A happy river and lake evaluation method based on intuitionistic fuzzy analytic hierarchy process
Patent Information
- Application Number
- CN202610799254.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-04
- Publication Date
- 2026-09-25
AI Technical Summary
然而，当前主流的幸福河湖评价方法多沿用传统层次分析法或多指标综合评价模型，这些方法在处理专家判断时往往要求给出精确的数值标度，难以有效表达人类决策过程中固有的模糊性与不确定性；尤其是在涉及多主体（居民、管理者、学者）共同参与的评价场景中，不同群体的价值取向存在显著分化，传统方法无法同时刻画专家对指标比较时的“支持度”、“反对度”与“犹豫度”，导致大量宝贵的主观判断信息被简化或丢失
（1）通过引入直觉模糊矩阵，能够同时捕捉专家判断中的支持、反对和犹豫三种心理状态，信息表达维度更丰富，更符合人类决策的认知实际；通过直觉模糊层次分析法处理模糊与不确定信息，减少了传统方法中因强制精确化而带来的主观偏差，使权重计算和指标评分更为客观；该方法既能处理定量数据，也能处理定性描述，特别适用于像幸福河湖这样包含大量社会、经济、人文等难以精确量化指标的复杂系统评价；
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Abstract
Description
Technical Field
[0001] This invention relates to the field of water culture evaluation technology, and in particular to a method for evaluating happy rivers and lakes based on intuitionistic fuzzy hierarchical analysis. Background Technology
[0002] In the context of comprehensively promoting the construction of happy rivers and lakes, scientifically and accurately evaluating the "happiness" level of rivers and lakes has become a key link in guiding high-quality development of water conservancy, optimizing resource allocation, and assessing governance effectiveness. However, current mainstream methods for evaluating happy rivers and lakes mostly rely on traditional analytic hierarchy process (AHP) or multi-index comprehensive evaluation models. These methods often require precise numerical scales when dealing with expert judgments, making it difficult to effectively express the inherent ambiguity and uncertainty in human decision-making processes. Especially in evaluation scenarios involving multiple stakeholders (residents, managers, and scholars), the value orientations of different groups are significantly differentiated. Traditional methods cannot simultaneously characterize the "support," "opposition," and "hesitation" of experts when comparing indicators, leading to the simplification or loss of a large amount of valuable subjective judgment information.
[0003] To address these issues, some existing technologies have attempted to introduce fuzzy set theory to enhance the ability to handle uncertain information. However, this approach still struggles to fully express the hesitant mindset of experts who are "neither completely supportive nor completely opposed" in their judgments. Furthermore, existing frameworks generally lack systematic adaptation to the typical characteristics of small and medium-sized rivers, such as "weak data foundation, complex and conflicting functions, and multiple governance entities." The discriminative power and interpretability of the evaluation results need to be improved. Summary of the Invention
[0004] In view of this, the present invention proposes a happiness river and lake evaluation method based on intuitionistic fuzzy hierarchical analysis to solve the technical problems mentioned in the background art, which is that the traditional hierarchical analysis method uses precise numerical scoring, which is difficult to effectively handle the fuzziness and hesitation in multi-subject evaluation; and that the existing fuzzy comprehensive evaluation method only considers the degree of membership and ignores the uncertainty information contained in the degree of non-membership and hesitation.
[0005] The technical solution of this invention is implemented as follows: This invention proposes a method for evaluating the happiness of rivers and lakes based on intuitionistic fuzzy hierarchical analysis, including: Establish a multi-level evaluation indicator system that includes the target layer, criterion layer, and indicator layer; Multiple subjects are invited to use intuitionistic fuzzy numbers to compare the importance of each element in the same level relative to the level above, and an intuitionistic fuzzy judgment matrix R is constructed. Based on the intuitionistic fuzzy judgment matrix R, the local weights of each layer element are calculated using the intuitionistic fuzzy hierarchical analysis method. Through hierarchical single sorting and consistency test, the global weight vector W of each evaluation index relative to the overall goal is finally synthesized. Obtain the evaluation value matrix Q; Using a five-element semi-partial subtraction set-pair potential model, the global weight vector W and the evaluation value matrix Q are aggregated to obtain the comprehensive evaluation value and corresponding level of the river and lake.
[0006] In some optional implementations, preferably, the step of calculating the local weights of each layer element using the intuitionistic fuzzy judgment matrix R and then synthesizing the global weight vector W of each evaluation index relative to the overall objective through hierarchical single sorting and consistency checks includes: For each expert's constructed intuitionistic fuzzy judgment matrix, the intuitionistic fuzzy weighted average operator is used to aggregate group opinions to obtain a comprehensive intuitionistic fuzzy judgment matrix. Based on the aforementioned comprehensive intuitionistic fuzzy judgment matrix, the intuitionistic fuzzy weight of each element is calculated using the intuitionistic fuzzy geometric average method. The intuitive fuzzy weights are defuzzified to obtain accurate weight values, and then normalized. Perform a consistency check in an intuitively ambiguous environment; if the check fails, provide feedback to the experts to adjust the judgment matrix.
