A comprehensive evaluation method for remediation effect of heavy metal contaminated farmland soil

Abstract

This invention relates to the field of farmland soil remediation technology, and in particular to a comprehensive evaluation method for the remediation effect of heavy metal-contaminated farmland soil. The method includes: constructing a four-criteria hierarchical evaluation system encompassing pollution load, ecotoxicity, soil health, and remediation cost; selecting at least three time points covering the initial, middle, and late stages within the remediation cycle, and simultaneously collecting index data for the divided remediation units; after standardizing the data, integrating subjective and objective weights using a combined weighting method based on the optimization of the sum of squared deviations; calculating a comprehensive evaluation index for each node, determining grade thresholds and classifying them using dynamic clustering; generating a spatiotemporal evolution map, identifying key limiting factors for "medium" and "poor" grade units, and outputting a diagnostic report containing dynamic evaluation, heterogeneity analysis, and optimization suggestions. This invention provides comprehensive evaluation, reliable data, and objective results, and can support precise control of remediation projects.

Description

Technical Field

[0001] This invention relates to the field of farmland soil remediation technology, specifically a comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil. Background Technology

[0002] Soil remediation effectiveness assessment is a crucial step in determining whether a remediation project has achieved its intended goals and guiding the optimization and adjustment of remediation strategies. Its scientific rigor and accuracy directly determine the implementation quality and ecological benefits of the remediation project. However, current mainstream methods for assessing the effectiveness of heavy metal-contaminated farmland soil remediation still have many shortcomings, failing to meet the comprehensive requirements of "ecological safety, soil function restoration, and economic feasibility" in remediation projects. These shortcomings are mainly reflected in the following aspects: The assessment is limited in scope and lacks comprehensiveness: Existing assessment methods often focus on the single dimension of "pollution load reduction," judging the remediation effect solely by detecting changes in the total amount of heavy metals in the soil, while ignoring key dimensions such as the content of available heavy metals, ecotoxicity, soil health, and remediation costs. This single-dimensional assessment can easily lead to problems such as "the total amount of heavy metals in the soil meets the standards after remediation, but the available form is still high and the ecological risk has not been reduced" or "the remediation effect meets the standards, but the cost is too high to be promoted," failing to fully reflect the comprehensive value of the remediation project. Data collection lacks spatiotemporal representativeness and dynamism: Most assessment methods adopt a "single-point static sampling" model, collecting only a small number of soil samples for testing after the remediation project is completed. This does not cover key stages such as the initial, middle, and late stages of remediation, and cannot capture the dynamic changing trends of indicators during the remediation process. At the same time, the remediation units are not divided according to the spatial heterogeneity of soil pollution, and only the overall area is used as the sampling object. This results in the data failing to reflect the remediation differences in areas with different levels of pollution, and the assessment results are easily distorted due to sampling bias, making it difficult to support the accurate judgment of dynamic remediation effects. The weighting calculation methods are one-sided and lack objectivity and rationality: Determining the weights of indicators is the core link of comprehensive evaluation. Existing methods mostly adopt a single subjective weighting method or an objective weighting method. The single subjective weighting method relies too much on expert experience, is easily affected by subjective bias, and is difficult to reflect the objective information of the data itself. The single objective weighting method determines the weights based only on the degree of data dispersion, which may ignore key indicators that are crucial to ecological security but have low data dispersion. This leads to a disconnect between the weight allocation and the actual evaluation needs, affecting the reliability of the comprehensive evaluation results.

[0003] To address the above issues, a comprehensive evaluation method for the remediation effect of heavy metal-contaminated farmland soil is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil includes the following steps: S1. Construct a multi-dimensional dynamic assessment index system: Establish an assessment system that includes four criteria layers: pollution load, ecotoxicity, soil health, and remediation cost. Among them, the pollution load criterion layer includes indicators based on the total amount and available content of target heavy metals in the soil, and the ecotoxicity criterion layer includes indicators based on the potential ecological risk index and the heavy metal enrichment coefficient of edible parts of crops. S2. Spatiotemporal data collection during the repair period: After the repair project is implemented, at least three discontinuous time points, sample data of each indicator in the indicator system will be collected synchronously for the multiple repair units. S3. Data Standardization and Combined Weight Calculation: The collected sample data is standardized and a combined weighting method based on the optimization of the sum of squared deviations is adopted. The subjective weights determined by the analytic hierarchy process and the objective weights determined by the entropy weight method are combined to obtain the comprehensive weights of each indicator. S4. Calculation of dynamic comprehensive evaluation index and classification of effect level: Based on the standardized data and comprehensive weight, the comprehensive evaluation index of each repair unit is calculated at each time node; and based on the comprehensive evaluation index dataset of all repair units at all time nodes, the dynamic clustering method is used to determine the classification threshold, and the repair effect level of each repair unit at different time nodes is classified accordingly. S5. Generating spatiotemporal diagnostics and optimization suggestions for repair effects: S5-1. Generate a spatiotemporal evolution map: Visualize the comprehensive evaluation index and repair effect level at different time points in space to generate a spatiotemporal evolution map of the repair effect. S5-2. Identify key limiting factors: For repair units with a repair effect level of "medium" or "poor", calculate the correlation between the evaluation index of each criterion layer and the comprehensive evaluation index, and identify the criterion layer with the highest correlation as the key limiting dimension of the repair effect of the unit; and further identify the indicators with high comprehensive weight and low standardized value as key limiting factors within the limiting dimension. S5-3, Output Diagnostic Report: Based on the spatiotemporal evolution map and the identification results of key limiting factors, generate a diagnostic report that includes dynamic evaluation of the repair process, spatial heterogeneity analysis and targeted optimization suggestions.

[0006] As a preferred approach, an assessment system is established that includes four criteria: pollution load, ecotoxicity, soil health, and remediation cost. This system comprises the following steps: S1-1. Determine the specific assessment indicators under the criteria layer: Under the pollution load criteria layer, select the total amount and available content of target heavy metals in the soil; under the ecotoxicity criteria layer, select the potential ecological risk index based on soil and crop properties and the heavy metal enrichment coefficient of edible parts of crops; under the soil health criteria layer, select key indicators reflecting the physicochemical properties and biological characteristics of the soil; under the remediation cost criteria layer, select direct and indirect cost indicators covering remediation materials, labor, energy consumption, and subsequent maintenance. S1-2. Determine the data sources and quantification methods for each specific indicator: For the indicators in the pollution load and soil health criteria layer, determine the quantification method through field sampling and laboratory analysis; for the indicators in the ecotoxicity criteria layer, determine the calculation method based on soil pollutant concentration and toxicity response parameters as well as crop sample test results; for the indicators in the remediation cost criteria layer, determine the accounting method based on actual remediation project investment and financial data. S1-3. Construct a complete hierarchical structure of indicators: Take the comprehensive assessment of remediation effect as the target layer, take the four dimensions of pollution load, ecotoxicity, soil health and remediation cost as the criteria layer, and take the specific indicators determined in step S1-1 as the scheme layer, forming an assessment indicator system for subsequent data collection and calculation.

