Regional agricultural water resource safety dynamic assessment method and system

Abstract

The invention discloses a regional agricultural water resource safety dynamic evaluation method and system, and relates to the technical field of agricultural water resource safety guarantee evaluation. Comprising the steps that a user performs order relation analysis method-variable coefficient method-SMI-P evaluation model, order relation analysis method-CRITIC method-TOPSIS evaluation model, order relation analysis method-entropy weight method-fuzzy comprehensive evaluation model and order relation analysis method-variable coefficient method-TOPSIS evaluation model according to evaluation requirements; and through selection of an analytic hierarchy process-entropy weight method-SMI-P evaluation model and an analytic hierarchy process-entropy weight method-fuzzy comprehensive evaluation model, comprehensive evaluation of agricultural water resources is realized. According to the agricultural water resource safety guarantee evaluation method with multiple levels and high comprehensiveness, water resource supply, demand balance, climate change, soil characteristics, farmer behaviors, social and economic conditions and the like are comprehensively considered, and an all-around evaluation system is constructed.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural water resource security evaluation, and particularly to a method and system for dynamic assessment of regional agricultural water resource security. Background Art

[0002] Existing agricultural water resource evaluation methods mainly focus on water resource supply-demand balance, remote sensing monitoring or single-factor-based analysis. However, these methods generally have limitations, such as lack of comprehensiveness, poor adaptability, high complexity and difficulty in application. Traditional models mostly focus on the evaluation of water resource supply or single indicators, ignoring the impacts of multiple factors such as agricultural production behavior, social and economic development, and climate change.

[0003] Most traditional agricultural water resource security evaluation models mainly focus on the single measurement of water resource supply and demand, often ignoring multiple influencing factors such as climate change, land use, soil quality, irrigation management, farmers' behavior, and social and economic factors. This approach leads to limited evaluation results and is difficult to comprehensively predict and ensure the long-term sustainability of agricultural production. On the other hand, most existing technologies use fixed parameters and single hydrological models for analysis, lacking the flexible adaptability to different regions, climates and agricultural production modes, and unable to be effectively promoted and applied.

[0004] In view of these deficiencies, the present invention proposes a multi-level and highly comprehensive agricultural water resource security evaluation framework, which can flexibly adapt to the hydrological conditions and agricultural production demands of different regions, and at the same time combines farmers' irrigation decision-making behaviors for dynamic assessment, providing a more comprehensive and accurate water resource security guarantee evaluation.

[0005] Therefore, proposing a method and system for dynamic assessment of regional agricultural water resource security to solve the difficulties existing in the prior art is an urgent problem for those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a method and system for dynamic assessment of regional agricultural water resource security. The present invention constructs an all-round evaluation system through a multi-level and highly comprehensive agricultural water resource security guarantee evaluation method, comprehensively considering water resource supply, demand balance, climate change, soil characteristics, farmers' behavior and social and economic conditions.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for dynamic assessment of regional agricultural water resource security includes the following steps:

[0009] Construct an order relation analysis method - coefficient of variation method - SMI - P evaluation model;

[0010] Construct an order relation analysis method - CRITIC method - TOPSIS evaluation model;

[0011] Construct an order relation analysis method - entropy weight method - fuzzy comprehensive evaluation model;

[0012] Construct an order relation analysis method - coefficient of variation method - TOPSIS evaluation model;

[0013] Construct an analytic hierarchy process - entropy weight method - SMI - P evaluation model;

[0014] Construct an analytic hierarchy process - entropy weight method - fuzzy comprehensive evaluation model;

[0015] Users can select an order relation analysis method - coefficient of variation method - SMI - P evaluation model, an order relation analysis method - CRITIC method - TOPSIS evaluation model, an order relation analysis method - entropy weight method - fuzzy comprehensive evaluation model, an order relation analysis method - coefficient of variation method - TOPSIS evaluation model, an analytic hierarchy process - entropy weight method - SMI - P evaluation model, and an analytic hierarchy process - entropy weight method - fuzzy comprehensive evaluation model according to the evaluation requirements, so as to realize the comprehensive evaluation of agricultural water resources.

[0016] Optionally, the specific evaluation steps of the order relation analysis method are as follows:

[0017] Determine the index order: Let the evaluated object be A, and the corresponding evaluation index be x 1 , x 2 , x 3 , x 4 …x m , if the importance degree of the evaluation index is greater than x j , then it is recorded as x i > x j , and determine the importance degree ranking among the indexes to obtain

[0018] Judge the ratio of the importance degrees of the indexes: Further quantify the importance according to the importance degree ranking among the indexes. Assume that the ratio of the importance degrees of is w k-1 / w k , then the ratio of the two is:

[0019]

[0020] In the formula, r k is the tone operator;

[0021] Calculate the weight: Obtain the weight of index m through the following formula

[0022]

[0023] wm-1 = r k w m 。

[0024] Optionally, the specific evaluation steps of the coefficient of variation method are as follows:

[0025] Normalize the index data: Negative index: Positive index: x' ij = x ij , where k is an arbitrary specified coefficient, which is 0.1; max|x j | represents the maximum value of the absolute value of the j-th column index;

[0026] Standardize the data:

[0027] Mean of each index:

[0028] Calculate the standard deviation of each index: where n is the number of years;

[0029] Calculate the coefficient of variation of each index:

[0030] Calculate the weights:

[0031] Combined weights: The linear weighted method is used to calculate the combined weights, that is where ω i is the combined weight of the i-th index, is the weight obtained by the order relation analysis method of the i-th index, is the weight obtained by the coefficient of variation method of the i-th index, and α is the weight coefficient, and its value range is [0,1].

[0032] Optionally, the specific evaluation steps of the SMI-P evaluation method are as follows:

[0033] The SMI-P evaluation method is a single-index quantification - multi-index synthesis - multi-criterion integration evaluation method, which is divided into three parts: First, perform single-index quantification on each index, secondly, perform multi-index synthesis processing on each index, and finally perform multi-criterion integration on each subsystem;

[0034] Single-index quantification: Through the fuzzy membership function μ k (x) = f k (x), map each index uniformly to [0,1], and the membership degree μ k∈[0,1], the piecewise linear membership function quantization method is adopted; in the index system, each index has a membership degree. To quantitatively describe the membership degree of a single index, the following assumptions are made: there are representative values for each index, namely the worst value, the relatively poor value, the passing value, the relatively good value, and the best value;

[0035] A positive index refers to an index whose membership degree increases as the index value increases, and a reverse index refers to an index whose membership degree decreases as the index value increases. Let a, b, c, d, and e be the worst value, the relatively poor value, the passing value, the relatively good value, and the best value of a certain index respectively;

[0036] The membership degree calculation formula for positive indexes is as follows:

[0037]

[0038] The membership degree calculation formula for reverse indexes is as follows:

[0039]

[0040] Among them, μ k is the membership degree of the kth index; x k is the index value; a k , b k , c k , d k , e k are the characteristic values of each index; a k is the worst value; b k is the relatively poor value; c k is the passing value; d k is the relatively good value; e k is the best value;

[0041] Multi-index comprehensive processing: The multi-index weighted calculation method is adopted, and it is calculated by weighting according to the membership degree of a single index, that is Among them, w k is the relative weight of each index to its criterion, n is the number of evaluation indexes in each criterion layer, μ k is the membership degree of the kth index, and G t is the t criterion layer index;

[0042] Multi-criterion integration: It is calculated by the method of weighted average or exponential weight weighting, that is Among them, D is the level of high-quality development of regional water conservancy; w t is the weight of the t criterion.

