Method for evaluating sustainable development of water-food-ecosystem in irrigation district based on sdgs

Abstract

The present application relates to the field of regional sustainable development evaluation, in particular to a method for evaluating the sustainable development of water and food ecosystems in irrigation areas based on SDGs, comprising: obtaining basic data of water and food ecosystems in irrigation areas; constructing an evaluation index system conforming to the SDGs framework, including water security, food security, ecological security and climate action indicators; constructing a fuzzy topological space and analyzing the topological characteristics of the indicators; defining a fuzzy neighborhood operator and constructing a multi-dimensional evaluation matrix reflecting the mutual influence between indicators; applying multi-layer fuzzy filters for evaluation optimization; calculating the comprehensive evaluation index and determining the development level; introducing fuzzy topology theory to accurately depict the complex correlation between water and food ecosystems; describing the mutual influence and transmission path between indicators based on fuzzy neighborhood operators; applying multi-layer fuzzy filters to form an adaptive and dynamic evaluation mechanism, realizing the deep integration of the SDGs framework and regional evaluation.

Description

Technical Field

[0001] This invention relates to the field of regional sustainable development assessment, and specifically to a method for assessing the sustainable development of irrigation district water and food ecosystems based on SDGs. Background Technology

[0002] Existing evaluation methods for sustainable development in irrigation districts suffer from the following shortcomings: First, the evaluation indicator system is not closely integrated with the international SDGs framework, making it difficult to reflect the consistency between regional development and global sustainable development goals, particularly with insufficient attention to SDGs-13 (climate action), and a lack of evaluation indicators for climate change adaptation and disaster resilience. Second, the use of simple fuzzy comprehensive evaluation or analytic hierarchy process (AHP) fails to adequately consider the complex relationships between indicators and the overall system, making it difficult to accurately characterize the dynamic evolution of the system. Third, the determination of indicator weights is too subjective, resulting in a lack of robustness in the evaluation results. Fourth, it is difficult to handle the nonlinear relationships between indicators and the time lag effect of the system, making it difficult for the evaluation results to support actual decision-making. Fifth, the classification of evaluation levels is not scientific enough, especially the naming of negative levels, which lacks risk-oriented descriptions.

[0003] Therefore, there is an urgent need to develop an evaluation method that is deeply integrated with the SDGs framework, can scientifically characterize the complex relationships and dynamic evolution of the water-food ecosystem in irrigation areas, and fully considers the impact of climate change, so as to provide a scientific basis for the sustainable development of irrigation areas. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating the sustainable development of irrigation area water and food ecosystems based on SDGs. By integrating the SDGs framework with fuzzy topology theory, a scientific, systematic and comprehensive evaluation system is constructed to achieve accurate evaluation of the sustainable development status of irrigation area water and food ecosystems.

[0005] This invention proposes a method for evaluating the sustainable development of irrigation area water-food ecosystems based on SDGs, including:

[0006] Obtain basic data on the water-food ecosystem of the irrigation area, including water security data, food security data, ecological security data, and climate action data;

[0007] Based on the aforementioned basic data, an evaluation index system conforming to the SDGs framework is constructed, which includes water security indicators, food security indicators, ecological security indicators, and climate action indicators.

[0008] A fuzzy topological space is constructed, and topological characteristic analysis is performed on the indicators in the evaluation index system to determine the correlation between the indicators.

[0009] Based on the fuzzy topological space, a fuzzy neighborhood operator is defined, and a multi-dimensional evaluation matrix reflecting the mutual influence between indicators is constructed.

[0010] The multidimensional evaluation matrix is ​​optimized by applying a multi-layer fuzzy filter to obtain the optimized evaluation result.

[0011] Based on the optimized evaluation results, the comprehensive evaluation index for the sustainable development of the irrigation district's water and food ecosystem is calculated, and the sustainable development level of the irrigation district is determined.

[0012] Preferably, the construction of an evaluation index system conforming to the SDGs framework specifically includes:

[0013] Based on the goals related to water security, food security, ecological security, and climate action in the SDGs framework, indicators related to sustainable development of irrigation districts are selected.

[0014] Based on the relationship between water and food ecosystems in irrigation districts, a basic indicator system is constructed, including irrigation district water production and use, irrigation district wastewater discharge, water resource utilization, arable land use, grain output, grain waste, natural population growth rate, natural arable land growth rate, land desertification, soil erosion, pesticide residues, ecological water consumption ratio, domestic sewage treatment rate, drinking water source compliance rate, arable land salinization ratio, crop disaster rate, investment per unit area of ​​irrigation district renovation, and farmland drought and flood protection rate.

[0015] The indicator data in the basic indicator system are standardized to convert indicators of different dimensions into comparable standard values.

[0016] Preferably, the construction of the fuzzy topological space specifically includes:

[0017] The standardized evaluation indicators are regarded as the basic elements of the topological space, and the fuzzy proximity is defined based on the inherent correlation between the indicators.

[0018] The connectivity conditions of the topological space are determined, forming four sub-topological spaces: water security, food security, ecological security, and climate action.

[0019] Analyze the connectivity, centrality, and betweenness of the indicators in the topological space to identify key indicator nodes and bridging points;

[0020] Generate feature vectors that reflect the topological characteristics of the indicators, providing topological support for subsequent evaluation.

[0021] Preferably, the definition of the fuzzy neighborhood operator and the construction of a multidimensional evaluation matrix reflecting the mutual influence between indicators specifically include:

[0022] Based on the index correlation matrix, a fuzzy neighborhood range is defined for each index;

[0023] Design neighborhood influence propagation rules to quantify how changes in indicators affect neighboring indicators;

[0024] Based on the SDGs framework, five levels of evaluation were determined: good, relatively good, average, medium risk, and high risk. The membership degree of each indicator to different levels was calculated.

[0025] By integrating neighborhood influence and membership information, a three-dimensional evaluation matrix is ​​constructed to reflect the relationship between indicators, regions, and evaluation levels.

[0026] Preferably, the optimization process of the multidimensional evaluation matrix using a multi-layer fuzzy filter specifically includes:

[0027] Construct a family of fuzzy filters based on the SDGs framework, and design dedicated filters for different types of system characteristics;

[0028] Optimize the basic weight configuration by applying the linear correlation characteristics among the primary filter treatment indicators;

[0029] The nonlinear coupling effect between the indicators of the secondary filter treatment is applied to adjust the influence propagation rules of the nonlinearly related indicators.

[0030] A three-stage filter is applied to enhance the robustness of the evaluation system and to address the impact of time lag effects and external shocks on the evaluation.

[0031] Verify the consistency and stability of the evaluation results after the application of multilayer filters.

