Soil pollutant detection and evaluation method and system based on high-density resistivity

Abstract

The invention relates to the technical field of soil pollution monitoring, in particular to a soil pollutant detection and evaluation method and system based on high-density resistivity. The method comprises the following steps: acquiring soil information data; performing soil uniformity analysis on the soil information data to generate soil uniformity data; performing electrode arrangement mode selection based on the soil uniformity data to generate soil electrode arrangement mode data; performing soil resistivity data acquisition according to the soil electrode arrangement mode data to obtain soil resistivity data; performing resistivity inversion on the soil resistivity data to generate a soil resistivity distribution diagram; and performing resistivity anomaly identification on the soil resistivity distribution map to generate abnormal resistivity identification data. By optimizing soil uniformity analysis, electrode arrangement, resistivity inversion and pollutant diffusion analysis, the accuracy in the soil pollutant detection and evaluation process is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of soil pollution monitoring, and in particular to a soil pollutant detection and evaluation method and system based on high-density resistivity. Background Art

[0002] The high-density resistivity method was first used in mineral exploration and water resource detection, and then gradually applied to environmental pollution monitoring. The initial resistivity measurement method was relatively simple, mainly relying on monopolar and bipolar electrode devices, with limited detection depth and resolution. With the advancement of technology, the high-density resistivity method came into being. By increasing the density of measurement points and improving the data collection method, it has realized the identification of pollutants at deeper layers and smaller scales below the surface. With the development of computer technology, the high-density resistivity method has been widely used, and the accuracy and speed of data processing have been significantly improved. Later, the emergence of three-dimensional inversion algorithms enabled resistivity data to more accurately depict the spatial distribution of underground pollutants, especially in detecting organic matter, heavy metals and other pollutants in soil. In recent years, high-density resistivity data analysis methods combined with artificial intelligence and machine learning have further improved detection accuracy. By integrating different electrode arrangements and multi-frequency signals, the high-density resistivity method has gradually become a core tool for environmental geophysical detection, providing a scientific basis for soil pollution control and environmental remediation. However, when the traditional high-density resistivity method is used in areas with poor soil uniformity, the electrode arrangement and data collection are easily affected, resulting in inaccurate results. At the same time, the distribution and diffusion of pollutants are evaluated as independent links, ignoring the long-term impact of pollution diffusion on soil quality and groundwater, which leads to low accuracy in the process of soil pollutant detection and evaluation. Summary of the invention

[0003] Based on this, it is necessary to provide a soil contaminant detection and evaluation method and system based on high-density resistivity to solve at least one of the above technical problems.

[0004] To achieve the above object, a soil contaminant detection and evaluation method based on high-density resistivity is provided, the method comprising the following steps: Step S1: acquiring soil information data; performing soil uniformity analysis on the soil information data to generate soil uniformity data; selecting an electrode arrangement mode based on the soil uniformity data to generate soil electrode arrangement mode data; collecting soil resistivity data based on the soil electrode arrangement mode data to obtain soil resistivity data; Step S2: Perform resistivity inversion on the soil resistivity data to generate a soil resistivity distribution map; perform resistivity anomaly identification on the soil resistivity distribution map to generate abnormal resistivity identification data; perform resistivity change area segmentation on the soil resistivity distribution map using the resistivity anomaly identification data to generate a high conductivity pollution area and a low conductivity pollution area; Step S3: Perform pollutant diffusion analysis on the high conductivity pollution area to generate pollutant diffusion data for the high conductivity pollution area; perform groundwater pollution risk assessment on the soil resistivity distribution map based on the pollutant diffusion data for the high conductivity pollution area to generate hazard assessment data for the high conductivity pollution area; perform soil quality degradation assessment on the low conductivity pollution area to generate hazard assessment data for the low conductivity pollution area; Step S4: regional risk labeling is performed on the high conductivity pollution area hazard assessment data and the low conductivity pollution area hazard assessment data to generate a soil regional pollutant risk labeling map; pollution decision-making is constructed based on the soil regional pollutant risk labeling map to perform soil pollutant control operations.

[0005] The present invention can comprehensively understand the physical and chemical properties of the soil, identify the uniformity level of the soil, and provide a basis for the subsequent selection of electrode arrangement mode by collecting and analyzing soil information data. The results of soil uniformity analysis can help determine the appropriate electrode arrangement mode and improve the accuracy and effect of resistivity measurement. By inverting and identifying anomalies in soil resistivity data, the spatial distribution map of soil resistivity can be accurately drawn, the resistivity abnormal area can be identified, and these areas can be divided. The soil resistivity distribution map obtained by resistivity inversion provides data support for the precise positioning of the polluted area, can clearly distinguish high conductivity pollution areas and low conductivity pollution areas, and guide subsequent pollution assessment and control. By analyzing the diffusion of pollutants in high conductivity pollution areas, the diffusion path of pollutants can be tracked and the risk of groundwater pollution can be assessed. This process helps to predict the trend of pollution diffusion and provide a scientific basis for environmental protection measures. By assessing the degradation of soil quality in low conductivity pollution areas, the degree and impact of pollution can be quantified, providing clear guidance for key areas of pollution control. By integrating the hazard assessment data of high-conductivity and low-conductivity pollution areas and generating soil regional pollutant risk annotation maps, the pollution risk levels of different areas can be clearly displayed. Through pollution decision-making construction, specific governance strategies are formulated to achieve efficient governance of soil pollutants. The visualization of the map helps decision makers quickly understand the spatial distribution and severity of pollution, provide scientific guidance for actual soil pollution control operations, and ensure the accuracy and effectiveness of governance actions. The comprehensive application of these four steps realizes accurate detection, risk assessment and governance decision-making of soil pollution through scientific data analysis and model selection. Each step provides data support for pollution identification, diffusion prediction, risk assessment and governance plan formulation, which not only improves the efficiency of pollution control, but also reduces resource waste in the governance process, optimizes environmental remediation strategies, and effectively improves soil environmental quality. Therefore, the present invention improves the accuracy of soil pollutant detection and assessment processes by optimizing soil uniformity analysis, electrode arrangement, resistivity inversion and pollutant diffusion analysis.

[0006] Preferably, step S1 comprises the following steps: Step S11: Acquire soil information data; Step S12: performing soil uniformity analysis on the soil information data to generate soil uniformity data; performing electrode arrangement mode selection based on the soil uniformity data to generate soil electrode arrangement mode data, wherein the soil electrode arrangement mode data includes a shallow layer detection arrangement mode and a deep layer detection arrangement mode; Step S13: confirming the boundary of the target area of ​​soil based on the shallow detection arrangement mode and the deep detection arrangement mode to obtain soil detection target area boundary data; dividing the soil information data into measurement grids based on the soil detection target area boundary data to generate soil measurement grid data; Step S14: collecting soil resistivity data on the soil measurement grid data to obtain soil resistivity data.

[0007] The present invention obtains accurate and comprehensive soil information data, which is the basis of the entire soil resistivity measurement, ensures that the data used in the subsequent steps are reliable and representative, and provides data support for the entire detection process. By analyzing the uniformity of the soil, the heterogeneous areas in the soil can be identified, providing a basis for the selection of the electrode arrangement mode. In this way, the arrangement of the electrodes can be optimized, the measurement accuracy and efficiency can be improved, and the detection errors caused by soil heterogeneity can be avoided. According to the soil uniformity data, the appropriate electrode arrangement mode (such as shallow detection and deep detection mode) can be selected to effectively optimize the electrode configuration for different soil characteristics, thereby ensuring the accuracy and sensitivity of different detection depths. By clarifying the boundaries of the target area of ​​soil detection, the accuracy and pertinence of the measurement can be improved, the reasonable division of the measurement grid can be ensured, and the overall data quality can be avoided from being affected by unnecessary regional interference. Scientific grid division can refine the soil information data and improve the accuracy of data acquisition. This process ensures uniform coverage of the measurement points, making the data more representative and accurate. Resistivity data is the core of soil detection. By collecting resistivity data point by point in the measurement grid, detailed soil resistivity distribution can be obtained, providing accurate basic data for subsequent data analysis and processing, and ultimately achieving accurate evaluation of soil properties and structure.

[0008] Preferably, step S12 comprises the following steps: Step S121: extracting soil particle morphological characteristics from the soil information data to obtain soil particle morphological characteristic data; dividing the soil information data into soil layers based on the soil particle morphological characteristic data to generate soil layer division data; Step S122: Calculate the soil layer height of the soil layer division data to generate soil layer height data; perform layer particle distribution analysis on the soil layer division data through the soil layer height data to generate a soil layer particle distribution map; calculate the distribution height difference of the soil layer particle distribution map to obtain distribution height difference data; Step S123: calculating the uniformity of soil particle distribution by using the distribution height difference data to obtain soil uniformity data; comparing the soil uniformity data with a preset soil uniformity threshold, and when the soil uniformity data is greater than or equal to the preset soil uniformity threshold, generating uniform soil data; when the soil uniformity data is less than the preset soil uniformity threshold, generating non-uniform soil data; Step S124: performing multi-modal electrode arrangement selection on the uniform soil data and the non-uniform soil data according to the soil layer division data to generate soil electrode arrangement pattern data, wherein the soil electrode arrangement pattern data includes a shallow detection arrangement pattern and a deep detection arrangement pattern.

[0009] The present invention can help understand the physical properties of soil, such as particle size, morphology, etc., by extracting soil particle morphological characteristics, which provides key data for subsequent soil layer division. Through particle morphological characteristic data, the differences between different soil types and layers can be identified, providing an accurate basis for electrode arrangement. Soil layer division makes the characteristics of different layers of soil clear, which provides more detailed guidance for the design of measurement grids and the selection of electrode arrangement patterns. Through layer division, different detection depths can be selected in a targeted manner to improve the accuracy and flexibility of detection. By calculating the height of the soil layer, the soil distribution range of different layers can be clarified, providing physical boundaries for subsequent measurement and detection. This helps to reasonably allocate the layout of the electrode array, ensure the accuracy of detection at different depths, and generate a layer particle distribution map to more intuitively display the particle distribution of each layer of the soil, thereby helping to identify the structural characteristics of the soil. This is crucial for the analysis of properties such as resistivity distribution and permeability of the soil, and helps to determine the most suitable electrode arrangement mode. By calculating the distribution height difference, the distribution unevenness of soil layer particles can be quantified, providing a basis for subsequent soil uniformity analysis. This can reveal the heterogeneity of the soil and help determine the resistivity differences between different soil layers, which in turn affects the choice of detection mode. The calculation of the uniformity of soil particle distribution helps determine the uniformity of the soil. If the soil uniformity is high, the selection of the electrode arrangement mode and the setting of the detection depth can be simplified, improving the detection efficiency and accuracy. If the soil is uneven, the electrode arrangement mode will be adjusted according to the needs of different areas to ensure the best detection effect. By comparing with the preset soil uniformity threshold, it is possible to quickly determine whether the soil meets the predetermined uniformity standard. This helps to choose whether to use a more sophisticated measurement method or adjust the electrode arrangement mode according to the actual uniformity of the soil to ensure the reliability of the measurement results. According to different soil layer data and soil uniformity information, the appropriate electrode arrangement mode (shallow detection or deep detection mode) can be flexibly selected. This multi-modal electrode arrangement selection not only improves the detection accuracy, but also optimizes the electrode configuration according to the actual soil conditions, avoiding unnecessary energy waste and inaccurate measurements.