[0043] Optionally, the specific evaluation steps of the CRITIC method are as follows:

[0044] Standardization processing: Standardize the positive and negative indexes. Positive indexes: Negative index:

[0045] Calculate the information carrying capacity: Calculate the conflict between evaluation indicators. The conflict reflects the correlation degree between different indicators. If there is a significant positive correlation, the smaller the conflict value. Let the conflict between indicator j and the other indicators be f j : Among them, r ij represents the Pearson correlation coefficient between indicator i and indicator j, and n is the number of indicators; finally, calculate the information carrying capacity: C Jj =σ j -f j

[0046] Calculate the weight:

[0047] Combined weight: Use the combined weighting method to weight the indicators, and use the linear weighting method to calculate the combined weight, that is Among them, ω i is the combined weight of the i-th indicator, is the weight obtained by the order relation analysis method of the i-th indicator, is the weight obtained by the CRITIC method of the i-th indicator, and α is the weight coefficient, and its value range is [0,1].

[0048] Optionally, the specific evaluation steps of the TOPSIS evaluation method are as follows:

[0049] Standardize the data to eliminate the influence of dimensions. After processing, a dimensionless decision matrix r=(r ij ) m×n is obtained, and then the weighted decision matrix Z = r ij *W 组合 ;

[0050] Determine the optimal value T + and the worst value

[0051] Calculate the weighted Euclidean distance between the sample and the ideal solution

[0052] Among them, z ij is the standardized value of the j-th indicator of the i-th sample;

[0053] Determine the closeness C of the i-th indicator i : Among them, C i ∈[0,1], C iThe closer the value is to 1, the closer the sample value is to the ideal solution, and the better the calculation result. Sorting is carried out according to the closeness degree.

[0054] Optionally, the specific evaluation steps of the entropy weight method are as follows:

[0055] Standardization processing: Since the dimensions and attributes of each index are different, in order to make the indexes comparable, first, standardize the indexes. Standardize the positive and negative indexes. Positive index: Negative index:

[0056] Calculate the data after standardization processing, calculate the proportion of the j-th evaluation index of the i-th evaluation object, and construct the normalized original data evaluation matrix P. The expression is:

[0057]

[0058] Among them, p mn represents the weight p ij of the j-th evaluation index of the i-th evaluation object;

[0059] Calculate the information entropy of the j-th index:

[0060]

[0061] Among them, m is the number of years;

[0062] Calculate the entropy weight method weight of the j-th index:

[0063] Combined weight: Use the combined weighting method to weight the indexes, and use the linear weighted method to calculate the combined weight, that is Among them, ω i is the combined weight of the i-th index, is the weight obtained by the order relation analysis method of the i-th index, is the weight obtained by the entropy weight method of the i-th index, and α is the weight coefficient, and the value range is [0,1].

[0064] Optionally, the specific evaluation steps of the fuzzy comprehensive evaluation method are as follows:

[0065] Determine the domain of influence factors of the thing to be evaluated: n evaluation factors, u = {u 1 , u 2 , ……, u n};

[0066] Determine the domain of evaluation grades of the thing to be evaluated: v = {v 1 , v 2 , ……, vm}, that is, the evaluation level set, and each evaluation level set is equivalent to a fuzzy subset;

[0067] Establish a fuzzy relation matrix R: Select a triangular distribution function to construct the membership function of each index. The solution formula for the membership degree function is where, r mn represents the membership degree of the evaluated object to the v i level fuzzy subset from the perspective of factor u i ;

[0068] Determine the fuzzy weight vector of the evaluation factors: A = (a 1 , a 2 ,..., a m ), where A represents the combined weight, and a m is the weight of the m-th index;

[0069] The fuzzy comprehensive evaluation model is shown as follows:

[0070]

[0071] where, b n represents the membership degree of the evaluated object to the v i level fuzzy subset as a whole.

[0072] Optionally, the specific evaluation steps of the analytic hierarchy process and the order relation analysis method are as follows:

[0073] Construct a judgment matrix: The judgment matrix is used to judge the importance of the indicators at a certain level and the relevant factors of the indicators at the upper level. It is obtained by pairwise comparison of the importance and is represented by the quantified relative weight a ij . Specifically, if there are n elements, then the matrix A = (a ij ) n×n is called the judgment matrix;

[0074] Characteristics of the judgment matrix: a ij > 0, a ij = 1, when i = j, a ij = 1;

[0075] Determine the weights of the evaluation indicators: Assume that A is a completely consistent judgment matrix, then there exists a ij a jk = a ik , 1 ≤ i, j, k ≤ n. The relevant formula for the n-order judgment matrix C is where, CI is the consistency index, used to measure whether the consistency of the judgment matrix can be accepted; λ max is the largest eigenvalue of the judgment matrix; Among them, CR is the consistency ratio, and RI is the average random consistency index. If CR < 0.1, it is considered that the judgment matrix meets the consistency requirement, and the weight obtained at this time is the index weight.

[0076] A regional agricultural water resources security dynamic assessment system applies the regional agricultural water resources security dynamic assessment method of any one of the above, and includes an index library module, a data source module, an evaluation model management module, a method parameter library module, an evaluation method library module, a water security assessment module, a notice and announcement management module, an operation guide management module, and a system management module connected in sequence;

[0077] The index library module includes an index sub-module and an index system construction sub-module, and is used for adding, editing, deleting, and searching predefined indexes;

[0078] The data source module is used to realize the import and viewing of data;

[0079] The evaluation model management module includes a create model sub-module and an existing model sub-module, and is used for creating an evaluation model and searching for existing models;

[0080] The method parameter library module is used to store RI parameters;

[0081] The evaluation method library module is used to summarize methods so that users can understand the relevant professional background;

[0082] The water security assessment module includes a create evaluation sub-module and an evaluation result sub-module, and is used to display the results after the create evaluation in the evaluation results;

[0083] The notice and announcement management module is used to display the name, release time, publisher, and detailed information of the announcement;

[0084] The operation guide management module is used to provide users with system operation guides;

[0085] The system management module is used to manage the announcement information released by the system, supports retrieval, viewing, modification, and deletion operations on the announcement, and ensures the management and maintenance of the announcement information.

[0086] Through the above technical solutions, compared with the prior art, the present invention provides a regional agricultural water resources security dynamic assessment method and system, which has the following beneficial effects:

[0087] (1) The present invention adopts a multi-level and comprehensive evaluation method for agricultural water resource security, comprehensively considering water resource supply, demand balance, climate change, soil characteristics, farmers' behaviors, and social and economic conditions, etc., to construct an all-round evaluation system. This evaluation model can not only more accurately analyze and predict the sustainability of agricultural water resources, but also provide operable policy suggestions and technical guidance according to the specific needs and challenges of different regions. Through multi-dimensional analysis, the model can identify potential risks and weak links in water resource security, providing a scientific decision-making basis for agricultural water resource management, thereby effectively improving the level of agricultural water resource security and promoting the sustainable development of agricultural production.

