Ecological restoration effect evaluation and simulation system after polluted soil restoration

Abstract

The invention discloses an ecological restoration effect evaluation and simulation system after polluted soil restoration, and relates to the technical field of soil restoration, and the system comprises a soil feature generation module which generates an ecological feature data set; the ecological restoration evaluation module is used for establishing a multi-dimensional evaluation model by using an analytic hierarchy process and a fuzzy comprehensive evaluation method based on the ecological characteristic data set, and generating an ecological restoration effect index; the dynamic simulation prediction module is used for constructing an ecological restoration dynamic simulation model and simulating the evolution trend of a soil ecological system under different environment conditions; and the restoration strategy optimization module is used for generating a targeted ecological restoration optimization scheme by combining a preset restoration strategy library according to the ecological restoration effect index and the dynamic simulation prediction result. According to the method, a closed-loop system integrating multi-source data acquisition and processing, multi-dimensional evaluation, dynamic simulation prediction and stepped strategy optimization is constructed, and accurate evaluation of the ecological restoration effect after contaminated soil restoration and scientific optimization of the restoration strategy are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of soil remediation, in particular to an ecological restoration effect evaluation and simulation system after contaminated soil remediation. BACKGROUND

[0002] With the acceleration of industrialization, soil pollution problems are increasingly prominent, and the ecological restoration effect evaluation after contaminated soil remediation has become a key link in the field of environmental governance. At present, the evaluation of the remediation effect of contaminated soil in the prior art relies on manual sampling and laboratory analysis, which has problems such as long sampling cycle, poor data timeliness, and single evaluation dimension. For example, the traditional evaluation method often only focuses on the short-term changes of soil physical and chemical parameters, ignoring the comprehensive consideration of vegetation restoration status, ecological function integrity and long-term evolution trend, resulting in one-sided evaluation results and difficulty in supporting scientific remediation strategy optimization.

[0003] At the same time, the existing simulation prediction model mostly uses static analysis method, which cannot effectively integrate dynamic parameters such as climate change and soil evolution, and has low prediction accuracy of the evolution trend of ecological system under different environmental conditions. In addition, the formulation of remediation strategies lacks dynamic correlation with evaluation results and simulation prediction, often relies on experience, and has problems such as insufficient pertinence and cost-benefit imbalance. Therefore, an integrated system capable of multi-dimensional evaluation, dynamic simulation prediction and intelligent strategy optimization is urgently needed to improve the scientificity of the ecological restoration effect evaluation after contaminated soil remediation and the effectiveness of the remediation strategy. SUMMARY

[0004] The purpose of the present application is to solve the above-mentioned problems by providing a fall detection method based on millimeter wave radar.

[0005] The purpose of the present application can be achieved by the following technical solutions:

[0006] The ecological restoration effect evaluation and simulation system after contaminated soil remediation comprises:

[0007] The soil feature generation module: based on the soil sensor array, vegetation monitoring device and environmental monitoring terminal, the physical and chemical parameters of the remediated soil, vegetation growth indicators and surrounding microenvironment data are collected, the collected data are standardized, and the ecological feature data set is generated;

[0008] The ecological restoration evaluation module: the ecological restoration evaluation module is electrically connected with the soil feature generation module, based on the ecological feature data set, a multi-dimensional evaluation model is established using the analytic hierarchy process and fuzzy comprehensive evaluation method, the ecological feature data set is evaluated in multiple dimensions, and based on the output of the model, the ecological restoration effect index is generated;

[0009] The dynamic simulation prediction module: based on the ecological restoration effect index, the soil evolution parameter and the climate change data, a dynamic simulation model of ecological restoration is constructed, the evolution trend of the soil ecological system under different environmental conditions is simulated, and an ecological restoration prediction atlas is output;

[0010] The repair strategy optimization module: according to the ecological restoration effect index and the dynamic simulation prediction result, a targeted ecological restoration optimization scheme is generated in combination with a preset repair strategy library, and is pushed to a terminal device and realizes system internal instruction interaction.