[0010] Preferably, step S124 includes: Extracting the soil layer division data in terms of layer quantity to obtain soil layer quantity data; performing soil layer pattern analysis on the soil layer division data based on the soil layer quantity data to generate shallow soil pattern data and deep soil pattern data; Perform Wenner electrode arrangement on uniform soil data according to shallow soil pattern data to generate a shallow first detection mode; perform Pole-Dipole electrode arrangement on non-uniform soil data according to shallow soil pattern data to generate a shallow second detection mode; Schlumberger electrodes are arranged on uniform soil data according to deep soil pattern data to generate a first deep detection mode; Dipole-Dipole electrodes are arranged on non-uniform soil data according to deep soil pattern data to generate a second deep detection mode; The shallow first detection mode and the shallow second detection mode are integrated to generate a shallow detection arrangement mode; the deep first detection mode and the deep second detection mode are integrated to generate a deep detection arrangement mode.

[0011] The present invention can accurately identify the hierarchical structure of the soil by extracting the number of soil layers, thereby providing basic information for the configuration of the electrode. This helps to better understand the hierarchical characteristics of the soil and determine the detection depth and arrangement pattern of each layer of soil. The analysis of the soil layer pattern can generate different electrode arrangement patterns for different soil layer characteristics. This analysis helps to determine the measurement method of shallow and deep soils, so that the electrode arrangement is more in line with the actual structure of the soil, and improves the depth and accuracy of the detection. The Wenner electrode arrangement mode is very suitable for uniform soil and can provide relatively accurate resistivity data in shallow detection. Through this arrangement, the resistivity characteristics of shallow soil can be effectively captured and the reliability of the data can be improved. The Pole-Dipole electrode arrangement is suitable for non-uniform soil and can better detect the resistivity differences of non-uniform soil layers. For non-uniform soil, the use of this mode can increase the depth and resolution of the detection and obtain more accurate measurement results. The Schlumberger electrode arrangement is suitable for the detection of deep soil and can provide high-precision resistivity measurement in uniform soil. Deep detection can better adapt to the resistivity characteristics of the soil, reduce errors, and improve measurement accuracy. The Dipole-Dipole electrode arrangement mode is particularly effective for deep detection of non-uniform soils. It can further reveal the resistivity differences between different soil layers and optimize the deep detection results. It can penetrate deep into non-uniform soils and provide more accurate resistivity measurements. By integrating the shallow detection mode, the characteristics of both uniform and non-uniform soils can be considered in a complete detection framework, improving the comprehensiveness and accuracy of shallow measurements. The integrated mode can adapt to a variety of soil conditions and improve the overall detection effect. After integrating the deep detection mode, the detection strategy of uniform and non-uniform soils can be optimized at the same time to ensure high-quality data in deep soil measurements. Through integration, the efficiency of deep detection can be improved and the comprehensiveness and depth of data can be ensured.

[0012] Preferably, step S2 comprises the following steps: Step S21: performing data preprocessing on the soil resistivity data to generate standard soil resistivity data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S22: Perform resistivity inversion on the standard soil resistivity to generate a soil resistivity distribution map; perform change amplitude calculation on the soil resistivity distribution map to obtain resistivity change amplitude data; perform resistivity anomaly identification on the soil resistivity distribution map based on the resistivity change amplitude data to generate abnormal resistivity identification data; Step S23: confirming the suspected pollution area on the soil resistivity distribution map through the resistivity anomaly identification data to obtain the suspected pollution area data; performing resistivity change trend analysis on the suspected pollution area data to generate resistivity change trend data; Step S24: Classifying the suspected contaminated area data into abnormal areas according to the resistivity change trend data, and generating high conductivity contaminated areas and low conductivity contaminated areas.

[0013] The present invention can ensure the quality of input data through data preprocessing, solve the problems of data missing and noise interference, and improve the accuracy of subsequent analysis. Through data cleaning, missing value filling and standardization, the standardized data provides a reliable basis for resistivity inversion and anomaly detection, thereby enhancing the robustness of the entire analysis process. Resistivity inversion is to convert the measured resistivity data into a spatial distribution map of the soil, which can visualize the change of soil resistivity. The soil resistivity distribution map provides an intuitive basis for subsequent detection and classification of contaminated areas, helping researchers to identify potential pollution hotspots. By calculating the amplitude of resistivity change, the spatial fluctuation of soil resistivity can be revealed, and the significant change area of ​​resistivity in the soil can be further identified. The amplitude of change data provides a quantitative basis for identifying soil resistivity anomalies. Resistivity anomaly identification can accurately capture areas in the soil that are different from normal levels, especially those affected by pollution or other abnormal factors. The generated abnormal resistivity identification data can help locate problematic soil areas and provide a basis for pollution source tracking and soil improvement. By combining resistivity anomaly identification with soil resistivity distribution map, the contaminated area can be determined more accurately. The confirmation of suspected contaminated areas provides clear target areas for subsequent in-depth analysis and actual pollution control measures. The analysis of resistivity change trends helps to understand the evolution of contaminated areas and whether the pollution is expanding or slowing down. Through trend analysis, important time dimension information can be provided for tracing the source of soil pollution, the speed of pollution spread and the scope of impact. Through in-depth analysis of resistivity change trends, contaminated areas can be divided into high conductivity pollution areas and low conductivity pollution areas. This classification helps to identify pollution types more accurately. High conductivity areas are usually associated with soils with higher pollutant concentrations, while low conductivity areas are areas with lower pollutant concentrations. Through this classification, more targeted solutions can be provided for the control strategies of different pollution types.

[0014] Preferably, performing resistivity inversion on the standard soil resistivity comprises: The standard soil resistivity is spatially calibrated using the soil electrode arrangement pattern data to generate soil resistivity spatial calibration data; the soil resistivity spatial calibration data is divided into soil spatial regional grid units to generate soil spatial grid units, wherein each soil spatial grid unit contains a local soil resistivity value; The theoretical apparent resistivity value of the soil space grid unit is simulated by the forward algorithm to generate the forward simulation resistivity value; the error between the forward simulation resistivity value and the local soil resistivity value is calculated to generate the resistivity error value; the resistivity error value is used to adjust the resistivity of the local soil resistivity value to generate the local adjusted resistivity; The local adjusted resistivity is iteratively inverted to generate the global adjusted resistivity of the soil; the global adjusted resistivity of the soil is interpolated to generate resistivity distribution interpolation data; the resistivity distribution interpolation data is visualized to generate a soil resistivity distribution map.

[0015] The present invention helps to eliminate spatial errors in soil resistivity data through spatial calibration, so that the data can more accurately reflect the actual soil characteristics. By considering the soil electrode arrangement pattern, it can be ensured that the calibrated resistivity data has good matching in different areas, providing high-quality input data for subsequent inversion. Dividing the soil area into grid units can convert the soil resistivity distribution into processable discrete data, making further analysis and calculation more detailed and accurate. The local resistivity value of each grid unit provides an important basis for subsequent forward simulation and error calculation. The forward algorithm can simulate the theoretical resistivity value according to the spatial distribution of soil resistivity. This process helps to infer the soil resistivity distribution under given conditions and provide a theoretical reference value for comparison with actual data. Error calculation is a key step to ensure data quality. By calculating the error between the forward simulation resistivity value and the actual measurement value, the difference in data can be quantified, helping to discover potential problems or anomalies, and providing a basis for further optimizing the soil resistivity value. The deviation in the actual measurement data can be corrected by error adjustment to ensure that the local soil resistivity value is more accurate. The adjusted local resistivity provides a more accurate input for generating the global resistivity, which helps to improve the accuracy of the final resistivity distribution. Iterative inversion is an optimization process that can gradually approach the true global resistivity of the soil by continuously adjusting the local resistivity. This process can effectively reduce the errors in the calculation, making the final resistivity more consistent with the actual soil conditions and improving the accuracy of the inversion. Data interpolation can fill the gaps between grid cells to form continuous resistivity distribution data. Interpolated data can make the soil resistivity distribution smoother, facilitate subsequent visualization and analysis, and avoid errors caused by data sparsity. Through data visualization, the resistivity distribution map can intuitively display the spatial variation of resistivity in the soil, providing researchers with an effective basis for decision-making. The soil resistivity distribution map helps to better understand the nature of the soil, the degree of contamination, and the treatment plan, and provides scientific support for subsequent soil management.

[0016] Preferably, step S3 comprises the following steps: Step S31: performing resistivity time series monitoring on the high-conductivity pollution area to generate resistivity time series images of the high-conductivity pollution area; performing pollutant diffusion analysis on the resistivity time series images of the high-conductivity pollution area to generate pollutant diffusion data of the high-conductivity pollution area; Step S32: screening the soil resistivity distribution map for the lowest resistance extreme value area to obtain the resistance extreme value area; locating the pollutant concentration area in the resistance extreme value area through the pollutant diffusion data of the high conductivity pollution area to generate pollutant diffusion concentration center point data; conducting groundwater pollution risk assessment on the high conductivity pollution area through the pollutant diffusion concentration center point data to generate high conductivity pollution area hazard assessment data; Step S33: Sampling the soil profile of the low-conductivity pollution area to generate the soil plane surface of the low-conductivity pollution area; performing pollution horizontal migration analysis on the soil plane surface of the low-conductivity pollution area to generate pollution horizontal migration data; performing vertical migration restriction analysis on the soil plane surface of the conductive pollution area to generate pollution vertical migration data; Step S34: Based on the pollution horizontal migration data and the pollution vertical migration data, soil quality degradation assessment is performed on the low conductivity pollution area to generate low conductivity pollution area hazard assessment data.