[0088] (2) The system platform of the present invention adopts a modular design, dividing different functions into independent modules for easy management and use. The platform has an index library management function, allowing users to add, edit, delete, and search for indexes, and at the same time supporting the construction and integration of index systems. Users can create personalized evaluation models, select different evaluation methods and index templates to meet diverse evaluation needs. The evaluation method library module provides a variety of combination and splitting methods, enriching the evaluation means and enhancing the scientificity and flexibility of the evaluation. Two sub-modules, namely creating evaluation and evaluation results, are specifically designed for water security evaluation, demonstrating the professionalism of the platform in a specific field. The platform takes into account both the research needs of water conservancy professionals and the possibility of use by non-water conservancy professionals. The platform can update the operation guide in a timely manner, providing users with the latest operation guidance and technical support. Brief Description of the Drawings

[0089] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0090] Figure 1 It is a flowchart of a dynamic evaluation method for regional agricultural water resource security provided by the present invention;

[0091] Figure 2 It is a structural block diagram of a dynamic evaluation system for regional agricultural water resource security provided by the present invention;

[0092] Figure 3 It is an evaluation result diagram of the order relation analysis method - coefficient of variation method - SMI - P evaluation model provided by the present invention;

[0093] Figure 4 It is an evaluation result diagram of the order relation analysis method - CRITIC method - TOPSIS evaluation model provided by the present invention;

[0094] Figure 5 It is the evaluation result graph of the order relation analysis method - entropy weight method - fuzzy comprehensive evaluation model provided by the present invention;

[0095] Figure 6 It is the evaluation result graph of the order relation analysis method - coefficient of variation method - TOPSIS evaluation model provided by the present invention;

[0096] Figure 7 It is the evaluation result graph of the analytic hierarchy process - entropy weight method - SMI - P evaluation model provided by the present invention;

[0097] Figure 8 It is the analytic hierarchy process - entropy weight method - fuzzy comprehensive evaluation model provided by the present invention. Detailed implementation manners

[0098] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0099] The analytic hierarchy process is a systematic and hierarchical analysis method that combines qualitative and quantitative methods. By establishing a hierarchical structure, complex decision-making problems are decomposed into multiple component factors, and a hierarchical structure model is formed according to the dominance relationship. The total ranking of the relative importance of decision-making schemes is determined through pairwise comparison methods. It has strong operability and requires less quantitative data information, and is applicable to complex decision-making problems with multiple objectives, multiple criteria, or unstructured characteristics.

[0100] The order relation analysis method is a subjective weight assignment method that determines the weight according to the importance degree between indicators. The decision maker first determines the most important indicator, and then determines the second most important indicator, and explains how much more important the most important indicator is than the second most important indicator. It has clear logic and is applicable to the situation where the decision maker determines the indicator weights based on his own judgment.

[0101] The entropy weight method is an objective weight assignment method that determines the weight by calculating the information entropy of the indicators. The smaller the information entropy, the greater the variation degree of its indicator values, the greater the amount of information provided, and the greater the role played in the comprehensive evaluation, and the greater its weight should be. It is applicable to the situation where the data volume is large and the data distribution has certain characteristics.

[0102] The coefficient of variation method is an objective weight assignment method that measures the degree of dispersion or fluctuation of data by calculating the coefficient of variation of the data, that is, the ratio of the standard deviation to the average value. It can intuitively reflect the fluctuation of the data.

[0103] The CRITIC method is an objective weighting method that comprehensively measures the objective weights of indicators based on the comparison intensity of evaluation indicators and the conflict between indicators. It takes into account the variability of indicators while considering the correlation between indicators. It can quantify the importance of different criteria and obtain objective evaluation results. It is applicable to judging data stability and suitable for analyzing data with a certain correlation between indicators or factors.

[0104] Currently, common evaluation methods include the TOPSIS method, the fuzzy comprehensive evaluation method, and the SMI-P method. The TOPSIS method is a distance-based multi-criteria decision-making method that evaluates the pros and cons of alternatives by calculating the distances between each alternative and the ideal optimal solution and the worst solution. The advantage of the TOPSIS method is that it avoids the subjectivity of data, does not require an objective function, does not need to pass tests, and can well describe the comprehensive influence of multiple influencing indicators. There are no strict restrictions on data distribution, sample size, or the number of indicators. It is suitable for both small-sample data and large systems with multiple evaluation units and multiple indicators, and is relatively flexible and convenient.

[0105] The fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics that uses fuzzy sets and fuzzy relation matrices to handle the uncertainty and fuzziness in evaluation problems. This method can reflect the relative importance between evaluation indicators, making the comprehensive evaluation result more scientific and reasonable. It can effectively integrate multiple different evaluation indicators, making the comprehensive evaluation result more accurate and comprehensive.

[0106] The SMI-P method is an integrated multi-criteria evaluation method that conducts evaluations through three steps: single-index quantification, multi-index synthesis, and multi-criteria integration. The advantage of the SMI-P method is that it can comprehensively obtain new information brought by index data and is suitable for complex system evaluations. This method is particularly effective when dealing with large datasets and can handle problems with multiple inputs and outputs.

[0107] Subjective weighting methods are based on the principle of "function-driven", objective weighting methods are based on the principle of "difference-driven", and combined weighting methods generally use mathematical statistics methods to combine subjective and objective weights, taking into account both subjectivity and objectivity, and are a more scientific and accurate index weighting method. Finally, 6 sets of evaluation models are formed.

[0108] Referring to Figure 1 As shown, the present invention discloses a method for dynamically evaluating the safety of regional agricultural water resources, including the following steps:

[0109] Construct an order relation analysis method - coefficient of variation method - SMI-P evaluation model;

[0110] Construct the order relation analysis method - CRITIC method - TOPSIS evaluation model;

[0111] Construct the order relation analysis method - entropy weight method - fuzzy comprehensive evaluation model;

[0112] Construct the order relation analysis method - coefficient of variation method - TOPSIS evaluation model;

[0113] Construct the analytic hierarchy process - entropy weight method - SMI - P evaluation model;

[0114] Construct the analytic hierarchy process - entropy weight method - fuzzy comprehensive evaluation model;

[0115] Users can select the order relation analysis method - coefficient of variation method - SMI - P evaluation model, order relation analysis method - CRITIC method - TOPSIS evaluation model, order relation analysis method - entropy weight method - fuzzy comprehensive evaluation model, order relation analysis method - coefficient of variation method - TOPSIS evaluation model, analytic hierarchy process - entropy weight method - SMI - P evaluation model, and analytic hierarchy process - entropy weight method - fuzzy comprehensive evaluation model according to the evaluation requirements, so as to realize the comprehensive evaluation of agricultural water resources.