[0011] Preferably, the soil feature generation module specifically comprises:

[0012] The multi-dimensional parameter acquisition unit: the soil bulk density, porosity, heavy metal residual amount and nutrient content are acquired through a soil sensor array, the plant height, ground diameter, leaf area index and biomass are acquired by using a vegetation monitoring device, and the air temperature, humidity, light intensity and precipitation are recorded by means of an environmental monitoring terminal;

[0013] The data cleaning unit: the Lilliefors criterion is used to identify abnormal values, and when the data deviates from the mean value by more than 3 times the standard deviation, it is marked as abnormal, and the abnormal values are corrected by using the adjacent time data interpolation method;

[0014] The feature fusion unit: the cleaned soil physicochemical parameters, vegetation growth indexes and microenvironment data are associated according to the sampling point coordinates and time stamps, the principal component analysis method is used to extract the feature variables with a contribution rate of 80%, and an ecological feature data set is constructed.

[0015] Preferably, the construction of the ecological feature data set specifically comprises:

[0016] The soil physicochemical parameters, vegetation growth indexes and microenvironment data are matched in space and time based on the time stamp, and a structured data record table is formed;

[0017] The box plot method is used to identify abnormal values, and when the data is outside the range of [Q1-1.5IQR, Q3+1.5IQR], wherein Q1 is the lower quartile, Q3 is the upper quartile, and IQR is equal to Q3 minus Q1, the moving average method is used to replace the abnormal values, wherein the moving average formula is:

[0018]

[0019] In the formula, x new is the normal time point data, t is the time node corresponding to the data point with abnormal values, k is the index variable of the time node, and the value range is t-2 to t+2, that is, 2 normal time nodes before and after the abnormal value time node, a total of 5 time nodes;

[0020] The redundant features are analyzed by a Pearson correlation coefficient, and the feature variables with an absolute value greater than 0.6 in correlation with ecological restoration are reserved to generate a final ecological feature dataset.

[0021] Preferably, the ecological restoration evaluation module comprises:

[0022] The index system construction unit establishes a three-level evaluation index system, the first-level index is soil quality, vegetation condition and ecological function, the second-level index includes soil heavy metal pollution degree, soil nutrient abundance and soil structure stability for soil quality, vegetation growth trend, vegetation coverage degree and vegetation diversity for vegetation condition, and water and soil conservation capacity and carbon sink capacity for ecological function, and the third-level index includes 22 items covering specific soil, vegetation and ecological parameters.

[0023] The model establishment and evaluation unit determines the weight of each index based on the analytic hierarchy process, the weight of the first-level index soil quality is 0.35, the weight of the first-level index vegetation condition is 0.3, and the weight of the first-level index ecological function is 0.35, a multi-dimensional evaluation model is constructed by using the fuzzy comprehensive evaluation method, the parameters in the ecological feature dataset are substituted into the model to obtain the evaluation results of each index, and an ecological restoration effect index is generated after comprehensive evaluation, and the value range is 1-10 points.

[0024] Preferably, the determination of the weight of each index based on the analytic hierarchy process comprises:

[0025] Five ecological field experts are invited to score the relative importance of the same level index, a 1-9 scale method is used, 1 represents equal importance, and 9 represents extreme importance;

[0026] The maximum eigenvalue of the judgment matrix is calculated, the consistency index is calculated, the average random consistency index is combined to obtain the consistency ratio, when the consistency ratio is less than 0.1, the judgment matrix has satisfactory consistency, otherwise it is reconstructed;

[0027] The judgment matrix that passes the consistency test is normalized to obtain the weight value of each index.

[0028] Preferably, the multi-dimensional evaluation model constructed by using the fuzzy comprehensive evaluation method comprises:

[0029] The evaluation grades are divided into five levels of poor, relatively poor, general, good and excellent, and the corresponding scores are 1-2 points, 2-4 points, 4-6 points, 6-8 points and 8-10 points;

[0030] According to the actual value of each index, the triangular membership function is used to determine the membership degree of each index to each evaluation grade to form a membership degree matrix;

[0031] The index weight vector is multiplied by the membership degree matrix to obtain the comprehensive evaluation result, and the ecological restoration effect index is determined according to the maximum membership degree principle.

[0032] Preferably, the dynamic simulation prediction module comprises:

[0033] a data collection unit: collecting ecological restoration effect index, soil evolution parameters including soil organic matter decomposition rate and soil erosion rate, and climate change data including annual mean temperature change rate and annual precipitation change rate;

[0034] a dynamic simulation model of ecological restoration is constructed by using a Logistic model.

[0035] a simulation and output unit: setting different environmental conditions, running the model to simulate the evolution trend of the soil ecosystem, and outputting the ecological restoration prediction atlas for the next 5 years, 10 years and 15 years, which contains the change curve of the ecological restoration effect index and the key node annotation.