[0017] The present invention can dynamically capture the change of resistivity in the high conductivity pollution area through resistivity time series monitoring, and provide real-time data support for the diffusion of pollutants. Pollutant diffusion analysis helps to reveal the expansion trend and scope of pollution sources, and provides a scientific basis for the implementation of pollution prevention and control measures. Resistance extreme value area screening can identify the areas with the lowest resistivity in the soil, which are usually hot spots for pollutant accumulation. Through the location of the accumulation area, the accumulation center of pollutants can be further analyzed, providing an accurate basis for the risk assessment of groundwater pollution, helping to formulate countermeasures and reduce the hazards of groundwater pollution. Through soil profile sampling and analysis, the soil properties and distribution of pollutants in low conductivity pollution areas can be more accurately understood. Pollution horizontal migration analysis can reveal the migration trend of pollutants in the horizontal direction, and vertical migration restriction analysis can help evaluate whether pollutants will expand to important resource levels such as groundwater, providing strong data support for soil management. By combining pollution horizontal migration data and pollution vertical migration data, the soil quality degradation in low conductivity pollution areas can be comprehensively evaluated. This helps to identify the severity of soil pollution, provide a quantitative basis for the design of soil remediation and pollution control strategies, and effectively predict the potential harm of polluted areas to the ecological environment.

[0018] Preferably, step S33 includes the following steps: Step S331: sampling the soil profile of the low-conductivity pollution area to generate the soil profile of the low-conductivity pollution area; Step S332: performing pollutant concentration detection on the soil surface of the low-conductivity pollution area to generate pollutant concentration data for the pollution area; calculating the pollutant horizontal diffusion rate on the soil surface of the low-conductivity pollution area using the pollutant concentration data for the pollution area to obtain pollutant horizontal diffusion data; calculating the pollutant horizontal migration trend based on the pollutant horizontal diffusion data and the pollutant concentration data for the pollution area to generate pollution horizontal migration data; Step S333: performing pollutant vertical concentration distribution analysis on the soil surface of the conductive pollution area to generate pollutant vertical concentration distribution data; performing vertical migration barrier layer identification on the soil surface of the conductive pollution area according to the pollutant vertical concentration distribution data to generate pollutant vertical migration barrier layer data; Step S334: Calculate the depth trend of vertical migration of pollution through the pollutant vertical migration barrier layer data and the pollutant vertical concentration distribution data to generate vertical migration data of pollution.

[0019] The present invention can obtain the detailed soil layer structure of the low-conductivity contaminated area through soil profile sampling, and provide the necessary sample data for subsequent pollutant detection and diffusion analysis. This helps to accurately understand the depth of the contaminated layer and its distribution range. Through pollutant concentration detection and horizontal diffusion rate calculation, the distribution of pollutants in the soil and their horizontal diffusion trend can be deeply understood, and then the diffusion range of pollutants can be predicted. The calculation of the horizontal migration trend of pollutants helps to evaluate the diffusion degree of pollutants in the future, thereby providing a basis for the timely formulation of pollution prevention and control measures. Through the vertical concentration distribution analysis of pollutants, the distribution characteristics of pollutants in different soil depths can be evaluated, helping to identify the migration direction and obstruction layer of pollutants. Identifying the vertical migration barrier layer helps to determine whether pollutants will further penetrate into the groundwater layer or deep soil, providing a scientific basis for the location of pollution sources and groundwater protection. The vertical migration depth situation calculation of pollution combines the vertical concentration distribution of pollutants and the migration barrier layer data, which helps to deeply understand the potential vertical migration path and depth of pollutants. This can predict whether pollutants will affect the groundwater layer and other deep soil resources, and evaluate the extension depth of pollution, providing key support for the formulation of soil remediation and groundwater protection plans.

[0020] Preferably, step S4 comprises the following steps: Step S41: integrating the high conductivity pollution area hazard assessment data and the low conductivity pollution area hazard assessment data to generate soil area pollutant detection assessment data; performing soil area risk labeling on the soil area pollutant detection assessment data to generate soil area pollutant risk labeling data; Step S42: Visualize the soil area pollutant risk labeling data to generate a soil area pollutant risk labeling map; construct pollution decisions based on the soil area pollutant risk labeling map to perform soil pollutant control operations.

[0021] The present invention can comprehensively evaluate the pollution level of the entire soil area by integrating the hazard assessment data of high-conductivity and low-conductivity pollution areas, and provide a unified data basis for subsequent risk assessment and governance work. By risk-marking these assessment data, the potential hazard areas of pollutants in the soil can be marked, helping to identify areas that need to be treated first, and improving the accuracy and efficiency of pollution control. Data visualization can convert complex pollution risk information into intuitive and easy-to-understand maps, which is convenient for relevant personnel to quickly understand the pollution status of the soil area, especially the distribution and degree of danger of pollutants. This visual display provides clearer and more effective support for the formulation of pollution decisions. Decision makers can formulate governance plans based on the labeled layers on the map, accurately locate pollution sources and governance target areas, thereby achieving efficient soil pollution governance operations. By marking and visualizing the risks of soil pollutants, the operability of pollution assessment and governance is enhanced. The risk-marked map not only helps to intuitively understand the spatial distribution of soil pollution, but also provides a scientific basis for soil pollution governance work, ensuring the accuracy and pertinence of governance operations, thereby effectively reducing the potential hazards of soil pollution and promoting the smooth progress of environmental remediation work.

[0022] In this specification, a soil contaminant detection and evaluation system based on high-density resistivity is provided, which is used to perform the above-mentioned soil contaminant detection and evaluation method based on high-density resistivity. The soil contaminant detection and evaluation system based on high-density resistivity includes: The electrode arrangement module is used to obtain soil information data; perform soil uniformity analysis on the soil information data to generate soil uniformity data; select an electrode arrangement mode based on the soil uniformity data to generate soil electrode arrangement mode data; collect soil resistivity data based on the soil electrode arrangement mode data to obtain soil resistivity data; The abnormal soil identification module is used to perform resistivity inversion on soil resistivity data to generate a soil resistivity distribution map; perform resistivity anomaly identification on the soil resistivity distribution map to generate abnormal resistivity identification data; perform resistivity change area segmentation on the soil resistivity distribution map using the resistivity anomaly identification data to generate high conductivity pollution areas and low conductivity pollution areas; The hazard assessment module is used to analyze the diffusion of pollutants in highly conductive pollution areas and generate pollutant diffusion data for highly conductive pollution areas; to conduct groundwater pollution risk assessment on soil resistivity distribution maps based on pollutant diffusion data for highly conductive pollution areas and generate hazard assessment data for highly conductive pollution areas; to conduct soil quality degradation assessment on low conductive pollution areas and generate hazard assessment data for low conductive pollution areas; The risk decision module is used to carry out regional risk labeling for the hazard assessment data of high conductivity pollution areas and the hazard assessment data of low conductivity pollution areas, and generate a soil regional pollutant risk labeling map; pollution decision-making is constructed based on the soil regional pollutant risk labeling map to execute soil pollutant control operations.

[0023] The beneficial effect of the present invention is that the electrode arrangement is matched with the soil characteristics through soil uniformity analysis, and the accuracy of data acquisition is optimized. This can improve the quality of resistivity data and reduce the errors caused by soil uniformity differences, thereby providing reliable basic data for subsequent steps. The soil resistivity distribution map generated by resistivity inversion provides spatial distribution information for the contaminated area, and resistivity anomaly identification further distinguishes high conductivity and low conductivity contaminated areas. This enables the contaminated area to be accurately located, helps to identify the source of pollution and the degree of pollution, and lays the foundation for subsequent analysis. Through the analysis of pollutant diffusion in high conductivity contaminated areas, the propagation trend of pollutants can be understood, thereby predicting potential threats to groundwater. Soil quality degradation assessment in low conductivity contaminated areas helps to assess the health of the soil and the possibility of its future recovery, and improves the scientificity and pertinence of environmental governance decisions. Through the generated soil regional pollutant risk labeling map, the risk level of the contaminated area can be clearly identified, providing clear priorities and strategies for pollution control. This step helps to formulate an efficient soil pollution control plan, ensure optimal resource allocation, and help reduce governance costs and improve the effect of pollution remediation. Therefore, the present invention improves the accuracy in the process of soil pollutant detection and assessment by optimizing soil uniformity analysis, electrode arrangement, resistivity inversion and pollutant diffusion analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a soil contaminant detection and evaluation method based on high-density resistivity; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 4 , a soil contaminant detection and evaluation method based on high-density resistivity, the method comprising the following steps: Step S1: acquiring soil information data; performing soil uniformity analysis on the soil information data to generate soil uniformity data; selecting an electrode arrangement mode based on the soil uniformity data to generate soil electrode arrangement mode data; collecting soil resistivity data based on the soil electrode arrangement mode data to obtain soil resistivity data; Step S2: Perform resistivity inversion on the soil resistivity data to generate a soil resistivity distribution map; perform resistivity anomaly identification on the soil resistivity distribution map to generate abnormal resistivity identification data; perform resistivity change area segmentation on the soil resistivity distribution map using the resistivity anomaly identification data to generate a high conductivity pollution area and a low conductivity pollution area; Step S3: Perform pollutant diffusion analysis on the high conductivity pollution area to generate pollutant diffusion data for the high conductivity pollution area; perform groundwater pollution risk assessment on the soil resistivity distribution map based on the pollutant diffusion data for the high conductivity pollution area to generate hazard assessment data for the high conductivity pollution area; perform soil quality degradation assessment on the low conductivity pollution area to generate hazard assessment data for the low conductivity pollution area; Step S4: regional risk labeling is performed on the high conductivity pollution area hazard assessment data and the low conductivity pollution area hazard assessment data to generate a soil regional pollutant risk labeling map; pollution decision-making is constructed based on the soil regional pollutant risk labeling map to perform soil pollutant control operations.