[0116] Furthermore, the specific evaluation steps of the order relation analysis method are as follows:

[0117] Determine the index order: Let the object to be evaluated be A, and the corresponding evaluation index be x 1 , x 2 , x 3 , x 4 …x m , if the importance degree of the evaluation index is greater than x j , then it is recorded as x i > x j , determine the importance degree ranking among the indexes to obtain

[0118] Judge the ratio of the importance degree of the indexes: Further quantify the importance according to the importance degree ranking among the indexes. Assume that the ratio of the importance degree of judgment is w k-1 / w k , then the ratio of the two is:

[0119]

[0120] In the formula, r k is the tone operator;

[0121] Calculate the weight: Obtain the weight of index m through the following formula

[0122]

[0123] wm-1 = r k w m 。

[0124] Furthermore, the specific evaluation steps of the coefficient of variation method are as follows:

[0125] Positive normalization of index data: Negative index: Positive index: x' ij = x ij , where k is an arbitrarily specified coefficient, which is 0.1; max|x j | represents the maximum value of the absolute value of the j-th column index;

[0126] Data standardization:

[0127] Mean of each index:

[0128] Calculate the standard deviation of each index: where n is the number of years;

[0129] Calculate the coefficient of variation of each index:

[0130] Calculate the weight:

[0131] Combined weight: The combined weight is obtained by the linear weighted method, that is where ω i is the combined weight of the i-th index, is the weight obtained by the order relation analysis method of the i-th index, is the weight obtained by the coefficient of variation method of the i-th index, and α is the weight coefficient, and its value range is [0,1].

[0132] Furthermore, the specific evaluation steps of the SMI-P evaluation method are as follows:

[0133] The SMI-P evaluation method is a single-index quantification - multi-index synthesis - multi-criterion integration evaluation method, which is divided into three parts: First, each index is quantified individually, second, each index is comprehensively processed with multiple indexes, and finally each subsystem is integrated with multiple criteria;

[0134] Single-index quantification: Through the fuzzy membership function μ k (x) = fk(x), each index is uniformly mapped to [0,1], and the membership degree μ k ∈[0,1], and the piecewise linear membership function quantization method is adopted; in the index system, each index has a membership degree. In order to quantitatively describe the membership degree of a single index, the following assumptions are made: Each index has representative values, namely the worst value, the poorer value, the passing value, the better value and the best value;

[0135] A positive index refers to an index whose membership degree increases as the index value increases, and a reverse index refers to an index whose membership degree decreases as the index value increases. Let a, b, c, d, and e be the worst value, the poorer value, the passing value, the better value, and the best value of a certain index respectively;

[0136] The membership degree calculation formula for positive indexes is as follows:

[0137]

[0138] The membership degree calculation formula for reverse indexes is as follows:

[0139]

[0140] Among them, μ k is the membership degree of the kth index; x k is the index value; a k , b k , c k , d k , e k are the characteristic values of each index; a k is the worst value; b k is the poorer value; c k is the passing value; d k is the better value; e k is the best value;

[0141] Multi-index comprehensive processing: Adopt the multi-index weighted calculation method, and calculate according to the membership degree of a single index weighted by the weight, that is Among them, w k is the relative weight of each index to its criterion, n is the number of evaluation indexes in each criterion layer, μ k is the membership degree of the kth index, and G t is the index of the t criterion layer;

[0142] Multi-criterion integration: Calculate by using the method of weighted average or exponential weight weighting, that is Among them, D is the high-quality development level of regional water conservancy; w t is the weight of the t criterion.

[0143] Furthermore, the specific evaluation steps of the CRITIC method are as follows:

[0144] Standardization processing: Standardize positive and negative indexes. Positive indexes: Negative indexes:

[0145] Calculate the information carrying capacity: Calculate the conflict between evaluation indicators. The conflict reflects the correlation degree between different indicators. If there is a significant positive correlation, the smaller the conflict value. Let the conflict degree between indicator j and the remaining indicators be f j : Among them, r ij represents the Pearson correlation coefficient between indicator i and indicator j, and n is the number of indicators; finally, calculate the information carrying capacity: C Jj =σ j -f j

[0146] Calculate the weight:

[0147] Combined weight: Use the combined weighting method to weight the indicators, and use the linear weighting method to calculate the combined weight, that is Among them, ω i is the combined weight of the i-th indicator, is the weight obtained by the order relation analysis method of the i-th indicator, is the weight obtained by the CRITIC method of the i-th indicator, and α is the weight coefficient, and its value range is [0,1].

[0148] Furthermore, the specific evaluation steps of the TOPSIS evaluation method are as follows:

[0149] Standardize the data to eliminate the influence of dimensions. After processing, a dimensionless decision matrix r=(r ij ) m×n is obtained, and then the weighted decision matrix Z=r ij *W 组合 ;

[0150] Determine the optimal value T + and the worst value

[0151] Calculate the weighted Euclidean distance between the sample and the ideal solution

[0152] Among them, z ij is the value after standardization of the j-th indicator of the i-th sample;

[0153] Determine the closeness C i of the i-th indicator: Among them, C i ∈[0,1], the closer the value of C i is to 1, the closer the sample value is to the ideal solution, and the better the calculation result. Sort according to the closeness.

[0154] Furthermore, the specific evaluation steps of the entropy weight method are as follows:

[0155] Standardization: Since the dimensions and attributes of various indicators are different, in order to make the indicators comparable, the indicators are first standardized. Positive and negative indicators are standardized. Positive indicators: Negative indicators:

[0156] Calculate the data after standardization, calculate the proportion of the j-th evaluation indicator of the i-th evaluation object, and construct the normalized original data evaluation matrix P. The expression is:

[0157]

[0158] Among them, p mn represents the weight p ij of the j-th evaluation indicator of the i-th evaluation object;

[0159] Calculate the information entropy of the j-th indicator:

[0160]

[0161] Among them, m is the number of years;

[0162] Calculate the weight of the j-th indicator by the entropy weight method:

[0163] Combined weight: The combined weighting method is used to weight the indicators, and the linear weighting method is used to calculate the combined weight, that is Among them, ω i is the combined weight of the i-th indicator, is the weight obtained by the order relation analysis method of the i-th indicator, is the weight obtained by the entropy weight method of the i-th indicator, and α is the weight coefficient, and its value range is [0,1].

[0164] Furthermore, the specific evaluation steps of the fuzzy comprehensive evaluation method are as follows:

[0165] Determine the domain of influence factors of the thing to be evaluated: n evaluation factors, u = {u 1 , u 2 , ……, u n};

[0166] Determine the domain of evaluation grades of the thing to be evaluated: v = {v 1 , v 2 , ……, v m}, that is, the evaluation grade set, and each evaluation grade set is equivalent to a fuzzy subset;

[0167] Establish a fuzzy relation matrix R: Select the triangular distribution function to construct the membership function of each indicator. The solution formula of the membership degree function is Among them, r mn represents the membership degree of the evaluated object to the fuzzy sub - level of v i from the perspective of factor u i grade;

[0168] Determine the fuzzy weight vector of the evaluation factors: A=(a 1 , a 2 ,..., a m ), where A represents the combined weight, and a m is the weight of the m - th index;

[0169] The fuzzy comprehensive evaluation model is shown as follows:

[0170]

[0171] Among them, b n represents the membership degree of the evaluated object to the fuzzy subset of v i grade as a whole.