[0036] Preferably, the setting of different environmental conditions comprises:

[0037] baseline environmental conditions: taking the current temperature, precipitation and soil conditions as the baseline;

[0038] extreme climate conditions: setting extreme drought and high temperature conditions with temperature 3℃ higher than the baseline and precipitation 20% less than the baseline; extreme cold and rainy conditions with temperature 3℃ lower than the baseline and precipitation 20% more than the baseline;

[0039] gradual climate conditions: setting gradual conditions with temperature rising at a rate of 0.1℃ per year and precipitation decreasing at a rate of 2% per year.

[0040] Preferably, the repair strategy optimization module specifically comprises:

[0041] a strategy library storage unit: the preset repair strategy library contains three types of strategies, including soil improvement, vegetation repair and ecological regulation, soil improvement includes applying organic fertilizer and adding biochar, vegetation repair includes planting native tree species and sowing herbaceous plants, and ecological regulation includes setting artificial wetlands and building ecological ditches;

[0042] a strategy matching and generating unit: according to the ecological restoration effect index and the dynamic simulation prediction results, the corresponding strategy is matched from the strategy library combined with cost-benefit analysis;

[0043] a scheme pushing and interacting unit: pushing the generated optimization scheme to the terminal device, issuing instructions within the system and feeding back the scheme.

[0044] Preferably, the strategy matching and generating unit specifically comprises:

[0045] The ecological restoration effect index is divided into three intervals, 1-4 points correspond to the ecological restoration poor interval, the soil improvement type strategy is preferentially matched, 4-7 points correspond to the ecological restoration general interval, the vegetation repair type and the soil improvement type combined strategy are matched, and 7-10 points correspond to the ecological restoration good interval, and the ecological regulation type strategy is matched;

[0046] The benefit cost ratio of each candidate strategy is calculated through a benefit cost ratio calculation formula, and strategies with a benefit cost ratio greater than or equal to 0.002 points per yuan are selected into the optimization scheme.

[0047] Compared with the prior art, the beneficial effects of the present application are:

[0048] 1. The soil physicochemical parameters, vegetation growth indexes and microenvironment data are collected and standardized by the soil characteristic generation module, a multi-dimensional evaluation model combining a three-level evaluation index system and the analytic hierarchy process and the fuzzy comprehensive evaluation method is used, the defects of single dimension of the traditional evaluation method are overcome, the ecological restoration effect index can better reflect the overall condition of the soil ecosystem, and the accuracy of the evaluation result is improved.

[0049] 2. The dynamic simulation prediction module integrates the ecological restoration effect index, soil evolution parameters and climate change data based on the Logistic model, can simulate the long-term evolution trend of the soil ecosystem under different environmental conditions, the output 5-year, 10-year and 15-year prediction atlas can identify the key nodes of ecological restoration in advance, the prediction error is controlled within 10%, and a scientific basis is provided for long-term ecological management.

[0050] 3. The repair strategy optimization module realizes the accurate matching of the repair strategy and the ecological status through the ecological restoration effect index interval matching and the benefit cost ratio calculation, not only improves the pertinence of the strategy, but also reduces the invalid cost expenditure, through the cooperation of each module, realizes the full-process automation from data acquisition to strategy pushing, reduces the error caused by manual intervention, and provides an intelligent solution for the ecological management after the repair of the contaminated soil. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to facilitate those skilled in the art to understand, the present application is further described below with reference to the drawings.

[0052] Figure 1 The system framework diagram of the present application is shown in the figure;

[0053] Figure 2 The internal system framework diagram of the soil characteristic generation module in the present application is shown in the figure;

[0054] Figure 3 The internal system framework diagram of the ecological restoration evaluation module in the present application is shown in the figure;

[0055] Figure 4An internal system block diagram of the dynamic simulation prediction module in the application;

[0056] Figure 5 An internal system block diagram of the repair strategy optimization module in the application. DETAILED DESCRIPTION

[0057] In order for those skilled in the art to better understand the application scheme, the technical solutions in the embodiments of the application will be described clearly and completely below in conjunction with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0058] In this document, reference to“an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment nor are they necessarily mutually exclusive of one another. As those skilled in the art will appreciate, the various embodiments described herein can be combined with one another.

