Evaluation method and system for ecological restoration effect of surface mine

Abstract

The invention relates to the technical field of ecological restoration evaluation, in particular to a surface mine ecological restoration effect evaluation method and system.The surface mine ecological restoration effect evaluation method comprises the following steps that based on ecological restoration targets and standards, a decision tree algorithm is adopted, potential evaluation indexes are analyzed and screened, data in the aspects of biological diversity and soil quality are processed, and the evaluation result is obtained; and classifying the data to generate a refined evaluation index system. According to the method, through application of the decision tree algorithm, screening and classification of evaluation indexes are more scientific and systematic, comprehensiveness and pertinence of an evaluation system are ensured, the genetic algorithm and the simulated annealing algorithm are applied, the global optimization capability is introduced in the weight adjustment process, and the evaluation accuracy is improved. The accuracy of weight configuration and the adaptability of an evaluation system are remarkably improved, the long-term and short-term memory network model and the support vector machine are combined, the long-term dependency relationship in ecological restoration effect time sequence data is captured, and the accuracy of future trend prediction and the reliability of a classification result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological restoration evaluation, and in particular to an open-pit mine ecological restoration effect evaluation method and system. Background Art

[0002] The field of ecological restoration evaluation technology focuses on evaluating the effects of restoring the environment through the implementation of various ecological restoration measures after the damage caused by human activities to the natural environment. This includes restoring soil quality, improving biodiversity, improving water quality, and increasing vegetation coverage. Ecological restoration evaluation technology covers a series of methods and means, such as ecological indicator evaluation, remote sensing technology, and biological monitoring, aiming to provide scientific and systematic evaluation results to guide subsequent ecological restoration work and environmental management.

[0003] Among them, the open-pit mine ecological restoration effect evaluation method is a method specifically for evaluating the effect of open-pit mine ecological restoration work. Its purpose is to systematically evaluate the implementation effect of ecological restoration measures through scientific evaluation systems and technical means. Including evaluating the effectiveness of soil erosion control, biodiversity restoration, water quality improvement, vegetation restoration, etc. Through this evaluation method, the effectiveness of ecological restoration work can be clarified, providing a basis for subsequent optimization of restoration plans and environmental protection measures, with the goal of achieving long-term sustainable development and restoration of the ecological environment in mining areas.

[0004] Traditional evaluation methods rely on simple linear weighting or single algorithm processing, which makes it difficult to effectively capture the complex relationship between evaluation indicators, resulting in insufficient accuracy and comprehensiveness of evaluation results. Traditional methods have limited ability to handle fuzzy and uncertain information, making it difficult to accurately predict the future trend of ecological restoration effects, affecting the reliability and practicality of evaluation results. These deficiencies lead to biased evaluation results, affect the decision-making and optimization of ecological restoration projects, and limit the continuous improvement and supervision of ecological restoration effects. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an open-pit mine ecological restoration effect evaluation method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a method for evaluating the ecological restoration effect of an open-pit mine, comprising the following steps: S1: Based on the ecological restoration goals and standards, the decision tree algorithm is used to analyze and screen potential evaluation indicators, process data on biodiversity and soil quality, and classify the data to generate a detailed evaluation indicator system; S2: Based on the refined evaluation index system, a genetic algorithm is used to preliminarily set the weight distribution of the index, adjust the weight to match the needs of the evaluation model, and analyze the weight distribution during the adjustment process to generate an adjusted weight configuration; S3: Based on the adjusted weight configuration, execute the fuzzy logic method, perform fuzzification processing on the indicators in the evaluation system, perform reasoning according to the fuzzy rule set, calculate the contribution of each indicator to the overall evaluation result, and generate a fuzzy logic scoring record; S4: Based on the fuzzy logic scoring record, a simulated annealing algorithm is used to identify problems in the scoring system, gradually reduce the energy, adjust the evaluation index weights, and optimize the weight adjustment process to generate an accuracy optimization score; S5: Based on the accuracy optimization score, the long short-term memory network model is used to process the time series data of ecological restoration effects, capture long-term dependencies through the repeated structure of the network layer, and predict the development trend of future ecological restoration effects to generate long-term trend prediction records; S6: Based on the long-term trend prediction record, apply a support vector machine to continuously analyze the prediction results, classify and verify the data in the long-term trend prediction record using the processed feature space and decision boundary, and evaluate the classification results to generate verified prediction data; S7: Based on the verified prediction data, a multi-criteria decision analysis method is used to analyze the evaluation results, and the data of differentiated dimensions are weighted and integrated in combination with ecological, social and economic factors. The results of the weighted integration are summarized to generate a comprehensive evaluation result.

[0007] As a further scheme of the present invention, the refined evaluation index system includes ecological diversity index, soil stability rating, water body restoration index, soil nutrient status index, and vegetation coverage percentage. The fuzzy logic scoring record includes the fuzzy contribution value of the indicator, the fuzzy scoring value, and the evaluation level division. The accuracy optimization scoring includes the optimized indicator weight value, the adjusted scoring value, and the confidence interval of the evaluation result. The long-term trend prediction record includes the predicted growth rate of vegetation coverage, the predicted change trend of soil organic matter, and the expected progress of water body restoration. The comprehensive evaluation results include the overall ecological restoration rating, social impact analysis records, economic benefit assessment results, and a list of improvement measures.

[0008] As a further solution of the present invention, based on the ecological restoration goals and standards, a decision tree algorithm is used to analyze and screen potential evaluation indicators, process data on biodiversity and soil quality, and classify the data to generate a refined evaluation index system. Specifically, the steps are as follows: S101: Based on ecological restoration goals and standards, the C4.5 decision tree algorithm was used to analyze and screen potential evaluation indicators associated with biodiversity and soil quality. Through the construction and pruning of the decision tree, the indicators that affect the evaluation process were identified to generate a set of key evaluation indicators; S102: Based on the key evaluation indicator set, the indicators are grouped using the K-means clustering algorithm, and the indicators are clustered into differentiated groups using distance calculation in the iterative process, each group represents a matching evaluation dimension, and the indicator group division result is generated; S103: Based on the indicator grouping result, the information gain ratio method is used to identify the indicator that contributes best to the evaluation system from the group, and the information gain ratio is used to evaluate the criticality of multiple indicators to generate a refined evaluation indicator system.

[0009] As a further solution of the present invention, based on the refined evaluation index system, a genetic algorithm is used to preliminarily set the weight distribution of the index, adjust the weight to match the needs of the evaluation model, and analyze the weight distribution during the adjustment process. The steps of generating the adjusted weight configuration are specifically as follows: S201: Based on the refined evaluation index system, a genetic algorithm is used to preliminarily set the weights of the indexes, adjust the weight distribution, and match the requirements of the evaluation model to generate a preliminary weight optimization; S202: Based on the preliminary weight optimization, a simulated annealing algorithm is applied to adjust the weight configuration in detail, to find the global optimal solution of weight allocation, to reduce the deviation of the local optimal solution, and to generate a weight detail adjustment result; S203: Based on the weight detail adjustment results, a multi-objective optimization algorithm is used to optimize the weight configuration, search for the optimal combination of multi-indicator weights, determine the multiple aspects of the evaluation system that reflect the ecological restoration effect, and generate an adjusted weight configuration.

[0010] As a further solution of the present invention, based on the adjusted weight configuration, a fuzzy logic method is executed to fuzzify the indicators in the evaluation system, reasoning is performed according to the fuzzy rule set, and the contribution of each indicator to the overall evaluation result is calculated. The steps of generating a fuzzy logic scoring record are specifically as follows: S301: Based on the adjusted weight configuration, fuzzy set theory is used to perform fuzzification processing on each evaluation indicator, a fuzzy level is assigned to the indicator, and a fuzzy rule set is used for reasoning to determine the contribution of each indicator to the overall evaluation result, and a fuzzy contribution score is generated; S302: Based on the fuzzy contribution score, a fuzzy comprehensive evaluation model is used to integrate the contribution of multiple indicators, and a fuzzy comprehensive evaluation result is generated by constructing a fuzzy relationship matrix and applying fuzzy operators; S303: Based on the fuzzy comprehensive evaluation result, fuzzy logic reasoning is performed, and defuzzification technology is used to convert the fuzzy evaluation result into an evaluation grade or a numerical value, and an evaluation judgment basis is provided to generate a fuzzy logic scoring record.