[0007] As a preferred approach, spatiotemporal data acquisition during the repair period includes the following steps: S2-1. Determine monitoring time nodes and remediation units: Based on the remediation project plan and remediation cycle, select at least three discontinuous time nodes. These time nodes should cover the initial, middle and late stages of remediation. At the same time, based on the spatial distribution characteristics of farmland soil pollution, divide the remediation area into multiple independent remediation units to ensure that the pollution characteristics within each remediation unit are relatively consistent. S2-2. Develop a synchronous sampling plan: Based on the time nodes and remediation units determined in step S2-1, conduct synchronous field sampling for each remediation unit at each time node to collect sample data required for the pollution load, ecotoxicity, soil health, and remediation cost criteria layers in the indicator system. Specifically, the samples for the pollution load criterion layer are obtained by collecting soil samples and conducting laboratory analysis of the total amount and available content of target heavy metals; the samples for the ecotoxicity criterion layer are obtained by simultaneously collecting soil and crop samples and calculating the potential ecological risk index and the heavy metal enrichment coefficient of the edible parts of crops based on the test results; the samples for the soil health criterion layer are obtained by collecting soil samples and conducting laboratory analysis of physicochemical properties and biological characteristics; and the data for the remediation cost criterion layer are obtained by collecting remediation project investment records and financial accounting data. S2-3, Sample Data Processing and Dataset Construction: Perform laboratory analysis or calculation on the samples collected in step S2-2 to obtain the specific values ​​of each indicator, and organize and record the indicator data of each repair unit at each time point to form a structured raw dataset for subsequent data standardization and weight calculation.

[0008] As a preferred approach, the collected sample data is standardized, and a combined weighting method based on the optimization of the sum of squared deviations is used. This method integrates the subjective weights determined by the analytic hierarchy process (AHP) and the objective weights determined by the entropy weight method to obtain the comprehensive weights of each indicator, including: S3-1. Data Standardization Processing: Standardize the collected sample data to convert each indicator value into a dimensionless standardized value. For positive and negative indicators, corresponding standardization methods are used respectively. S3-2, Subjective weight calculation: The analytic hierarchy process is adopted. By constructing the judgment matrix of the indicator system, calculating the eigenvectors and verifying consistency, the subjective weight of each indicator is determined. S3-3, Objective weight calculation: Using the entropy weight method, based on the standardized data, the information entropy of each indicator is calculated, and the entropy weight of each indicator is calculated based on the information entropy as the objective weight; S3-4. Combined weight calculation: The combined weighting method based on the optimization of the sum of squared deviations is adopted to combine subjective weights and objective weights. By solving the optimization problem that minimizes the sum of squared deviations of subjective weights and objective weights, the comprehensive weight of each indicator is obtained.

[0009] As a preferred approach, the comprehensive evaluation index and repair effect level at different time points are visualized spatially to generate a spatiotemporal evolution map of the repair effect, including the following steps: S5-1-1 Spatial Data Preparation: Based on the repair units divided in step S2-1, obtain the spatial location information of each repair unit, and associate it with the comprehensive evaluation index and repair effect level of each time node calculated in step S4 to form a comprehensive evaluation dataset with spatial attributes. S5-1-2, Spatial Interpolation Processing: Using the center point of each repair unit as the representative point of spatial location, the spatial interpolation method is used to interpolate and calculate the comprehensive evaluation index, generating a continuous spatial distribution surface covering the entire repair area; S5-1-3, Level Partition Visualization: Based on the repair effect level classification threshold determined in step S4, the interpolated comprehensive evaluation index spatial distribution surface is divided into different level regions, and different colors or patterns are used to render each level region. S5-1-4 Spatiotemporal Sequence Integration: Arrange the spatial distribution maps of the repair effect levels generated at each time point in chronological order to form a sequence map that reflects the spatiotemporal dynamic changes of the repair effect, and intuitively show the evolution trend of the repair effect in each region.

[0010] As a preferred approach, for repair units with a repair effectiveness level of "medium" or "poor", the correlation between the evaluation index of each criterion layer and the comprehensive evaluation index is calculated. The criterion layer with the highest correlation is identified as the key constraint dimension of the repair effectiveness of the unit. Furthermore, within this constraint dimension, indicators with high comprehensive weight and low standardized values ​​are identified as key constraint factors, including the following steps: S5-2-1, Calculation of Criterion Layer Assessment Index: Based on the standardized data in step S3-1 and the comprehensive weights obtained in step S3-4, the assessment indices of the four criteria layers for each remediation unit at the corresponding time nodes are calculated respectively: pollution load, ecotoxicity, soil health and remediation cost. S5-2-2, Correlation Analysis: For repair units with a repair effect level of "medium" or "poor", grey relational analysis is used to calculate the correlation between the evaluation index of each criterion layer and the comprehensive evaluation index, forming a correlation sequence. S5-2-3, Key Constraint Dimension Identification: Select the criterion layer with the highest correlation from the correlation sequence and identify it as the key constraint dimension of the repair unit's repair effect at the current time point; S5-2-4. Determination of Key Limiting Factors: Within the identified key limiting dimensions, extract the standardized values ​​and comprehensive weights of all indicators under that dimension, and select the indicators with the highest comprehensive weights and significantly lower standardized values ​​as the key limiting factors affecting the repair effect of the repair unit.

[0011] As a preferred approach, based on the spatiotemporal evolution map and the identification results of key limiting factors, a diagnostic report is generated that includes dynamic evaluation of the repair process, spatial heterogeneity analysis, and targeted optimization suggestions, including: S5-3-1. Dynamic evaluation of the repair process: Based on the spatiotemporal evolution map generated in step S5-1, analyze the changes in the effect level of each repair unit at different time nodes, and evaluate the overall progress speed and effect stability of the repair project from the time dimension. S5-3-2, Spatial Heterogeneity Analysis: Based on the spatial distribution characteristics of the remediation effect in the spatiotemporal evolution map, identify the spatial difference pattern of the remediation effect, and analyze the causes of spatial heterogeneity in combination with the background characteristics of farmland soil and the implementation of remediation measures. S5-3-3, Optimization suggestion generation: Based on the key limiting factors identified in step S5-2, and in combination with their respective criterion layers and specific indicator characteristics, targeted suggestions for adjusting remediation strategies are proposed, including optimization of remediation material application, improvement of agronomic measures, and cost control measures. S5-3-4. Diagnostic Report Integration: The dynamic evaluation of the restoration process, spatial heterogeneity analysis, and targeted optimization suggestions are systematically integrated to form a comprehensive diagnostic report that includes textual descriptions, charts, and specific improvement plans, providing decision support for the precise control of the restoration project.