[0007] In some optional implementations, preferably, the invitation of multiple subjects uses intuitionistic fuzzy numbers to compare the importance of each element within the same level relative to the level above, constructing an intuitionistic fuzzy judgment matrix R, including: Evaluation experts from diverse backgrounds, including riverside residents, water conservancy management departments, and researchers, were invited to use intuitionistic fuzzy numbers to perform pairwise comparisons of the three-layer elements and establish an intuitionistic fuzzy judgment matrix R. The intuitionistic fuzzy numbers are represented as (μ, ν, π), where μ represents the degree of membership, ν represents the degree of non-membership, and π represents the degree of hesitation.
[0008] In some optional implementations, preferably, the establishment of a multi-level evaluation index system comprising a target layer, a criterion layer, and an indicator layer includes: The target layer is the "Comprehensive Evaluation Score of Happy Rivers and Lakes", and the criteria layer includes C1 Safe and Tranquil River Index, C2 Healthy River Index, C3 Beautiful River Index, C4 Cultural River Index, C5 Intelligent River Index and C6 Harmonious River Index. The index layer consists of multiple lower-level parameter indicators under each indicator of the criteria layer.
[0009] In some optional implementations, preferably, it also includes: identifying key factors that restrict the construction of happy rivers and lakes by introducing an obstacle degree model, and proposing optimization suggestions.
[0010] In some alternative implementations, preferably, by introducing an obstacle degree model, key factors restricting the construction of happy rivers and lakes are identified, and optimization suggestions are proposed, including: Obstacles O for each indicator i Sort the indicators from largest to smallest and identify the top-ranked indicators as key constraint factors. Based on the criterion layer dimensions to which the key constraint factors belong and their specific connotations, and in combination with the actual situation of rivers and lakes, targeted optimization suggestions are output.
[0011] In some optional implementations, preferably, the invited experts transform the actual performance of each underlying indicator into a unified intuitive fuzzy evaluation value, forming an evaluation value matrix Q, including: For quantitative indicators, the measured values are mapped to intuitive fuzzy numbers through a preset membership function; For qualitative indicators, evaluation is conducted by experts or the public using intuitive fuzzy numbers.
[0012] In some optional implementations, preferably, the step of using a five-element semi-partial subtraction set-pair potential model to aggregate the global weight vector W and the evaluation value matrix Q to obtain the comprehensive evaluation value and corresponding level of the river or lake includes: The effectiveness of the construction of happy rivers and lakes is divided into 5 evaluation levels; Set pair analysis studies the intrinsic relationship between each element of a system and the whole system, and establishes a system structure model of the degree of deterministic and uncertain relationship between two related sets that conform to a certain attribute. Calculate the correlation coefficient μ of the index values for a sample of happy river and lake construction in a given region. ijk ; The correlation coefficient μ of the evaluation sample index value ijk The corresponding relative membership degree μ ijk * And the relative membership degree υ ijk * After normalization, the five-element relational component υ of the happiness river and lake index value is obtained. ijk ; Using the five-element relational component of the Happy Rivers and Lakes index value υ ijk Calculate the correlation coefficient μ of the evaluation index value ij And obtain the quinary contact number μ of the evaluation sample. i ; A comprehensive evaluation of the assessment sample is conducted, using the five-element relationship coefficient μ of the assessment sample. i Calculate the rating of the evaluation sample.
[0013] In some optional implementations, preferably, the calculation of the correlation coefficient μ of the index values of the happy river and lake construction sample in a given area is... ijk ,include: Based on the sample value x of index j of evaluation sample i ij With evaluation rating standards s kj Calculate μ ij1 μij2 μ ij3 μ ij4 and μ ij5 .
[0014] In some optional implementations, preferably, the set pair analysis studies the intrinsic relationship between each element of the system and the whole system, and establishes a system structure model that conforms to the degree of determinism and uncertainty of a certain attribute between two related sets, including: Based on the difference coefficients I1, I2, and I3 of the indicator sample values and the correlation components of the level 2, level 3, and level 4 evaluation standards, as well as the opposition coefficient J, the sameness component a, the opposition component c, and the difference components b1, b2, and b3, the expression for the quinary correlation coefficient μ is obtained.
