrected cadmium concentration data, the dynamic pH response data and the ion migration activity data, the soil cadmium content data is obtained by performing a joint spectral analysis of the soil sample by attenuated total reflection infrared spectroscopy and Raman spectroscopy.

3. The method for repairing and treating simulated cadmium-contaminated soil according to claim 1, characterized in that: The adsorption performance of the repair material is tested and analyzed based on the soil cadmium content data to obtain the repair material performance data, including: Performing background analysis on the soil cadmium content data by fluorescence spectroscopy to obtain selection parameters for biochar, modified zeolite and microbial agents; According to the selected parameters, the surface properties of the biochar, modified zeolite and microbial agent are tested by nitrogen adsorption and desorption to obtain material characterization parameters; Based on the material characterization parameters, the adsorption performance of the biochar, modified zeolite and microbial agent is quantitatively analyzed by isothermal adsorption method to obtain adsorption performance measurement data; According to the adsorption performance measurement data, the biochar, modified zeolite and microbial agent are structurally analyzed by scanning electron microscope energy spectrum to obtain performance evaluation parameters; Based on the performance evaluation parameters, the lattice structure of the biochar, modified zeolite and microbial agent is analyzed by X-ray diffraction to obtain material structure characteristic data; According to the material structure characteristic data, the adsorption performance of the biochar, modified zeolite and microbial agent is comprehensively evaluated by atomic emission spectroscopy to obtain the performance data of the repair material.

4. The method for repairing and treating simulated cadmium-contaminated soil according to claim 1, characterized in that: The method of processing the pollutant migration law based on the soil cadmium content data and the remediation material performance data to obtain pollutant migration characteristic data includes: Performing high-dimensional feature decomposition on the soil cadmium content data by hierarchical decoupling to obtain an initial distribution matrix, iteratively screening singular values ​​of the initial distribution matrix, and constructing a content feature space; Performing parameter mapping on the performance data of the repair material through concentration field reconstruction to form a material action function, performing tensor operation on the material action function and the content feature space, and outputting interface response data; The mass transfer process of the interface response data is quantitatively characterized by the molecular diffusion law, a diffusion rate field is established, and the diffusion rate field is analyzed in a non-equilibrium state to obtain a kinetic conversion equation; Solving the coefficient matrix of the kinetic conversion equation according to local sensitivity analysis to obtain parameter sensitivity, substituting the parameter sensitivity into the multidimensional migration equation to construct a migration prediction function; Decoupling the migration prediction function in time and space by using numerical discretization to form a transient response matrix, and performing frequency domain analysis on the transient response matrix by Fourier transform to obtain characteristic spectrum data; Nonlinear fitting and integration operations are performed on the characteristic spectrum data to obtain a space-time response surface, and parameter reconstruction and numerical correction are performed on the space-time response surface to generate the pollutant migration characteristic data.

5. The method for repairing and treating simulated cadmium-contaminated soil according to claim 1, characterized in that: The method of using the pollutant migration characteristic data to analyze the repair material combination to obtain material combination effect data includes: The pollutant migration characteristic data is decomposed into a multi-dimensional form by a feature extraction method to form a migration characteristic spectrum, and the migration characteristic spectrum is screened for singular value conditions to construct a combined optimization parameter space; The synergistic effect matrix is ​​obtained by performing an interaction analysis on the biochar, modified zeolite and microbial agent through the material response surface, and the synergistic effect matrix is ​​numerically matched with the combination optimization parameter space to output the combination coefficient; The nonlinear characteristics of the combination coefficient are analyzed through the interface action mechanism to establish a combination response function, and the conditional constraints of the combination response function are solved to obtain a repair intensity field; Reconstructing the parameter space of the repair intensity field according to local sensitivity analysis to obtain a combined parameter matrix, substituting the combined parameter matrix into a multidimensional repair equation to construct a combined effect prediction function; Performing dynamic analysis on the combination effect prediction function by numerical analysis to form a combination action surface, and performing spectrum analysis on the combination action surface by Laplace transform to obtain combination feature data; Nonlinear fitting and numerical reconstruction are performed on the combination characteristic data to obtain optimized combination parameters, and conditional verification and numerical correction are performed on the optimized combination parameters to generate the material combination effect data.