[0032] Preferably, the calculation of the comprehensive evaluation index for the sustainable development of the irrigation district's water-grain ecosystem specifically includes:

[0033] Determine the weighting coefficients for the four subsystems: water security, food security, ecological security, and climate action.

[0034] Based on the optimized evaluation results, the water security index, food security index, ecological security index and climate action index were calculated respectively.

[0035] The comprehensive evaluation index for sustainable development of the irrigation area's water-grain ecosystem is calculated by weighting the weight coefficients and corresponding indices of each subsystem.

[0036] Based on the magnitude of the comprehensive evaluation index, the sustainable development level of the irrigation area is divided into five levels: good, relatively good, average, medium risk, and high risk.

[0037] Preferably, the water security indicators correspond to SDGs-6 (Clean Water and Sanitation), including total agricultural irrigation water use, domestic water use, industrial water use, ecological and environmental water use, urban water supply, water use loss in canals, water use loss in channels, agricultural irrigation water use as a percentage of total water use, per capita comprehensive water use, and agricultural water use efficiency coefficient; the food security indicators correspond to SDGs-2 (Eliminating Hunger), including per capita food consumption, per capita food output, irrigated area as a percentage of total irrigated area, and per capita arable land area; the ecological security indicators correspond to SDGs-15 (Protecting Terrestrial Ecosystems), including soil erosion, pesticide residues, per capita ecological and environmental water use as a percentage of total irrigated area, land desertification, arable land salinization as a percentage of total arable land, drinking water source compliance rate, and domestic sewage treatment rate; the climate action indicators correspond to SDGs-13 (Climate Action), including crop disaster rate, investment in irrigation area renovation per unit area, and farmland drought and flood protection rate.

[0038] Preferably, the definition of fuzzy proximity based on the intrinsic correlation between indicators includes:

[0039] Calculate the correlation coefficients between the indicators to determine the initial degree of correlation;

[0040] Adjust the correlation value based on the physical meaning and system structure of the indicator;

[0041] Set a correlation threshold to determine proximity relationships;

[0042] Construct a fuzzy correlation matrix that reflects the proximity relationship between indicators.

[0043] Preferably, the construction of the multilayer fuzzy filter is based on the following principles:

[0044] The primary filter mainly targets the explicit linear correlation between indicators, focusing on eliminating redundant information and maintaining the balance of the evaluation.

[0045] Secondary filters primarily target implicit nonlinear coupling between indicators, focusing on enhancing the clustering effect of key indicators and revealing the internal structure of the system.

[0046] The third-level filter is mainly designed for the time dynamic characteristics of the system, focusing on enhancing the robustness of the evaluation results to outliers and external shocks.

[0047] The filter parameters are adaptively adjusted according to the characteristics of different irrigation areas to ensure the relevance and applicability of the evaluation.

[0048] Preferably, the determination of the weight coefficients for the four subsystems of water security, food security, ecological security, and climate action adopts a weight allocation method based on topological characteristics, specifically including:

[0049] The structural importance of the four subsystems in the fuzzy topological space is analyzed, including connectivity, centrality and bridging.

[0050] Determine the initial weight values ​​based on the priority of the corresponding objectives in the SDGs framework;

[0051] The weights are adjusted to take into account regional characteristics and development stages;

[0052] Verify the rationality of the weight allocation to ensure that the final weights meet the overall system balance requirements.

[0053] The beneficial effects of this invention include:

[0054] It has achieved an organic integration of the SDGs framework with the evaluation of sustainable development in irrigation districts, especially by incorporating relevant indicators from SDGs-13 (climate action), establishing a scientific mapping relationship from global goals to regional indicators, and enhancing the international comparability and policy guidance significance of the evaluation results.

[0055] By introducing fuzzy topology theory, an innovative index-related network model was constructed, which scientifically depicts the complex relationships among the four subsystems of water security, food security, ecological security, and climate action, overcoming the limitations of traditional evaluation methods that consider each subsystem in isolation.

[0056] A multidimensional evaluation matrix was constructed based on the fuzzy neighborhood operator, which accurately quantified the mutual influence and propagation path between indicators, and realized an accurate description of the dynamic evolution characteristics of the system.

[0057] The innovative application of multi-layer fuzzy filters for evaluation optimization has formed an adaptive and dynamic evaluation mechanism, which enhances the robustness and reliability of the evaluation results.

[0058] By adopting a risk-oriented classification method, negative levels are named medium risk and high risk, which more intuitively reflects the risk status of irrigation district development and improves the pertinence of decision support.

[0059] A complete evaluation and decision-making closed loop has been established, which has improved the practical value of the evaluation results. Attached Figure Description

[0060] Figure 1 This is an overall flowchart of the evaluation system method for sustainable development of irrigation area water and food ecosystem based on SDGs in this invention;

[0061] Figure 2 This is a schematic diagram of the fuzzy topology space construction module of the present invention;

[0062] Figure 3 This is a schematic diagram of the structure for constructing a multidimensional evaluation matrix using the fuzzy neighborhood operator of this invention;

[0063] Figure 4 This is a schematic diagram of the structure for optimizing the multilayer fuzzy filter of the present invention;

[0064] Figure 5 This is a flowchart illustrating the calculation process of the comprehensive evaluation index for sustainable development of irrigation districts in this invention.

[0065] Figure 6 This is a diagram showing the mapping relationship between the SDGs framework and the evaluation index system of this invention;

[0066] Figure 7 This is a hierarchical structure diagram of the evaluation index system of this invention;

[0067] Figure 8 This is a flowchart of the weight allocation process for the four subsystems of this invention. Detailed Implementation

[0068] Please refer to Figures 1 to 8 The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0069] like Figure 1 As shown, this invention provides a method for evaluating the sustainable development of irrigation area water and food ecosystems based on SDGs, which mainly includes six steps: acquiring basic data, constructing an evaluation index system, constructing a fuzzy topological space, defining fuzzy neighborhood operators to construct a multidimensional evaluation matrix, applying multi-layer fuzzy filters to optimize the evaluation, calculating the comprehensive evaluation index and determining the development level.

[0070] This invention first requires acquiring basic data on the water-food ecosystem of the irrigation area, including water security data, food security data, ecological security data, and climate action data. This data can be obtained through various channels, such as official statistics, remote sensing data, field surveys, and meteorological monitoring.

[0071] Water security data mainly includes information on total water resources, water consumption, water quality, and water conservancy facilities; food security data mainly includes information on arable land area, grain output, and grain consumption; ecological security data mainly includes information on land cover, soil erosion, land degradation, and biodiversity; and climate action data mainly includes information on meteorological disaster records, investment in irrigation infrastructure, and farmland disaster prevention capabilities.

[0072] Preferably, the time span of these data should cover 5 to 10 years to reflect the dynamic trends in irrigation district development. For example, data from 2015 to 2025 can be collected, which matches the implementation timeframe of the SDGs (2015 to 2030), and helps to assess the progress of irrigation districts in achieving the SDGs objectives.