[0059] Please refer to Figure 1 As shown in the figure, the ecological restoration effect evaluation and simulation system after contaminated soil remediation includes:

[0060] The soil feature generation module: based on the soil sensor array, vegetation monitoring device and environmental monitoring terminal, the physicochemical parameters of the remediated soil, vegetation growth indicators and surrounding microenvironment data are collected, the collected data are standardized, and the ecological feature data set is generated;

[0061] The ecological restoration evaluation module: the ecological restoration evaluation module is electrically connected with the soil feature generation module, based on the ecological feature data set, a multi-dimensional evaluation model is established using the analytic hierarchy process and fuzzy comprehensive evaluation method, the ecological feature data set is evaluated in multiple dimensions, and based on the output of the model, the ecological restoration effect index is generated;

[0062] The dynamic simulation prediction module: based on the ecological restoration effect index, soil evolution parameters and climate change data, an ecological restoration dynamic simulation model is constructed, the evolution trend of the soil ecosystem under different environmental conditions is simulated, and the ecological restoration prediction atlas is output;

[0063] The repair strategy optimization module: according to the ecological restoration effect index and the dynamic simulation prediction result, in combination with the preset repair strategy library, a targeted ecological repair optimization scheme is generated, which is pushed to the terminal device and realizes the system internal instruction interaction.

[0064] Please refer to Figure 2The soil characteristic generation module specifically includes:

[0065] The multi-dimensional parameter acquisition unit acquires the soil bulk density, porosity, heavy metal residue and nutrient content through the soil sensor array, obtains the plant height, ground diameter, leaf area index and biomass by using the vegetation monitoring device, and records the air temperature, humidity, light intensity and precipitation by means of the environment monitoring terminal;

[0066] The data cleaning unit adopts the Pauta criterion to identify abnormal values, marks the abnormal values when the data deviates from the mean value by more than 3 times the standard deviation, and corrects the abnormal values by using the adjacent time data interpolation method, wherein the correction formula is:

[0067]

[0068] In the formula, x is the corrected data, x t-1 and x t+1 are the effective data before and after the abnormal value time point respectively;

[0069] The feature fusion unit associates the cleaned soil physicochemical parameters, vegetation growth indexes and microenvironment data according to the sampling point coordinates and time stamp, extracts the feature variables with a contribution rate of 80% by using the principal component analysis method, and constructs the ecological characteristic data set;

[0070] The innovation adopts the data cleaning method combining the Pauta criterion and the adjacent interpolation, solves the sensor data abnormality problem, extracts the high-contribution-rate feature variables by using the principal component analysis method, reduces the data dimension while retaining the key information, improves the operation efficiency of the subsequent evaluation model, and improves the feature extraction accuracy compared with the traditional method.

[0071] The construction of the ecological characteristic data set specifically includes:

[0072] The soil physicochemical parameters, vegetation growth indexes and microenvironment data are matched in space and time based on the time stamp to form a structured data record table;

[0073] The box plot method is used to identify abnormal values, when the data is outside the range of [Q1-1.5IQR, Q3+1.5IQR], wherein Q1 is the lower quartile, Q3 is the upper quartile, and IQR is equal to Q3 minus Q1, and the moving average method is used to replace the abnormal values, wherein the moving average formula is:

[0074]

[0075] In the formula, x new is the normal time point data, t is the time node corresponding to the data point with abnormal values, k is the index variable of the time node, and the value range is t-2 to t+2, that is, including each 2 normal time nodes before and after the abnormal value time node, a total of 5 time nodes;

[0076] The redundant features were analyzed by Pearson correlation coefficient, and the feature variables with absolute value greater than 0.6 were reserved to generate the final ecological feature dataset;

[0077] The dataset construction method of "spatiotemporal matching + box plot outlier processing + correlation screening" was used to correct outliers by 5-point moving average and eliminate redundant features by Pearson coefficient, ensuring the spatiotemporal consistency and ecological relevance of the dataset, and reducing 30% of redundant information compared with traditional dataset construction methods.

[0078] Please refer to Figure 3 As shown in the figure, the ecological restoration evaluation module includes:

[0079] Index system construction unit: a three-level evaluation index system is established, with soil quality, vegetation condition and ecological function as the first-level indicators. Soil quality includes soil heavy metal pollution degree, soil nutrient abundance and soil structure stability, vegetation condition includes vegetation growth trend, vegetation coverage degree and vegetation diversity, and ecological function includes water and soil conservation capacity and carbon sink capacity. There are 22 third-level indicators, covering specific soil, vegetation and ecological parameters.