[0012] As can be seen from the technical solution provided by the present invention above, the comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil provided by the present invention has the following beneficial effects: The assessment system is more comprehensive, covering multiple core needs: This invention constructs a four-tiered assessment system that includes pollution load, ecotoxicity, soil health, and remediation cost, breaking through the limitations of traditional remediation effect assessments that only focus on the single dimension of "pollution removal." Among them, the pollution load criterion reflects the basic level of heavy metal pollution, the ecotoxicity criterion is related to food chain safety and ecological risks, the soil health criterion ensures the restoration of basic soil functions, and the remediation cost criterion takes into account economic feasibility. The four dimensions work together to cover the core needs of "ecological safety, soil function, and economic rationality," ensuring that the assessment results can fully reflect the comprehensive value of the remediation project and avoid the one-sidedness of the assessment caused by the lack of dimensions. The data collection is more representative in time and space, supporting dynamic assessment: This invention selects at least three discontinuous time nodes covering the initial, middle and late stages within the remediation cycle, and divides remediation units with consistent internal characteristics according to the spatial distribution of pollution, achieving "temporal and spatial synchronization" sampling; This collection method can accurately capture the dynamic change trend of each indicator during the remediation process, and avoids the interference of spatial heterogeneity of soil pollution on the data by sampling by unit, providing real and complete basic data for subsequent dynamic comprehensive assessment, avoiding the shortcomings of traditional "single-point static sampling" that cannot reflect the remediation process and regional differences; More scientific weighting calculation, balancing subjective and objective information: This invention adopts a combined weighting method based on the optimization of the sum of squared deviations, integrating the analytic hierarchy process (AHP) and the entropy weighting method. The AHP fully incorporates the experience and judgment of experts in soil remediation, ecological environment, and other fields, ensuring that the weights meet actual assessment needs. The entropy weighting method determines the weights based on the dispersion of standardized data, reflecting the objective information of the data itself. The two methods are optimized by minimizing the sum of squared deviations, avoiding the problem of single subjective weights being easily affected by experience biases, and also avoiding the defect of single objective weights potentially ignoring key indicators. This makes the comprehensive weights of various indicators more reasonable, providing a reliable basis for subsequent comprehensive assessment index calculations. The grading is more objective and dynamically matches the actual effect distribution: Based on the comprehensive evaluation index dataset of all time points and all remediation units, this invention uses a dynamic clustering method to determine the grade threshold, rather than relying on a fixed threshold set by humans. This method can automatically classify the grades as "excellent, good, medium, and poor" according to the actual effect distribution characteristics of the remediation project, making the grade classification more in line with the actual situation of the project. It avoids the grade deviation caused by the "one-size-fits-all" approach of the traditional fixed threshold method when facing different pollution scenarios and different remediation technologies, and ensures that the effect grade of each remediation unit has comparability both horizontally (between regions) and vertically (between times). More precise diagnosis and optimization, forming a closed loop of remediation regulation: This invention visualizes the spatiotemporal dynamic changes of remediation effects by generating spatiotemporal evolution maps, intuitively presenting the differences in remediation progress in different regions; at the same time, for "medium" and "poor" level units, it accurately identifies key limiting dimensions through correlation analysis, and further identifies key limiting factors with high comprehensive weight and low standardized values, avoiding the problem of "general talk" in traditional diagnosis; based on the key limiting factors, the targeted suggestions for optimizing the application of remediation materials, improving agronomic measures, and controlling costs can directly guide the precise regulation of remediation projects, forming a closed loop of "assessment-diagnosis-optimization", effectively improving remediation efficiency, reducing ineffective input, and ensuring that remediation projects advance towards the goal of "ecological compliance, usable soil, and controllable costs"; With high application value, this invention provides reliable decision support for remediation projects: The comprehensive diagnostic report output by this invention includes dynamic evaluation of the remediation process, spatial heterogeneity analysis, and targeted optimization suggestions, along with visualizations such as spatiotemporal evolution maps and key limiting factor charts, combining "conclusiveness" and "operability." The report can directly provide remediation project managers with clear decision-making basis, ensuring that the implementation direction of the remediation project is controllable and the effect is verifiable. At the same time, its standardized evaluation process (from indicator construction to report output) can be extended to different types of heavy metal contaminated farmland remediation projects, providing a technical paradigm for the standardized evaluation of farmland soil remediation effects, and helping to ensure the safe use of remediated farmland and sustainable agricultural development. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the overall process of a comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil according to the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0015] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific embodiments.

[0016] like Figure 1 As shown in the figure, this invention provides a comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil, including the following steps: S1. Construct a multi-dimensional dynamic assessment index system: Establish an assessment system that includes four criteria layers: pollution load, ecotoxicity, soil health, and remediation cost. Among them, the pollution load criterion layer includes indicators based on the total amount and available content of target heavy metals in the soil, and the ecotoxicity criterion layer includes indicators based on the potential ecological risk index and the heavy metal enrichment coefficient of edible parts of crops. S2. Spatiotemporal data collection during the repair period: After the repair project is implemented, at least three discontinuous time points, sample data of each indicator in the indicator system will be collected synchronously for the multiple repair units. S3. Data Standardization and Combined Weight Calculation: The collected sample data is standardized and a combined weighting method based on the optimization of the sum of squared deviations is adopted. The subjective weights determined by the analytic hierarchy process and the objective weights determined by the entropy weight method are combined to obtain the comprehensive weights of each indicator. S4. Calculation of dynamic comprehensive evaluation index and classification of effect level: Based on the standardized data and comprehensive weight, the comprehensive evaluation index of each repair unit is calculated at each time node; and based on the comprehensive evaluation index dataset of all repair units at all time nodes, the dynamic clustering method is used to determine the classification threshold, and the repair effect level of each repair unit at different time nodes is classified accordingly. S5. Generating spatiotemporal diagnostics and optimization suggestions for repair effects: S5-1. Generate a spatiotemporal evolution map: Visualize the comprehensive evaluation index and repair effect level at different time points in space to generate a spatiotemporal evolution map of the repair effect. S5-2. Identify key limiting factors: For repair units with a repair effect level of "medium" or "poor", calculate the correlation between the evaluation index of each criterion layer and the comprehensive evaluation index, and identify the criterion layer with the highest correlation as the key limiting dimension of the repair effect of the unit; and further identify the indicators with high comprehensive weight and low standardized value as key limiting factors within the limiting dimension. S5-3, Output Diagnostic Report: Based on the spatiotemporal evolution map and the identification results of key limiting factors, generate a diagnostic report that includes dynamic evaluation of the repair process, spatial heterogeneity analysis and targeted optimization suggestions.