[0011] As a further solution of the present invention, based on the fuzzy logic scoring record, a simulated annealing algorithm is used to identify problems in the scoring system, gradually reduce the energy, adjust the evaluation index weights, and optimize the weight adjustment process. The steps of generating the accuracy optimization score are specifically as follows: S401: Based on the fuzzy logic scoring record, a grey correlation analysis method is used to analyze the relationship between the indicators in the evaluation system and the evaluation results, identify the key indicators that affect the evaluation results, and generate a key indicator analysis record; S402: Based on the key indicator analysis record, the simulated annealing algorithm is applied to adjust the indicator weights, and the energy of the solution is gradually optimized through mining and development strategies, and the optimal weight allocation scheme is searched to generate weight optimization adjustment results; S403: Based on the weight optimization adjustment result, linear programming is used to calibrate the indicator weights, and in the evaluation system, the true criticality reflected by each indicator weight is verified to generate an accuracy optimization score.

[0012] As a further solution of the present invention, based on the accuracy optimization score, the long short-term memory network model is used to process the time series data of ecological restoration effects, capture long-term dependencies through the repeated structure of the network layer, and predict the development trend of future ecological restoration effects. The steps of generating long-term trend prediction records are specifically as follows: S501: Based on the accuracy optimization score, a long short-term memory network model is used to process the time series data of ecological restoration effects, capture the long-term dependencies in the data, analyze the data trends, predict the changes in future ecological restoration effects, and generate trend prediction data; S502: Based on the trend forecast data, using seasonal decomposition forecasting method, separating seasonal changes and trend components in time series data, analyzing development trends through seasonal adjustment, optimizing the accuracy of trend forecasting, and generating seasonal adjustment forecast data; S503: Based on the seasonally adjusted forecast data, a unit root test is used to evaluate the stability of the trend, and the reliability of the forecast trend is confirmed through statistical tests, the robustness of the forecast data is optimized, and a long-term trend forecast record is generated.

[0013] As a further solution of the present invention, based on the long-term trend prediction record, a support vector machine is applied to continuously analyze the prediction results, and the data in the long-term trend prediction record is classified and verified by using the processed feature space and decision boundary, and the classification results are evaluated. The steps of generating verified prediction data are specifically as follows: S601: Based on the long-term trend prediction record, a support vector machine is used to analyze, differentiate differentiated prediction trends, and trend data is classified and verified to generate a trend classification analysis record; S602: Based on the trend classification analysis record, a k-fold cross validation method is applied to verify the result, and the prediction performance of the model for new data is evaluated by alternately using the difference data subsets as training and test sets to generate enhanced prediction data; S603: Based on the enhanced prediction data, a weighted average method is used to summarize and compare the results of the difference verification rounds, analyze the consistency and deviation of the prediction data, and evaluate the accuracy of the ecological restoration effect prediction to generate verified prediction data.

[0014] As a further solution of the present invention, based on the verified prediction data, a multi-criteria decision analysis method is used to analyze the evaluation results, and the data of the differentiated dimensions are weighted and integrated in combination with ecological, social and economic factors, and the results of the weighted integration are summarized. The steps of generating a comprehensive evaluation result are specifically as follows: S701: Based on the verified prediction data, the target layer, criterion layer and indicator layer of the evaluation are defined by using the hierarchical analysis method, the weights of the multi-criterion layer factors are calculated by constructing a pairwise comparison matrix, and the consistency ratio is used to perform a consistency test to generate a multi-factor weight distribution table; S702: Based on the multi-factor weight distribution table, applying the fuzzy comprehensive evaluation method, determining the evaluation factor set and the evaluation level, constructing a fuzzy relationship matrix, and expressing the contribution degree of the difference factors to the evaluation level, and generating a fuzzy evaluation result matrix; S703: Based on the fuzzy evaluation result matrix, a weighted average method is used to calculate the weighted score corresponding to each evaluation level, perform defuzzification processing, and convert the fuzzy evaluation result into an evaluation score to generate a comprehensive evaluation result.

[0015] An open-pit mine ecological restoration effect evaluation system, the open-pit mine ecological restoration effect evaluation system is used to execute the open-pit mine ecological restoration effect evaluation method, the system includes an indicator analysis and screening module, a weight distribution adjustment module, a fuzzy logic scoring module, a problem identification and optimization module, a trend analysis and prediction module, and an evaluation and verification module; The indicator analysis and screening module uses the C4.5 decision tree algorithm to analyze and screen potential evaluation indicators based on the goals and standards of ecological restoration, screen indicators related to biodiversity and soil quality, group indicators using the K-means clustering algorithm, aggregate similar indicators, and use the information gain ratio to identify the indicators that contribute best to the evaluation process, generating a set of key evaluation indicators; The weight allocation adjustment module uses a genetic algorithm based on a set of key evaluation indicators to preliminarily allocate the weights of the indicators to match the requirements of the evaluation model, and uses a simulated annealing algorithm to refine the adjustment of the weight configuration. Through the simulated annealing process, it searches for the global optimal solution and generates the adjusted weight configuration; The fuzzy logic scoring module uses a fuzzy logic processing method based on the adjusted weight configuration to fuzzify the evaluation indicators, assign a fuzzy value to each indicator, perform reasoning and calculation according to the set fuzzy rule set, convert the fuzzy input into a scoring output, and generate a fuzzy logic scoring record; The problem identification and optimization module uses a simulated annealing algorithm based on fuzzy logic scoring records to identify problems in the scoring system, adjust the weights of evaluation indicators, search for the global optimal solution of weight configuration, and use linear programming to adjust and optimize weight distribution to generate accuracy optimization scores; The trend analysis and prediction module uses a long short-term memory network model based on accuracy optimization scoring to process time series data of ecological restoration effects, predicts future trends by learning long-term dependencies in the data, applies seasonal decomposition prediction method, analyzes seasonal changes and trends in time series, and generates long-term trend prediction records; The evaluation and verification module is based on long-term trend forecast records and uses support vector machines to analyze forecast data, classifies and verifies trends by building decision boundaries, uses hierarchical analysis to evaluate and weight the criticality of differential factors, and integrates multiple dimensional data to generate comprehensive evaluation results.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, through the application of decision tree algorithm, the screening and classification of evaluation indicators are made more scientific and systematic, ensuring the comprehensiveness and pertinence of the evaluation system. The use of genetic algorithm and simulated annealing algorithm introduces global optimization capability in the weight adjustment process, significantly improving the accuracy of weight configuration and the adaptability of the evaluation system. Fuzzy logic method provides an effective means for processing uncertain and fuzzy information, enhancing the flexibility and practicality of the evaluation process. The combination of long short-term memory network model and support vector machine not only captures the long-term dependencies in the time series data of ecological restoration effects, but also improves the accuracy of future trend predictions and the reliability of classification results. Through multi-standard decision analysis methods, ecological, social and economic factors are fully considered, making the evaluation results more comprehensive and objective, providing a solid scientific basis for subsequent decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the workflow of the present invention.

[0018] Figure 2 This is a detailed flow chart of S1 of the present invention.

[0019] Figure 3 This is a detailed flow chart of S2 of the present invention.

[0020] Figure 4 This is a detailed flow chart of S3 of the present invention.

[0021] Figure 5 This is a detailed flow chart of S4 of the present invention.

[0022] Figure 6 This is a detailed flow chart of S5 of the present invention.

[0023] Figure 7 This is a detailed flow chart of S6 of the present invention.

[0024] Figure 8 This is a detailed flow chart of S7 of the present invention.

[0025] Fig. 9 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] Embodiment 1 See also Figure 1 The present invention provides a technical solution: a method for evaluating the ecological restoration effect of an open-pit mine, comprising the following steps: S1: Based on the ecological restoration goals and standards, the decision tree algorithm is used to analyze and screen potential evaluation indicators, process data on biodiversity and soil quality, and classify the data to generate a detailed evaluation indicator system; S2: Based on the refined evaluation index system, the genetic algorithm is used to preliminarily set the weight distribution of the index, adjust the weight to match the needs of the evaluation model, and analyze the weight distribution during the adjustment process to generate the adjusted weight configuration; S3: Based on the adjusted weight configuration, the fuzzy logic method is executed to fuzzify the indicators in the evaluation system, reasoning is performed according to the fuzzy rule set, the contribution of each indicator to the overall evaluation result is calculated, and the fuzzy logic score record is generated; S4: Based on the fuzzy logic scoring records, the simulated annealing algorithm is used to identify the problems in the scoring system, gradually reduce the energy, adjust the evaluation index weights, and optimize the weight adjustment process to generate accuracy optimization scores; S5: Based on the accuracy optimization score, the long short-term memory network model is used to process the time series data of ecological restoration effects, capture long-term dependencies through the repeated structure of the network layer, and predict the development trend of future ecological restoration effects to generate long-term trend prediction records; S6: Based on the long-term trend forecast records, support vector machines are applied to continuously analyze the forecast results, and the data in the long-term trend forecast records are classified and verified using the processed feature space and decision boundaries, and the classification results are evaluated to generate verified forecast data; S7: Based on the verified prediction data, a multi-criteria decision analysis method is used to analyze the evaluation results. The data of differentiated dimensions are weighted and integrated in combination with ecological, social and economic factors. The results of weighted integration are summarized to generate comprehensive evaluation results.