[0073] Based on the acquired basic data, this invention constructs an evaluation index system that conforms to the SDGs framework. This system fully considers the goals related to water security, food security, ecological security, and climate action in the SDGs, and selects representative indicators in combination with the actual characteristics of the irrigation area.

[0074] like Figure 6 As shown, the mapping relationship between the evaluation indicator system and the SDGs framework reflects a four-level correspondence. SDGs-6 (Clean Water and Sanitation) corresponds to water security indicators, SDGs-2 (Eliminating Hunger) corresponds to food security indicators, SDGs-15 (Protecting Terrestrial Ecosystems) corresponds to ecological security indicators, and SDGs-13 (Climate Action) corresponds to climate action indicators. This mapping relationship ensures consistency between regional assessments and global sustainable development goals.

[0075] like Figure 7 As shown, the evaluation index system includes four subsystems: water security indicators, food security indicators, ecological security indicators, and climate action indicators. In one embodiment of the invention, these indicators are specifically selected as 18 basic indicators, including: irrigation area water production and use, irrigation area wastewater discharge, water resource utilization, arable land utilization, grain output, food waste, natural population growth rate, natural arable land growth rate, land desertification, soil erosion, pesticide residues, ecological water consumption ratio, domestic sewage treatment rate, drinking water source compliance rate, arable land salinization ratio, crop disaster rate, investment per unit area of ​​irrigation area renovation, and farmland drought and flood protection rate.

[0076] Preferably, these indicator data are standardized to convert indicators with different dimensions into comparable standard values. For exponential indicators, the following standardization formula is used:

[0077] ,

[0078] in, Let i be the standardized value of the i-th indicator. Let be the original value of the i-th indicator. This is the minimum value of the indicator among all evaluation objects. This represents the maximum value of the indicator across all evaluation objects. This formula maps the original indicator value to a range of 0 to 1, allowing for comparison of indicators with different dimensions.

[0079] For the specific gravity index, the following standardized formula is used:

[0080] ,

[0081] in, Let i be the standardized value of the i-th indicator. Let be the original value of the i-th indicator. This represents the total amount of the indicator. This formula converts the original proportion value into a standardized proportion value.

[0082] For inverse indicators (indicators where smaller values ​​are better), a reverse conversion is required:

[0083] ,

[0084] in, Let i be the standardized value of the i-th indicator. Let be the original value of the i-th indicator. and These are the minimum and maximum values ​​of the indicator, respectively. This formula converts a negative indicator into a positive one, so that a larger standardized value indicates better performance.

[0085] like Figure 2 As shown, this invention constructs a fuzzy topological space to perform topological characteristic analysis on the indicators in the evaluation index system. The construction of the fuzzy topological space includes a data acquisition module, an indicator standardization module, a fuzzy proximity definition module, a sub-topological space formation module, and a topological characteristic analysis module.

[0086] The standardized evaluation metrics are considered as the basic elements of the topological space. Each metric can be represented as a point. All indicators constitute the indicator set. , where n is the total number of indicators.

[0087] Fuzzy proximity is defined based on the inherent correlation between indicators. In one embodiment of the present invention, fuzzy proximity is determined through the following steps:

[0088] First, calculate the correlation coefficients between the indicators to determine the initial degree of association. The Pearson correlation coefficient is used for calculation.

[0089] ,

[0090] in, Let i be the correlation coefficient between index i and index j. For the i-th index value of the k-th sample, The average value of index i. The sample size is denoted by . This formula calculates the degree of linear correlation between two indicators, with a value ranging from -1 to 1. The larger the absolute value, the stronger the correlation.

[0091] Then, based on the physical meaning and system structure of the indicators, the correlation coefficient is adjusted. For example, for indicator pairs with a clear causal relationship, such as irrigation water efficiency and grain yield, the correlation coefficient can be appropriately increased even if it is not high; for indicator pairs that are only statistically correlated but lack a causal relationship, the correlation coefficient can be appropriately decreased. The adjusted correlation coefficient is denoted as... .

[0092] Next, a correlation threshold is set to determine proximity relationships. In this embodiment, a threshold is set. That is, when At that time, it is assumed that index i and index j have a proximity relationship.

[0093] Finally, a fuzzy correlation matrix R is constructed to reflect the proximity relationships between indicators. The elements of matrix R are... This represents the correlation between index i and index j, with diagonal elements all being 1, and the matrix is ​​a symmetric matrix.

[0094] The connectivity conditions of the topological space are determined, forming four sub-topological spaces: water security, food security, ecological security, and climate action. Connectivity is determined using the following methods:

[0095] set up , , , These are subsets of indicators corresponding to the four subsystems: water security, food security, ecological security, and climate action. Subtopological space. From subset It consists of all its possible unions, intersections, and complements, satisfying the axioms of topological spaces.

[0096] For each subsystem k (k=1,2,3,4), its corresponding subtopological space It includes all indicators and their topological relationships within the subsystem. Based on the subtopology space. This allows for further analysis of the internal connectivity of subsystems and the coupling relationships between subsystems.

[0097] The connectivity within a subsystem is measured by cohesion:

[0098] ,

[0099] in, Let k be the cohesion of subsystem k. Let k be the set of indexes contained in subsystem k. Let k be the number of indicators contained in subsystem k. Let represent the correlation between indicator i and indicator j. This formula calculates the average correlation between all indicator pairs within the subsystem, reflecting the tightness of the relationship within the subsystem. Higher cohesion indicates a tighter correlation between the indicators within the subsystem.

[0100] The connectivity between subsystems is measured by the degree of coupling:

[0101] ,

[0102] in, The degree of coupling between subsystem k and subsystem l. and These are the index sets of indicators contained in the two subsystems. and These represent the number of indicators contained in each of the two subsystems. This formula calculates the average correlation degree between all indicator pairs between the two subsystems, reflecting the degree of correlation between them. A higher coupling degree indicates a stronger mutual influence between the two subsystems.

[0103] Analyze the connectivity, centrality, and betweenness of the indicators in the topological space to identify key indicator nodes and bridging points.

[0104] Connectivity reflects the number of direct connections between an indicator and other indicators:

[0105] ,

[0106] in, Let i be the connectivity of the index. This is an indicator function that takes the value 1 when the condition within the parentheses is true, and 0 otherwise. This is the threshold for proximity relationships. The formula calculates the number of other metrics that are adjacent to metric i, reflecting the connectivity activity of metric i in the network.

[0107] Centrality reflects the centrality of an indicator within the entire network.