[0172] Specifically, for the membership degree calculation: for the positive - type index where the larger the index score, the better, and the negative - type index where the smaller the index score, the better, the triangular distribution is used to calculate the membership degree of the index (where S i in the positive - type index is the minimum value of the i - th grade interval, and S i in the negative - type index is the maximum value of the i - th grade interval), and the formula is as follows:

[0173] Positive - type index:

[0174]

[0175] Negative - type index:

[0176]

[0177]

[0178] Furthermore, the specific evaluation steps of the analytic hierarchy process and the order relation analysis method are as follows:

[0179] Construct a judgment matrix: The judgment matrix is used to judge the importance of the indicators at a certain level and the related factors of the indicators at the upper level. It is obtained by pairwise comparison of the importance and is represented by the quantified relative weight a ij . Specifically, if there are n elements, then the matrix A=(a ij ) n×n is called the judgment matrix;

[0180] Characteristics of the judgment matrix: a ij >0, a ij =1, when i = j, aij = 1;

[0181] Determine the weights of evaluation indicators: Assume that A is a completely consistent judgment matrix, then there exists a ij a jk = a ik , 1 ≤ i, j, k ≤ n, and the relevant formula for the n-order judgment matrix C is where CI is the consistency index, used to measure whether the consistency of the judgment matrix can be accepted; λ max is the maximum eigenvalue of the judgment matrix; where CR is the consistency ratio and RI is the average random consistency index. If CR < 0.1, it is considered that the judgment matrix meets the consistency requirements, and the weights obtained at this time are the indicator weights.

[0182] Specifically, the Analytic Hierarchy Process - Entropy Weight Method - Fuzzy Comprehensive Evaluation Model (A - E - P) and the Analytic Hierarchy Process - Entropy Weight Method - SMI - P Evaluation Model (A - E - S) both emphasize the combination of subjectivity and objectivity. However, in dealing with uncertainty, the former is stronger; the latter pays more attention to the satisfaction and preferences of users. The Ordinal Relationship Analysis Method - Coefficient of Variation Method - TOPSIS Evaluation Model (G - V - T) and the Ordinal Relationship Analysis Method - Entropy Weight Method - Fuzzy Comprehensive Evaluation Model (G - E - P) both adopt a simplified method for determining weights. However, the former focuses more on data - driven and the intuitiveness of results, while the latter pays more attention to the uncertainty in data. The Ordinal Relationship Analysis Method - CRITIC Method - TOPSIS Evaluation Model (G - C - T) and the Ordinal Relationship Analysis Method - Coefficient of Variation Method - SMI - P Evaluation Model (G - V - S) also adopt a simplified method for determining weights. However, the former pays more attention to the mutual relationship between indicators, while the latter pays more attention to the satisfaction and preferences of users.

[0183] In a specific embodiment, in an application case in a certain agricultural region, the method of the present invention is used for water resource security assessment.

[0184] (1) Selection of evaluation indicators:

[0185] According to the characteristics of the selected agricultural area, the following indicators are selected for agricultural water resource security assessment, as shown in Table 1, and the relevant data are shown in Table 2.

[0186] Table 1 Evaluation Index System of Jinghuiqu Irrigation Area

[0187]

[0188] Table 2 Data of Each Index in Jinghuiqu Irrigation Area

[0189] Index Unit 2014 2015 2016 2017 2018 2019 2020 2021 2022 A1 mm 591 612 480 609 493 580 697 949 670 A2 mm 1431 1381 1482 1354 1442 1421 1257 1485 1212 A3 mm 618 656 721 681 667 651 702 679 685 A4 <![CDATA[Ten thousand m 3 > 35595 37312 35158 35711 42830 44767 46212 46653 47462 A5 <![CDATA[Ten thousand m 3 > 47869 48644 49764 49063 47179 40626 41849 41365 28188 A6 ％ 17.8 18.2 15.1 20.7 15.3 16.6 20.5 23.4 19.2 A7 ％ 80 82 83.6 83.7 84 84.1 85.3 88.4 90 A8 ％ 70 75 73 78 79 82 85 89 90 A9 ％ 59 63 64 70 68 73 77 80 82 A10 — 0.451 0.458 0.447 0.478 0.462 0.512 0.522 0.551 0.546 A11 ％ 69.8 71.2 72.1 73.3 74.8 75.3 76.7 78.2 80.4 A12 pcs / mu 0.0132 0.0138 0.0138 0.0139 0.0142 0.0147 0.0149 0.0149 0.0149 A13 m -0.54 0.21 -0.71 0.34 -0.36 0.18 0.21 0.35 -0.27 A14 ％ 65 68 66 69 68 73 77 81 84 A15 — 17.1 17.2 16.5 16.8 16 16.9 16.8 16.2 15.9 A16 ℃ 16.2 15.3 15.7 15.7 15.9 14.9 14.6 16.7 17.2

[0190] (2) Determination of evaluation index thresholds: Based on consulting literature, existing standard materials, and the construction specifications for high-standard irrigated farmland, and seeking expert opinions, the evaluation grade standards for each index are finally determined. The evaluation grades are divided into five levels, and the summary results of each evaluation characteristic value are shown in Table 3.

[0191] Table 3 Evaluation thresholds of Jinghuiqu Irrigation Area

[0192]

[0193]

[0194] (3) Evaluation results: Six evaluation models are used to evaluate the agricultural water resources security of Jinghuiqu Irrigation Area. Based on consulting literature, existing standard materials, and the construction specifications for high-standard irrigated farmland, and seeking expert opinions, the evaluation grade standards for each index are finally determined. The evaluation grades are divided into five levels: very safe (0.80 < r ≤ 1.00), safe (0.60 < r ≤ 0.80), relatively safe (0.40 < r ≤ 0.60), generally safe (0.20 < r ≤ 0.40), and unsafe (0 ≤ r ≤ 0.20). Six evaluation models are used to rate the agricultural water resources security of Jinghuiqu Irrigation Area, and the evaluation results are as follows:

[0195] It can be seen from Figure 3 that the evaluation results of the order relation analysis method - coefficient of variation method - SMI - P model: The agricultural water resources security levels in 2021 and 2022 are "safe"; the agricultural water resources security levels in 2015, 2017, 2019, and 2020 are "relatively safe"; the agricultural water resources security levels in 2014, 2016, and 2018 are "generally safe".

[0196] It can be seen from Figure 4 that the evaluation results of the order relation analysis method - CRITIC method - TOPSIS model: The agricultural water resources security level from 2014 to 2022 is "relatively safe".

[0197] It can be seen from Figure 5 that in the order relation analysis method - entropy weight method - fuzzy comprehensive evaluation model, according to the principle of maximum membership degree of the fuzzy comprehensive evaluation method, it can be seen that the agricultural water resources security level in 2022 is "very safe"; the agricultural water resources security level in 2021 is "safe"; the agricultural water resources security levels in 2020 and 2017 are "relatively safe"; the agricultural water resources security levels in 2019, 2018, 2015, and 2014 are "generally safe"; the agricultural water resources security level in 2016 is "unsafe".