[0028] The detailed evaluation index system includes ecological diversity index, soil stability rating, water body restoration index, soil nutrient status index, and vegetation coverage percentage. The fuzzy logic scoring records include the fuzzy contribution value, fuzzy scoring value, and evaluation level division of the indicator. The accuracy optimization scoring includes the optimized indicator weight value, the adjusted scoring value, and the confidence interval of the evaluation result. The long-term trend prediction records include the predicted growth rate of vegetation coverage, the predicted change trend of soil organic matter, and the expected progress of water body restoration. The comprehensive evaluation results include the overall ecological restoration rating, social impact analysis records, economic benefit assessment results, and a list of improvement measures.

[0029] In step S1, the decision tree algorithm is used to analyze and screen data such as biodiversity and soil quality in the field of ecological restoration. The decision tree algorithm processes a large number of data sets of potential evaluation indicators based on the goals and standards of ecological restoration. This process involves calculating information gain or Gini impurity to select the best segmentation attributes, thereby refining the evaluation index system. The decision tree iteratively splits the data set until it reaches the preset maximum depth or the lower limit of the node data volume, generating a series of refined evaluation indicators, such as ecological diversity index, soil stability rating, etc. This step realizes the construction of an evaluation index system from macro to micro.

[0030] In step S2, a genetic algorithm is used to preliminarily set and adjust the indicator weights in the refined evaluation indicator system. The genetic algorithm simulates natural selection and genetic mechanisms, and optimizes the indicator weight configuration through operations such as population initialization, selection, crossover and mutation. The initial population is based on randomly assigned weights. The fitness function evaluates the degree of match of each individual (i.e., a set of weight configurations) to the evaluation model. A new generation of population is generated through crossover and mutation operations, and the weight allocation is gradually optimized to enhance the accuracy and adaptability of the evaluation model. This process generates an adjusted weight configuration.

[0031] In step S3, the fuzzy logic method is used to fuzzify the evaluation indicators and calculate the contribution of each indicator. The fuzzy logic method converts the specific values ​​of the evaluation indicators into fuzzy values, such as "high", "medium", and "low", by constructing a fuzzy rule set, and performs reasoning based on the fuzzy rule set. This process involves defining fuzzy variables, building membership functions and rule bases, and implementing reasoning mechanisms. Through fuzzification and fuzzy reasoning, the calculated fuzzy logic score records reflect in detail the contribution of each indicator to the overall evaluation results, providing more flexible and adaptable decision support for the evaluation.

[0032] In step S4, the simulated annealing algorithm is used to identify problems in the scoring system and adjust the weights. The simulated annealing algorithm simulates the annealing process in the physical process to gradually reduce the system energy in a probabilistic manner to find the global optimal solution. In the optimization of the evaluation index weights, the algorithm starts from a high temperature and randomly selects the weight configuration for small adjustments. The adjusted configuration is evaluated through the energy function (i.e., the fitness of the evaluation index weight configuration). As the temperature gradually decreases, the algorithm reduces the probability of accepting a worse solution and eventually converges to the optimal weight configuration. By optimizing the evaluation index weights, the accuracy and reliability of the evaluation system are improved.

[0033] In step S5, the long short-term memory network model is used to process the time series data of ecological restoration effects. The long short-term memory network can effectively capture the long-term dependencies in the time series data through its unique gating mechanism and predict the development trend of the ecological restoration effect. This step includes building a network model, training the model to adapt to the characteristics of the time series data, and predicting future trends through the repeated structure of the network layer. The generated long-term trend prediction records reflect the future changing trends of indicators such as vegetation coverage and soil organic matter, providing a predictive basis for ecological restoration work.

[0034] In step S6, support vector machines are used to continuously analyze and verify the long-term trend forecast records. The support vector machine effectively classifies and verifies the forecast results by constructing the optimal decision boundary in the feature space. This step involves selecting a suitable kernel function, training the model to minimize misclassification, and classifying and verifying the data in the long-term trend forecast records. The generated verified forecast data provides a reliable verification mechanism for the evaluation method through highly accurate classification results.

[0035] In step S7, a multi-criteria decision analysis method is used to comprehensively analyze the evaluation results. By analyzing and integrating the evaluation results of different dimensions (such as ecology, society, and economy), the evaluation indicators are weighted and integrated. During the operation, the algorithm considers the relative importance of each factor, performs weighted summation, and summarizes the final comprehensive evaluation result, which reflects the comprehensive benefits of the ecological restoration project.

[0036] See also Figure 2 ,Based on the ecological restoration goals and standards, the decision tree algorithm is used to analyze and screen potential evaluation indicators, process data on biodiversity and soil quality, and classify the data. The specific steps to generate a detailed evaluation indicator system are as follows: S101: Based on the ecological restoration goals and standards, the C4.5 decision tree algorithm is used to analyze and screen potential evaluation indicators related to biodiversity and soil quality. Through the construction and pruning of the decision tree, the indicators that affect the evaluation process are identified, and a set of key evaluation indicators is generated; data preparation and input, data on various potential evaluation indicators related to biodiversity and soil quality are collected, including but not limited to plant species diversity, soil organic matter content, heavy metal content, etc.; The C4.5 algorithm constructs a decision tree by using the features (i.e., potential evaluation indicators) and results (labels related to ecological restoration goals and standards) in the training data set; To prevent overfitting, that is, the decision tree is too complex and can only predict the training data well but not generalize, C4.5 introduces a pruning step. Pruning simplifies the decision tree by removing branches that do not provide additional information. Extraction of key evaluation indicators,The evaluation indicators corresponding to the nodes in the higher layers (close to the root) and the non-pruned part of the tree are regarded as key evaluation indicators, which play a decisive role in distinguishing different categories (i.e., meeting or not meeting the ecological restoration standards).

[0037] Key evaluation indicators are the most influential indicators in the ecological restoration evaluation process. They can significantly distinguish different categories of ecological restoration effects and are the factors that most influence evaluation decisions.

[0038] S102: Based on the key evaluation indicator set, the K-means clustering algorithm is used to group the indicators, and the distance calculation in the iterative process is used to cluster the indicators into differentiated groups, each group representing a matching evaluation dimension, and the indicator group division result is generated; It includes biodiversity dimensions (such as species richness, species evenness, protection of endemic species, etc.), soil quality dimensions (such as soil structure, organic matter content, pollutant content (such as heavy metals), and soil fertility and water retention capacity), water quality dimensions (including the reduction of pollutants in water bodies and the improvement of aquatic biodiversity, etc.), ecosystem function dimensions (such as carbon fixation, nitrogen cycle, habitat provision, etc.), and landscape restoration dimensions.

[0039] S103: Based on the indicator grouping results, the information gain ratio method is used to identify the indicators that contribute best to the evaluation system from the group, and the information gain ratio is used to evaluate the criticality of multiple indicators to generate a refined evaluation indicator system; Identify evaluation indicators that can specifically reflect the treatment effect. These indicators should cover all relevant environmental and ecological dimensions. For example, biodiversity can be assessed by species richness and community structure, and soil quality can be assessed by organic matter content, pollutant levels and soil structure; Apply data analysis methods such as decision tree, cluster analysis and information gain ratio to analyze the contribution of these indicators to distinguish different restoration effects; Evaluate the correlation between indicators through statistical methods and identify potential redundant indicators; Combine different evaluation indicators and methods to comprehensively evaluate the treatment effect from multiple perspectives. For example, the improvement of biodiversity and soil quality, as well as the impact on the function of the entire ecosystem, can be considered at the same time; Regular adjustments will be made based on ongoing environmental monitoring data. The weights and scores of evaluation indicators will be updated based on new data to ensure that the evaluation system can accurately reflect the current status and trends of ecological restoration.