[0108] ,

[0109] in, Let i be the centrality of the index. Let be the total number of indicators. This formula calculates the average correlation between indicator i and all other indicators, reflecting the centrality of indicator i in the entire system. Indicators with higher centrality are more important in the system.

[0110] Betweenness reflects the importance of the indicator as a bridging point:

[0111] ,

[0112] in, Let i be the betweenness of index i. The total number of shortest paths from index s to index t. Let represent the number of shortest paths passing through index i. This formula calculates the proportion of shortest paths between all index pairs that pass through index i, reflecting the bridging effect of index i in connecting different parts. Indicators with higher betweenness numbers have a more significant bridging effect in the system.

[0113] Generate feature vectors that reflect the topological characteristics of the indicators, providing topological structure support for subsequent evaluation. The feature vectors are represented as follows:

[0114] ,

[0115] in, Let i be the characteristic vector of index i. For connectivity, For centrality, For betweenness, This represents the cohesion of subsystem k to which index i belongs. This vector comprehensively reflects the network location characteristics and subsystem affiliation characteristics of the index.

[0116] like Figure 3 As shown, based on the constructed fuzzy topological space, this invention defines a fuzzy neighborhood operator and constructs a multidimensional evaluation matrix reflecting the mutual influence between indicators. This module includes fuzzy topological space input, neighborhood operator definition, influence propagation rule design, membership degree calculation, and three-dimensional evaluation matrix construction.

[0117] Based on the index correlation matrix, a fuzzy neighborhood range is defined for each index. The fuzzy neighborhood of index i... Defined as:

[0118] ,

[0119] in, For the fuzzy neighborhood of index i, Let j be the topological space point corresponding to index j. The correlation between indicator i and indicator j. The threshold is set for proximity relationships. This definition indicates that the fuzzy neighborhood of index i includes all other indices whose correlation with index i exceeds the threshold.

[0120] The invention designs neighborhood influence propagation rules to quantify how changes in indicators affect neighboring indicators. In one embodiment, the influence propagation employs a weighted propagation model:

[0121] ,

[0122] in, Let j be the change in index j. The change in index i To influence the weight, Let be the fuzzy neighborhood of index j. Influence weights. Based on correlation Sure:

[0123] ,

[0124] in, Let i be the weight of the influence of index i on index j. This represents the correlation degree. The formula normalizes the correlation degree, ensuring that the sum of the influence weights of all neighboring indicators on the target indicator is 1.

[0125] Based on the SDGs framework, a five-level evaluation system was established: Good, Fair, Average, Medium Risk, and High Risk. The membership degree of each indicator to different levels was calculated. The evaluation level division was based on the segmentation of the comprehensive evaluation index.

[0126] good: ;

[0127] better: ;

[0128] generally: ;

[0129] Medium risk: ;

[0130] High risk: .

[0131] SDI stands for Sustainable Development Index. This classification method adopts a risk-oriented naming strategy, naming negative levels as medium risk and high risk, which more intuitively reflects the risk status of irrigation district development and improves the pertinence of decision support.

[0132] For a single indicator Its membership degree to the k-th level Calculation using fuzzy membership functions:

[0133] ,

[0134] in, Let be the membership degree of index i to the k-th level. and These represent the lower and upper limits of the k-th level, respectively. The function uses a trapezoidal fuzzy number representation: the membership degree is 1 when the index value is within the central interval of the level, decreases linearly when it is within the transition interval, and becomes 0 when it exceeds the range of adjacent levels.

[0135] By integrating neighborhood influence and membership information, a three-dimensional evaluation matrix is ​​constructed to reflect the relationships between indicators, regions, and evaluation levels. The three-dimensional evaluation matrix M is defined as follows:

[0136] ,

[0137] in, This represents the comprehensive membership degree of the i-th indicator to the k-th evaluation level in the j-th evaluation region. The matrix has three dimensions: the first dimension is the indicator dimension (i = 1 to n), the second dimension is the region dimension (j = 1 to m), and the third dimension is the level dimension (k = 1 to 5). The calculation of the matrix elements comprehensively considers both the direct membership degree of the indicators and the influence of their neighborhood.

[0138] ,

[0139] in, To comprehensively determine membership degree, For direct membership degree, As the neighborhood influence weight, This is the neighborhood influence coefficient, ranging from 0 to 1. When, only direct membership is considered; when At that time, it is entirely determined by the neighborhood index. In this embodiment, preferably... That is, the overall membership degree consists of 70% direct membership degree and 30% neighborhood influence.

[0140] like Figure 4 As shown, this invention applies a multi-level fuzzy filter to optimize a multi-dimensional evaluation matrix. The multi-level fuzzy filter system includes a multi-dimensional evaluation matrix input, a first-level filter (linear correlation processing), a second-level filter (nonlinear coupling processing), a third-level filter (robustness enhancement), and an optimized evaluation result output.

[0141] A family of fuzzy filters based on the SDGs framework is constructed, and dedicated filters are designed for different types of system characteristics. Fuzzy Filter Family It includes three levels of filters, each optimized for different system characteristics.

[0142] The linear correlation between indicators is applied using a first-level filter to optimize the basic weight configuration. (First-level filter) Primarily targeting the explicit linear relationship between indicators, its mathematical expression is as follows:

[0143] ,

[0144] in, This is the evaluation matrix after primary filter processing. This is the original evaluation matrix. This is the weight adjustment matrix. The formula represents a linear transformation of the original evaluation matrix M using the weight adjustment matrix W. The weight adjustment matrix W is determined based on correlation analysis of the indicators, balancing the weights of highly linearly correlated indicator groups to avoid information redundancy.

[0145] In one embodiment of the present invention, the weight adjustment matrix W is constructed based on the following principle: identifying indicator pairs with a correlation coefficient exceeding 0.7, and reducing the weight of the indicator with lower importance. For example, if the correlation coefficient between indicator A and indicator B is 0.85, and indicator A has a higher centrality, then the weight of indicator A is kept at 1.0, and the weight of indicator B is adjusted to 0.7. The weight adjustment matrix W is a diagonal matrix, with the diagonal elements representing the adjustment weights of each indicator.

[0146] A second-order filter is applied to address the nonlinear coupling effect between indices, adjusting the propagation rules of the influence of nonlinearly correlated indices. Second-order filter It mainly targets the implicit nonlinear coupling between indicators, and its mathematical expression is as follows:

[0147] ,

[0148] in, This is the evaluation matrix after secondary filter processing. It is a nonlinear mapping function. This is a set of nonlinear parameters. The formula represents the matrix after processing the first-stage filter. Applying nonlinear mapping functions The transformation is performed, where P is a set of parameters that control the transformation characteristics.