[0017] In this embodiment, step S1 involves establishing a structured, multi-dimensional hierarchical framework of indicators by clarifying the core dimensions and specific indicators for assessing remediation effectiveness. This provides a clear target for spatiotemporal data collection during the subsequent remediation cycle, and provides a unified standard for data standardization, combined weight calculation, and remediation effectiveness grading. This ensures the relevance, systematicity, and standardization of the entire assessment process, laying the foundation for a comprehensive assessment of the remediation effectiveness of heavy metal-contaminated farmland soil. The detailed steps are as follows: S1-1. Determine the specific evaluation indicators under the criteria level: Under the pollution load criterion layer, the total amount and available content of target heavy metals in the soil are selected to reflect the overall pollution level of heavy metals in the soil and the actual mobile and transformable active components. Under the ecotoxicity criteria, a potential ecological risk index based on soil and crop properties and a heavy metal enrichment coefficient in the edible parts of crops are selected. The former is used to assess the potential harm of soil heavy metals to the ecosystem, while the latter is used to measure the risk of heavy metals entering the food chain through crops. Under the soil health criteria layer, key indicators reflecting the soil physicochemical properties and biological characteristics are selected. Soil physicochemical property indicators may include soil pH, organic matter content, and soil texture, while biological characteristic indicators may include soil microbial community structure and microbial activity, in order to assess the basic functional status of the soil after remediation. Under the repair cost criterion layer, direct and indirect cost indicators covering repair materials, labor, energy consumption and subsequent maintenance are selected. Direct costs include repair material procurement costs, labor operation costs, energy consumption costs, etc., while indirect costs include material replenishment costs and equipment maintenance costs during the subsequent maintenance process, etc., in order to measure the economic feasibility of the repair project. S1-2. Determine the data sources and quantification methods for each specific indicator: For the indicators in the pollution load and soil health criteria layer, a method for quantification through field sampling and laboratory analysis was determined. Specifically, soil samples were collected in the remediation area according to the specifications. After the samples were sent to the laboratory, the total amount of target heavy metals in the soil was analyzed using instruments such as atomic absorption spectrophotometer and inductively coupled plasma mass spectrometry. The effective content of target heavy metals was analyzed using standard methods such as DTPA extraction method, thereby obtaining the specific values ​​of the indicators in the criteria layer. For the indicators in the ecotoxicity criteria layer, a method was determined to calculate them based on soil pollutant concentrations, toxicity response parameters, and crop sample test results. The potential ecological risk index is calculated by converting the concentrations of each heavy metal in the soil into pollution coefficients and then combining them with the toxicity response coefficients of each heavy metal. The heavy metal enrichment coefficient of the edible part of crops is calculated by collecting samples of the edible parts of mature crops, analyzing the content of the target heavy metals in the laboratory, and then calculating the ratio of the target heavy metal content in the corresponding soil. For the indicators in the remediation cost criterion layer, a method for accounting based on actual remediation project input and financial data was determined. After collecting soil samples on-site, the soil pH value was measured in the laboratory using a pH meter, the soil organic matter content was determined using the potassium dichromate oxidation-external heating method, and the soil texture was determined using the sieving method. The soil microbial community structure was analyzed by the soil microbial culture and counting method, and the microbial activity was measured by the soil respiration intensity method, thereby obtaining quantitative data for each indicator in the criterion layer. For the indicators in the repair cost criteria layer, a method for accounting based on the actual investment and financial data of the repair project is determined. The cost of repair materials is calculated by collecting the purchase list of repair materials in the repair project contract, the labor cost is calculated based on the labor hours and wage standards in the construction records, the energy cost is calculated based on the energy consumption data and energy unit price in the equipment operation records, and the post-maintenance cost is calculated by combining the post-maintenance plan and historical maintenance cost records. Finally, the specific values ​​of direct and indirect cost indicators are summarized. S1-3. Construct a complete hierarchical structure of indicators: The target layer of the indicator system is clearly defined, and the comprehensive evaluation of the remediation effect is set as the target layer. This layer is the core of the entire indicator system and governs the indicators at all subsequent levels. The ultimate goal of the evaluation is to comprehensively measure the remediation effect of heavy metal contaminated farmland soil. The criteria layer of the indicator system is defined, with four dimensions: pollution load, ecotoxicity, soil health, and remediation cost. These four dimensions cover the key aspects of remediation effect assessment from the perspectives of pollution degree, ecological risk, soil function, and economic cost, respectively, providing support for the target layer. The indicator system is divided into scheme layers. The specific indicators determined in step S1-1 are set as scheme layers. The scheme layer indicators are the specific refinements of the criterion layer. Each criterion layer corresponds to the scheme layer indicators below it, forming a three-level progressive indicator hierarchy structure of "target layer - criterion layer - scheme layer". The three-level hierarchical structure is integrated and verified to ensure logical coherence between indicators at each level. The indicators at the scheme level can fully reflect the evaluation requirements of the corresponding criteria level, and the four dimensions of the criteria level can fully support the comprehensive evaluation of the target level, ultimately forming an evaluation indicator system for subsequent data collection and calculation.

[0018] In this embodiment, step S2 serves to obtain the sample data required for each criterion layer in the evaluation index system by synchronously sampling multiple time nodes and regional repair units covering the entire repair cycle. This provides real, complete, and spatiotemporally representative raw data support for subsequent data standardization processing, combined weight calculation, and dynamic evaluation of repair effects, ensuring that the repair effect evaluation results can reflect the actual situation at different stages and in different regions, thereby improving the scientific nature and accuracy of the evaluation. The detailed steps are as follows: S2-1. Determine monitoring time points and repair units: Selection of monitoring time nodes: Based on the remediation project plan and remediation cycle, at least three discontinuous time nodes should be selected. The selected time nodes should fully cover the initial, middle and late stages of remediation in order to comprehensively capture the dynamic changes of soil indicators during the remediation process and avoid the one-sided judgment of the remediation progress due to the lack of time nodes. Remediation Unit Division: Based on the spatial distribution characteristics of farmland soil pollution, the entire remediation area is divided into multiple independent remediation units. During the division process, it is necessary to ensure that the pollution characteristics within each remediation unit are relatively consistent, such as small differences in core attributes such as soil heavy metal pollution concentration, pollution range, and soil texture, so as to ensure the representativeness of subsequent sampling data within the unit and reduce the interference of spatial heterogeneity on single sample data. S2-2. Develop a synchronous sampling plan: Sampling synchronization planning: Based on the monitoring time nodes and repair units determined in step S2-1, on-site synchronous sampling work is organized uniformly for each repair unit at each monitoring time node to ensure that the sampling conditions and sampling process of different repair units are consistent at the same time node, and that the sampling method of the same repair unit is continuous at different time nodes, so as to avoid data comparison distortion due to asynchronous sampling. Sample collection methods for each criterion layer: Pollution load criterion layer sample collection: Soil samples were collected from the corresponding remediation units and sent to the laboratory. Professional analytical methods were used to detect and analyze the total amount and available content of the target heavy metals in the soil, thereby obtaining the required index data for the criterion layer. Ecotoxicity criterion layer sample collection: Soil samples and crop samples growing in the corresponding remediation unit were collected simultaneously. Based on the laboratory's detection results of pollutant concentration and toxicity response parameters in the soil samples, and the detection results of heavy metal content in the crop samples, the potential ecological risk index and the heavy metal enrichment coefficient of the edible part of the crop were calculated to obtain the index data of the criterion layer. Soil health criterion layer sample collection: Soil samples were collected from the corresponding remediation units and analyzed in the laboratory using standardized testing methods to obtain the physicochemical properties (such as soil pH value, organic matter content, etc.) and biological characteristics (such as soil microbial community structure, microbial activity, etc.) of the soil criterion layer. Data collection for the cost criterion layer: By collecting actual input records during the implementation of the repair project, including repair material purchase lists, labor hour records, equipment energy consumption statistics, etc., and at the same time organizing the project financial accounting data, relevant data on direct costs (such as repair material costs, labor costs, and energy consumption costs) and indirect costs (such as subsequent maintenance costs) are extracted to form the indicator data of this criterion layer. S2-3, Sample Data Processing and Dataset Construction: Sample data detection and calculation: Laboratory testing and analysis of soil and crop samples collected in step S2-2 to obtain specific values ​​of each indicator of pollution load, ecotoxicity, and soil health criteria layer; and to organize and calculate the collected remediation project input records and financial accounting data to determine the specific values ​​of each indicator of the remediation cost criteria layer. Structured dataset formation: All criterion-level indicator data of each repair unit at each monitoring time node are organized and recorded according to the logical relationship of "repair unit-time node-criterion-specific indicator-indicator value" to construct a structured original dataset. This dataset needs to clearly present the spatiotemporal attributes and indicator affiliation of each data, providing a direct data foundation for the subsequent data standardization processing and combined weight calculation in step S3.