[0040] Use the information gain ratio to evaluate the importance of each indicator. Select indicators with higher information gain ratios; The indicator with the highest information gain ratio is selected from each group as the representative of this dimension. The indicator is the one that best reflects the key aspects of ecological restoration effects. Integrate the selected optimal indicators to form an evaluation indicator system.

[0041] In sub-step S101, the C4.5 decision tree algorithm is used to analyze and screen potential evaluation indicators related to biodiversity and soil quality. The specific operation process involves collecting relevant data on the ecosystem, including vegetation type, soil composition, water quality indicators, etc. The data format is a structured data table. Each row represents an observation sample and the columns contain the values ​​of various evaluation indicators. The C4.5 algorithm selects the optimal splitting attribute by calculating the information gain rate, thereby gradually constructing a decision tree. During the construction process, the decision tree is pruned. The pruning algorithm compares the information gain of the evaluation node with the threshold to remove overly complex branches to prevent overfitting and ensure the generalization ability of the model. The decision tree model generated by this process can identify and screen key evaluation indicators that are directly related to the goals and standards of ecological restoration, forming a set of key evaluation indicators. The operation result effectively identifies indicators that have a significant impact on the ecological evaluation process, providing a scientific and accurate evaluation basis for ecological restoration.

[0042] In sub-step S102, based on the key evaluation indicator set, the K-means clustering algorithm is used to group the indicators. This operation involves taking the key evaluation indicators as input data. The data format is also a structured data table. Each row represents an evaluation indicator, and the columns include the characteristic values ​​of the indicators. When the K-means clustering algorithm is initialized, K indicators are randomly selected as group centers. The Euclidean distance between each indicator and the center of each group is calculated, and the indicators are classified into the nearest group according to the minimum distance principle. The position of the group center is iteratively optimized until the distribution of indicators within the group no longer changes. This process clusters the indicators into groups with differentiated characteristics. Each group represents an evaluation dimension. The generated indicator group division results provide a perspective for comprehensive evaluation of ecological restoration effects from different dimensions.

[0043] In sub-step S103, based on the indicator grouping results, the information gain ratio method is used to identify the indicators with the best contribution to the evaluation system from each group. During the operation, the information gain ratio of the indicators in each group to the evaluation results is first calculated, that is, the ratio of the information gain of the indicator to its own information entropy is considered to measure the importance of the indicator in the evaluation system, and the indicator with the highest information gain ratio is selected as the representative of the group to form a detailed evaluation indicator system. This system not only reflects the contribution of each indicator to the evaluation results, but also reduces the complexity of the evaluation process through carefully selected indicators, and improves the efficiency and accuracy of the evaluation. Through this series of operations, the evaluation indicator system finally generated provides a scientific basis for ecological restoration work, optimizes the evaluation process, and ensures the objectivity and reliability of the evaluation results.

[0044] See also Figure 3 ,Based on the refined evaluation index system, the genetic algorithm is used to preliminarily set the weight distribution of the indicators, adjust the weights to match the needs of the evaluation model, and analyze the weight distribution during the adjustment process. The specific steps for generating the adjusted weight configuration are as follows: S201: Based on the refined evaluation index system, the genetic algorithm is used to preliminarily set the weights of the indicators, adjust the weight distribution, and match the requirements of the evaluation model to generate preliminary weight optimization; S202: Based on the preliminary weight optimization, the simulated annealing algorithm is applied to make detailed adjustments to the weight configuration, to find the global optimal solution of the weight allocation, to reduce the deviation of the local optimal solution, and to generate the weight detail adjustment result; S203: Based on the detailed adjustment results of the weights, a multi-objective optimization algorithm is used to optimize the weight configuration, search for the optimal combination of multi-indicator weights, determine the multiple aspects of the evaluation system that reflect the effects of ecological restoration, and generate the adjusted weight configuration.

[0045] In sub-step S201, the weights of each indicator in the refined evaluation index system are preliminarily set and adjusted through a genetic algorithm to match the requirements of the evaluation model and generate preliminary weight optimization results. When the genetic algorithm is started, a set of indicator weights is first randomly generated as a population. Each individual represents a weight configuration. The data format of the weight value is a real number ranging from 0 to 1. The algorithm continuously iterates and optimizes the weight configuration by simulating the process of natural selection, including selection, crossover (hybridization) and mutation operations. The selection operation evaluates the performance of each individual based on the fitness function, which is defined according to the effect of the weight configuration in the evaluation model. Individuals with excellent performance are given priority for reproduction. The crossover operation allows excellent individuals to exchange some genes (ie, weight configuration) to generate new individuals. The mutation operation randomly changes certain weights in individual individuals and introduces new genetic diversity. After multiple generations of iterations, the algorithm tends to be stable. The optimal individual at this time represents the preliminary optimized weight configuration, which can better balance the contribution of each evaluation indicator in the evaluation system and provide a basis for subsequent optimization.

[0046] In sub-step S202, based on the preliminary weight optimization results, the simulated annealing algorithm is applied to make detailed adjustments to the weight configuration. The simulated annealing algorithm simulates the annealing process in physics to achieve the global optimal solution. In the initial stage, the algorithm sets a higher "temperature", randomly selects weight configurations, and calculates their fitness. As the "temperature" gradually decreases, the algorithm reduces the probability of accepting poor solutions and increases the search intensity for better solutions. The adjustment of the weight configuration is achieved by randomly changing the existing weight values ​​by a small amplitude. After the adjustment, the fitness of the new configuration is evaluated. If the new configuration provides better fitness or is within the probability of accepting a poor solution at the current temperature of the algorithm, the new configuration is accepted. This process continues until the "temperature" drops below the predetermined threshold. At this time, the algorithm reaches a stable state, and the generated weight configuration is the global optimal or close to the global optimal solution, which reduces the deviation caused by the local optimal solution and optimizes the accuracy and reliability of the evaluation model.

[0047] In sub-step S203, based on the detailed adjustment results of the weights, a multi-objective optimization algorithm is used to further optimize the weight configuration. The multi-objective optimization algorithm considers multiple optimization objectives at the same time and searches for the optimal combination of multi-indicator weights to ensure that the evaluation system can comprehensively reflect multiple aspects of the ecological restoration effect. During the operation, the algorithm evaluates multiple performance indicators corresponding to each weight configuration, such as the comprehensiveness, sensitivity and discrimination of the evaluation. Through the iterative search process, the algorithm identifies the weight configuration that performs balanced on all considered performance indicators, and achieves a reasonable distribution of the importance of different evaluation indicators. This optimization process ensures that the weight configuration of the evaluation system can both highlight the role of key evaluation indicators and not ignore other important indicators. The generated adjusted weight configuration file provides a quantitative and optimized decision-making basis for the evaluation model, and improves the accuracy and operability of the ecological restoration evaluation.

[0048] See also Figure 4 Based on the adjusted weight configuration, the fuzzy logic method is executed to fuzzify the indicators in the evaluation system, and reasoning is performed according to the fuzzy rule set to calculate the contribution of each indicator to the total evaluation result. The specific steps for generating the fuzzy logic scoring record are as follows: S301: Based on the adjusted weight configuration, fuzzy set theory is used to perform fuzzification processing on each evaluation indicator, a fuzzy grade is assigned to the indicator, and a fuzzy rule set is used for reasoning to determine the contribution of each indicator to the overall evaluation result, and a fuzzy contribution score is generated; S302: Based on the fuzzy contribution score, the fuzzy comprehensive evaluation model is used to integrate the contribution of multiple indicators, and the fuzzy comprehensive evaluation result is generated by constructing a fuzzy relationship matrix and applying fuzzy operators; S303: Based on the fuzzy comprehensive evaluation results, fuzzy logic reasoning is performed, and defuzzification technology is used to convert the fuzzy evaluation results into evaluation grades or values, and an evaluation judgment basis is provided to generate a fuzzy logic scoring record.