[0029] The present invention can comprehensively understand the physical and chemical properties of the soil, identify the uniformity level of the soil, and provide a basis for the subsequent selection of electrode arrangement mode by collecting and analyzing soil information data. The results of soil uniformity analysis can help determine the appropriate electrode arrangement mode and improve the accuracy and effect of resistivity measurement. By inverting and identifying anomalies in soil resistivity data, the spatial distribution map of soil resistivity can be accurately drawn, the resistivity abnormal area can be identified, and these areas can be divided. The soil resistivity distribution map obtained by resistivity inversion provides data support for the precise positioning of the polluted area, can clearly distinguish high conductivity pollution areas and low conductivity pollution areas, and guide subsequent pollution assessment and control. By analyzing the diffusion of pollutants in high conductivity pollution areas, the diffusion path of pollutants can be tracked and the risk of groundwater pollution can be assessed. This process helps to predict the trend of pollution diffusion and provide a scientific basis for environmental protection measures. By assessing the degradation of soil quality in low conductivity pollution areas, the degree and impact of pollution can be quantified, providing clear guidance for key areas of pollution control. By integrating the hazard assessment data of high-conductivity and low-conductivity pollution areas and generating soil regional pollutant risk annotation maps, the pollution risk levels of different areas can be clearly displayed. Through pollution decision-making construction, specific governance strategies are formulated to achieve efficient governance of soil pollutants. The visualization of the map helps decision makers quickly understand the spatial distribution and severity of pollution, provide scientific guidance for actual soil pollution control operations, and ensure the accuracy and effectiveness of governance actions. The comprehensive application of these four steps realizes accurate detection, risk assessment and governance decision-making of soil pollution through scientific data analysis and model selection. Each step provides data support for pollution identification, diffusion prediction, risk assessment and governance plan formulation, which not only improves the efficiency of pollution control, but also reduces resource waste in the governance process, optimizes environmental remediation strategies, and effectively improves soil environmental quality. Therefore, the present invention improves the accuracy of soil pollutant detection and assessment processes by optimizing soil uniformity analysis, electrode arrangement, resistivity inversion and pollutant diffusion analysis.

[0030] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a soil contaminant detection and evaluation method based on high-density resistivity of the present invention. In this example, the soil contaminant detection and evaluation method based on high-density resistivity includes the following steps: Step S1: acquiring soil information data; performing soil uniformity analysis on the soil information data to generate soil uniformity data; selecting an electrode arrangement mode based on the soil uniformity data to generate soil electrode arrangement mode data; collecting soil resistivity data based on the soil electrode arrangement mode data to obtain soil resistivity data; In an embodiment of the present invention, soil samples are collected at multiple representative points in the target area by using soil sampling equipment (such as a soil drill, a soil sampler, etc.). The sampling points need to be scientifically selected based on regional geological characteristics, soil type, degree of pollution, and other factors to ensure that the sampling data is representative. Through laboratory analysis or field analysis technology (such as laser induced breakdown spectroscopy, X-ray fluorescence analysis, etc.), the collected soil samples are comprehensively analyzed to obtain basic information of the soil, including soil type, particle size distribution, moisture content, mineral composition, chemical properties, pH, and other data. Based on the acquired soil information data, a statistical analysis method is used to perform uniformity analysis on the spatial distribution and physical and chemical properties of the soil. Common methods include variance analysis, standard deviation calculation, Kriging interpolation, etc. The uniformity of the soil is quantitatively evaluated to generate soil uniformity data to reflect the uniformity of the soil in the target area. The uniformity data is usually displayed in the form of a chart, a spatial distribution map, or a parameter value. Based on the soil uniformity data, combined with the requirements of resistivity measurement (such as measurement depth, resolution, detection accuracy, etc.), a suitable electrode arrangement mode is selected. Common electrode arrangement modes include Wenner four-electrode method, Schlumberger four-electrode method, Dipole-Dipole method, etc. Through simulation or actual testing, the applicability of different electrode arrangement modes under the soil conditions is evaluated, and the arrangement mode that can best meet the soil resistivity acquisition needs is selected to generate soil electrode arrangement mode data, describing the installation method, spacing and depth of the electrodes. According to the selected electrode arrangement mode, the electrodes are installed at the soil sampling points to collect soil resistivity data. Use a geological resistivity meter (such as AGI or other brands of resistivity measurement equipment) for data collection to ensure that the electrodes are installed stably and in good contact. According to different measurement targets and electrode arrangement modes, set appropriate measurement parameters (such as current intensity, electrode spacing, scanning depth, etc.), and perform multiple data collections to ensure the accuracy and reliability of the data. Through reverse calculation or interpolation, soil resistivity data at different depths and spatial positions can be obtained. These data can help evaluate the conductivity characteristics of the soil and further support related analyses such as soil pollution, remediation, and permeability.

[0031] Step S2: Perform resistivity inversion on the soil resistivity data to generate a soil resistivity distribution map; perform resistivity anomaly identification on the soil resistivity distribution map to generate abnormal resistivity identification data; perform resistivity change area segmentation on the soil resistivity distribution map using the resistivity anomaly identification data to generate a high conductivity pollution area and a low conductivity pollution area; In an embodiment of the present invention, the soil resistivity data (including information such as electrode spacing, depth, and measurement location) collected in step S1 is organized as input data to prepare for inversion analysis. A suitable resistivity inversion algorithm is selected, such as an inversion algorithm based on the least squares method, a simulated annealing algorithm, a genetic algorithm, and the like. Common resistivity inversion methods include gradient-based inversion and nonlinear inversion, and the like. The collected soil resistivity data is processed using a resistivity inversion algorithm to calculate and generate a two-dimensional or three-dimensional distribution map of the soil resistivity. During the inversion process, the physical properties of the soil (such as soil moisture, particle size, etc.) are used for auxiliary correction to improve the accuracy of the inversion results. Through resistivity inversion, a spatial distribution map of soil resistivity is obtained to show the soil resistivity conditions at different depths and in different regions. The map can be displayed using a chromatogram or a contour map to reflect changes in the soil conductivity area. Based on the soil resistivity distribution map, statistical analysis or machine learning methods (such as K-means clustering, support vector machine, etc.) are used to identify resistivity anomalies. First, calculate the mean and standard deviation of the resistivity data, and mark the data with large deviations from the mean as outliers. Set appropriate resistivity thresholds or standard deviation multiples (such as ±2σ, ±3σ) as the identification criteria for abnormal resistivity. According to the conventional resistivity range of the soil, identify areas with higher or lower resistivity. Through calculation, identify areas where the resistivity deviates significantly from the normal range, and generate resistivity anomaly identification data. These data usually include information such as the location of the abnormal area, the size and type of the abnormal value. Based on the resistivity anomaly identification data, segment the resistivity distribution map. Through segmentation algorithms (such as image segmentation, regional growing algorithms, etc.), the soil resistivity distribution map is divided into multiple regions, focusing on identifying high conductivity pollution areas and low conductivity pollution areas. Identify areas with lower resistivity (i.e., areas with higher conductivity), which usually represent areas with high concentrations of pollutants in the soil, such as heavy metals, oil pollution, etc. By analyzing the resistivity values ​​of these areas, evaluate the distribution range and concentration of pollutants. Identify areas with higher resistivity (i.e., areas with lower conductivity), which are related to areas far from the pollution source or with less pollution. Based on the segmentation results of the resistivity change area, the spatial distribution data of the high conductivity pollution area and the low conductivity pollution area are generated. The pollution area is intuitively displayed through different color markings or area division diagrams.

[0032] Step S3: Perform pollutant diffusion analysis on the high conductivity pollution area to generate pollutant diffusion data for the high conductivity pollution area; perform groundwater pollution risk assessment on the soil resistivity distribution map based on the pollutant diffusion data for the high conductivity pollution area to generate hazard assessment data for the high conductivity pollution area; perform soil quality degradation assessment on the low conductivity pollution area to generate hazard assessment data for the low conductivity pollution area; In an embodiment of the present invention, a pollutant diffusion model for a specific high-conductivity contaminated area is selected. Common pollutant diffusion models include conventional diffusion equation models, numerical simulation models (such as finite element method, finite difference method), and pollutant transport models based on physical and chemical principles. Based on the existing soil resistivity distribution map and resistivity anomaly area, the diffusion process of pollutants is analyzed using information such as soil moisture, soil texture, pollutant type, and concentration. Simulate the horizontal diffusion process of pollutants in the soil and calculate their diffusion rate and path. Common methods include Laplace equation, Fick diffusion law, pollutant migration simulation, etc. According to the simulation results, pollutant diffusion data is generated, including information such as the diffusion range, speed, and time change of pollutants in high-conductivity contaminated areas. These data are usually presented in the form of diffusion maps, concentration distribution maps, etc., showing the diffusion process of pollutants in different times and spaces. According to the pollutant diffusion data in the high-conductivity contaminated area, the risk of pollutants penetrating into groundwater is analyzed. Assess the relationship between soil conductivity and groundwater flow to determine whether and how pollutants penetrate into the groundwater layer. Use groundwater flow models (such as Darcian flow models, gradient flow models, etc.) to simulate the infiltration path of pollutants. According to the diffusion and penetration of pollutants, combined with factors such as groundwater flow rate, soil permeability, and pollutant properties, the risk of groundwater contamination is assessed. Quantitative risk assessment methods (such as probabilistic risk assessment, exposure-response assessment, etc.) can be used to quantitatively analyze the possibility of groundwater contamination. By analyzing the diffusion of pollutants and the risk of groundwater contamination, hazard assessment data for high-conductivity contaminated areas are generated. This data contains information such as the risk of pollutant diffusion, the possibility of groundwater contamination, and the scope of impact, which is used to assess the pollution risk level and governance priority of the area, and select a soil quality degradation assessment model for specific low-conductivity contaminated areas. These models include factors such as changes in soil organic matter, changes in pH, and accumulation of pollutants. Commonly used soil degradation assessment models include humus dynamics models and soil acid-base reaction models. Based on the physical and chemical properties of the soil, the soil quality degradation in low-conductivity contaminated areas is analyzed, including factors such as changes in soil compaction, mineral changes, and accumulation of pollutant concentrations. For low-conductivity contaminated areas, focus on changes in soil pH, nutrient loss, and the impact of pollutants on soil microorganisms. Combined with the results of soil quality degradation analysis, hazard assessment data for low-conductivity contaminated areas are generated. Assess the degree of soil quality degradation and the potential impact of this degradation on soil functions (such as plant growth, groundwater filtration, etc.), help identify high-risk areas and formulate corresponding remediation measures.