[0198] It can be seen from Figure 6It can be seen that the evaluation results of the order relation analysis method - coefficient of variation method - TOPSIS model are as follows: the agricultural water resources security levels in 2015, 2017, 2019, 2020, and 2021 are "very safe"; the agricultural water resources security level in 2022 is "relatively safe"; the agricultural water resources security level in 2018 is "generally safe"; the agricultural water resources security levels in 2014 and 2016 are "unsafe".

[0199] It can be seen from Figure 7 that the evaluation results of the analytic hierarchy process - entropy weight method - SMI-P model are as follows: the agricultural water resources security level in 2021 is "safe"; the agricultural water resources security levels in 2015, 2017, 2019, 2020, and 2022 are "relatively safe"; the agricultural water resources security levels in 2014, 2016, and 2018 are "generally safe".

[0200] It can be seen from Figure 8 that in the analytic hierarchy process - entropy weight method - fuzzy comprehensive evaluation model, according to the maximum membership degree principle of the fuzzy comprehensive evaluation method, it can be seen that the agricultural water resources security levels in 2021 and 2022 are "safe"; the agricultural water resources security levels in 2020, 2019, 2018, and 2017 are "relatively safe"; the agricultural water resources security levels in 2016, 2015, and 2014 are "generally safe". The calculation results of this model are consistent with those of the order relation analysis method - entropy weight method - fuzzy comprehensive evaluation model.

[0201] (4) Analysis of evaluation results:

[0202] Through the evaluation results of 6 sets of models in the Jinghuiqu irrigation area, the highest and lowest evaluation indexes were selected as representative years. After comparing and analyzing the changes in the values ​​of positive and negative indicators of the original data, a relatively optimal evaluation model was obtained. Through the analysis of data from 2014, 2015, 2016, 2017 and 2018, the rainfall in 2014 was relatively low, the evaporation of the water surface was high, and the irrigation water utilization coefficient was low, reflecting that the water resource utilization efficiency was low, the groundwater level dropped and the salinization index was high. Therefore, the agricultural water resource security index was the lowest in 2014; in 2015, the rainfall increased, the soil moisture content increased, and the groundwater level rose, reflecting that the water exchange capacity of the soil and groundwater in the region increased. Therefore, the agricultural water resource security index in 2015 increased compared with 2014; in 2016, the rainfall was the least, the water demand of grain crops increased, the evaporation of the water surface increased, and the groundwater level dropped significantly. The 2016 agricultural water resources security index decreased compared with 2015, indicating that the sustainable utilization rate of water resources was low. The 2017 agricultural water resources security index decreased compared with 2015. The 2017 rainfall increased, the soil moisture content increased significantly, and the groundwater level rose, indicating that the water resource utilization efficiency was improved and the water-saving capacity of the irrigation area was improved. The 2017 agricultural water resources security index increased compared with 2016. The 2018 rainfall decreased significantly compared with 2017, and the evaporation of the water surface was high, which means that excessive water loss may aggravate the contradiction between supply and demand of water resources in the irrigation area. The relative decrease in the irrigation water utilization coefficient indicates that the effective utilization rate of irrigation water has decreased. The 2018 agricultural water resources security index decreased compared with 2017.

[0203] The rainfall in 2019, 2020 and 2021 increased year by year, and the soil moisture content and groundwater level increased year by year. This shows that in the irrigation area, the utilization efficiency of water resources has increased year by year, and the water-saving efficiency has also increased significantly. Water resources are relatively sufficient, and irrigation in the irrigation area is guaranteed. Therefore, the agricultural water resources security index in 2019, 2020 and 2021 increased year by year, and the agricultural water resources security index was the highest in 2021. In 2022, the rainfall decreased and the soil moisture content decreased, resulting in a deterioration in the water exchange capacity between the soil and groundwater, which led to a decrease in groundwater supply. Therefore, the agricultural water resources security index in 2022 decreased compared with 2021.

[0204] After the above analysis and comparison, the evaluation results of the "Sequence S" model are consistent with the selected representative years. Therefore, it can be concluded that 2021 is the year with the highest agricultural water resources security index and 2014 is the year with the lowest agricultural water resources security index, which matches the "Sequence S" evaluation results. Therefore, it can be concluded that the "Sequence S" evaluation model is the optimal evaluation model among the evaluation results of the Jinghuiqu Irrigation District.

[0205] As can be seen from the evaluation model results combined with "sequence change S": the agricultural water resource security levels in 2021 and 2022 are "safe"; the agricultural water resource security levels in 2015, 2017, 2019 and 2020 are "relatively safe"; the agricultural water resource security levels in 2014, 2016 and 2018 are "moderately safe". In 2021 and 2022, the rainfall was abundant, the water resource utilization efficiency was significantly improved, and the popularization of water-saving facilities, etc., promoted the full utilization of water resources in the irrigation area. Therefore, the agricultural water resource security levels in 2021 and 2022 are "safe"; in 2015, 2017, 2019 and 2020, the rainfall was relatively sufficient and the soil moisture content was relatively high, indicating that in these 4 years, the water exchange capacity between the soil and groundwater in the irrigation area was good, which ensured the irrigation capacity of the irrigation area. However, the water surface evaporation in these 4 years was relatively high, resulting in excessive water loss, and the effective utilization rate of water resources still needed to be improved. Therefore, the agricultural water resource security levels in 2015, 2017, 2019 and 2020 are "relatively safe"; in 2014, 2016 and 2018, the rainfall was less, and problems such as excessive groundwater extraction and land salinization were obvious, and the sustainable utilization efficiency of water resources was relatively low. Therefore, the agricultural water resource security levels in 2014, 2016 and 2018 are "moderately safe".

[0206] Corresponding to Figure 1 the method described above, the embodiment of the present invention also provides a regional agricultural water resource security dynamic evaluation system, the structure diagram of which is as shown in Figure 2 and includes a sequentially connected index library module, data source module, evaluation model management module, method parameter library module, evaluation method library module, water security evaluation module, notice and announcement management module, operation guide management module and system management module;

[0207] The index library module includes an index sub-module and an index system construction sub-module, and is used for performing operations such as adding, editing, deleting, and searching predefined indexes;

[0208] The data source module is used to realize the import and viewing of data;

[0209] The evaluation model management module includes a create model sub-module and an existing model sub-module, and is used for creating evaluation models and searching for existing models;

[0210] The method parameter library module is used for storing RI parameters;

[0211] The evaluation method library module is used for summarizing methods so that users can understand the relevant professional background;

[0212] The water safety assessment module, including a creation evaluation sub-module and an evaluation result sub-module, is used to display the results after the creation evaluation in the evaluation results;

[0213] The notice and announcement management module is used to display the name, release time, publisher, and detailed information of the announcements;

[0214] The operation guide management module is used to provide users with system operation guides;

[0215] The system management module is used to manage the announcement information released by the system, support operations such as retrieving, viewing, modifying, and deleting announcements, and ensure the management and maintenance of announcement information.

[0216] Specifically, the indicator library module. The indicator library includes an indicator sub-module and an indicator system construction sub-module. The indicator sub-module performs operations such as adding, editing, deleting, and searching for predefined indicators. The indicator system construction sub-module can view the detailed information of the constructed indicators based on the existing predefined indicators. At the same time, it integrates them into the indicator system for subsequent operations.