[0049] In sub-step S301, each evaluation indicator is fuzzified by fuzzy set theory, and the fuzzy grade assignment of the indicator is realized and reasoning is performed using the fuzzy rule set. At the beginning of the operation, the adjusted weight configuration and the evaluation indicator value are used as input. The data format of the indicator value is real number, which represents the specific measurement value of the ecological restoration effect. The fuzzification process involves converting these real values ​​into membership in the fuzzy set. The membership ranges from 0 to 1, indicating the degree to which the indicator value corresponds to different fuzzy grades. This process is performed according to predefined fuzzy grades (such as "low", "medium", and "high") and corresponding membership functions. The membership function defines the mapping rules from real values ​​to fuzzy grade membership. Subsequently, according to the fuzzy rule set, the contribution of each indicator to the overall evaluation result is determined by fuzzy reasoning. The fuzzy rule set is defined based on expert knowledge and historical data. For example, "If the soil quality index is high, the contribution to the ecological restoration evaluation is high". Finally, this step generates a fuzzy contribution score. This score exists in the form of a fuzzy grade, which intuitively represents the contribution of each indicator to the evaluation result, providing a basis for further comprehensive evaluation.

[0050] In sub-step S302, based on the fuzzy contribution score, the fuzzy comprehensive evaluation model is used to integrate the contribution of multiple indicators. This process involves constructing a fuzzy relationship matrix, which records the membership of each evaluation indicator at each fuzzy level, and applying fuzzy operators (such as fuzzy weighted average) to integrate this information to generate a fuzzy comprehensive evaluation result. The fuzzy operator weights the membership according to the adjusted weight configuration to reflect the importance of each indicator in the evaluation system. The integrated fuzzy evaluation result provides a comprehensive perspective, comprehensively considers the contribution of all evaluation indicators, and effectively captures the multidimensionality and complexity of the evaluation system. The generated fuzzy comprehensive evaluation result is a set of fuzzy levels, which reflects the overall performance of the ecological restoration effect and provides a basis for the final evaluation decision.

[0051] In sub-step S303, based on the fuzzy comprehensive evaluation results, fuzzy logic reasoning is performed and defuzzification technology is used to convert the fuzzy evaluation results into specific evaluation grades or values. The defuzzification technology involves selecting a specific method (such as the centroid method, the maximum membership method) to extract a clear value or grade from the fuzzy comprehensive evaluation results as the final evaluation of the ecological restoration effect. This process takes into account all potential fuzzy grades and their corresponding memberships in the fuzzy comprehensive evaluation results, ensuring that the conversion results can accurately reflect the meaning of the comprehensive evaluation. The generated fuzzy logic scoring record details the evaluation result determination process and the final evaluation grade, providing a clear and quantitative basis for the evaluation and decision-making of ecological restoration projects, and enhancing the transparency and credibility of the evaluation results.

[0052] See also Figure 5 Based on the fuzzy logic scoring records, the simulated annealing algorithm is used to identify the problems in the scoring system, gradually reduce the energy, adjust the evaluation index weights, and optimize the weight adjustment process. The specific steps for generating accuracy optimization scores are as follows: S401: Based on the fuzzy logic scoring record, the relationship between the indicators in the evaluation system and the evaluation results is analyzed by using the grey correlation analysis method, the key indicators that affect the evaluation results are identified, and the key indicator analysis records are generated; S402: Based on the key indicator analysis records, the simulated annealing algorithm is applied to adjust the indicator weights, and the energy of the solution is gradually optimized through mining and development strategies, and the optimal weight allocation scheme is searched to generate weight optimization adjustment results; S403: Based on the weight optimization adjustment results, linear programming is used to calibrate the indicator weights, and in the evaluation system, the true criticality reflected by each indicator weight is verified to generate an accuracy optimization score.

[0053] In sub-step S401, the relationship between the indicators in the evaluation system and the evaluation results is analyzed by the grey correlation analysis method to identify the key indicators that affect the evaluation results. The core of the grey correlation analysis method is to calculate the correlation between the evaluation indicators and the evaluation results. The data format is the actual value of the evaluation indicator and the fuzzy logic score record. First, the actual value of the evaluation indicator is standardized to eliminate the dimensional influence between different indicators and ensure the consistency of the analysis. Subsequently, the grey correlation between each indicator and the evaluation result is calculated based on the standardized data. The calculation basis of the correlation is the degree of influence of the indicator change on the evaluation result change. By comparing the correlation of different indicators, the key indicators that have the greatest impact on the evaluation results can be identified. The generated key indicator analysis record lists the correlation of each indicator in detail, which provides a scientific basis for subsequent weight adjustment and optimization of the evaluation system, ensuring that the evaluation system pays more attention to indicators that have a significant impact on the evaluation results.

[0054] In sub-step S402, based on the key indicator analysis record, the simulated annealing algorithm is applied to adjust the indicator weights. The simulated annealing algorithm searches for the optimal weight distribution scheme through the principle of energy minimization in the simulated annealing process. The operation process starts with setting an initial temperature and initializing the weight configuration based on the data in the key indicator analysis record. The weight configuration is randomly adjusted and the impact of the adjusted weight configuration on the evaluation system is calculated, that is, the evaluation result based on the new weight configuration is calculated using the evaluation model and compared with the optimization target. If the new configuration improves the accuracy or comprehensiveness of the evaluation result, the new weight configuration is accepted. As the temperature gradually decreases, the algorithm gradually reduces the range of change of the weight configuration and increases the search depth for the current optimal solution. This process is iterated repeatedly until the cooling end condition is reached or the optimization result meets the preset accuracy requirement. The generated weight optimization adjustment result records the optimal weight of each indicator in detail. These weights reflect the actual impact of the indicator on the evaluation result, provide a refined adjustment plan for the evaluation system, and improve the accuracy and effectiveness of the evaluation system.

[0055] In sub-step S403, based on the weight optimization adjustment results, linear programming is used to calibrate the indicator weights, and the true criticality reflected by each indicator weight is verified in the evaluation system. The application purpose of linear programming is to find the optimal weight configuration while satisfying a series of constraints to maximize the accuracy of the evaluation system. During the operation, the weight optimization adjustment results are used as input variables of the linear programming model, and the accuracy of the evaluation results is used as the optimization goal. The constraints include the reasonable range and total limit of the indicator weights, and the importance ranking based on the key indicator analysis records. By solving the linear programming model, a set of optimal indicator weight configurations are obtained, which can maximize the accuracy of the evaluation system. The generated accuracy optimization score records the impact of each indicator weight adjustment on the evaluation accuracy before and after, and demonstrates the contribution of weight adjustment to improving the accuracy of the evaluation system, ensuring that the evaluation system can effectively reflect the multi-dimensional characteristics of ecological restoration effects in practical applications.

[0056] See also Figure 6 Based on the accuracy optimization score, the long short-term memory network model is used to process the time series data of ecological restoration effects. The long-term dependencies are captured through the repeated structure of the network layer, and the development trend of the future ecological restoration effects is predicted. The specific steps for generating long-term trend prediction records are as follows: S501: Based on the accuracy optimization score, the long short-term memory network model is used to process the time series data of ecological restoration effects, capture the long-term dependencies in the data, analyze the data trends, predict the changes in the ecological restoration effects in the future, and generate trend prediction data; S502: Based on the trend forecast data, the seasonal decomposition forecast method is used to separate the seasonal changes and trend components in the time series data, analyze the development trend through seasonal adjustment, optimize the accuracy of trend forecast, and generate seasonal adjustment forecast data; S503: Based on seasonally adjusted forecast data, use unit root test to evaluate the stability of the trend, confirm the reliability of the forecast trend through statistical tests, optimize the robustness of the forecast data, and generate long-term trend forecast records.

[0057] In sub-step S501, the long short-term memory network (LSTM) model is used to process the time series data of ecological restoration effects, aiming to capture the long-term dependencies in the data and analyze the data trends, so as to predict the changes in the ecological restoration effects in the future. The time series data format is the ecological restoration effect indicators recorded in chronological order, such as vegetation coverage, soil moisture content, etc. Each record contains a timestamp and the corresponding indicator value. The LSTM model effectively handles the long-distance dependency problem in the time series data through its unique gating mechanism. During the model training process, the time series data is first preprocessed, including normalization processing to improve the efficiency and stability of model training. Then, the processed data is input into the LSTM model for training. The model learns the patterns and trends in the data through repeated iterations. Finally, the trained LSTM model is used to predict the changes in the ecological restoration effects in the future and generate trend prediction data. The data is displayed in a time series format, reflecting the expected changing trend of the ecological restoration effects in the future, and providing a scientific basis for the planning and management of ecological restoration projects.