[0019] In this embodiment, step S3 aims to eliminate the dimensional differences between different indicators through data standardization, ensuring the comparability of indicator data. Simultaneously, it employs a combined weighting method that integrates subjective and objective factors to calculate the comprehensive weight, balancing expert judgment with objective data information. This provides unified and reasonable basic data and weighting basis for subsequent dynamic comprehensive evaluation index calculations, ensuring the scientific rigor and fairness of the evaluation results. The detailed steps are as follows: S3-1, Data Standardization Processing: Purpose and scope of standardization: Standardize all indicator data in the structured raw dataset constructed in step S2, convert indicator values ​​of different dimensions (such as heavy metal content in mg / kg, cost in yuan, pH value dimensionless, etc.) and orders of magnitude into dimensionless standardized values, and eliminate the interference of dimensional differences on subsequent weight calculation and evaluation index synthesis. Distinguishing between positive and negative indicators: Clearly define the attributes of the indicators. For positive indicators, the larger the index value, the more beneficial the remediation effect (such as soil organic matter content and microbial activity in the soil health criterion layer). For negative indicators, the larger the index value, the more detrimental the remediation effect (such as total soil heavy metal content and available content in the pollution load criterion layer, and heavy metal enrichment coefficient of edible parts of crops in the ecotoxicity criterion layer). Implementation of differentiated standardization methods: Positive indicators are standardized using a positive standardization method, which maps the original values ​​of the indicators to the [0,1] interval through a formula, ensuring that the larger the original value of the indicator, the closer the standardized value is to 1; negative indicators are standardized using a negative standardization method, which maps the original values ​​of the indicators to the [0,1] interval through a formula, ensuring that the larger the original value of the indicator, the closer the standardized value is to 0, and finally obtaining a standardized dataset for all indicators; S3-2, Subjective Weight Calculation: Application framework of Analytic Hierarchy Process (AHP): The AHP is used to determine the subjective weight of each indicator. This method is based on the three-level indicator hierarchy structure of "target layer - criterion layer - scheme layer" constructed in step S1. A judgment matrix is ​​constructed by experts judging the importance of the indicators to realize the quantification of subjective experience. Judgment Matrix Construction: Experts in soil remediation, ecological environment, and agricultural science were invited to conduct pairwise importance comparisons of indicators within the same level based on their contribution to the remediation effect assessment. The judgment matrix elements were assigned values ​​according to the 1-9 scale (1 indicates that the two indicators are equally important, 3 indicates that the former is slightly more important than the latter, 5 indicates that the former is significantly more important than the latter, 7 indicates that the former is strongly more important than the latter, 9 indicates that the former is extremely more important than the latter, 2, 4, 6, and 8 are the median values ​​of the above adjacent judgments, and the reciprocal indicates that the latter is more important than the former), thus forming the judgment matrix for each level. Eigenvector Calculation and Consistency Check: The largest eigenvalue and its corresponding eigenvector of the judgment matrix are calculated using methods such as the sum method and the root method. After normalizing the eigenvectors, the initial subjective weights of each indicator are obtained. At the same time, the consistency index CI (CI=(λmax-n) / (n-1), where λmax is the largest eigenvalue of the judgment matrix and n is the order of the judgment matrix) is calculated, and the consistency ratio CR (CR=CI / RI) is calculated in combination with the average random consistency index RI (a fixed value determined according to the value of n). If CR<0.1, it indicates that the consistency of the judgment matrix meets the requirements and the initial subjective weights are valid. If CR≥0.1, experts need to be invited to readjust the judgment matrix until the consistency check is passed, and finally the subjective weights of each indicator are determined. S3-3, Calculation of Objective Weights: Entropy weight method calculation logic: The objective weight of each indicator is determined based on the standardized dataset obtained in step S3-1 using the entropy weight method. This method reflects the dispersion of the data by calculating the information entropy of the indicator. The greater the dispersion (the smaller the information entropy), the greater the contribution of the indicator to the evaluation result, and the higher the corresponding objective weight. Indicator weighting calculation: The standardized value x of the i-th repair unit and the j-th indicator in the standardized dataset. ij Calculate the proportion p of the j-th index in all repair units. ij The calculation formula is p ij =x ij / ∑(i=1 to m)x ij (where m is the total number of repair units), if x ij If all are 0, then define p ij =1 / m, to ensure the rationality of the specific gravity calculation; Information entropy and entropy weight calculation: Calculate the information entropy ej of the j-th indicator based on its weight pij. The calculation formula is ej. j =-k×∑(i=1 to m)pij ×lnp ij（ Where k = 1 / lnm, used to normalize the information entropy to the [0,1] interval); then according to the information entropy e j Calculate the entropy weight w of the j-th index. j The calculation formula is w j =(1-e j ) / ∑(j=1 to n)(1-e j (where n is the total number of indicators), entropy weight w j That is, the objective weight of each indicator; S3-4, Calculation of Combined Weights: The principle of the combined weighting method: The combined weighting method based on the optimization of the sum of squared deviations is adopted. The subjective weight ωj obtained in step S3-2 and the objective weight wj obtained in step S3-3 are integrated. The core objective is to construct an optimization model so that the combined weights reflect both the subjective judgment of experts and the objective information of the data, while minimizing the deviation between the subjective weights and the objective weights, thus ensuring the rationality and stability of the weights. Optimization Model Construction and Solution: Let the comprehensive weight of the j-th indicator be α. j Construct the optimization objective function that minimizes the sum of squared deviations: min∑(j=1 to n)[(α j -ω j )²+(α j -w j )²], while simultaneously satisfying the constraint condition ∑(j=1 to n)α j =1 and α j ≥0 (j=1 to n); by differentiating the objective function and solving it in combination with the constraints, the comprehensive weight α is obtained. j The analytical solution is used to determine the comprehensive weight of all indicators, forming a weight system for subsequent comprehensive evaluation index calculation.