[0149] The design principle is to enhance the clustering effect of key indicators and reveal the internal structure of the system. In one embodiment of the invention, for the indicator groups that form clusters, the geometric mean method is used to enhance their overall effect:

[0150] ,

[0151] in, It is a nonlinear mapping function. to Let p be the values ​​of the indicators within the cluster. This represents the geometric mean of p index values. This geometric mean form can better reflect the synergistic effect of multiple indicators. Compared with the arithmetic mean, the geometric mean is more sensitive to extreme values ​​and can better reflect the weakest link effect.

[0152] A three-stage filter is applied to enhance the robustness of the evaluation system and address the impact of time lag effects and external shocks on the evaluation. Three-stage filter Primarily focusing on the system's time-dynamic characteristics, its mathematical expression is as follows:

[0153] ,

[0154] in, This is the evaluation matrix after three-stage filter processing. For time series mapping functions, For time parameter set, This represents the set of external impact parameters. The formula expresses the matrix after processing the secondary filter. Applying time series mapping functions The transformation is performed, with T and S controlling the time characteristics and external impact characteristics, respectively.

[0155] The design principle is to reduce the impact of outliers and enhance time continuity. In one embodiment of the invention, a moving average method is used for time smoothing:

[0156] ,

[0157] in, This is the smoothed index value at time t. The index value at time t is the original value. and These are the index values ​​at time t minus 1 and time t plus 1, respectively. , , For the weighting coefficients, satisfying In a preferred embodiment, , , This weighting configuration, while maintaining the dominance of the current value, also appropriately considers the influence of previous and subsequent times, which can effectively mitigate the abnormal impact of extreme events.

[0158] The consistency and stability of the evaluation results after the application of multilayer filters are verified. In one embodiment of the present invention, a convergence index is used for verification:

[0159] ,

[0160] in, Let C denote the matrix norm, and C be the convergence index. This formula calculates the relative magnitude of the difference between the matrices before and after optimization. This represents the norm of the difference between the matrices before and after optimization. This represents the norm of the original matrix. When C is less than a preset threshold... When the value is typically 0.1, the filter optimization is considered to have converged.

[0161] For example, if the norm of the original evaluation matrix M is 10.0, the optimized matrix... If the norm of the difference between the matrix and the original matrix is ​​0.8, then the convergence index is... The value being less than the threshold of 0.1 indicates that the optimization has converged. This shows that the optimization process of the multilayer filter effectively adjusts the evaluation results while maintaining basic consistency with the original evaluation, without excessive deviation.

[0162] like Figure 5 As shown, after optimizing the multi-layer filter, this invention calculates the comprehensive evaluation index for the sustainable development of the irrigation area's water-grain ecosystem, and determines the sustainable development level of the irrigation area accordingly. The process includes inputting the optimization evaluation results, determining the weights of the four subsystems, calculating the indices of the four subsystems, calculating the comprehensive evaluation index, and determining the development level.

[0163] like Figure 8 As shown, the weight coefficients for four subsystems—water security, food security, ecological security, and climate action—are first determined. In one embodiment of the invention, a weight allocation method based on topological characteristics is employed.

[0164] The structural importance of the four subsystems in the fuzzy topological space is analyzed, including connectivity, centrality, and bridging. Connectivity reflects the tightness of connections between indicators within the subsystem, and is calculated using the following formula:

[0165] ,

[0166] in, For the connectivity of subsystem s, For the index set of indicators contained in subsystem s, Let be the number of indicators contained in subsystem s. Let represent the correlation between indicator i and indicator j. This formula calculates the average correlation between all indicator pairs within the subsystem, reflecting the tightness of the relationship within the subsystem.

[0167] Centrality reflects the core position of a subsystem within the overall system, and is calculated using the following formula:

[0168] ,

[0169] in, For the centrality of subsystem s, Let i be the centrality of the index. Let be the number of indicators contained in subsystem s. This formula calculates the average centrality of all indicators within the subsystem, reflecting the coreness of the subsystem within the overall system.

[0170] Bridging performance reflects the importance of a subsystem as a bridging point, and is calculated using the following formula:

[0171] ,

[0172] in, For the bridging property of subsystem s, Let i be the betweenness of index i. Let be the number of metrics contained in subsystem s. This formula calculates the average betweenness of all metrics within the subsystem, reflecting the importance of the subsystem in connecting its different parts.

[0173] Taking into account the three topological characteristics, the basic formula for the subsystem weights is:

[0174] ,

[0175] in, Let be the weight of the s-th subsystem. The average centrality of this subsystem. The formula represents the average betweenness of the subsystem, with the denominator being the sum of the products of the centrality and betweenness of the four subsystems. This formula considers both the centrality and bridging effect of the subsystems, comprehensively reflecting their importance in the overall system.

[0176] Initial weight values ​​are determined by considering the priorities of the corresponding objectives within the SDGs framework. Within the SDGs framework, the global priorities for SDGs-6 (Clean Water and Sanitation), SDGs-2 (Eliminating Hunger), SDGs-15 (Protecting Terrestrial Ecosystems), and SDGs-13 (Climate Action) can be set to 0.30, 0.30, 0.25, and 0.15, respectively. This reflects that water security and food security are the most fundamental guarantees, followed by ecological protection, while climate action, although important, has a relatively low weight in short-term assessments.

[0177] The weights should be adjusted based on regional characteristics and development stages. For example, for severely water-scarce irrigation areas, the weight of the water security subsystem can be increased by 10% to 20%; for ecologically fragile areas, the weight of the ecological security subsystem can be increased by 10% to 20%; and for irrigation areas prone to climate disasters, the weight of the climate action subsystem can be increased by 10% to 15%.

[0178] Verify the rationality of the weight allocation to ensure that the final weights meet the overall system balance requirements. Generally, the sum of the weights of the four subsystems should be 1, and the weight of a single subsystem should be between 0.1 and 0.5. In one embodiment of the present invention, after the above calculations and adjustments, the weight coefficients of the four subsystems of an irrigation district are as follows: water security 0.35, food security 0.30, ecological security 0.20, and climate action 0.15.

[0179] Next, based on the optimized evaluation results, the Water Security Index (WSI), Food Security Index (FSI), Ecological Security Index (ESI), and Climate Action Index (CAI) are calculated respectively:

[0180] ,

[0181] ,

[0182] ,

[0183] ,

[0184] in, , , , These are indexes for water security, food security, ecological security, and climate action indicators, respectively. For the optimized evaluation matrix elements, This represents the level value for level k. In this embodiment, , , , , These correspond to five risk levels: good, relatively good, average, medium risk, and high risk. These four formulas calculate the safety index of each of the four subsystems using a weighted summation method, with the weights being the risk level values. .

[0185] Then, based on the weight coefficients and corresponding indices of each subsystem, the comprehensive evaluation index (SDI) for sustainable development of the irrigation district's water-grain ecosystem is calculated using a weighted average:

[0186] ,

[0187] in, , , , These are the weights for the water security, food security, ecological security, and climate action subsystems, respectively. The formula sums the indices of the four subsystems according to their weights to obtain the final comprehensive evaluation index, SDI.