[0033] Step S4: Perform regional risk labeling on the high conductivity pollution area hazard assessment data and the low conductivity pollution area hazard assessment data to generate a soil regional pollutant risk labeling map; construct pollution decisions based on the soil regional pollutant risk labeling map to perform soil pollutant control operations.

[0034] In an embodiment of the present invention, the hazard assessment data of high conductivity pollution areas and low conductivity pollution areas are integrated. The integration process needs to generate comprehensive regional risk data based on the assessment data of pollutant diffusion, groundwater pollution risk, soil quality degradation, etc. in each area. These data include information such as pollutant concentration, diffusion rate, risk level, impact on groundwater, etc. A specific risk labeling model (such as based on weighted scoring method, fuzzy logic method, geographic information system (GIS) analysis, etc.) is selected to divide and label the integrated hazard assessment data. Through this model, the system can classify according to the pollution risk level (such as high, medium, and low) of each area. Based on the hazard assessment data, GIS technology is used to spatially label the polluted areas according to their risk levels. In the soil regional risk labeling map, the pollutant risk can be represented by color, symbol or density layer. For example, high-risk areas are marked in red, low-risk areas are marked in green, and a soil regional pollutant risk labeling map containing soil pollutant risk levels is generated. The map provides intuitive geographic information for pollution control, displays pollutant diffusion areas and their potential hazards, and facilitates regional division, resource allocation and decision-making. Based on the soil regional pollutant risk labeling map, select the appropriate pollution control decision support model. Common decision models include the analytic hierarchy process (AHP), fuzzy comprehensive evaluation method, artificial intelligence optimization algorithm (such as genetic algorithm, simulated annealing method), etc. These models can comprehensively consider factors such as the degree of harm of pollutants, control costs, and technical feasibility to help formulate control plans. In the process of pollution decision-making, the following factors need to be considered: such as reducing pollutant concentrations, restoring soil quality, and protecting groundwater resources. According to the risk labeling map, high-risk areas are given priority, especially those with rapid pollution spread and high potential for groundwater pollution. Select appropriate soil pollution control technology based on regional characteristics (such as pollutant type, concentration, soil type, etc.). For example, use methods such as bioremediation, chemical remediation, or physical remediation. Consider the economic feasibility of control in the decision-making process, optimize resource allocation, and formulate phased and regional control plans. Through the selected decision support model, combined with information such as the degree of pollution, risk level, and remediation cost of each region, generate specific pollution control strategies. This includes specific control areas, implementation schedules, budgets, and technical requirements. Based on the results of the pollution decision-making process, a specific soil pollutant control operation plan is formulated and control work is carried out, including various control methods such as pollution source isolation, pollutant removal, and soil improvement.

[0035] Preferably, step S1 comprises the following steps: Step S11: Acquire soil information data; Step S12: performing soil uniformity analysis on the soil information data to generate soil uniformity data; performing electrode arrangement mode selection based on the soil uniformity data to generate soil electrode arrangement mode data, wherein the soil electrode arrangement mode data includes a shallow layer detection arrangement mode and a deep layer detection arrangement mode; Step S13: confirming the boundary of the target area of ​​soil based on the shallow detection arrangement mode and the deep detection arrangement mode to obtain soil detection target area boundary data; dividing the soil information data into measurement grids based on the soil detection target area boundary data to generate soil measurement grid data; Step S14: collecting soil resistivity data on the soil measurement grid data to obtain soil resistivity data.

[0036] In an embodiment of the present invention, soil information data is collected from different soil depths and locations by using soil detection equipment (such as soil sensors or remote sensing technology). The data should include physical properties of the soil (such as moisture, temperature, density, texture, etc.) and chemical properties (such as pH, salinity, etc.). The collected soil information data is formatted into structured data suitable for analysis, such as a table or a three-dimensional data set, and the uniformity of the soil sample is evaluated using statistical analysis methods or spatial analysis techniques (such as geographic information system GIS). According to the analysis results, the variation pattern of the soil in different regions is determined. The uniformity of the soil sample is quantified using methods such as standard deviation and coefficient of variation, and the obtained soil uniformity data may include soil uniformity scores in different regions. Based on the soil uniformity data, a suitable electrode arrangement mode is selected. If the soil uniformity is good, a relatively simple shallow detection arrangement mode is selected; if the soil layers are clear or uneven, a deep detection arrangement mode is selected. The shallow detection arrangement mode is suitable for situations where the soil is uniform and the detection depth is shallow. The deep detection arrangement mode is suitable for situations where the soil is uneven and deeper detection is required. Combined with the shallow and deep detection arrangement modes, the boundaries of the target area to be detected are clearly defined through soil uniformity data and electrode arrangement mode. The boundary data of the target area can be generated by preliminary measurement of soil resistivity or delineation of geographic information data. According to the detection mode and soil characteristics, the spatial division of the boundary is carried out in combination with the terrain or soil type. According to the boundary data of the target area, the soil area is divided into standardized measurement grids for subsequent resistivity measurement. According to the detection accuracy requirements, the size and shape of the measurement grid are set. Regular rectangular or circular grids are usually used to improve the consistency and operability of the data. The generated soil measurement grid data should contain information such as grid location coordinates, grid size and number. The resistivity instrument is used to collect resistivity data for the delineated measurement grid. Resistivity tests are carried out at each grid point and data is recorded. Resistivity measurement methods such as quadrupole method and dipole method are used to adjust the working mode of the measurement instrument according to the detection mode (shallow or deep). The collected resistivity data is preliminarily processed, such as denoising and data interpolation, to ensure the accuracy and continuity of the data. The final soil resistivity data should include the resistivity value of each measurement point, measurement depth, corresponding grid number and other information for subsequent analysis and modeling.

[0037] Preferably, step S12 comprises the following steps: Step S121: extracting soil particle morphological characteristics from the soil information data to obtain soil particle morphological characteristic data; dividing the soil information data into soil layers based on the soil particle morphological characteristic data to generate soil layer division data; Step S122: Calculate the soil layer height of the soil layer division data to generate soil layer height data; perform layer particle distribution analysis on the soil layer division data through the soil layer height data to generate a soil layer particle distribution map; calculate the distribution height difference of the soil layer particle distribution map to obtain distribution height difference data; Step S123: calculating the uniformity of soil particle distribution by using the distribution height difference data to obtain soil uniformity data; comparing the soil uniformity data with a preset soil uniformity threshold, and when the soil uniformity data is greater than or equal to the preset soil uniformity threshold, generating uniform soil data; when the soil uniformity data is less than the preset soil uniformity threshold, generating non-uniform soil data; Step S124: performing multi-modal electrode arrangement selection on the uniform soil data and the non-uniform soil data according to the soil layer division data to generate soil electrode arrangement pattern data, wherein the soil electrode arrangement pattern data includes a shallow detection arrangement pattern and a deep detection arrangement pattern.

[0038] In an embodiment of the present invention, the morphological characteristics of soil particles, such as the size, shape, surface roughness, etc. of the particles, are obtained by using a soil particle analysis instrument (such as a laser particle size analyzer, a scanning electron microscope, etc.). Specific particle morphological data is extracted by image analysis or physical testing. An analysis method based on image processing is used to extract parameters such as length, width, and thickness of the particles, and the morphological index of the particles (such as aspect ratio, roundness, etc.) is calculated. Based on these morphological characteristics, soil particles can be clustered and analyzed to identify different types of particles. The obtained soil particle morphological characteristic data include the average diameter, shape factor, surface texture characteristics, etc. of the particles. According to the morphological characteristics of the soil particles, combined with other physical and chemical properties of the soil (such as particle density, soil texture, etc.), the soil is divided into layers. The soil layer division can be determined based on particle size, sedimentation, or uniformity of particle distribution. Cluster analysis or hierarchical algorithms (such as K-means clustering, hierarchical clustering, etc.) are used to divide the soil into several layers according to the size, shape, and other characteristics of the soil particles. The obtained soil layer division data includes information such as soil category, thickness, and particle characteristics of each layer. According to the soil layer division data, the soil height of each layer is calculated. This can be determined by indicators such as soil thickness and sedimentation depth of the layer. Use geographic information system (GIS) or measuring instruments (such as depth sensors) to obtain the height information of each layer. The obtained soil layer height data will include soil height information of each layer, such as the vertical distance from the surface to the deep layer. Through the soil layer height data, the distribution of particles in each soil layer is analyzed. This analysis can use statistical methods or image analysis technology to draw a longitudinal distribution map of particles. According to the distribution of particles, aggregation analysis, distribution pattern recognition, etc. are performed to generate a distribution map of particles. The obtained soil layer particle distribution map may include information such as the number, distribution pattern, and density of particles in each layer. By analyzing the layer particle distribution map, the height difference of particle distribution is calculated. The height difference reflects the degree of change of soil particles between each layer. By calculating the standard deviation or coefficient of variation of the particle distribution of each layer, the distribution height difference data is obtained, and the generated distribution height difference data contains quantitative information on the difference in particle distribution between each layer. According to the distribution height difference data, the distribution uniformity of soil particles is calculated. Uniformity can be quantified by statistical indicators such as difference and coefficient of variation, reflecting the uniformity of soil particles in the target area. Based on the distribution height difference data, the uniformity is calculated using methods such as variance analysis, standard deviation or distribution index. The obtained soil uniformity data is a digital indicator that indicates the uniformity of soil particle distribution. The calculated soil uniformity data is compared with the preset soil uniformity threshold. The threshold can be set based on experience or soil characteristics. If the soil uniformity data is greater than or equal to the preset threshold, it is judged as uniform soil, otherwise it is non-uniform soil. Based on the comparison results, uniform soil data or non-uniform soil data is generated.According to the soil layer division data and the uniform soil data or the non-uniform soil data, select the appropriate electrode arrangement mode. The shallow detection arrangement mode is suitable for soils with good uniformity, while the deep detection arrangement mode is suitable for non-uniform soils or soils that require deeper detection. The shallow detection arrangement mode is suitable for situations where the soil uniformity is good and the detection range is shallow, and a smaller electrode array is usually used. The deep detection arrangement mode is suitable for situations where the soil uniformity is poor or deeper detection is required, and a larger or specially arranged electrode array is usually used. The soil electrode arrangement pattern data finally generated will include the specific electrode layout information of the shallow detection mode and the deep detection mode.