[0020] In this embodiment, step S4 combines the standardized indicator data with the comprehensive weight; through quantitative calculation, it obtains the core indicator of the repair effect of each repair unit at different time points—the comprehensive evaluation index; then, based on the index data of the entire cycle and the entire region, it objectively determines the level threshold using a dynamic clustering method; finally, it realizes the classification of the dynamic repair effect of each repair unit; providing a quantitative level basis for the subsequent spatiotemporal diagnosis and optimization suggestions of the repair effect; the detailed steps are as follows: Step S4-1: Calculation of Dynamic Comprehensive Evaluation Index: Calculation basis and scope: Based on the standardized dataset obtained in step S3-1 (containing dimensionless standardized values ​​of all repair units, all time points, and all indicators) and the comprehensive weight system determined in step S3-4 (each indicator corresponds to a unique comprehensive weight), the comprehensive evaluation index is calculated for each repair unit at each time point to ensure coverage of all monitoring time points (early, middle, and late stages of repair) and all divided repair units within the repair cycle, reflecting the evaluation characteristics of "dynamic" and "comprehensive". Calculation method implementation: The weighted summation method is used to calculate the comprehensive evaluation index. For a certain repair unit at a certain time point, the comprehensive evaluation index is calculated using this formula. The contribution of each indicator to the repair effect is summed according to its weight to obtain a quantitative value that comprehensively reflects the repair effect of the repair unit at the current time point. The higher the index, the better the repair effect. Results Compilation: The comprehensive evaluation index of each repair unit at each time point was calculated one by one; then it was organized according to the correspondence of "repair unit number - time point - comprehensive evaluation index" to form a dynamic comprehensive evaluation index dataset; clearly showing the effect change trend of different repair units within the repair cycle; providing basic data for subsequent level classification; Step S4-2: Determining the hierarchical threshold (based on dynamic clustering method): Dataset preparation: Collect the dynamic comprehensive evaluation index dataset formed in step S4-1; this dataset should cover the comprehensive evaluation index of all repair units at all monitoring time points; ensure that the data samples can represent the effect distribution characteristics of the entire repair project (including the effect differences of different regions and different repair stages); avoid threshold division deviations due to incomplete samples; Dynamic clustering analysis implementation: The above dataset is clustered using a dynamic clustering method. The core logic is to group samples with similar indices into the same category based on the natural distribution characteristics of the comprehensive evaluation index. The boundary point between categories is the grading threshold for the remediation effect level. Specific operations include: First, the dataset is standardized (to ensure cluster stability, the index values ​​can be normalized again; this is different from the standardization of the index in step S3-1 and is only used for cluster calculation); then, a reasonable number of clusters is set (based on the actual needs of farmland soil remediation effect assessment, it is usually divided into four levels: "excellent," "good," "medium," and "poor"; the number of clusters is 4); finally, through iterative calculation, the similarity of indices within the same category is maximized, and the difference in indices between different categories is maximized, ultimately resulting in 4 cluster categories and their corresponding category intervals. Grading threshold extraction: Extract the boundary values ​​of different category intervals from the dynamic clustering results; use them as grading thresholds for the repair effect level; for example, if the index intervals obtained after clustering are [0,0.3), [0.3,0.6), [0.6,0.8), [0.8,1.0], then the corresponding grading thresholds are 0.3, 0.6, and 0.8; respectively serving as the dividing lines between "poor" and "medium", "medium" and "good", and "good" and "excellent"; ensuring that the thresholds can objectively reflect the distribution pattern of the index data, rather than being subjectively set. Step S4-3: Classification of Repair Effect Levels for Repair Units: Establishment of grade matching rules: Based on the grading thresholds determined in step S4-2, establish a correspondence rule between the comprehensive evaluation index and the repair effect level; for example: a comprehensive evaluation index ≥ 0.8 corresponds to the "Excellent" level; 0.6 ≤ index < 0.8 corresponds to the "Good" level; 0.3 ≤ index < 0.6 corresponds to the "Medium" level; and index < 0.3 corresponds to the "Poor" level (the specific thresholds need to be based on the actual clustering results); ensure that each index interval has a unique corresponding grade label; Unit-by-unit, time-node-by-time rating determination: Based on the above rules, for each repair unit, its comprehensive evaluation index and repair effect level are matched one by one at each monitoring time node. For example, the comprehensive evaluation index of repair unit A in the early stage of repair is 0.25, corresponding to the "poor" level; the index increases to 0.55 in the middle stage of repair, corresponding to the "medium" level; the index reaches 0.78 in the later stage of repair, corresponding to the "good" level. The rating change trajectory of each repair unit within the repair cycle is fully recorded. Summary of grading results: The grading effect of all repair units at each time point is summarized according to the structure of "repair unit-time point-comprehensive evaluation index-repair effect level" to form a grading effect dataset. This dataset can not only intuitively reflect the dynamic repair process of a single repair unit, but also reflect the effect differences between different repair units. It provides a direct grading basis for drawing the spatiotemporal evolution map and identifying key limiting factors in step S5.