[0188] Finally, based on the magnitude of the comprehensive evaluation index, the sustainable development level of the irrigation district is divided into five levels: good, relatively good, average, medium risk, and high risk.

[0189] good: ;

[0190] better: ;

[0191] generally: ;

[0192] Medium risk: ;

[0193] High risk: .

[0194] These thresholds are determined based on expert experience and historical data and can be adjusted according to the characteristics of different regions. For example, the threshold standards can be appropriately increased for economically developed regions and appropriately decreased for less developed regions. Using the naming convention of medium and high risk, compared to the previous less safe and unsafe, more intuitively conveys risk warning information and helps decision-makers take timely countermeasures.

[0195] Through the above steps, this invention achieves a scientific evaluation of the sustainable development status of the irrigation area's water-grain ecosystem, providing a reliable basis for sustainable management and decision-making in the irrigation area.

[0196] In one embodiment of the present invention, the water safety indicators correspond to SDGs-6 (Clean Water and Sanitation) and include the following specific indicators:

[0197] Total agricultural irrigation water consumption: reflects the scale of agricultural water use in the irrigation area, expressed in 100 million cubic meters per year;

[0198] Domestic water consumption: reflects the amount of water used by residents, measured in 100 million cubic meters per year;

[0199] Industrial water use: reflects the amount of water used in industrial production, measured in 100 million cubic meters per year;

[0200] Ecological and environmental water use: reflects the amount of water required to maintain the ecosystem, measured in 100 million cubic meters per year;

[0201] Urban water supply: Reflects the water supply volume of the urban water supply system, measured in 100 million cubic meters per year;

[0202] Water loss in irrigation canals: reflects water loss in irrigation canals, expressed in 100 million cubic meters per year;

[0203] Water utilization loss in channels: reflects the loss of water transport in channels, in 100 million cubic meters per year;

[0204] Agricultural irrigation water consumption as a percentage of total water consumption: This reflects the proportion of agricultural water use, expressed as a percentage.

[0205] Per capita comprehensive water consumption: reflects the level of per capita water resource utilization, and the unit is cubic meters per person per year;

[0206] Agricultural water use efficiency coefficient: reflects the efficiency of agricultural water use, dimensionless.

[0207] These indicators comprehensively reflect the status of water resource acquisition, utilization, management and protection in the irrigation area, and correspond to the specific goals in SDGs-6 regarding sustainable water resource management, improved water resource utilization efficiency and protection of aquatic ecosystems.

[0208] In one embodiment of the present invention, the food security indicators correspond to SDGs-2 (the elimination of hunger) and include the following specific indicators:

[0209] Per capita grain consumption: reflects the level of grain consumption, expressed in kilograms per person per year;

[0210] Per capita grain output: reflects grain production capacity, measured in kilograms per person per year;

[0211] Irrigated area percentage: Reflects the level of development of irrigated agriculture, expressed as a percentage.

[0212] Per capita arable land area: reflects the amount of arable land resources owned, and the unit is mu per person.

[0213] These indicators comprehensively reflect the status of food production, consumption and resource utilization in the irrigation district, and correspond to the specific goals in SDGs-2 regarding eliminating hunger, achieving food security, improving nutrition and promoting sustainable agriculture.

[0214] In one embodiment of the present invention, the ecological security indicators correspond to SDGs-15 (Protecting Terrestrial Ecosystems) and include the following specific indicators:

[0215] Soil and water loss: reflects the state of land degradation, measured in square kilometers or percentages;

[0216] Pesticide residues: Reflect the degree of agricultural environmental pollution, expressed in milligrams per kilogram or the rate of exceeding the standard;

[0217] Per capita ecological water use ratio: reflects the degree of ecological water security, expressed as a percentage.

[0218] Land desertification: Reflects the degree of land desertification, measured in square kilometers or percentages;

[0219] Farmland salinization percentage: Reflects the degree of farmland salinization, expressed as a percentage (%).

[0220] Drinking water source compliance rate: reflects the degree of drinking water safety assurance, expressed as a percentage (%).

[0221] Domestic sewage treatment rate: reflects the level of sewage treatment, expressed in percent.

[0222] These indicators comprehensively reflect the ecological environment quality and protection status of the irrigation area, and correspond to the specific goals in SDGs-15 regarding the protection, restoration and promotion of sustainable use of terrestrial ecosystems, prevention of desertification, and halting and reversing land degradation.

[0223] In one embodiment of the present invention, the climate action indicators correspond to SDGs-13 (climate action) and include the following specific indicators:

[0224] Crop disaster rate: This reflects the degree of impact of climate disasters on agricultural production, expressed as a percentage (%). The calculation formula is: affected area divided by total sown area multiplied by 100%. This indicator directly reflects the irrigation district's ability to withstand extreme weather events; a lower disaster rate indicates stronger climate adaptability. For example, if an irrigation district had a sown area of ​​100,000 hectares in 2024 and an affected area of ​​5,000 hectares, then the crop disaster rate would be 5%.

[0225] Investment per unit area in irrigation district renovation: This reflects the level of investment in infrastructure construction and climate adaptability improvement in the irrigation district, expressed in tens of thousands of yuan per hectare. This indicator reflects the financial investment in improving disaster prevention and mitigation capabilities within the irrigation district; higher investment levels generally indicate stronger disaster resistance. For example, if an irrigation district has invested a cumulative 500 million yuan in irrigation and drainage system renovation over the past five years, and the district covers an area of ​​100,000 hectares, then the investment per unit area is 50,000 yuan per hectare.

[0226] Farmland drought and flood protection rate: This reflects the ability of farmland in an irrigated area to withstand drought and flood disasters, expressed as a percentage (%). The calculation formula is: the area of ​​farmland with drought and flood protection capacity divided by the total farmland area multiplied by 100%. This indicator comprehensively reflects the sophistication of irrigation and drainage facilities and the level of climate risk management in the irrigation area. A higher drought and flood protection rate indicates a more solid foundation for the sustainable development of the irrigation area. For example, if an irrigation area has a total farmland area of ​​80,000 hectares, of which 60,000 hectares have drought and flood protection capacity, then the farmland drought and flood protection rate is 75%.

[0227] These three indicators comprehensively reflect the irrigation district's capacity and resilience to cope with climate change, corresponding to the specific goals in SDGs-13 regarding enhancing the capacity to withstand climate-related disasters, improving the capacity to adapt to climate change, and integrating climate change measures into policies. By incorporating climate action indicators, this invention addresses the shortcomings of existing evaluation systems that do not adequately focus on climate change, making the evaluation more comprehensive and scientific.