[0058] In sub-step S502, based on the trend forecast data, the seasonal decomposition forecasting method is used to separate the seasonal changes and trend components in the time series data. The seasonal decomposition forecasting method decomposes the time series into trend components, seasonal components and random components, and analyzes the impact of each component on the time series separately. This method first identifies and extracts the seasonal patterns in the time series, and then removes these seasonal influences from the original data, leaving the trend components and random noise. Next, the remaining trend components are analyzed and predicted, and finally the seasonal patterns are reintroduced into the trend forecast to obtain a comprehensive forecast result after seasonal adjustment. The generated seasonally adjusted forecast data shows in detail the predicted trend of future ecological restoration effects after considering seasonal changes, which improves the accuracy and practicality of trend forecasts and provides more accurate time series forecast support for ecological restoration work.

[0059] In sub-step S503, based on the seasonally adjusted forecast data, the unit root test is used to evaluate the stability of the trend, the reliability of the forecast trend is confirmed through statistical tests, and the robustness of the forecast data is optimized. The unit root test is a statistical method used to analyze the stability of time series. The core of the test is to determine whether there is a unit root in the time series, that is, whether the time series is non-stationary. During the operation, the seasonally adjusted forecast data is first subjected to a unit root test, and the stability of the time series is judged by the test results. If the test shows that the time series is stationary, the forecast trend is considered to be reliable; conversely, if the time series is non-stationary, the data needs to be further processed by difference processing to obtain a stable time series. After completing the stability analysis and necessary data processing, the generated long-term trend forecast record details the stability and reliability of the forecast trend, ensures the robustness of the forecast results, and provides data support for the long-term planning and decision-making of ecological restoration projects.

[0060] See also Figure 7 ,Based on the long-term trend forecast records, the support vector machine is applied to continuously analyze the forecast results, and the data in the long-term trend forecast records are classified and verified using the processed feature space and decision boundaries, and the classification results are evaluated. The specific steps for generating verified forecast data are as follows: S601: Based on the long-term trend prediction records, support vector machines are used for analysis to distinguish differentiated prediction trends, and trend data are classified and verified to generate trend classification analysis records; S602: Based on the trend classification analysis records, the k-fold cross validation method is applied to verify the results, and the prediction performance of the model for new data is evaluated by using the difference data subsets as training and test sets in turn, thereby generating enhanced prediction data; S603: Based on the enhanced prediction data, the weighted average method is used to summarize and compare the results of the difference verification rounds, analyze the consistency and deviation of the prediction data, and evaluate the accuracy of the ecological restoration effect prediction to generate verified prediction data.

[0061] In sub-step S601, based on the long-term trend forecast records, support vector machine (SVM) is used for analysis to distinguish differentiated forecast trends, and to classify and verify trend data. Support vector machine is a powerful supervised learning algorithm suitable for classification problems of high-dimensional data. This process first involves formatting the data of the long-term trend forecast records, including trend values ​​of time series, stability test results, etc., and converting them into feature vectors suitable for SVM processing. Subsequently, based on these feature vectors, a classification model is constructed using the SVM algorithm. Model training involves selecting appropriate kernel functions (such as linear kernels, radial basis kernels, etc.) and adjusting model parameters (such as penalty coefficient C and kernel function parameters) to achieve the most optimized classification boundary. Through the training and verification process of the SVM model, trend data can be effectively classified into different forecast trend categories, such as rising trends, falling trends, and stable trends. The generated trend classification analysis records describe in detail the characteristics and distribution of each trend category, providing an in-depth understanding and basis for subsequent trend analysis and forecasting.

[0062] In sub-step S602, based on the trend classification analysis records, the k-fold cross-validation method is applied to verify the results. The k-fold cross-validation is a widely used model evaluation method. The data set is divided into k subsets of equal size, and one of the subsets is used as a test set and the rest as a training set in turn, so as to evaluate the performance of the model in k different training and testing rounds. This method aims to improve the accuracy and generalization ability of the model in predicting new data. During the operation, the value of k is first determined, and then the data is segmented k times based on the trend classification analysis records. After each segmentation, the SVM model is used for training and testing, and the accuracy and performance indicators of each test are recorded. After k rounds of training and testing, the average value of the model performance indicators is calculated to evaluate the overall prediction performance of the model. The generated enhanced prediction data includes the model's average accuracy, precision, recall and other performance indicators, providing a comprehensive evaluation of the model's prediction performance and ensuring the reliability and effectiveness of the prediction model in practical applications.

[0063] In sub-step S603, based on the enhanced prediction data, the weighted average method is used to summarize and compare the results of different verification rounds. The weighted average method is a statistical method that calculates the overall average by assigning different weights to the results of different verification rounds, thereby performing a comprehensive evaluation based on the importance of the results of each round. During the operation, weights are assigned to each round of verification according to its stability and accuracy, and then the weighted average is calculated to summarize the prediction performance of all verification rounds. This method can not only analyze the consistency and deviation of the prediction data between different verification rounds, but also comprehensively evaluate the accuracy of the prediction of ecological restoration effects. The generated verified prediction data records in detail the model performance indicators after weighted averaging, such as weighted average accuracy and weighted average recall. These data reflect the robustness and accuracy of the prediction model in multiple verifications, and provide data support for the accurate prediction of ecological restoration effects.

[0064] See also Figure 8 Based on the verified prediction data, the evaluation results are analyzed using a multi-criteria decision analysis method. The data of the differentiated dimensions are weighted and integrated in combination with ecological, social and economic factors, and the results of the weighted integration are summarized. The specific steps for generating comprehensive evaluation results are as follows: S701: Based on the verified prediction data, the hierarchical analysis method is used to define the target layer, criterion layer and indicator layer of the evaluation, and the weights of the multi-criteria layer factors are calculated by constructing a pairwise comparison matrix, and the consistency ratio is used to perform consistency test to generate a multi-factor weight distribution table; S702: Based on the multi-factor weight distribution table, the fuzzy comprehensive evaluation method is applied to determine the evaluation factor set and the evaluation level, construct a fuzzy relationship matrix, and express the contribution degree of the difference factors to the evaluation level, and generate a fuzzy evaluation result matrix; S703: Based on the fuzzy evaluation result matrix, the weighted score corresponding to each evaluation level is calculated using the weighted average method, and defuzzification is performed. The fuzzy evaluation results are converted into evaluation scores to generate comprehensive evaluation results.

[0065] In sub-step S701, the target layer, criterion layer and indicator layer of the evaluation are defined through the analytic hierarchy process (AHP) to systematically process and analyze the multi-dimensional evaluation problem of ecological restoration effects. First, the evaluation system is divided into three levels according to the evaluation purpose: the top target layer represents the overall goal of the evaluation, that is, the comprehensive evaluation of the ecological restoration effect; the middle criterion layer contains several evaluation criteria, such as biodiversity, soil quality, water quality, etc.; the bottom indicator layer is composed of specific quantifiable evaluation indicators, which are sub-factors under the criterion layer. Next, a pairwise comparison matrix is ​​constructed to compare the elements in the criterion layer and the indicator layer with each other to evaluate their relative importance to the upper layer goal. Through expert scoring or relevant data support, a weight is given to each comparison, and the weight vector is calculated. Subsequently, the consistency ratio (CR) is used for consistency test to ensure the rationality and logic of the pairwise comparison. Through this series of operations, the generated multi-factor weight distribution table lists the weights of each criterion and indicator in detail, providing a quantitative basis for further comprehensive evaluation and decision analysis.

[0066] In sub-step S702, based on the multi-factor weight distribution table, the fuzzy comprehensive evaluation method is applied to determine the evaluation factor set and evaluation level to further deepen the evaluation analysis. First, the evaluation factor set is determined according to the evaluation objectives and the weight distribution table, including various criteria and indicators, and the corresponding evaluation levels are defined, such as excellent, good, medium, poor, etc. Then, a fuzzy relationship matrix is ​​constructed to represent the membership of each evaluation factor at different evaluation levels, reflecting the contribution of difference factors to the evaluation level. The construction of the fuzzy relationship matrix is ​​based on the weight of each factor and the evaluation data. By calculating the membership of each factor to each evaluation level, the relative contribution and influence of each factor in the complex evaluation system are shown. The generated fuzzy evaluation result matrix reflects in detail the performance of ecological restoration effects in multiple dimensions, providing fuzzy logic support for comprehensive evaluation.