[0039] Preferably, step S124 includes: Extracting the soil layer division data in terms of layer quantity to obtain soil layer quantity data; performing soil layer pattern analysis on the soil layer division data based on the soil layer quantity data to generate shallow soil pattern data and deep soil pattern data; Perform Wenner electrode arrangement on uniform soil data according to shallow soil pattern data to generate a shallow first detection mode; perform Pole-Dipole electrode arrangement on non-uniform soil data according to shallow soil pattern data to generate a shallow second detection mode; Schlumberger electrodes are arranged on uniform soil data according to deep soil pattern data to generate a first deep detection mode; Dipole-Dipole electrodes are arranged on non-uniform soil data according to deep soil pattern data to generate a second deep detection mode; The shallow first detection mode and the shallow second detection mode are integrated to generate a shallow detection arrangement mode; the deep first detection mode and the deep second detection mode are integrated to generate a deep detection arrangement mode.

[0040] In the embodiment of the present invention, the soil layer division data is hierarchically counted to obtain relevant data such as the number and thickness of each layer. The obtained soil layer quantity data contains the quantity information of each layer, which is helpful for subsequent pattern analysis. The distribution pattern of shallow soil and deep soil is divided by the number of layers and the soil hierarchical structure. Cluster analysis or regularized pattern recognition technology can be used to generate shallow soil pattern data and deep soil pattern data for subsequent detection mode selection. For uniform soil data in shallow soil, the Wenner electrode arrangement mode is adopted. In this mode, the electrodes are arranged at equal intervals, which is suitable for shallow soil with good uniformity and helps to uniformly detect shallow resistivity. The Pole-Dipole electrode arrangement mode is adopted for non-uniform soil data in shallow soil. The electrodes are arranged into a monopole-dipole structure, which is suitable for detecting changes in shallow non-uniform soil. For uniform soil data in deep soil, the Schlumberger electrode arrangement mode is adopted. The arrangement mode with a large distance between the central electrode and the outer electrode is suitable for the detection of deep uniform soil, which is conducive to deepening the detection range. For the inhomogeneous soil data in deep soil, the Dipole-Dipole electrode arrangement mode is used. The dipole electrode arrangement structure is suitable for deep detection of inhomogeneous soil and can provide higher resolution. The shallow first detection mode and the shallow second detection mode are integrated to generate a unified shallow detection arrangement mode. The Wenner electrode arrangement and the Pole-Dipole electrode arrangement are combined to form a comprehensive detection mode suitable for shallow layers. The deep first detection mode and the deep second detection mode are integrated to generate a unified deep detection arrangement mode. The Schlumberger electrode arrangement and the Dipole-Dipole electrode arrangement are combined to form a comprehensive detection mode suitable for deep layers.

[0041] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: performing data preprocessing on the soil resistivity data to generate standard soil resistivity data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S22: Perform resistivity inversion on the standard soil resistivity to generate a soil resistivity distribution map; perform change amplitude calculation on the soil resistivity distribution map to obtain resistivity change amplitude data; perform resistivity anomaly identification on the soil resistivity distribution map based on the resistivity change amplitude data to generate abnormal resistivity identification data; Step S23: confirming the suspected pollution area on the soil resistivity distribution map through the resistivity anomaly identification data to obtain the suspected pollution area data; performing resistivity change trend analysis on the suspected pollution area data to generate resistivity change trend data; Step S24: Classifying the suspected contaminated area data into abnormal areas according to the resistivity change trend data, and generating high conductivity contaminated areas and low conductivity contaminated areas.

[0042] In an embodiment of the present invention, the soil resistivity data is preliminarily cleaned to remove outliers and noise to ensure data quality. Statistical methods (such as removing extreme values) or machine learning algorithms are used for anomaly detection and cleaning. For the cleaned data, missing values ​​are filled to complete the data set. Interpolation, mean filling or machine learning methods (such as KNN filling) can be used. The filled soil resistivity data is standardized to eliminate dimensional differences and make the data conform to a unified scale. Use Z-score or Min-Max standardization to adjust the data to a unified range. Resistivity inversion is performed on the standard soil resistivity data to generate a soil resistivity distribution map. The surface resistivity measurement is converted into a spatial distribution map using a geophysical inversion algorithm (such as gradient descent method, finite element method) to generate a soil resistivity distribution map, reflecting the resistivity distribution of different regions in the soil. The soil resistivity distribution map is calculated for the change amplitude to identify the change of resistivity in different regions. Differential calculation or percentage change method is used to generate resistivity change amplitude data for each region, and resistivity change amplitude data is obtained for identifying abnormal areas. Based on the resistivity change amplitude data, the soil resistivity distribution map is anomaly identified. The resistivity change amplitude threshold is set, and the area exceeding the threshold is marked as an abnormal area. The abnormal resistivity identification data is generated to identify and locate the abnormal soil area. The soil resistivity distribution map is analyzed using the abnormal resistivity identification data to confirm the polluted area. By comparing the abnormal resistivity distribution with the known pollutant characteristics, the suspicious polluted area is screened out and the suspicious polluted area data is generated for further analysis. The resistivity change trend analysis is performed on the suspicious polluted area data to identify the resistivity change pattern over time or space. Time series analysis or spatial trend analysis is used to evaluate the resistivity change trend in the suspected polluted area, and the resistivity change trend data is obtained to reflect the resistivity dynamic characteristics of the polluted area. The suspicious polluted area is classified by the resistivity change trend data to distinguish between high conductivity polluted areas and low conductivity polluted areas. Based on the conductivity difference, the threshold division method is used to classify the area into high conductivity (areas with higher pollutant concentrations) and low conductivity areas, and the high conductivity polluted area and low conductivity polluted area data are generated to provide a basis for subsequent treatment and remediation plans.

[0043] Preferably, performing resistivity inversion on the standard soil resistivity comprises: The standard soil resistivity is spatially calibrated using the soil electrode arrangement pattern data to generate soil resistivity spatial calibration data; the soil resistivity spatial calibration data is divided into soil spatial regional grid units to generate soil spatial grid units, wherein each soil spatial grid unit contains a local soil resistivity value; The theoretical apparent resistivity value of the soil space grid unit is simulated by the forward algorithm to generate the forward simulation resistivity value; the error between the forward simulation resistivity value and the local soil resistivity value is calculated to generate the resistivity error value; the resistivity error value is used to adjust the resistivity of the local soil resistivity value to generate the local adjusted resistivity; The local adjusted resistivity is iteratively inverted to generate the global adjusted resistivity of the soil; the global adjusted resistivity of the soil is interpolated to generate resistivity distribution interpolation data; the resistivity distribution interpolation data is visualized to generate a soil resistivity distribution map.

[0044] In an embodiment of the present invention, soil electrode arrangement pattern data is collected, which means that electrodes are placed in the soil of the measurement area in a specific arrangement to record resistivity data at different locations. Different electrode arrangements (such as vertical, horizontal or trapezoidal arrangements) will affect the accuracy and range of the measurement data. The standard soil resistivity data is combined with the electrode arrangement data for spatial calibration to generate "soil resistivity spatial calibration data". This step uses the electrode arrangement data to adjust and correct the original soil resistivity value to ensure that the data reflects the spatial characteristics and geological structure of the soil. Based on the calibrated soil resistivity data, the soil area is divided into multiple small units (grid units). These grid units can be divided into different sizes according to actual needs to accommodate finer or larger-scale measurements. A representative soil resistivity value is calculated in each grid unit to obtain a "local soil resistivity value" for subsequent simulation and adjustment. The resistivity value of each grid unit is usually expressed as a center point value or an average value to simplify subsequent model processing. The resistivity value of the soil spatial grid unit is theoretically calculated using a forward algorithm (such as finite difference or finite element method) to simulate the apparent resistivity value of the area, which is called the "forward simulation resistivity value". The forward algorithm uses the existing soil resistivity model to calculate the theoretical apparent resistivity value under the electrode arrangement so as to compare it with the actual measured resistivity. Different forward simulation resistivity values ​​are calculated for different electrode arrangements and depths to ensure that the generated simulation data are comparable with the actual measured data. The forward simulation resistivity value is compared with the local soil resistivity value one by one to calculate the error value, namely the "resistivity error value". Error calculation often uses absolute error or relative error to quantify the deviation between the two. The resistivity error value is used to adjust the local soil resistivity to generate the "local adjusted resistivity". The adjustment method can use iterative optimization techniques such as least squares method or gradient descent method to minimize the resistivity error, thereby improving the accuracy of the model. The adjusted resistivity value is fed back to the soil spatial grid unit to prepare for the next iterative inversion. The locally adjusted resistivity value is used as input for multiple iterative inversions. Each iteration uses error information to optimize the resistivity value and gradually reduce the resistivity error until the preset convergence condition is reached. After each iteration, a "soil global adjusted resistivity" is generated, which is the comprehensive resistivity distribution of the entire measurement area. The inversion process usually sets a certain number of iterations or error thresholds to ensure the reliability and stability of the results. The soil global adjusted resistivity data is interpolated to generate "resistivity distribution interpolation data". The interpolation method can choose inverse distance weighted interpolation (IDW), Kriging interpolation or other interpolation algorithms for specific geological data to make the resistivity distribution smoother. The interpolated resistivity data is visualized to generate a "soil resistivity distribution map". Visualization usually generates a color distribution map to show the spatial distribution of resistivity, which is convenient for subsequent analysis and geological research.

[0045] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: performing resistivity time series monitoring on the high-conductivity pollution area to generate resistivity time series images of the high-conductivity pollution area; performing pollutant diffusion analysis on the resistivity time series images of the high-conductivity pollution area to generate pollutant diffusion data of the high-conductivity pollution area; Step S32: screening the soil resistivity distribution map for the lowest resistance extreme value area to obtain the resistance extreme value area; locating the pollutant concentration area in the resistance extreme value area through the pollutant diffusion data of the high conductivity pollution area to generate pollutant diffusion concentration center point data; conducting groundwater pollution risk assessment on the high conductivity pollution area through the pollutant diffusion concentration center point data to generate high conductivity pollution area hazard assessment data; Step S33: Sampling the soil profile of the low-conductivity pollution area to generate the soil plane surface of the low-conductivity pollution area; performing pollution horizontal migration analysis on the soil plane surface of the low-conductivity pollution area to generate pollution horizontal migration data; performing vertical migration restriction analysis on the soil plane surface of the conductive pollution area to generate pollution vertical migration data; Step S34: Based on the pollution horizontal migration data and the pollution vertical migration data, soil quality degradation assessment is performed on the low conductivity pollution area to generate low conductivity pollution area hazard assessment data.