[0228] In one embodiment of the present invention, the specific implementation steps for defining fuzzy proximity based on the intrinsic correlation between indicators include:

[0229] First, the correlation coefficients between the indicators are calculated to determine the initial degree of association. The Pearson correlation coefficient is then used to calculate the degree of linear correlation between the indicators.

[0230] Then, based on the physical meaning and system structure of the indicators, the correlation coefficient values ​​are adjusted. For example, if there is a clear causal relationship between irrigation water efficiency and grain yield, the correlation coefficient should be appropriately increased even if it is not high; while some indicators, although statistically correlated, lack a clear causal mechanism, and their correlation coefficient should be appropriately decreased. The adjustment principle is based on the following considerations: for indicator pairs with strong physical causal relationships, the correlation coefficient should be increased by 10% to 20%; for indicator pairs with statistical correlation but weak causal relationships, the correlation coefficient should be decreased by 10% to 20%.

[0231] Next, a correlation threshold is set to determine proximity relationships. In this embodiment, a threshold is set. That is, when the adjusted correlation is greater than or equal to 0.5, the two indicators are considered to have a proximity relationship.

[0232] Finally, a fuzzy correlation matrix R is constructed to reflect the proximity relationships between the indicators. Matrix R is a symmetric matrix with diagonal elements of 1.0 and off-diagonal elements representing the adjusted correlation degree between the indicators. For example, for 18 evaluation indicators of an irrigation district, an 18×18 fuzzy correlation matrix might be obtained, where the correlation degree between total agricultural irrigation water consumption and the effective utilization coefficient of agricultural water might be 0.85, indicating a high correlation between the two; while the correlation degree between total agricultural irrigation water consumption and the domestic sewage treatment rate might only be 0.25, indicating a weak correlation between the two.

[0233] In one embodiment of the present invention, the construction of the multilayer fuzzy filter is based on the following principles:

[0234] The primary filter primarily targets explicit linear correlations between indicators, focusing on eliminating redundant information and maintaining the balance of the evaluation. Its design principle is to identify highly linearly correlated indicator groups and appropriately adjust their weights to avoid redundant calculations. For example, if the correlation coefficient between the indicators of irrigation water production and use and water resource utilization is 0.92, far exceeding the threshold of 0.7, then information redundancy may exist. In this case, the primary filter will appropriately reduce the weight of one indicator based on its importance (based on centrality). If water resource utilization is more important, its weight may remain at 1.0, while the weight of irrigation water production and use may be reduced from 1.0 to 0.7.

[0235] The secondary filter primarily targets implicit nonlinear couplings between indicators, focusing on strengthening the clustering effect of key indicators and revealing the internal structure of the system. Its design principle is to identify groups of indicators with nonlinear relationships and enhance their overall effect through nonlinear mapping. For example, irrigation water efficiency, grain yield, and per capita arable land area may have nonlinear coupling relationships, and their combined effect is not a simple linear superposition. The secondary filter uses a geometric average method to strengthen their synergistic effect, better reflecting the weakest link effect—that is, when one indicator is significantly lower than others, it significantly lowers the overall evaluation.

[0236] The three-stage filter primarily targets the time-dynamic characteristics of the system, focusing on enhancing the robustness of evaluation results to outliers and external shocks. Its design principle considers the time-series characteristics of the indicators to mitigate the impact of abnormal fluctuations. For example, a certain irrigation district experienced a severe flood in 2023, resulting in an abnormal 50% drop in grain yield. Traditional evaluation methods might yield abnormally low results, while the three-stage filter uses a moving average to smooth the 2023 grain yield data with those from 2022 and 2024, significantly reducing the short-term impact of extreme events and providing a more robust long-term evaluation result.

[0237] Furthermore, the filter parameters are adaptively adjusted according to the characteristics of different irrigation districts to ensure the relevance and applicability of the evaluation. For example, for irrigation districts with abundant water resources but low utilization efficiency, the weight of water resource utilization efficiency-related indicators in the first-stage filter can be strengthened; for irrigation districts with water shortages, the weight of water conservation-related indicators can be strengthened; and for irrigation districts prone to climate disasters, the weight coefficient of time smoothing treatment in the third-stage filter can be increased to enhance robustness to extreme events.

[0238] In one embodiment of the present invention, the weight coefficients of the four subsystems—water security, food security, ecological security, and climate action—are determined using a weight allocation method based on topological characteristics. The specific steps include:

[0239] First, the structural importance of the four subsystems in the fuzzy topological space is analyzed, including connectivity, centrality, and bridging. These three topological properties reflect the importance of the subsystems in the overall evaluation system from different perspectives.

[0240] Then, initial weight values ​​are determined by considering the priorities of the corresponding targets within the SDGs framework. While the SDGs framework provides a priority reference for global sustainable development goals, it needs to be adjusted based on the specific circumstances of each irrigation district. For example, in arid irrigation districts with severe water shortages, the weight of water security should be significantly higher than that of other subsystems; in ecologically fragile irrigation districts, the weight of ecological security should be appropriately increased.

[0241] Next, the weights should be adjusted based on regional characteristics and development stages. Different irrigation districts face different main problems and development priorities, and the weight allocation should reflect these differences. For example, for irrigation districts with severe water shortages, the weight of the water security subsystem can be increased by 10% to 20%; for ecologically fragile areas, the weight of the ecological security subsystem can be increased by 10% to 20%; and for irrigation districts prone to climate disasters, the weight of the climate action subsystem can be increased by 10% to 15%.

[0242] Finally, the rationality of the weight allocation is verified to ensure that the final weights meet the overall system balance requirements. Generally, the sum of the weights of the four subsystems should be 1.0, and the weight of a single subsystem should be between 0.1 and 0.5 to avoid an imbalance in the evaluation caused by an excessively high weight for any subsystem. In one embodiment of the present invention, after the above calculations and adjustments, the weight coefficients of the four subsystems of an irrigation district are as follows: water security 0.35, food security 0.30, ecological security 0.20, and climate action 0.15. This indicates that in the sustainable development evaluation of this irrigation district, water security occupies the most important position, followed by food security, then ecological security, and although climate action has a relatively low weight, it cannot be ignored.