[0067] In sub-step S703, based on the fuzzy evaluation result matrix, the weighted score corresponding to each evaluation level is calculated using the weighted average method, and defuzzification is performed. This operation converts the fuzzy evaluation results into specific evaluation scores, making the evaluation results more intuitive and easy to understand. During the operation, first, the weighted score of each evaluation level is calculated based on the membership degree and corresponding weight of each evaluation level in the fuzzy evaluation result matrix. Then, through defuzzification methods, such as the maximum membership principle or the center of gravity method, the fuzzy evaluation is converted into a specific numerical score to reflect the overall evaluation level of the ecological restoration effect. The generated comprehensive evaluation results are presented in a clear numerical form, which not only takes into account the multi-dimensional factors of the evaluation system, but also facilitates the interpretation and application of the evaluation results, providing a basis for the effect evaluation and future planning of ecological restoration projects.

[0068] See also Fig. 9, an open-pit mine ecological restoration effect evaluation system, the open-pit mine ecological restoration effect evaluation system is used to implement the above-mentioned open-pit mine ecological restoration effect evaluation method, the system includes an indicator analysis and screening module, a weight distribution adjustment module, a fuzzy logic scoring module, a problem identification and optimization module, a trend analysis and prediction module, and an evaluation and verification module; The indicator analysis and screening module uses the C4.5 decision tree algorithm to analyze and screen potential evaluation indicators based on the goals and standards of ecological restoration, screen indicators related to biodiversity and soil quality, and use the K-means clustering algorithm to group indicators and aggregate similar indicators. It uses the information gain ratio to identify the indicators that contribute best to the evaluation process and generate a set of key evaluation indicators. The weight allocation adjustment module uses genetic algorithms to preliminarily allocate the weights of indicators based on the key evaluation indicator set to match the requirements of the evaluation model. It uses simulated annealing algorithms to refine the adjustment of weight configurations. Through the simulated annealing process, it searches for the global optimal solution and generates the adjusted weight configuration. The fuzzy logic scoring module uses fuzzy logic processing methods based on the adjusted weight configuration to fuzzify the evaluation indicators, assign fuzzy values ​​to each indicator, perform reasoning and calculation according to the set fuzzy rule set, convert fuzzy input into scoring output, and generate fuzzy logic scoring records; The problem identification and optimization module uses simulated annealing algorithm based on fuzzy logic scoring records to identify problems in the scoring system, adjust the weights of evaluation indicators, search for the global optimal solution of weight configuration, and use linear programming to adjust and optimize weight distribution to generate accuracy optimization scores; The trend analysis and prediction module uses a long short-term memory network model based on accuracy optimization scoring to process time series data on ecological restoration effects. It predicts future trends by learning long-term dependencies in the data, applies seasonal decomposition prediction methods, analyzes seasonal changes and trends in time series, and generates long-term trend prediction records. The evaluation and verification module is based on long-term trend forecast records and uses support vector machines to analyze forecast data. It classifies and verifies trends by building decision boundaries, uses the analytic hierarchy process to evaluate and weight the criticality of differential factors, and integrates data from multiple dimensions to generate comprehensive evaluation results.

[0069] Through the C4.5 decision tree algorithm and K-means clustering algorithm in the indicator analysis and screening module, key evaluation indicators are accurately identified and aggregated, ensuring the pertinence and efficiency of the evaluation. This process not only improves the processing speed of the evaluation system, but also ensures the comprehensiveness and depth of the evaluation, so that the evaluation process can cover all aspects of ecological restoration, including the comprehensive assessment of biodiversity and soil quality, providing a scientific and reasonable evaluation basis for ecological restoration.

[0070] The application of the weight distribution adjustment module, through the combination of genetic algorithm and simulated annealing algorithm, realizes the dynamic adjustment and optimization of indicator weights. This weight optimization mechanism not only improves the flexibility and adaptability of the evaluation, but also ensures the accuracy and reliability of the evaluation results. Through the search for the global optimal solution, the evaluation system can flexibly adjust the weights of evaluation indicators in a complex ecological restoration environment, and accurately reflect the actual contribution of each indicator to the ecological restoration effect.

[0071] The introduction of the fuzzy logic scoring module provides a richer and more flexible way of expressing the evaluation results through fuzzy processing of evaluation indicators and fuzzy logic reasoning. This fuzzy logic scoring mechanism enables the evaluation results to better handle and express uncertainty and fuzzy information, improves the accuracy and practicality of the evaluation, and enables decision makers to make more reasonable and accurate decisions based on the fuzzy logic scoring records.

[0072] The trend analysis and prediction module provides long-term trend prediction and analysis for ecological restoration through the application of long-short-term memory network models and seasonal decomposition prediction methods, enhancing the foresight and predictive capabilities of the evaluation system. This long-term trend prediction not only helps analyze the future development trend of ecological restoration effects, but also provides a scientific basis for the adjustment and optimization of ecological restoration strategies.

[0073] The design of the evaluation and verification module further improves the accuracy and scientificity of the evaluation results through the comprehensive application of support vector machines and hierarchical analysis method. This module can not only effectively analyze and verify the predicted data, but also comprehensively consider data from multiple dimensions to generate comprehensive comprehensive evaluation results. This multi-dimensional and multi-angle evaluation and verification mechanism provides a scientific basis and comprehensive decision-making support for the evaluation and subsequent improvement of ecological restoration projects.

[0074] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for evaluating the ecological restoration effect of an open-pit mine, characterized in that: The following steps are involved: Based on ecological restoration goals and standards, a decision tree algorithm is used to analyze and screen potential evaluation indicators, process data on biodiversity and soil quality, and classify the data to generate a detailed evaluation indicator system; Based on the refined evaluation index system, a genetic algorithm is used to preliminarily set the weight distribution of the index, adjust the weight to match the needs of the evaluation model, and analyze the weight distribution during the adjustment process to generate an adjusted weight configuration; Based on the adjusted weight configuration, a fuzzy logic method is executed to fuzzify the indicators in the evaluation system, reasoning is performed according to the fuzzy rule set, the contribution of each indicator to the overall evaluation result is calculated, and a fuzzy logic scoring record is generated; Based on the fuzzy logic scoring records, a simulated annealing algorithm is used to identify problems in the scoring system, gradually reduce the energy, adjust the evaluation index weights, and optimize the weight adjustment process to generate an accuracy optimization score; Based on the accuracy optimization score, the long short-term memory network model is used to process the time series data of ecological restoration effects, capture long-term dependencies through the repeated structure of the network layer, and predict the development trend of future ecological restoration effects to generate long-term trend prediction records; Based on the long-term trend forecast record, the support vector machine is applied to continuously analyze the forecast results, the processed feature space and decision boundary are used to classify and verify the data in the long-term trend forecast record, and the classification results are evaluated to generate verified forecast data; Based on the verified prediction data, a multi-criteria decision analysis method is used to analyze the evaluation results. The data of differentiated dimensions are weighted and integrated in combination with ecological, social and economic factors, and the results of weighted integration are summarized to generate comprehensive evaluation results.

2. The open-pit mine ecological restoration effect evaluation method according to claim 1, characterized in that: The detailed evaluation index system includes ecological diversity index, soil stability rating, water body restoration index, soil nutrient status index, and vegetation coverage percentage. The fuzzy logic scoring record includes the fuzzy contribution value of the indicator, the fuzzy scoring value, and the evaluation grade division. The accuracy optimization scoring includes the optimized indicator weight value, the adjusted scoring value, and the confidence interval of the evaluation result. The long-term trend prediction record includes the predicted growth rate of vegetation coverage, the predicted change trend of soil organic matter, and the expected progress of water body restoration. The comprehensive evaluation results include the overall ecological restoration rating, social impact analysis records, economic benefit assessment results, and a list of improvement measures.

3. The open-pit mine ecological restoration effect evaluation method according to claim 1, characterized in that: Based on the ecological restoration goals and standards, the decision tree algorithm is used to analyze and screen potential evaluation indicators, process data on biodiversity and soil quality, and classify the data. The specific steps to generate a detailed evaluation indicator system are as follows: Based on the ecological restoration goals and standards, the C4.5 decision tree algorithm was used to analyze and screen potential evaluation indicators associated with biodiversity and soil quality. Through the construction and pruning of the decision tree, the indicators that affect the evaluation process were identified, and a set of key evaluation indicators was generated. Based on the key evaluation indicator set, the K-means clustering algorithm is used to group the indicators, and the distance calculation in the iterative process is used to cluster the indicators into differentiated groups, each group representing a matching evaluation dimension, to generate an indicator group division result; Based on the index grouping results, the information gain ratio method is used to identify the index that contributes best to the evaluation system from the group, and the information gain ratio is used to evaluate the criticality of multiple indicators to generate a detailed evaluation index system.