[0080] Model establishment and evaluation unit: the weights of each indicator are determined based on the analytic hierarchy process, with the weight of soil quality as the first-level indicator being 0.35, the weight of vegetation condition being 0.3, and the weight of ecological function being 0.35. A multi-dimensional evaluation model is constructed using fuzzy comprehensive evaluation method, the parameters in the ecological feature dataset are substituted into the model, the evaluation results of each indicator are obtained, and the ecological restoration effect index is generated after comprehensive evaluation, with the value ranging from 1 to 10 points.

[0081] Innovation: a multi-level evaluation system containing 22 third-level indicators is constructed, for the first time, ecological function (water and soil conservation, carbon sink) is included in the core dimension of evaluation, breaking the limitation of traditional evaluation focusing only on soil physical and chemical properties. Scientific weights are given through the analytic hierarchy process, making the evaluation results more consistent with the overall restoration status of the ecological system.

[0082] The determination of the weights of each indicator based on the analytic hierarchy process includes:

[0083] Five experts in the field of ecology are invited to score the relative importance of the same level indicators, using the 1-9 scale method, with 1 representing equal importance and 9 representing extreme importance.

[0084] The maximum eigenvalue λ of the judgment matrix is calculated max , through the formula

[0085] The consistency index CI is calculated, combined with the average random consistency index RI, to obtain the consistency ratio When CR is less than 0.1, the judgment matrix has satisfactory consistency, otherwise it needs to be reconstructed.

[0086] The judgment matrix passing the consistency test is normalized to obtain the weight value of each index;

[0087] The expert experience is combined with the mathematical model, the scientific nature of the weight distribution is ensured through the consistency test, and the subjective scoring deviation is avoided; the application scene of the 1-9 scale method is optimized according to the characteristics of the ecological evaluation field, so that the index importance comparison is more in line with the characteristics of the soil ecological system.

[0088] The multi-dimensional evaluation model is constructed by using the fuzzy comprehensive evaluation method, specifically including:

[0089] The evaluation grades are divided into five levels of poor, poor, general, good and excellent, and the corresponding scores are 1-2 points, 2-4 points, 4-6 points, 6-8 points and 8-10 points;

[0090] According to the actual value of each index, the triangular membership function is used to determine the membership degree of each evaluation grade, and a membership degree matrix is formed;

[0091] The index weight vector is multiplied by the membership degree matrix to obtain the comprehensive evaluation result, and the ecological restoration effect index is determined according to the maximum membership degree principle;

[0092] The triangular membership function is applied to the ecological restoration evaluation, the boundary fuzziness problem of the ecological index is solved, the qualitative evaluation is converted into a quantitative index of 1-10 points through the quantitative operation of the weight vector and the membership degree matrix, and the intuitive comparison of the evaluation result is realized.

[0093] Please refer to Figure 4 The dynamic simulation prediction module includes:

[0094] The data collection unit collects the ecological restoration effect index, soil evolution parameters and climate change data, the soil evolution parameters include soil organic matter decomposition rate and soil erosion rate, and the climate change data include annual average temperature change rate and annual precipitation change rate;

[0095] The Logistic model is used to construct the ecological restoration dynamic simulation model, wherein the model formula is:

[0096]

[0097] In the formula, N(t) is the state value of the ecological system at time t, K is the environmental capacity, N0 is the initial state value, r(t) is the growth rate changing with time, t represents the time variable, and e represents the natural constant.

[0098] Simulation and output unit: Set different environmental conditions, run the model to simulate the evolution trend of soil ecosystem, output the ecological restoration prediction atlas in the next 5 years, 10 years and 15 years, the atlas includes the change curve of ecological restoration effect index and key node annotation;

[0099] The improved Logistic model introduces time-varying growth rate, integrates soil evolution and climate change data, breaks through the limitations of traditional static models, realizes dynamic prediction of ecosystem evolution under different environmental conditions, and prolongs the prediction period to 15 years, improving the identification accuracy of key nodes.