[0217] The data source module mainly realizes the import and viewing of data, realizes the batch import of source data, but the data for batch import needs to download the template in advance, and can also be searched according to keywords, time, evaluation objects, etc.

[0218] The evaluation model management module includes a create model sub-module and an existing model sub-module. The create model sub-module is responsible for creating an evaluation model. First, it is necessary to fill in the name of the evaluation model (which can be used as a template later). Next, select the comprehensive evaluation method. The evaluation method is a combined type, and it is necessary to select a subjective weighting method and an objective weighting method to operate in combination. Finally, select the evaluation indicator template created in the previous step. The existing model sub-module is used to search for existing models, view the detailed information of existing models, and delete models.

[0219] The method parameter library module. The method parameter module only involves the RI parameter in the V1 version. The RI value is an essential predefined value for AHP calculation, but the RI value involved in each calculation is sometimes unified and sometimes not unified. Therefore, the system can store the RI value in the system in advance according to the user's calculation habits and directly select it when calculating.

[0220] The evaluation method library module is a summary of the methods used in the system calculation for users to understand the relevant professional background. The methods included are all split methods in the combined methods. There are a total of six combined methods, and there are eight split methods.

[0221] The water safety assessment module includes a creation evaluation sub-module and an evaluation result sub-module. The result after the creation evaluation will be displayed in the evaluation result. The creation evaluation sub-module can first customize the name of the evaluation result, then select the date span, then select the specific evaluation object, and finally select the created evaluation template to conduct a safety evaluation on the specified irrigation area. The evaluation result can be viewed in the evaluation result sub-module. Part of the interface of the evaluation result sub-module displays all evaluation results. This part can also perform fuzzy queries and search for evaluation results by keywords. At the same time, the evaluation results can be classified and displayed according to different irrigation areas. The content details of each evaluation result include: result name, evaluation template name, evaluation model name, evaluation object, data start time, data end time, and calculation status. There is also a list for the detailed display of the evaluation result, and the results of each day in the selected span date time period are displayed.

[0222] The notice and announcement management module is used to display the name, release time, publisher, and detailed information of the announcements. Users can search for relevant announcements by entering keywords to facilitate quick finding of the required information;

[0223] The operation guide management module is used to provide users with a system operation guide to help users quickly understand how to create projects, set relevant information, and view water safety evaluation results. This module also covers the content of various modules such as the index library, data source, evaluation model management, method parameter library, evaluation method library, RI management, notice and announcement, operation guide, and system management, aiming to help users quickly get started and proficiently use the system;

[0224] The system management module is used to manage the announcement information released by the system, supporting operations such as retrieving, viewing, modifying, and deleting announcements to ensure more efficient and convenient management and maintenance of announcement information.

[0225] In another specific embodiment, after logging in to the platform, the system automatically jumps to the "home page drop-down menu" page of the function main page. On the home page drop-down menu page, the user can click on the index library to pop up the index and index system construction sub-page options. Click on the index or the index system construction button to perform the jump. After clicking, you can jump here, and you can add new indexes by clicking on "new". Here, you need to enter the index name, unit, index attribute label, and finally click "ok" to complete the creation. As known from the creation of the template by the foregoing model, the subsequent templates involved will all be saved to the sub-module of the existing templates. The model templates involved in creating the evaluation all come from the six combined methods formed by the eight evaluation methods demonstrated in the evaluation model module; by selecting the evaluation template, you can view the evaluation results of the selected corresponding model in the evaluation result.

[0226] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For related parts, reference can be made to the description in the method section.

[0227] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dynamic assessment of regional agricultural water resources security, characterized in that: The following steps are involved: Construct the ordinal relationship analysis method-coefficient of variation method-SMI-P evaluation model; Construct the ordinal relationship analysis method-CRITIC method-TOPSIS evaluation model; Construct the order relationship analysis method-entropy weight method-fuzzy comprehensive evaluation model; Construct the ordinal relationship analysis method-coefficient of variation method-TOPSIS evaluation model; Construct the analytic hierarchy process-entropy weight method-SMI-P evaluation model; Construct the analytic hierarchy process-entropy weight method-fuzzy comprehensive evaluation model; According to the evaluation needs, users can select the ordinal relationship analysis method-coefficient of variation method-SMI-P evaluation model, the ordinal relationship analysis method-CRITIC method-TOPSIS evaluation model, the ordinal relationship analysis method-entropy weight method-fuzzy comprehensive evaluation model, the ordinal relationship analysis method-coefficient of variation method-TOPSIS evaluation model, the hierarchical analysis method-entropy weight method-SMI-P evaluation model and the hierarchical analysis method-entropy weight method-fuzzy comprehensive evaluation model to achieve a comprehensive evaluation of agricultural water resources.

2. A method for dynamic assessment of regional agricultural water resources security according to claim 1, characterized in that: The specific evaluation steps of the ordinal relation analysis method are as follows: Determine the order of indicators: Let the object to be evaluated be A, and the corresponding evaluation indicators be x1, x2, x3, x4…x m , if the importance of the evaluation index is greater than x j , then record it as x i >x j , determine the importance ranking between indicators Judgment of the ratio of the importance of indicators: further quantify the importance according to the ranking of the importance of the indicators, assuming that the judgment The importance ratio is w k-1 / w k , then the ratio of the two is: In the formula, r k is the tone operator; Calculate the weight: The weight of indicator m is obtained by the following formula w m-1 =r k w m 。 3. A method for dynamic assessment of regional agricultural water resources security according to claim 1, characterized in that: The specific evaluation steps of the coefficient of variation method are as follows: Positive indicator data: Negative indicator: Positive indicator: x' ij =x ij , where k is a specified arbitrary coefficient, which is 0.1; max|x j | represents the maximum absolute value of the index in the jth column; Data Standardization: The mean value of each indicator is: Calculate the standard deviation for each metric: Where n is the number of years; Calculate the coefficient of variation for each indicator: Calculate weights: Combination weight: The combination weight is obtained by linear weighting method, that is Among them, ω i is the combined weight of the ith indicator, is the weight obtained by the order relationship analysis method of the i-th indicator, is the weight obtained by the coefficient of variation method of the ith indicator, α is the weight coefficient, and its value range is [0,1].