[0243] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for evaluating the sustainable development of irrigation and food ecosystems in irrigated areas based on the SDGs, characterized in that... include: Obtain basic data on the water-food ecosystem of the irrigation area, including water security data, food security data, ecological security data, and climate action data; Based on the aforementioned basic data, an evaluation index system conforming to the SDGs framework is constructed, which includes water security indicators, food security indicators, ecological security indicators, and climate action indicators. A fuzzy topological space is constructed, and topological characteristic analysis is performed on the indicators in the evaluation index system to determine the correlation between the indicators. Based on the fuzzy topological space, a fuzzy neighborhood operator is defined, and a multi-dimensional evaluation matrix reflecting the mutual influence between indicators is constructed. The multidimensional evaluation matrix is ​​optimized by applying a multi-layer fuzzy filter to obtain the optimized evaluation result. Based on the optimized evaluation results, the comprehensive evaluation index for the sustainable development of the irrigation district's water and food ecosystem is calculated, and the sustainable development level of the irrigation district is determined. The construction of the fuzzy topological space specifically includes: The standardized evaluation indicators are regarded as the basic elements of the topological space, and the fuzzy proximity is defined based on the inherent correlation between the indicators. The connectivity conditions of the topological space are determined, forming four sub-topological spaces: water security, food security, ecological security, and climate action. Analyze the connectivity, centrality, and betweenness of the indicators in the topological space to identify key indicator nodes and bridging points; Generate feature vectors that reflect the topological characteristics of the indicators, providing topological structure support for subsequent evaluation; The definition of the fuzzy neighborhood operator and the construction of a multidimensional evaluation matrix reflecting the mutual influence between indicators specifically include: Based on the index correlation matrix, a fuzzy neighborhood range is defined for each index; Design neighborhood influence propagation rules to quantify how changes in indicators affect neighboring indicators; Based on the SDGs framework, five levels of evaluation were determined: good, relatively good, average, medium risk, and high risk. The membership degree of each indicator to different levels was calculated. By integrating neighborhood influence and membership information, a three-dimensional evaluation matrix is ​​constructed to reflect the relationship between indicators, regions, and evaluation levels. The optimization of the multidimensional evaluation matrix by applying a multi-layer fuzzy filter specifically includes: Construct a family of fuzzy filters based on the SDGs framework, and design dedicated filters for different types of system characteristics; Optimize the basic weight configuration by applying the linear correlation characteristics among the primary filter treatment indicators; The nonlinear coupling effect between the indicators of the secondary filter treatment is applied to adjust the influence propagation rules of the nonlinearly related indicators. A three-stage filter is applied to enhance the robustness of the evaluation system and to address the impact of time lag effects and external shocks on the evaluation. Verify the consistency and stability of the evaluation results after the application of multilayer filters.

2. The method for evaluating the sustainable development of irrigation area water-food ecosystems based on SDGs according to claim 1, characterized in that, The construction of an evaluation index system that conforms to the SDGs framework specifically includes: Based on the goals related to water security, food security, ecological security, and climate action in the SDGs framework, indicators related to sustainable development of irrigation districts are selected. Based on the relationship between water and food ecosystems in irrigation districts, a basic indicator system is constructed, including irrigation district water production and use, irrigation district wastewater discharge, water resource utilization, arable land use, grain output, grain waste, natural population growth rate, natural arable land growth rate, land desertification, soil erosion, pesticide residues, ecological water consumption ratio, domestic sewage treatment rate, drinking water source compliance rate, arable land salinization ratio, crop disaster rate, investment per unit area of ​​irrigation district renovation, and farmland drought and flood protection rate. The indicator data in the basic indicator system are standardized to convert indicators of different dimensions into comparable standard values.

3. The method for evaluating the sustainable development of irrigation area water-food ecosystems based on SDGs according to claim 1, characterized in that, The calculation of the comprehensive evaluation index for the sustainable development of the irrigation district's water-grain ecosystem specifically includes: Determine the weighting coefficients for the four subsystems: water security, food security, ecological security, and climate action. Based on the optimized evaluation results, the water security index, food security index, ecological security index and climate action index were calculated respectively. The comprehensive evaluation index for sustainable development of the irrigation area's water-grain ecosystem is calculated by weighting the weight coefficients and corresponding indices of each subsystem. Based on the magnitude of the comprehensive evaluation index, the sustainable development level of the irrigation area is divided into five levels: good, relatively good, average, medium risk, and high risk.

4. The method for evaluating the sustainable development of irrigation area water and food ecosystems based on SDGs according to claim 2, characterized in that, The water security indicators correspond to SDGs-6 (Clean Water and Sanitation) and include total agricultural irrigation water use, domestic water use, industrial water use, ecological and environmental water use, urban water supply, water use loss in canals, water use loss in channels, agricultural irrigation water use as a percentage of total water use, per capita comprehensive water use, and agricultural water use efficiency coefficient. The food security indicators correspond to SDGs-2 (Eliminating Hunger) and include per capita grain consumption, per capita grain output, irrigated area as a percentage of total irrigated area, and per capita arable land area. The ecological security indicators correspond to SDGs-15 (Protecting Terrestrial Ecosystems) and include soil erosion, pesticide residues, per capita ecological and environmental water use as a percentage of total irrigated area, land desertification, arable land salinization as a percentage of total arable land, drinking water source compliance rate, and domestic sewage treatment rate. The climate action indicators correspond to SDGs-13 (Climate Action) and include crop disaster rate, investment in irrigation area renovation per unit area, and farmland drought and flood protection rate.

5. The method for evaluating the sustainable development of irrigation area water-food ecosystems based on SDGs according to claim 1, characterized in that, The definition of fuzzy proximity based on the intrinsic correlation between indicators includes: Calculate the correlation coefficients between the indicators to determine the initial degree of correlation; Adjust the correlation value based on the physical meaning and system structure of the indicator; Set a correlation threshold to determine proximity relationships; Construct a fuzzy correlation matrix that reflects the proximity relationship between indicators.

6. The method for evaluating the sustainable development of irrigation area water and food ecosystems based on SDGs according to claim 1, characterized in that, The construction of the multilayer fuzzy filter is based on the following principles: The primary filter mainly targets the explicit linear correlation between indicators, focusing on eliminating redundant information and maintaining the balance of the evaluation. Secondary filters primarily target implicit nonlinear coupling between indicators, focusing on enhancing the clustering effect of key indicators and revealing the internal structure of the system. The third-level filter is mainly designed for the time dynamic characteristics of the system, focusing on enhancing the robustness of the evaluation results to outliers and external shocks. The filter parameters are adaptively adjusted according to the characteristics of different irrigation areas to ensure the relevance and applicability of the evaluation.

7. The method for evaluating the sustainable development of irrigation area water-food ecosystems based on SDGs according to claim 3, characterized in that, The weighting coefficients for the four subsystems—water security, food security, ecological security, and climate action—are determined using a topologically based weighting method, specifically including: The structural importance of the four subsystems in the fuzzy topological space is analyzed, including connectivity, centrality and bridging. Determine the initial weight values ​​based on the priority of the corresponding objectives in the SDGs framework; The weights are adjusted to take into account regional characteristics and development stages; Verify the rationality of the weight allocation to ensure that the final weights meet the overall system balance requirements.

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