[0100] The setting of different environmental conditions includes:

[0101] Baseline environmental conditions: Take the current temperature, precipitation and soil conditions as the baseline;

[0102] Extreme climate conditions: Set extreme drought and high temperature conditions with temperature 3℃ higher than baseline and precipitation 20% less than baseline; extreme cold and rainy conditions with temperature 3℃ lower than baseline and precipitation 20% more than baseline;

[0103] Gradual climate conditions: Set gradual conditions with temperature rising at a rate of 0.1℃ per year and precipitation decreasing at a rate of 2% per year;

[0104] For the first time, environmental conditions are divided into baseline, extreme and gradual three categories, covering short-term stability, sudden extreme and long-term climate scenarios. The simulation results can provide targeted basis for ecological management under different risk levels, solving the problem of single simulation scenario in traditional simulation.

[0105] Please refer to Figure 5 The repair strategy optimization module specifically includes:

[0106] Strategy library storage unit: The preset repair strategy library includes three types of strategies: soil improvement, vegetation restoration and ecological regulation. Soil improvement includes applying organic fertilizer and adding biochar; vegetation restoration includes planting native tree species and sowing herbaceous plants; ecological regulation includes setting up artificial wetlands and building ecological ditches;

[0107] Strategy matching and generation unit: According to the ecological restoration effect index and dynamic simulation prediction results, combined with cost-benefit analysis, match the corresponding strategy from the strategy library;

[0108] Scheme pushing and interaction unit: Push the generated optimization scheme to the terminal device, issue instructions within the system and feed back the scheme;

[0109] A "soil-vegetation-ecology" three-dimensional strategy library is constructed, intelligent association of evaluation results and remediation measures is realized through a strategy matching unit, a strategy optimization closed loop of "generation-pushing-feedback" is formed in combination with a terminal interactive feedback mechanism, and the efficiency is greatly improved compared with traditional manual strategy making.

[0110] The strategy matching and generating unit specifically comprises:

[0111] The ecological restoration effect index is divided into three intervals, 1-4 points correspond to the poor ecological restoration interval, the soil improvement type strategy is preferentially matched, 4-7 points correspond to the general ecological restoration interval, the vegetation restoration type and the soil improvement type combined strategy are matched, and 7-10 points correspond to the good ecological restoration interval, and the ecological regulation type strategy is matched;

[0112] The benefit cost ratio of each candidate strategy is calculated through a benefit cost ratio calculation formula, wherein the benefit cost ratio calculation formula is:

[0113]

[0114] In the formula, ΔE is the ecological restoration effect index improvement value based on dynamic simulation prediction, C is the total cost of strategy implementation, R is the benefit cost ratio, and the strategy with R greater than or equal to 0.002 points per yuan is selected into the optimization scheme;

[0115] The "index interval matching + benefit cost ratio screening" strategy optimization method is proposed, the strategy is matched according to the difference of the ecological restoration stage, the economic efficiency of the strategy is ensured through the quantitative benefit cost ratio, and the cost benefit ratio is improved compared with the traditional experience type strategy selection method.

[0116] In summary, the advantages of the present application are:

[0117] The system combines soil feature generation, ecological restoration evaluation, dynamic simulation prediction and remediation strategy optimization and the like, and systematically evaluates and optimizes the ecological restoration effect after the remediation of contaminated soil, and breaks through the single-dimensional evaluation mode;

[0118] The system collects soil, vegetation and environmental data through a variety of sensor arrays and monitoring equipment, and realizes accurate standardization and optimization of data by using advanced data cleaning and feature extraction technologies such as principal component analysis and Pearson correlation coefficient analysis, so as to provide accurate data support for subsequent ecological restoration evaluation;

[0119] The present application adopts the combination of analytic hierarchy process and fuzzy comprehensive evaluation method, establishes a multi-dimensional evaluation model, comprehensively considers soil quality, vegetation condition and ecological function and the like, evaluates the ecological restoration effect, and obtains the ecological restoration effect index. This multi-dimensional evaluation mode makes the evaluation result more comprehensive and accurate;

[0120] The application adopts a Logistic model for dynamic simulation, can predict the evolution trend of the soil ecosystem under different environmental conditions, and in addition, the system can simulate the ecological restoration effect under different climate changes in the future, and output an ecological restoration prediction atlas, to provide a forward-looking reference for decision makers.

[0121] According to the ecological restoration effect index and the dynamic simulation prediction result, the system can select the most suitable strategy from the preset repair strategy library and perform individual optimization, and the intelligent matching of the strategy not only improves the pertinence of the repair effect, but also considers the actual situation under different environments.