[0021] In this embodiment, step S5 transforms the quantitative data of the repair effect into a visualized spatiotemporal map, intuitively presenting the dynamic changes and spatial differences in the repair process. Simultaneously, it accurately identifies the key dimensions and factors that constrain the repair effect, and finally integrates the analysis results to form a diagnostic report containing dynamic evaluation, causes of differences, and adjustment plans, providing direct decision support for the precise control and optimization of the repair project. The detailed steps are as follows: Step S5-1: Generate a spatiotemporal evolution map: S5-1-1 Spatial Data Preparation: Based on the repair units divided in step S2-1, obtain the spatial location information of each repair unit and associate it with the comprehensive evaluation index and repair effect level of each time node calculated in step S4 to ensure that the spatial attributes of each repair unit are completely matched with its corresponding repair effect data, forming a comprehensive evaluation dataset with spatial attributes that includes spatial location, time node, evaluation index, and effect level. S5-1-2 Spatial Interpolation Processing: Taking the center point of each repair unit as the spatial location representative point of the unit, the comprehensive evaluation index is interpolated using the spatial interpolation method. Through this calculation, the index value of the discrete repair unit is transformed into a continuous spatial distribution surface covering the entire repair area, so that the comprehensive evaluation index at any location in the repair area can be queried through the surface, eliminating blank data areas between repair units and fully reflecting the spatial distribution characteristics of the repair effect. S5-1-3, Visualization of Grade Zoning: Based on the repair effect grade grading threshold determined in step S4, the continuous spatial distribution surface of the interpolated comprehensive evaluation index is divided into regions corresponding to the "Excellent", "Good", "Medium", and "Poor" grades according to the threshold. Different colors or patterns are used to render the regions of different grades (e.g., dark green for "Excellent", light green for "Good", yellow for "Medium", and red for "Poor"), so that the regions of different grades are clearly distinguishable in space, forming a spatial distribution map of the repair effect grade at a single time point. S5-1-4. Spatiotemporal Sequence Integration: The spatial distribution maps of repair effect levels generated at each monitoring time point (early, middle, and late stages of repair) are arranged in chronological order to form a spatiotemporal sequence map of repair effect. This map allows for a direct observation of the trend of effect level changes in different regions during the repair cycle (e.g., a region gradually improves from "poor" to "good"), as well as the differences in effect between different regions at the same time point, thus fully presenting the spatiotemporal dynamic evolution of the repair effect. Step S5-2: Identify key limiting factors: S5-2-1 Calculation of Criterion Layer Assessment Index: Based on the standardized data obtained in step S3-1 and the comprehensive weights determined in step S3-4, the assessment indexes of the four criterion layers for each remediation unit at the corresponding time node are calculated respectively: pollution load, ecotoxicity, soil health, and remediation cost. The calculation method is to sum the standardized values ​​of all indicators under each criterion layer according to the comprehensive weight of each indicator to obtain the assessment index of that criterion layer, which reflects the degree of contribution of a single criterion layer to the remediation effect. S5-2-2, Correlation Analysis: For repair units with a repair effect level of "medium" or "poor", grey relational analysis is used to calculate the correlation between the four criterion-level evaluation indices and the comprehensive evaluation index of the unit. Through calculation, the four correlation values ​​corresponding to each "medium" or "poor" level unit are obtained, forming a correlation sequence. This sequence reflects the degree of correlation between each criterion level and the overall repair effect. S5-2-3. Key Limiting Dimension Identification: From the correlation sequence of each "medium" or "poor" level remediation unit, select the criterion layer with the highest correlation and identify the criterion layer as the key limiting dimension of the remediation effect of the remediation unit at the current time point; for example, if the pollution load criterion layer of a certain "poor" level unit has the highest correlation, then the pollution load dimension is the key limiting dimension that restricts the remediation effect of the unit. S5-2-4. Determination of Key Limiting Factors: Within the identified key limiting dimensions, extract the standardized values ​​and comprehensive weights of all indicators under that dimension; screen these indicators, selecting those with high comprehensive weights (i.e., those with significant impact on that dimension) and significantly low standardized values ​​(i.e., those with poor performance), and identify them as key limiting factors affecting the remediation effect of the remediation unit; for example, if the key limiting dimension is pollution load, and the indicator "available content of heavy metals in soil" under that dimension has a high weight and a low standardized value, then that indicator is a key limiting factor; Step S5-3: Output the diagnostic report: S5-3-1 Dynamic Evaluation of Repair Progress: Based on the spatiotemporal evolution map generated in step S5-1, analyze the changes in the repair effect level of each repair unit at different time nodes; statistically analyze the changes in the number of units of each level in the overall repair area from the time dimension (such as the increase in the proportion of "excellent" and "good" level units and the decrease in the proportion of "medium" and "poor" level units) to evaluate the overall progress speed of the repair project; at the same time, observe the stability of the level changes of individual units (such as whether there are repeated fluctuations in level) to judge the stability of the repair effect and form a dynamic evaluation result of the repair progress. S5-3-2, Spatial Heterogeneity Analysis: Based on the spatial distribution characteristics of remediation effects in the spatiotemporal evolution map, identify the spatial differences in remediation effects (e.g., the overall effect of one area is better than another, or there are significant differences in local effects within the same area); combine the background characteristics of farmland soil (e.g., soil texture, differences in initial pollution concentration) and the implementation of remediation measures (e.g., differences in the amount of remediation materials added, differences in construction technology) to analyze the specific reasons for spatial heterogeneity (e.g., areas with high initial pollution concentrations are more difficult to remediate, resulting in delayed effects), and form conclusions on spatial heterogeneity analysis. S5-3-3, Optimization Recommendation Generation: Based on the key limiting factors identified in step S5-2, and considering their respective criterion layers and specific indicator characteristics, targeted remediation strategy adjustment recommendations are proposed. If the key limiting factor is the "available content of heavy metals in soil" in the pollution load criterion layer, optimization recommendations for remediation material application are proposed (e.g., increasing the dosage of amendments that reduce the availability of heavy metals). If the key limiting factor is the "soil organic matter content" in the soil health criterion layer, recommendations for improving agronomic measures are proposed (e.g., increasing the application of organic fertilizer). If the key limiting factor is the "post-remediation maintenance cost" in the remediation cost criterion layer, cost control measures are proposed (e.g., optimizing the maintenance cycle and selecting low-cost maintenance materials). S5-3-4. Integration of Diagnostic Reports: Systematically integrate the dynamic evaluation results of the restoration process, the conclusions of spatial heterogeneity analysis, and targeted optimization suggestions to form a comprehensive diagnostic report. The report should include textual descriptions (clearly explaining the evaluation conclusions and causal analysis), charts and graphs (including spatiotemporal evolution maps, distribution charts of key limiting factors, etc.), and specific improvement plans (clearly defining the implementation steps and parameter requirements of adjustment measures). Ensure that the report is complete, logically clear, and highly operable, providing direct decision support for the precise control of the restoration project.

[0022] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil, characterized in that: Includes the following steps: S1. Construct a multi-dimensional dynamic assessment index system: Establish an assessment system that includes four criteria layers: pollution load, ecotoxicity, soil health, and remediation cost. Among them, the pollution load criterion layer includes indicators based on the total amount and available content of target heavy metals in the soil, and the ecotoxicity criterion layer includes indicators based on the potential ecological risk index and the heavy metal enrichment coefficient of edible parts of crops. S2. Spatiotemporal data collection during the repair period: After the repair project is implemented, at least three discontinuous time points, sample data of each indicator in the indicator system will be collected synchronously for the multiple repair units. S3. Data Standardization and Combined Weight Calculation: The collected sample data is standardized and a combined weighting method based on the optimization of the sum of squared deviations is adopted. The subjective weights determined by the analytic hierarchy process and the objective weights determined by the entropy weight method are combined to obtain the comprehensive weights of each indicator. S4. Calculation of dynamic comprehensive evaluation index and classification of effect level: Based on the standardized data and comprehensive weight, the comprehensive evaluation index of each repair unit is calculated at each time node; and based on the comprehensive evaluation index dataset of all repair units at all time nodes, the dynamic clustering method is used to determine the classification threshold, and the repair effect level of each repair unit at different time nodes is classified accordingly. S5. Generating spatiotemporal diagnostics and optimization suggestions for repair effects: S5-1. Generate a spatiotemporal evolution map: Visualize the comprehensive evaluation index and repair effect level at different time points in space to generate a spatiotemporal evolution map of the repair effect. S5-2. Identify key limiting factors: For repair units with a repair effect level of "medium" or "poor", calculate the correlation between the evaluation index of each criterion layer and the comprehensive evaluation index, and identify the criterion layer with the highest correlation as the key limiting dimension of the repair effect of the unit; and further identify the indicators with high comprehensive weight and low standardized value as key limiting factors within the limiting dimension. S5-3, Output Diagnostic Report: Based on the spatiotemporal evolution map and the identification results of key limiting factors, generate a diagnostic report that includes dynamic evaluation of the repair process, spatial heterogeneity analysis and targeted optimization suggestions.

2. The comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil according to claim 1, characterized in that: Establish an assessment system comprising four criteria layers: pollution load, ecotoxicity, soil health, and remediation costs. This includes the following steps: S1-1. Determine the specific assessment indicators under the criteria layer: Under the pollution load criteria layer, select the total amount and available content of target heavy metals in the soil; under the ecotoxicity criteria layer, select the potential ecological risk index based on soil and crop properties and the heavy metal enrichment coefficient of edible parts of crops; under the soil health criteria layer, select key indicators reflecting the physicochemical properties and biological characteristics of the soil; under the remediation cost criteria layer, select direct and indirect cost indicators covering remediation materials, labor, energy consumption, and subsequent maintenance. S1-2. Determine the data sources and quantification methods for each specific indicator: For the indicators in the pollution load and soil health criteria layer, determine the quantification method through field sampling and laboratory analysis; for the indicators in the ecotoxicity criteria layer, determine the calculation method based on soil pollutant concentration and toxicity response parameters as well as crop sample test results; for the indicators in the remediation cost criteria layer, determine the accounting method based on actual remediation project investment and financial data. S1-3. Construct a complete hierarchical structure of indicators: Take the comprehensive assessment of remediation effect as the target layer, take the four dimensions of pollution load, ecotoxicity, soil health and remediation cost as the criteria layer, and take the specific indicators determined in step S1-1 as the scheme layer, forming an assessment indicator system for subsequent data collection and calculation.