[0046] In the embodiment of the present invention, electrode sensors are evenly arranged in the high conductivity pollution area, and the monitoring depth and monitoring frequency are selected to adapt to the pollutant diffusion characteristics in the area. Electrode sensors are used to regularly collect resistivity data, record data and form a time series to ensure the integrity and continuity of the monitoring data. The collected data is processed into a resistivity time series image by a resistivity imaging system to intuitively analyze the change of resistivity over time. A diffusion model (such as a Gaussian model) is used to analyze the resistivity change trend in the time series image, extract the pollutant diffusion trajectory, and generate pollutant diffusion data in the high conductivity pollution area. The area with the lowest resistance is screened out from the soil resistivity distribution map, and the point with serious pollution is locked. By analyzing the pollutant diffusion data in the resistivity extreme value area, the concentration gradient calculation method is used to determine the diffusion center of the pollutant, and the aggregation center point data is generated. Based on the pollutant diffusion center point and the soil layer structure, a groundwater pollution diffusion model is established, and the pollution risk is assessed by simulating the groundwater pollution path, and finally the hazard assessment data of the high conductivity pollution area is generated. Representative sample points are selected in the low conductivity pollution area for stratified sampling, and soil samples of different depths are collected. The pollutant concentration in the soil sample is tested, and the horizontal migration rate of the pollutants is calculated by combining the diffusion model to generate the horizontal migration data of the pollution. The permeability of the soil layer and the vertical migration capacity of the pollutants are analyzed by using equipment such as the permeability coefficient meter to generate the vertical migration data of the pollution. The horizontal migration data of the pollution and the vertical migration data are integrated, and the soil quality degradation evaluation model is used to analyze the degree of soil degradation in the low-conductivity pollution area. Based on the results of the soil degradation analysis, a hazard assessment report is output to provide support for pollution control decisions and generate hazard assessment data for the low-conductivity pollution area.

[0047] Preferably, step S33 includes the following steps: Step S331: sampling the soil profile of the low-conductivity pollution area to generate the soil profile of the low-conductivity pollution area; Step S332: performing pollutant concentration detection on the soil surface of the low-conductivity pollution area to generate pollutant concentration data for the pollution area; calculating the pollutant horizontal diffusion rate on the soil surface of the low-conductivity pollution area using the pollutant concentration data for the pollution area to obtain pollutant horizontal diffusion data; calculating the pollutant horizontal migration trend based on the pollutant horizontal diffusion data and the pollutant concentration data for the pollution area to generate pollution horizontal migration data; Step S333: performing pollutant vertical concentration distribution analysis on the soil surface of the conductive pollution area to generate pollutant vertical concentration distribution data; performing vertical migration barrier layer identification on the soil surface of the conductive pollution area according to the pollutant vertical concentration distribution data to generate pollutant vertical migration barrier layer data; Step S334: Calculate the depth trend of vertical migration of pollution through the pollutant vertical migration barrier layer data and the pollutant vertical concentration distribution data to generate vertical migration data of pollution.

[0048] In an embodiment of the present invention, sampling points are selected in low-conductivity pollution areas according to soil type and pollution diffusion conditions to ensure that the distribution of sample points is representative. Use professional sampling equipment such as geological drills to perform stratified sampling at each sample point, and record information such as the depth, soil structure and humidity of each layer. Generate soil profile data from the sampling results to characterize the vertical soil distribution characteristics of low-conductivity pollution areas. The collected soil samples are tested for pollutant concentrations, including heavy metals, volatile organic compounds, etc., to generate pollutant concentration data for the polluted area. Combined with the pollutant concentration data and the layered structure of the soil profile, the diffusion equation is used to calculate the horizontal diffusion rate of pollutants in the soil to obtain horizontal diffusion data of pollutants. Based on the horizontal diffusion data and concentration data, the horizontal migration trend of pollutants in the region is analyzed by numerical simulation methods to generate pollution horizontal migration data, reflecting the diffusion trend of pollutants and the potential pollution range. Vertical concentration detection is performed on soil samples at different depths in the soil profile to form vertical concentration distribution data of pollutants, which helps identify the aggregation characteristics of pollutants in the vertical direction. According to the vertical concentration distribution data, combined with the soil stratification, permeability and chemical reaction characteristics, the barrier layers (such as clay layers or hard layers) that hinder the vertical migration of pollutants are identified, and the pollutant vertical migration barrier layer data is generated. Based on the pollutant vertical concentration distribution data and vertical migration barrier layer data, the vertical migration depth of pollutants is simulated and the situation is evaluated in combination with the geological model, and the depth and trend of the downward migration of pollutants are calculated. Through the vertical migration depth analysis, the vertical migration data of pollution is generated to provide a diffusion risk assessment of pollutants in the vertical distribution of soil, providing support for groundwater pollution prevention and control.

[0049] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes: Step S41: integrating the high conductivity pollution area hazard assessment data and the low conductivity pollution area hazard assessment data to generate soil area pollutant detection assessment data; performing soil area risk labeling on the soil area pollutant detection assessment data to generate soil area pollutant risk labeling data; Step S42: Visualize the soil area pollutant risk labeling data to generate a soil area pollutant risk labeling map; construct pollution decisions based on the soil area pollutant risk labeling map to perform soil pollutant control operations.

[0050] In an embodiment of the present invention, by collecting the hazard assessment data of high conductivity pollution areas and the hazard assessment data of low conductivity pollution areas, the two types of data are converted into a unified format and normalized to ensure the compatibility and consistency of the data. The evaluation data is integrated and analyzed using a weighted average or hierarchical clustering algorithm, and comprehensive soil regional pollutant detection and assessment data is generated according to the type, concentration and migration characteristics of the pollutants. Based on the soil regional pollutant detection and assessment data, the risk level of the polluted area is divided using a risk grading model (such as a matrix risk assessment method). The main considerations include pollutant concentration, migration rate, soil properties and the potential harm of pollutants to the environment. For different risk levels (such as low, medium and high risks), color marking and classification marking are performed to generate soil regional pollutant risk marking data to intuitively display the spatial distribution characteristics of pollution risks. The soil regional pollutant risk marking data is visualized using a geographic information system (GIS) to generate a soil regional pollutant risk marking map. Different types of pollution risk areas are distinguished on the map by different colors, symbols or legends (for example, green represents low risk, yellow represents medium risk, and red represents high risk), ensuring that the map is clear and intuitive, and is easy to interpret and make subsequent decisions. Make pollution control decisions based on the soil regional pollutant risk labeling map. According to the principle of giving priority to high-risk areas, formulate specific control operation plans, including pollutant removal, soil remediation and post-monitoring. In the control decision, resource allocation, control methods (such as physical remediation, chemical treatment or biological remediation), personnel arrangements and time schedules are included to form a complete soil pollutant control operation plan. Finally, carry out soil pollution control operations and record the progress and effects of control for subsequent tracking and evaluation.

[0051] In this specification, a soil contaminant detection and evaluation system based on high-density resistivity is provided, which is used to perform the above-mentioned soil contaminant detection and evaluation method based on high-density resistivity. The soil contaminant detection and evaluation system based on high-density resistivity includes: The electrode arrangement module is used to obtain soil information data; perform soil uniformity analysis on the soil information data to generate soil uniformity data; select an electrode arrangement mode based on the soil uniformity data to generate soil electrode arrangement mode data; collect soil resistivity data based on the soil electrode arrangement mode data to obtain soil resistivity data; The abnormal soil identification module is used to perform resistivity inversion on soil resistivity data to generate a soil resistivity distribution map; perform resistivity anomaly identification on the soil resistivity distribution map to generate abnormal resistivity identification data; perform resistivity change area segmentation on the soil resistivity distribution map using the resistivity anomaly identification data to generate high conductivity pollution areas and low conductivity pollution areas; The hazard assessment module is used to analyze the diffusion of pollutants in highly conductive pollution areas and generate pollutant diffusion data for highly conductive pollution areas; to conduct groundwater pollution risk assessment on soil resistivity distribution maps based on pollutant diffusion data for highly conductive pollution areas and generate hazard assessment data for highly conductive pollution areas; to conduct soil quality degradation assessment on low conductive pollution areas and generate hazard assessment data for low conductive pollution areas; The risk decision module is used to carry out regional risk labeling for the hazard assessment data of high conductivity pollution areas and the hazard assessment data of low conductivity pollution areas, and generate a soil regional pollutant risk labeling map; pollution decision-making is constructed based on the soil regional pollutant risk labeling map to execute soil pollutant control operations.

[0052] The beneficial effect of the present invention is that the electrode arrangement is matched with the soil characteristics through soil uniformity analysis, and the accuracy of data acquisition is optimized. This can improve the quality of resistivity data and reduce the errors caused by soil uniformity differences, thereby providing reliable basic data for subsequent steps. The soil resistivity distribution map generated by resistivity inversion provides spatial distribution information for the contaminated area, and resistivity anomaly identification further distinguishes high conductivity and low conductivity contaminated areas. This enables the contaminated area to be accurately located, helps to identify the source of pollution and the degree of pollution, and lays the foundation for subsequent analysis. Through the analysis of pollutant diffusion in high conductivity contaminated areas, the propagation trend of pollutants can be understood, thereby predicting potential threats to groundwater. Soil quality degradation assessment in low conductivity contaminated areas helps to assess the health of the soil and the possibility of its future recovery, and improves the scientificity and pertinence of environmental governance decisions. Through the generated soil regional pollutant risk labeling map, the risk level of the contaminated area can be clearly identified, providing clear priorities and strategies for pollution control. This step helps to formulate an efficient soil pollution control plan, ensure optimal resource allocation, and help reduce governance costs and improve the effect of pollution remediation. Therefore, the present invention improves the accuracy in the process of soil pollutant detection and assessment by optimizing soil uniformity analysis, electrode arrangement, resistivity inversion and pollutant diffusion analysis.