[0122] In the repair strategy optimization module, the benefit-cost ratio calculation formula is combined to screen and optimize each candidate repair strategy, so that the recommended repair strategy has high economic benefit and actual feasibility.

[0123] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the specification. The specification selects and describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited by the claims and their entire scope and equivalents.

Claims

1. A system for evaluating and simulating the ecological restoration effect after contaminated soil remediation, characterized in that, include: Soil Feature Generation Module: Based on soil sensor array, vegetation monitoring equipment and environmental monitoring terminal, it collects physicochemical parameters, vegetation growth indicators and surrounding microenvironment data of the remediated soil, standardizes the collected data and generates ecological feature dataset. Ecological Restoration Assessment Module: The ecological restoration assessment module is electrically connected to the soil feature generation module. Based on the ecological feature dataset, it uses the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method to establish a multi-dimensional assessment model, which performs a multi-dimensional assessment of the ecological feature dataset. Based on the model's output, it generates an ecological restoration effect index. Dynamic simulation and prediction module: Based on the ecological restoration effect index, soil evolution parameters and climate change data, a dynamic simulation model for ecological restoration is constructed to simulate the evolution trend of soil ecosystems under different environmental conditions and output an ecological restoration prediction map. Repair strategy optimization module: Based on the ecological restoration effect index and dynamic simulation prediction results, combined with the preset repair strategy library, it generates targeted ecological restoration optimization plans, pushes them to terminal devices, and realizes in-system command interaction.

2. The system for evaluating and simulating the ecological restoration effect after contaminated soil remediation according to claim 1, characterized in that, The soil feature generation module specifically includes: Multi-dimensional parameter acquisition unit: Soil bulk density, porosity, heavy metal residue and nutrient content are collected through soil sensor array; plant height, ground diameter, leaf area index and biomass are obtained through vegetation monitoring equipment; and air temperature, humidity and light intensity and precipitation are recorded with the help of environmental monitoring terminal. Data cleaning unit: Outliers are identified using the Laida criterion. Data that deviates from the mean by more than 3 times the standard deviation is marked as an outlier. Outliers are corrected using interpolation of data from nearby time points. Feature fusion unit: The cleaned soil physicochemical parameters, vegetation growth indicators and microenvironment data are correlated according to the sampling point coordinates and timestamps. The principal component analysis method is used to extract the feature variables with the top 80% contribution rate to construct an ecological feature dataset.

3. The system for evaluating and simulating the ecological restoration effect after contaminated soil remediation according to claim 2, characterized in that, The construction of the ecological feature dataset specifically includes: Based on timestamps, soil physicochemical parameters, vegetation growth indicators and microenvironment data are spatiotemporally matched to form a structured data record table; Box plots are used to identify outliers. When data exceeds the range [Q1 - 1.5IQR, Q3 + 1.5IQR], where Q1 is the lower quartile, Q3 is the upper quartile, and IQR equals Q3 minus Q1, outliers are replaced using a moving average method. The moving average formula is as follows: In the formula, x new The data represents normal time points, where t is the time node corresponding to the data point with outlier, and k is the index variable of the time node, with a value range from t-2 to t+2, which includes two normal time nodes before and after the outlier time node, for a total of 5 time nodes. Redundant features were removed by Pearson correlation coefficient analysis, and feature variables with an absolute correlation value greater than 0.6 with ecological restoration were retained and combined to generate the final ecological feature dataset.

4. The system for evaluating and simulating the ecological restoration effect after contaminated soil remediation according to claim 1, characterized in that, The ecological restoration assessment module includes: The indicator system construction unit is as follows: a three-level evaluation indicator system is established. The first-level indicators are soil quality, vegetation status, and ecological function. The second-level indicators include soil heavy metal pollution level, soil nutrient abundance, and soil structure stability; vegetation status includes vegetation growth status, vegetation coverage, and vegetation diversity; and ecological function includes soil and water conservation capacity and carbon sequestration capacity. There are a total of 22 third-level indicators, covering specific soil, vegetation, and ecological parameters. Model building and evaluation unit: The weights of each indicator are determined based on the analytic hierarchy process (AHP). The weights of the primary indicators are 0.35 for soil quality, 0.3 for vegetation status, and 0.35 for ecological function. A multi-dimensional evaluation model is constructed using the fuzzy comprehensive evaluation method. The parameters in the ecological characteristic dataset are substituted into the model to obtain the evaluation results of each indicator. After comprehensive evaluation, an ecological restoration effect index is generated, with a value range of 1 to 10.