4. The open-pit mine ecological restoration effect evaluation method according to claim 1, characterized in that: Based on the refined evaluation index system, the genetic algorithm is used to preliminarily set the weight distribution of the index, adjust the weight to match the needs of the evaluation model, and analyze the weight distribution during the adjustment process. The steps for generating the adjusted weight configuration are as follows: Based on the refined evaluation index system, a genetic algorithm is used to preliminarily set the weights of the indicators, adjust the weight distribution, and match the requirements of the evaluation model to generate a preliminary weight optimization; Based on the preliminary weight optimization, a simulated annealing algorithm is applied to adjust the weight configuration in detail, to find the global optimal solution of weight allocation, to reduce the deviation of the local optimal solution, and to generate the weight detail adjustment result; Based on the detailed adjustment results of the weights, a multi-objective optimization algorithm is used to optimize the weight configuration, search for the optimal combination of multi-indicator weights, determine the multiple aspects of the evaluation system that reflect the effects of ecological restoration, and generate the adjusted weight configuration.

5. The open-pit mine ecological restoration effect evaluation method according to claim 1, characterized in that: Based on the adjusted weight configuration, the fuzzy logic method is executed to fuzzify the indicators in the evaluation system, reasoning is performed according to the fuzzy rule set, and the contribution of each indicator to the overall evaluation result is calculated. The steps of generating the fuzzy logic scoring record are as follows: Based on the adjusted weight configuration, fuzzy set theory is used to fuzzify each evaluation indicator, assign a fuzzy level to the indicator, and reasoning is performed using a fuzzy rule set to determine the contribution of each indicator to the overall evaluation result, thereby generating a fuzzy contribution score; Based on the fuzzy contribution score, the fuzzy comprehensive evaluation model is used to integrate the contribution of multiple indicators, and the fuzzy comprehensive evaluation result is generated by constructing a fuzzy relationship matrix and applying fuzzy operators; Based on the fuzzy comprehensive evaluation results, fuzzy logic reasoning is performed, and defuzzification technology is used to convert the fuzzy evaluation results into evaluation grades or values, and an evaluation judgment basis is provided to generate a fuzzy logic scoring record.

6. The open-pit mine ecological restoration effect evaluation method according to claim 1, characterized in that: Based on the fuzzy logic scoring records, the simulated annealing algorithm is used to identify the problems in the scoring system, gradually reduce the energy, adjust the evaluation index weights, and optimize the weight adjustment process. The steps for generating accuracy optimization scores are as follows: Based on the fuzzy logic scoring records, the relationship between the indicators in the evaluation system and the evaluation results is analyzed by using the grey correlation analysis method, the key indicators that affect the evaluation results are identified, and the key indicator analysis records are generated; Based on the key indicator analysis records, the simulated annealing algorithm is applied to adjust the indicator weights, and the energy of the solution is gradually optimized through mining and development strategies, and the optimal weight allocation scheme is searched to generate weight optimization adjustment results; Based on the weight optimization adjustment results, linear programming is used to calibrate the indicator weights, and in the evaluation system, the true criticality reflected by each indicator weight is verified to generate an accuracy optimization score.

7. The open-pit mine ecological restoration effect evaluation method according to claim 1, characterized in that: Based on the accuracy optimization score, the long short-term memory network model is used to process the time series data of ecological restoration effects, capture long-term dependencies through the repeated structure of the network layer, and predict the development trend of future ecological restoration effects. The specific steps for generating long-term trend prediction records are as follows: Based on the accuracy optimization score, a long short-term memory network model is used to process the time series data of ecological restoration effects, capture the long-term dependencies in the data, analyze the data trends, predict future changes in ecological restoration effects, and generate trend prediction data; Based on the trend forecast data, seasonal decomposition forecasting method is used to separate seasonal changes and trend components in time series data, analyze development trends through seasonal adjustment, optimize the accuracy of trend forecasting, and generate seasonally adjusted forecast data; Based on the seasonally adjusted forecast data, the unit root test is used to evaluate the stability of the trend, and the reliability of the forecast trend is confirmed through statistical tests, the robustness of the forecast data is optimized, and a long-term trend forecast record is generated.

8. The open-pit mine ecological restoration effect evaluation method according to claim 1, characterized in that: Based on the long-term trend prediction record, the support vector machine is applied to continuously analyze the prediction results, the processed feature space and decision boundary are used to classify and verify the data in the long-term trend prediction record, and the classification results are evaluated. The steps of generating verified prediction data are specifically as follows: Based on the long-term trend forecast records, support vector machines are used to analyze, differentiate differentiated forecast trends, and trend data is classified and verified to generate trend classification analysis records; Based on the trend classification analysis records, a k-fold cross validation method is applied to verify the results, and the prediction performance of the model on new data is evaluated by using the difference data subsets as training and test sets in turn to generate enhanced prediction data; Based on the enhanced prediction data, the weighted average method is used to summarize and compare the results of the difference verification rounds, analyze the consistency and deviation of the prediction data, and evaluate the accuracy of the ecological restoration effect prediction to generate verified prediction data.

9. The open-pit mine ecological restoration effect evaluation method according to claim 1, characterized in that: Based on the verified prediction data, the evaluation results are analyzed using a multi-criteria decision analysis method. The data of the differentiated dimensions are weighted and integrated in combination with ecological, social and economic factors, and the results of the weighted integration are summarized. The specific steps for generating comprehensive evaluation results are as follows: Based on the verified prediction data, the hierarchical analysis method is used to define the target layer, criterion layer and indicator layer of the evaluation, and the weights of the multi-criterion layer factors are calculated by constructing a pairwise comparison matrix, and the consistency ratio is used to perform consistency test to generate a multi-factor weight distribution table; Based on the multi-factor weight distribution table, a fuzzy comprehensive evaluation method is applied to determine the evaluation factor set and the evaluation grade, a fuzzy relationship matrix is ​​constructed, and the contribution degree of the difference factors to the evaluation grade is expressed to generate a fuzzy evaluation result matrix; Based on the fuzzy evaluation result matrix, the weighted score corresponding to each evaluation level is calculated using the weighted average method, defuzzification is performed, and the fuzzy evaluation results are converted into evaluation scores to generate comprehensive evaluation results.

10. An open-pit mine ecological restoration effect evaluation system, characterized in that: According to the open-pit mine ecological restoration effect evaluation method according to any one of claims 1 to 9, the system includes an indicator analysis and screening module, a weight distribution adjustment module, a fuzzy logic scoring module, a problem identification and optimization module, a trend analysis and prediction module, and an evaluation and verification module; The indicator analysis and screening module uses the C4.5 decision tree algorithm to analyze and screen potential evaluation indicators based on the goals and standards of ecological restoration, screen indicators related to biodiversity and soil quality, group indicators using the K-means clustering algorithm, aggregate similar indicators, and use the information gain ratio to identify the indicators that contribute best to the evaluation process, generating a set of key evaluation indicators; The weight allocation adjustment module uses a genetic algorithm based on a set of key evaluation indicators to preliminarily allocate the weights of the indicators to match the requirements of the evaluation model, and uses a simulated annealing algorithm to refine the adjustment of the weight configuration. Through the simulated annealing process, it searches for the global optimal solution and generates the adjusted weight configuration; The fuzzy logic scoring module uses a fuzzy logic processing method based on the adjusted weight configuration to fuzzify the evaluation indicators, assign a fuzzy value to each indicator, perform reasoning and calculation according to the set fuzzy rule set, convert the fuzzy input into a scoring output, and generate a fuzzy logic scoring record; The problem identification and optimization module uses a simulated annealing algorithm based on fuzzy logic scoring records to identify problems in the scoring system, adjust the weights of evaluation indicators, search for the global optimal solution of weight configuration, and use linear programming to adjust and optimize weight distribution to generate accuracy optimization scores; The trend analysis and prediction module uses a long short-term memory network model based on accuracy optimization scoring to process time series data of ecological restoration effects, predicts future trends by learning long-term dependencies in the data, applies seasonal decomposition prediction method, analyzes seasonal changes and trends in time series, and generates long-term trend prediction records; The evaluation and verification module is based on long-term trend forecast records and uses support vector machines to analyze forecast data, classifies and verifies trends by building decision boundaries, uses hierarchical analysis to evaluate and weight the criticality of differential factors, and integrates multiple dimensional data to generate comprehensive evaluation results.

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