4. A method for dynamic assessment of regional agricultural water resources security according to claim 1, characterized in that: The specific evaluation steps of the SMI-P evaluation method are as follows: The SMI-P evaluation method is a single indicator quantification-multiple indicator synthesis-multiple criteria integration evaluation method, which is divided into three parts: first, quantify each indicator by a single indicator, then conduct multi-indicator comprehensive processing on each indicator, and finally conduct multi-criteria integration on each subsystem; Single indicator quantification: through fuzzy membership function μ k (x) = f k (x), all indicators are uniformly mapped to [0,1], and the membership degree μ k ∈[0,1], using piecewise linear membership function quantification method; in the index system, each index has a membership degree. In order to quantitatively describe the membership degree of a single index, the following assumptions are made: each index has a representative value, namely the worst value, the worse value, the passing value, the better value and the best value; A positive indicator refers to an indicator whose membership increases with the increase of the indicator value, and a negative indicator refers to an indicator whose membership decreases with the increase of the indicator value. Let a, b, c, d, and e be the worst value, relatively poor value, passing value, relatively good value, and optimal value of a certain indicator respectively; The calculation formula of the membership degree of the positive indicator is as follows: The membership calculation formula of the reverse indicator is as follows: Among them, μ k is the membership degree of the kth index; x k is the index value; a k , b k 、c k d k 、e k is the characteristic value of each indicator; a k is the worst value; b k is a poor value; c k is the passing value; d k is a better value; e k is the optimal value; Multi-index comprehensive processing: adopt multi-index weighted calculation method, according to the single index membership according to the weighted calculation, that is, Among them, w k is the weight of each indicator relative to its criterion, n is the number of evaluation indicators in each criterion layer, μ k is the kth index membership, G t is the t-criterion layer index; Multi-criteria integration: Calculated by weighted average or exponential weighting method, that is, Among them, D is the level of high-quality development of regional water conservancy; w t is the weight of the t criterion.

5. A method for dynamic assessment of regional agricultural water resources security according to claim 1, characterized in that: The specific evaluation steps of the CRITIC method are as follows: Standardization: Standardize the positive and negative indicators. Positive indicators: Negative indicators: Calculate the information carrying capacity: Calculate the conflict between the evaluation indicators. The conflict reflects the correlation between different indicators. If there is a significant positive correlation, the smaller the conflict value is, the smaller the conflict value is. Let the conflict between indicator j and the other indicators be f. j : Among them, r ij represents the Pearson correlation coefficient between indicator i and indicator j, and n is the number of indicators; finally, the information carrying capacity is calculated: C Jj =σ j -f j Calculate weights: Combination weight: The combination weighting method is used to weight the indicators, and the linear weighting method is used to calculate the combination weight, that is, Among them, ω i is the combined weight of the ith indicator, is the weight obtained by the order relationship analysis method of the i-th indicator, is the weight of the ith indicator obtained by the CRITIC method, α is the weight coefficient, and its value range is [0,1].

6. A method for dynamic assessment of regional agricultural water resources security according to claim 1, characterized in that: The specific evaluation steps of the TOPSIS evaluation method are as follows: The data is standardized to eliminate the dimension effect, and the dimensionless decision matrix r is obtained after processing. ij ) m×n , and then obtain the weighted decision matrix Z = r ij *W 组合 ; Determine the optimal value T + and the worst value T - : Calculate the weighted Euclidean distance between the sample and the ideal solution and Among them, z ij is the standardized value of the jth indicator of the i-th sample; Determine the closeness C of the i-th indicator i : Among them, C i ∈[0,1],C i The closer the value is to 1, the closer the sample value is to the ideal solution, the better the calculation result is, and the results are sorted according to the degree of closeness.

7. A method for dynamic assessment of regional agricultural water resources security according to claim 1, characterized in that: The specific evaluation steps of the entropy weight method are as follows: Standardization: The dimensions and attributes of various indicators are different. In order to make the indicators comparable, the indicators are first standardized, and the positive indicators and negative indicators are standardized. Positive indicators: Negative indicators: Calculate the standardized data, calculate the proportion of the jth evaluation index of the i-th evaluation object, and construct the normalized original data evaluation matrix P, which is expressed as: Among them, p mn The weight p of the jth evaluation index of the i-th evaluation object ij ; Calculate the message entropy of the jth indicator: in, m is the number of years; Calculate the entropy weight of the j-th indicator: Combination weight: The combination weighting method is used to weight the indicators, and the linear weighting method is used to calculate the combination weight, that is, Among them, ω i is the combined weight of the ith indicator, is the weight obtained by the order relationship analysis method of the i-th indicator, is the weight of the ith indicator obtained by the entropy weight method, α is the weight coefficient, and its value range is [0,1].

8. A method for dynamic assessment of regional agricultural water resources security according to claim 1, characterized in that: The specific evaluation steps of the fuzzy comprehensive evaluation method are as follows: Determine the domain of influencing factors of the thing being evaluated: n evaluation factors, u = {u1, u2, ..., u n }; Determine the evaluation level domain of the evaluated object: v = {v1, v2, ..., v m }, that is, the evaluation level set, each evaluation level set is equivalent to a fuzzy subset; Establish the fuzzy relationship matrix R: Select the triangular distribution function to construct the membership function of each indicator. The formula for solving the membership function is: Among them, r mn Indicates that the object being evaluated is from factor u i Let's see v i The membership degree of the hierarchical fuzzy sub-level; Determine the fuzzy weight vector of the evaluation factors: A = (a1, a2, ..., a m ), where A represents the combined weight, a m is the weight of the mth indicator; The fuzzy comprehensive evaluation model is shown as follows: Among them, b n Indicates the overall evaluation of the object. i The degree of membership of a hierarchical fuzzy subset.

9. A method for dynamic assessment of regional agricultural water resources security according to claim 1, characterized in that: The specific evaluation steps of the hierarchical analysis method are as follows: Constructing a judgment matrix: The judgment matrix is ​​used to judge the importance of a certain layer of indicators and the factors related to the previous layer of indicators. The importance is compared pairwise and the relative weights are quantified. ij Specifically, if there are n elements, then the matrix A formed by them is (a ij ) n×n It is called the judgment matrix; Characteristics of judgment matrix: a ij >0,a ij =1, When i=j,a ij =1; Determine the weight of evaluation index: Assuming A is a completely consistent judgment matrix, there exists a ij a jk =a ik ,1≤i,j,k≤n, the relevant formula of the n-order judgment matrix C is Among them, CI is the consistency index, which is used to measure whether the consistency of the judgment matrix is ​​acceptable; λ max is the maximum eigenvalue of the judgment matrix; Among them, CR is the consistency ratio, RI is the average random consistency index, if CR < 0.1, it is considered that the judgment matrix meets the consistency requirements, and the weight obtained at this time is the index weight.

10. A regional agricultural water resources security dynamic assessment system, characterized in that: A method for dynamic assessment of regional agricultural water resources security according to any one of claims 1 to 9 is applied, comprising an indicator library module, a data source module, an assessment model management module, a method parameter library module, an evaluation method library module, a water security assessment module, a notification management module, an operation guide management module and a system management module connected in sequence; The indicator library module includes an indicator submodule and an indicator system construction submodule, which is used to add, edit, delete, and search for predefined indicators; Data source module, used to import and view data; Evaluation model management module, including model creation submodule and existing model submodule, used to create evaluation models and search for existing models; Method parameter library module, used to store RI parameters; Evaluation method library module, used to summarize methods so that users can understand the relevant professional background; The water safety assessment module includes a creation assessment submodule and an assessment result submodule, which is used to display the result after the creation assessment is completed in the assessment result; The notification and announcement management module is used to display the name, release time, publisher and detailed information of the announcement; Operation guide management module, used to provide system operation guide for users; The system management module is used to manage the announcement information released by the system. It supports retrieval, viewing, modification and deletion of announcements to ensure the management and maintenance of announcement information.

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