[0053] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0054] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A soil contaminant detection and evaluation method based on high-density resistivity, characterized in that: The following steps are involved: Step S1: acquiring soil information data; performing soil uniformity analysis on the soil information data to generate soil uniformity data; selecting an electrode arrangement mode based on the soil uniformity data to generate soil electrode arrangement mode data; collecting soil resistivity data based on the soil electrode arrangement mode data to obtain soil resistivity data; Step S2: Perform resistivity inversion on the soil resistivity data to generate a soil resistivity distribution map; perform resistivity anomaly identification on the soil resistivity distribution map to generate abnormal resistivity identification data; perform resistivity change area segmentation on the soil resistivity distribution map using the resistivity anomaly identification data to generate a high conductivity pollution area and a low conductivity pollution area; Step S3: performing pollutant diffusion analysis on the high-conductivity pollution area to generate pollutant diffusion data for the high-conductivity pollution area; Conduct groundwater pollution risk assessment on soil resistivity distribution map through pollutant diffusion data in high conductivity pollution areas to generate hazard assessment data for high conductivity pollution areas; conduct soil quality degradation assessment on low conductivity pollution areas to generate hazard assessment data for low conductivity pollution areas; Step S4: regional risk labeling is performed on the high conductivity pollution area hazard assessment data and the low conductivity pollution area hazard assessment data to generate a soil regional pollutant risk labeling map; pollution decision-making is constructed based on the soil regional pollutant risk labeling map to perform soil pollutant control operations.

2. The soil contaminant detection and evaluation method based on high-density resistivity according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire soil information data; Step S12: performing soil uniformity analysis on the soil information data to generate soil uniformity data; performing electrode arrangement mode selection based on the soil uniformity data to generate soil electrode arrangement mode data, wherein the soil electrode arrangement mode data includes a shallow layer detection arrangement mode and a deep layer detection arrangement mode; Step S13: confirming the boundary of the target area of ​​soil based on the shallow detection arrangement mode and the deep detection arrangement mode to obtain soil detection target area boundary data; dividing the soil information data into measurement grids based on the soil detection target area boundary data to generate soil measurement grid data; Step S14: collecting soil resistivity data on the soil measurement grid data to obtain soil resistivity data.

3. The soil contaminant detection and evaluation method based on high-density resistivity according to claim 2 is characterized in that: Step S12 includes the following steps: Step S121: extracting soil particle morphological characteristics from the soil information data to obtain soil particle morphological characteristic data; dividing the soil information data into soil layers based on the soil particle morphological characteristic data to generate soil layer division data; Step S122: Calculate the soil layer height of the soil layer division data to generate soil layer height data; perform layer particle distribution analysis on the soil layer division data through the soil layer height data to generate a soil layer particle distribution map; calculate the distribution height difference of the soil layer particle distribution map to obtain distribution height difference data; Step S123: calculating the uniformity of soil particle distribution by using the distribution height difference data to obtain soil uniformity data; comparing the soil uniformity data with a preset soil uniformity threshold, and when the soil uniformity data is greater than or equal to the preset soil uniformity threshold, generating uniform soil data; when the soil uniformity data is less than the preset soil uniformity threshold, generating non-uniform soil data; Step S124: performing multi-modal electrode arrangement selection on the uniform soil data and the non-uniform soil data according to the soil layer division data to generate soil electrode arrangement pattern data, wherein the soil electrode arrangement pattern data includes a shallow detection arrangement pattern and a deep detection arrangement pattern.

4. The soil contaminant detection and evaluation method based on high-density resistivity according to claim 3 is characterized in that: Step S124 includes: Extracting the soil layer division data in terms of layer quantity to obtain soil layer quantity data; performing soil layer pattern analysis on the soil layer division data based on the soil layer quantity data to generate shallow soil pattern data and deep soil pattern data; Perform Wenner electrode arrangement on uniform soil data according to shallow soil pattern data to generate a shallow first detection mode; perform Pole-Dipole electrode arrangement on non-uniform soil data according to shallow soil pattern data to generate a shallow second detection mode; Schlumberger electrodes are arranged on uniform soil data according to deep soil pattern data to generate a deep first detection mode; Dipole-Dipole electrodes are arranged on non-uniform soil data according to deep soil pattern data to generate a deep second detection mode; The shallow first detection mode and the shallow second detection mode are integrated to generate a shallow detection arrangement mode; the deep first detection mode and the deep second detection mode are integrated to generate a deep detection arrangement mode.

5. The soil contaminant detection and evaluation method based on high-density resistivity according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing data preprocessing on the soil resistivity data to generate standard soil resistivity data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S22: Perform resistivity inversion on the standard soil resistivity to generate a soil resistivity distribution map; perform change amplitude calculation on the soil resistivity distribution map to obtain resistivity change amplitude data; perform resistivity anomaly identification on the soil resistivity distribution map based on the resistivity change amplitude data to generate abnormal resistivity identification data; Step S23: confirming the suspected pollution area on the soil resistivity distribution map through the resistivity anomaly identification data to obtain the suspected pollution area data; performing resistivity change trend analysis on the suspected pollution area data to generate resistivity change trend data; Step S24: Classifying the suspected contaminated area data into abnormal areas according to the resistivity change trend data, and generating high conductivity contaminated areas and low conductivity contaminated areas.

6. The soil contaminant detection and evaluation method based on high-density resistivity according to claim 5 is characterized in that: Resistivity inversion for standard soil resistivity includes: The standard soil resistivity is spatially calibrated using the soil electrode arrangement pattern data to generate soil resistivity spatial calibration data; the soil resistivity spatial calibration data is divided into soil spatial regional grid units to generate soil spatial grid units, wherein each soil spatial grid unit contains a local soil resistivity value; The theoretical apparent resistivity value of the soil space grid unit is simulated by the forward algorithm to generate the forward simulation resistivity value; the error between the forward simulation resistivity value and the local soil resistivity value is calculated to generate the resistivity error value; the resistivity error value is used to adjust the resistivity of the local soil resistivity value to generate the local adjusted resistivity; The local adjusted resistivity is iteratively inverted to generate the global adjusted resistivity of the soil; the global adjusted resistivity of the soil is interpolated to generate resistivity distribution interpolation data; the resistivity distribution interpolation data is visualized to generate a soil resistivity distribution map.

7. The soil contaminant detection and evaluation method based on high-density resistivity according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing resistivity time series monitoring on the high-conductivity pollution area to generate resistivity time series images of the high-conductivity pollution area; performing pollutant diffusion analysis on the resistivity time series images of the high-conductivity pollution area to generate pollutant diffusion data of the high-conductivity pollution area; Step S32: screening the soil resistivity distribution map for the lowest resistance extreme value area to obtain the resistance extreme value area; locating the pollutant concentration area in the resistance extreme value area through the pollutant diffusion data of the high conductivity pollution area to generate pollutant diffusion concentration center point data; conducting groundwater pollution risk assessment on the high conductivity pollution area through the pollutant diffusion concentration center point data to generate high conductivity pollution area hazard assessment data; Step S33: Sampling the soil profile of the low-conductivity pollution area to generate the soil plane surface of the low-conductivity pollution area; performing pollution horizontal migration analysis on the soil plane surface of the low-conductivity pollution area to generate pollution horizontal migration data; performing vertical migration restriction analysis on the soil plane surface of the conductive pollution area to generate pollution vertical migration data; Step S34: Based on the pollution horizontal migration data and the pollution vertical migration data, soil quality degradation assessment is performed on the low conductivity pollution area to generate low conductivity pollution area hazard assessment data.

8. The soil contaminant detection and evaluation method based on high-density resistivity according to claim 7 is characterized in that: Step S33 includes the following steps: Step S331: sampling the soil profile of the low-conductivity pollution area to generate the soil profile of the low-conductivity pollution area; Step S332: performing pollutant concentration detection on the soil surface of the low-conductivity pollution area to generate pollutant concentration data for the pollution area; calculating the pollutant horizontal diffusion rate on the soil surface of the low-conductivity pollution area using the pollutant concentration data for the pollution area to obtain pollutant horizontal diffusion data; calculating the pollutant horizontal migration trend based on the pollutant horizontal diffusion data and the pollutant concentration data for the pollution area to generate pollution horizontal migration data; Step S333: performing pollutant vertical concentration distribution analysis on the soil surface of the conductive pollution area to generate pollutant vertical concentration distribution data; performing vertical migration barrier layer identification on the soil surface of the conductive pollution area according to the pollutant vertical concentration distribution data to generate pollutant vertical migration barrier layer data; Step S334: Calculate the depth trend of vertical migration of pollution through the pollutant vertical migration barrier layer data and the pollutant vertical concentration distribution data to generate vertical migration data of pollution.

9. The soil contaminant detection and evaluation method based on high-density resistivity according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: integrating the high conductivity pollution area hazard assessment data and the low conductivity pollution area hazard assessment data to generate soil area pollutant detection assessment data; performing soil area risk labeling on the soil area pollutant detection assessment data to generate soil area pollutant risk labeling data; Step S42: Visualize the soil area pollutant risk labeling data to generate a soil area pollutant risk labeling map; construct pollution decisions based on the soil area pollutant risk labeling map to perform soil pollutant control operations.

10. A soil contaminant detection and evaluation system based on high-density resistivity, characterized in that: Used to perform the soil contaminant detection and evaluation method based on high-density resistivity as claimed in claim 1, the soil contaminant detection and evaluation system based on high-density resistivity comprises: The electrode arrangement module is used to obtain soil information data; perform soil uniformity analysis on the soil information data to generate soil uniformity data; select an electrode arrangement mode based on the soil uniformity data to generate soil electrode arrangement mode data; collect soil resistivity data based on the soil electrode arrangement mode data to obtain soil resistivity data; The abnormal soil identification module is used to perform resistivity inversion on soil resistivity data to generate a soil resistivity distribution map; perform resistivity anomaly identification on the soil resistivity distribution map to generate abnormal resistivity identification data; perform resistivity change area segmentation on the soil resistivity distribution map using the resistivity anomaly identification data to generate high conductivity pollution areas and low conductivity pollution areas; The hazard assessment module is used to analyze the diffusion of pollutants in highly conductive pollution areas and generate pollutant diffusion data for highly conductive pollution areas; to conduct groundwater pollution risk assessment on soil resistivity distribution maps based on pollutant diffusion data for highly conductive pollution areas and generate hazard assessment data for highly conductive pollution areas; to conduct soil quality degradation assessment on low conductive pollution areas and generate hazard assessment data for low conductive pollution areas; The risk decision module is used to carry out regional risk labeling for the hazard assessment data of high conductivity pollution areas and the hazard assessment data of low conductivity pollution areas, and generate a soil regional pollutant risk labeling map; pollution decision-making is constructed based on the soil regional pollutant risk labeling map to execute soil pollutant control operations.

Images