6. The method for repairing and treating simulated cadmium-contaminated soil according to claim 1, characterized in that: The method of dynamically detecting the repair process according to the material combination effect data to obtain the repair effect data includes: Separating and extracting the multi-dimensional cadmium ion migration characteristics of the material combination effect data by high-order spectrum decomposition to obtain a dynamic response spectrum; Decoupling calculation is performed on the repair process time sequence information of the dynamic response spectrum through ion migration flux analysis to form a mass transfer kinetic equation; The gradient field of the mass transfer kinetics equation is quantitatively analyzed through the interface reaction-diffusion mechanism to construct a molecular transfer function; The molecular transfer function is spatially discretized according to a concentration field reconstruction method to obtain multi-dimensional migration field data; Using parameter sensitivity analysis to perform nonlinear analysis on the multi-dimensional migration field data, and construct a repair response surface; Numerical discretization and time-space integration operations are performed according to the repair response surface to obtain the repair effect data.

7. The method for remediation of simulated cadmium-contaminated soil according to claim 1, characterized in that: The method of using the remediation effect data to evaluate the remediation plan and generate a cadmium contaminated soil remediation plan includes: The restoration effect data is subjected to multi-level decomposition mapping by a high-dimensional feature extraction algorithm to obtain an evaluation feature vector, and the evaluation feature vector is reconstructed through orthogonal transformation to obtain a solution evaluation matrix; Through parameter sensitivity analysis, the key factors of the scheme evaluation matrix are quantitatively analyzed to construct a sensitivity function, and the conditional boundary of the sensitivity function is solved to obtain an evaluation parameter field; Decoupling the time series characteristics of the evaluation parameter field through a dynamic response mechanism to generate a characteristic response spectrum, and performing nonlinear decomposition on the characteristic response spectrum to obtain a repair efficiency function; The spatial distribution of the repair efficiency function is locally analyzed according to the numerical reconstruction method to obtain the efficiency distribution field, and the efficiency distribution field is normalized through multi-dimensional mapping to construct a solution optimization function; The scheme optimization function is dynamically solved by recursive iteration to form a multi-dimensional optimization surface, and the multi-dimensional optimization surface is resampled by Lagrange interpolation to obtain a scheme characteristic spectrum; Non-equilibrium analysis and numerical integration operations are performed on the characteristic spectrum of the scheme to obtain scheme evaluation parameters, and multi-objective constraint optimization and parameter correction are performed on the scheme evaluation parameters to generate the cadmium-contaminated soil treatment scheme.

8. A restoration and treatment system for simulating cadmium-contaminated soil, used to implement the restoration and treatment method for simulating cadmium-contaminated soil as described in any one of claims 1 to 7, characterized in that: The remediation system for simulating cadmium-contaminated soil comprises: A detection module is used to detect and analyze cadmium-contaminated soil samples by atomic absorption spectroscopy to obtain soil cadmium content data; An analysis module, used to detect and analyze the adsorption performance of the repair material according to the soil cadmium content data, and obtain the performance data of the repair material; A processing module, used to process the pollutant migration law based on the soil cadmium content data and the remediation material performance data to obtain pollutant migration characteristic data; An evaluation module, used to use the pollutant migration characteristic data to perform data analysis on the repair material combination to obtain material combination effect data; An identification module, used to dynamically detect the repair process according to the material combination effect data to obtain repair effect data; A generation module is used to use the restoration effect data to conduct data evaluation on the restoration plan and generate a cadmium-contaminated soil treatment plan.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the method for remediation of simulated cadmium-contaminated soil as described in any one of claims 1 to 7 is implemented.

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