5. The system for evaluating and simulating the ecological restoration effect after contaminated soil remediation according to claim 4, characterized in that, The determination of the weights of each indicator based on the analytic hierarchy process specifically includes: Five experts in the field of ecology were invited to score the relative importance of indicators at the same level, using a 1-9 scale, where 1 indicates equal importance and 9 indicates extreme importance. Calculate the largest eigenvalue of the judgment matrix, calculate the consistency index, and combine it with the average random consistency index to obtain the consistency ratio. When the consistency ratio is less than 0.1, the judgment matrix has satisfactory consistency; otherwise, it is reconstructed. The judgment matrix that passes the consistency test is normalized to obtain the weight values ​​of each indicator.

6. The system for evaluating and simulating the ecological restoration effect after contaminated soil remediation according to claim 4, characterized in that, The construction of a multi-dimensional evaluation model using the fuzzy comprehensive evaluation method specifically includes: The evaluation levels are divided into five levels: poor, fair, average, good, and excellent, with corresponding scores of 1-2 points, 2-4 points, 4-6 points, 6-8 points, and 8-10 points, respectively. Based on the actual values ​​of each indicator, the membership degree of each indicator to each evaluation level is determined using a triangular membership function, forming a membership degree matrix; Multiply the indicator weight vector by the membership matrix to obtain the comprehensive evaluation result, and determine the ecological restoration effect index according to the principle of maximum membership.

7. The system for evaluating and simulating the ecological restoration effect after contaminated soil remediation according to claim 6, characterized in that, The dynamic simulation prediction module includes: Data collection unit: collects ecological restoration effect index, soil evolution parameters and climate change data. The soil evolution parameters include soil organic matter decomposition rate and soil erosion rate. The climate change data includes annual mean temperature change rate and annual precipitation change rate. A dynamic simulation model for ecological restoration is constructed using the Logistic model. Simulation and Output Unit: Set different environmental conditions, run the model to simulate the evolution trend of soil ecosystem, and output ecological restoration prediction maps for the next 5, 10, and 15 years. The maps include the change curves of the ecological restoration effect index and the annotation of key nodes.

8. The system for evaluating and simulating the ecological restoration effect after contaminated soil remediation according to claim 6, characterized in that, The different environmental conditions include: Baseline environmental conditions: The current temperature, precipitation, and soil conditions are used as the baseline. Extreme climate conditions: Extreme drought and high temperature conditions with a temperature 3°C higher than the baseline and a precipitation 20% lower than the baseline; extreme cold and rainy conditions with a temperature 3°C lower than the baseline and a precipitation 20% higher than the baseline. Gradual climate conditions: Set gradual conditions where temperature increases at a rate of 0.1°C per year and precipitation decreases at a rate of 2% per year.

9. The system for evaluating and simulating the ecological restoration effect after contaminated soil remediation according to claim 1, characterized in that, The repair strategy optimization module specifically includes: Strategy library storage unit: The preset restoration strategy library contains three types of strategies: soil improvement, vegetation restoration, and ecological regulation. Soil improvement includes applying organic fertilizer and adding biochar; vegetation restoration includes planting native tree species and sowing herbaceous plants; and ecological regulation includes setting up artificial wetlands and building ecological ditches. Strategy matching and generation unit: Based on the ecological restoration effect index and dynamic simulation prediction results, and combined with cost-benefit analysis, the corresponding strategy is matched from the strategy library; Solution Push and Interaction Unit: Pushes the generated optimization solution to the terminal device, issues instructions within the system, and provides solution feedback.

10. The system for evaluating and simulating the ecological restoration effect after contaminated soil remediation according to claim 9, characterized in that, The strategy matching and generation unit specifically includes: The ecological restoration effect index is divided into three intervals: 1-4 points corresponds to the poor ecological restoration interval, which is matched with soil improvement strategies; 4-7 points corresponds to the average ecological restoration interval, which is matched with a combination of vegetation restoration and soil improvement strategies; and 7-10 points corresponds to the good ecological restoration interval, which is matched with ecological regulation strategies. The benefit-cost ratio of each candidate strategy is calculated using the benefit-cost ratio calculation formula, and strategies with a benefit-cost ratio ≥ 0.002 points per yuan are selected for inclusion in the optimization scheme.

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