3. The comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil according to claim 1, characterized in that, The spatiotemporal data acquisition during the repair period includes the following steps: S2-1. Determine monitoring time nodes and remediation units: Based on the remediation project plan and remediation cycle, select at least three discontinuous time nodes. These time nodes should cover the initial, middle and late stages of remediation. At the same time, based on the spatial distribution characteristics of farmland soil pollution, divide the remediation area into multiple independent remediation units to ensure that the pollution characteristics within each remediation unit are relatively consistent. S2-2. Develop a synchronous sampling plan: Based on the time nodes and remediation units determined in step S2-1, conduct synchronous field sampling for each remediation unit at each time node to collect sample data required for the pollution load, ecotoxicity, soil health, and remediation cost criteria layers in the indicator system. Specifically, the samples for the pollution load criterion layer are obtained by collecting soil samples and conducting laboratory analysis of the total amount and available content of target heavy metals; the samples for the ecotoxicity criterion layer are obtained by simultaneously collecting soil and crop samples and calculating the potential ecological risk index and the heavy metal enrichment coefficient of the edible parts of crops based on the test results; the samples for the soil health criterion layer are obtained by collecting soil samples and conducting laboratory analysis of physicochemical properties and biological characteristics; and the data for the remediation cost criterion layer are obtained by collecting remediation project investment records and financial accounting data. S2-3, Sample Data Processing and Dataset Construction: Perform laboratory analysis or calculation on the samples collected in step S2-2 to obtain the specific values ​​of each indicator, and organize and record the indicator data of each repair unit at each time point to form a structured raw dataset for subsequent data standardization and weight calculation.

4. The comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil according to claim 1, characterized in that: The collected sample data were standardized, and a combined weighting method based on the sum of squared deviations optimization was used to integrate the subjective weights determined by the analytic hierarchy process (AHP) and the objective weights determined by the entropy weight method, resulting in the comprehensive weights of each indicator, including: S3-1. Data Standardization Processing: Standardize the collected sample data to convert each indicator value into a dimensionless standardized value. For positive and negative indicators, corresponding standardization methods are used respectively. S3-2, Subjective weight calculation: The analytic hierarchy process is adopted. By constructing the judgment matrix of the indicator system, calculating the eigenvectors and verifying consistency, the subjective weight of each indicator is determined. S3-3, Objective weight calculation: Using the entropy weight method, based on the standardized data, the information entropy of each indicator is calculated, and the entropy weight of each indicator is calculated based on the information entropy as the objective weight; S3-4. Combined weight calculation: The combined weighting method based on the optimization of the sum of squared deviations is adopted to combine subjective weights and objective weights. By solving the optimization problem that minimizes the sum of squared deviations of subjective weights and objective weights, the comprehensive weight of each indicator is obtained.

5. The comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil according to claim 1, characterized in that: Visualize the comprehensive evaluation index and repair effect level at different time points in space to generate a spatiotemporal evolution map of the repair effect, including the following steps: S5-1-1 Spatial Data Preparation: Based on the repair units divided in step S2-1, obtain the spatial location information of each repair unit, and associate it with the comprehensive evaluation index and repair effect level of each time node calculated in step S4 to form a comprehensive evaluation dataset with spatial attributes. S5-1-2, Spatial Interpolation Processing: Using the center point of each repair unit as the representative point of spatial location, the spatial interpolation method is used to interpolate and calculate the comprehensive evaluation index, generating a continuous spatial distribution surface covering the entire repair area; S5-1-3, Level Partition Visualization: Based on the repair effect level classification threshold determined in step S4, the interpolated comprehensive evaluation index spatial distribution surface is divided into different level regions, and different colors or patterns are used to render each level region. S5-1-4 Spatiotemporal Sequence Integration: Arrange the spatial distribution maps of the repair effect levels generated at each time point in chronological order to form a sequence map that reflects the spatiotemporal dynamic changes of the repair effect, and intuitively show the evolution trend of the repair effect in each region.

6. The comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil according to claim 1, characterized in that: For repair units with a repair effectiveness level of "medium" or "poor", the correlation between the evaluation index of each criterion layer and the comprehensive evaluation index is calculated. The criterion layer with the highest correlation is identified as the key constraint dimension of the repair effectiveness of the unit. Furthermore, within this constraint dimension, indicators with high comprehensive weight and low standardized values ​​are identified as key constraint factors, including the following steps: S5-2-1, Calculation of Criterion Layer Assessment Index: Based on the standardized data in step S3-1 and the comprehensive weights obtained in step S3-4, the assessment indices of the four criteria layers for each remediation unit at the corresponding time nodes are calculated respectively: pollution load, ecotoxicity, soil health and remediation cost. S5-2-2, Correlation Analysis: For repair units with a repair effect level of "medium" or "poor", grey relational analysis is used to calculate the correlation between the evaluation index of each criterion layer and the comprehensive evaluation index, forming a correlation sequence. S5-2-3, Key Constraint Dimension Identification: Select the criterion layer with the highest correlation from the correlation sequence and identify it as the key constraint dimension of the repair unit's repair effect at the current time point; S5-2-4. Determination of Key Limiting Factors: Within the identified key limiting dimensions, extract the standardized values ​​and comprehensive weights of all indicators under that dimension, and select the indicators with the highest comprehensive weights and significantly lower standardized values ​​as the key limiting factors affecting the repair effect of the repair unit.

7. The comprehensive evaluation method for the remediation effect of heavy metal contaminated farmland soil according to claim 1, characterized in that: Based on the spatiotemporal evolution map and the identification results of key limiting factors, a diagnostic report is generated, which includes dynamic evaluation of the repair process, spatial heterogeneity analysis, and targeted optimization suggestions, including: S5-3-1 Dynamic Evaluation of Repair Progress: Based on the spatiotemporal evolution map generated in step S5-1, analyze the changes in the effect level of each repair unit at different time nodes, and evaluate the overall progress speed and effect stability of the repair project from the time dimension. S5-3-2, Spatial Heterogeneity Analysis: Based on the spatial distribution characteristics of the remediation effect in the spatiotemporal evolution map, identify the spatial difference pattern of the remediation effect, and analyze the causes of spatial heterogeneity in combination with the background characteristics of farmland soil and the implementation of remediation measures. S5-3-3, Optimization suggestion generation: Based on the key limiting factors identified in step S5-2, and in combination with their respective criterion layers and specific indicator characteristics, targeted suggestions for adjusting remediation strategies are proposed, including optimization of remediation material application, improvement of agronomic measures, and cost control measures. S5-3-4. Diagnostic Report Integration: The dynamic evaluation of the restoration process, spatial heterogeneity analysis, and targeted optimization suggestions are systematically integrated to form a comprehensive diagnostic report that includes textual descriptions, charts, and specific improvement plans, providing decision support for the precise control of the restoration project.

Images