[0015] The happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis of the present invention has the following advantages over the prior art: (1) By introducing the intuitionistic fuzzy matrix, it is possible to capture the three psychological states of support, opposition and hesitation in expert judgment at the same time, which makes the information expression dimension richer and more in line with the cognitive reality of human decision-making; by using the intuitionistic fuzzy hierarchical analysis method to process fuzzy and uncertain information, the subjective bias caused by the forced precision in traditional methods is reduced, making the weight calculation and index scoring more objective; this method can handle both quantitative data and qualitative descriptions, and is particularly suitable for the evaluation of complex systems such as happy rivers and lakes that contain a large number of social, economic and humanistic indicators that are difficult to quantify precisely. (2) By constructing an intuitionistic fuzzy judgment matrix for each expert, the group opinions are aggregated using the intuitionistic fuzzy weighted average operator to obtain a comprehensive intuitionistic fuzzy judgment matrix; based on the comprehensive intuitionistic fuzzy judgment matrix, the intuitionistic fuzzy weight of each element is calculated using the intuitionistic fuzzy geometric average method; the intuitionistic fuzzy weight is defuzzified to obtain precise weight values, and then normalized; a consistency test is performed under the intuitionistic fuzzy environment, and if it fails, feedback is given to the experts to adjust the judgment matrix; by processing fuzzy and uncertain information mathematically, the subjective bias caused by forced precision in traditional methods is reduced, making the weight calculation and index scoring more objective. (3) By introducing the obstacle degree model, the key factors restricting the construction of happy rivers and lakes are identified, optimization suggestions are put forward, and scientific basis is provided for precise policy implementation, with stronger diagnostic function; (4) In the evaluation stage, a five-element semi-partial subtraction set pair potential model is constructed to transform the intuitive fuzzy evaluation value into a five-element connection number, and to finely divide the five levels from "happiness" to "unhappiness". The information loss problem of traditional set pair potential is overcome by the semi-partial subtraction allocation strategy. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating the happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis in an embodiment of the present invention. Figure 2 The index value correlation coefficient μ in the embodiments of the present invention ijk A schematic diagram of the curve. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0019] In the description of the embodiments of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention based on the specific circumstances.
[0020] In the description of the embodiments of the present invention, it should be noted that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of the present invention.
[0021] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.
[0022] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.
[0023] The following disclosure provides numerous different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. Additionally, examples of various specific processes and materials are provided in this invention; however, those skilled in the art will recognize the applicability of other processes and / or the use of other materials.
[0024] The technical solution will now be explained in detail: Reference Figure 1 and Figure 2 As shown in the embodiment of the present invention, a happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis is proposed, including: Step S1: Establish a multi-level evaluation index system that includes the target layer, criterion layer, and indicator layer; In step S1, the target layer G is the "Comprehensive Evaluation Score of Happy Rivers and Lakes," i.e., the effectiveness of the construction of happy rivers and lakes. The criterion layer C includes six dimensions: C1 Safe and Tranquil River, C2 Healthy River, C3 Beautiful River, C4 Cultural River, C5 Intelligent River, and C6 Harmonious River. The indicator layer consists of specific, quantifiable, or assessable indicators. The P element of the indicator layer is a specific elaboration of the content of the criterion layer, wherein: The C1 Anlan River includes the following indicators: P11 Flood control compliance rate, P12 Bank slope stability, P13 Flood disaster early warning capability, P14 Water supply guarantee rate, and P15 Flood disaster losses as a percentage of regional GDP. The C2 Healthy River indicator includes the following sub-indicators: P21 Water quality compliance rate of water function zones, P22 Ecological base flow guarantee rate, P23 Vertical connectivity of water system, P24 Natural condition of riverbank zone, and P25 Aquatic biodiversity. The C3 Beautiful River indicator includes the following sub-indicators: P31 Natural landscape preservation, P32 Waterfront landscape harmony, P33 Accessibility and quality of waterfront spaces, P34 Proportion of ecological transformation of hardened shorelines, and P35 Public satisfaction with the river landscape. The C4 River of Culture includes the following indicators: P41 Protection status of water cultural heritage, P42 Density and quality of water cultural carriers, P43 Integration degree of water cultural elements, P44 Annual frequency and influence of water culture publicity and education activities, and P45 Public awareness and recognition of water culture. The C5 Smart River system includes the following indicators: P51 Coverage and standard compliance rate of monitoring and sensing facilities, P52 Implementation degree of the four early warning functions, P53 Intelligent rate of river and lake inspection and problem handling, P54 Cross-departmental data sharing level, P55 Investment and operation and maintenance support intensity of smart water conservancy, and P56 Convenience of public information services. The C6 Harmonious River indicator includes: P61 the degree of improvement of the public participation mechanism, P62 the degree of improvement and implementation of the river and lake management and protection system, P63 the dynamic zeroing rate of the four types of river and lake pollution problems, P64 the effectiveness of the basin collaborative governance mechanism, and P65 the public's satisfaction with the happiness of the river. The correspondence between the criteria layer and the indicator layer, and the meaning of the indicators, are shown in Table 1 below: Table 1
[0025] Step S2: Invite multiple subjects to use intuitionistic fuzzy numbers to compare the importance of each element in the same level relative to the level above, and construct an intuitionistic fuzzy judgment matrix R; The intuitionistic fuzzy judgment matrix R in step S2 is obtained by multiple evaluation subjects defining a scaling table based on the relative importance in Table 2, and by using intuitionistic fuzzy numbers to compare the importance of each element in the same level with the element in the next higher level pairwise. Table 2 Factor i is more extremely important than factor j. (0.90, 0.10, 0.00) Factor i is much more important than factor j. (0.80, 0.15, 0.05) Factor i is significantly more important than factor j. (0.70, 0.20, 0.10) Factor i is slightly more important than factor j. (0.60, 0.25, 0.15) Factor i is as important as factor j (0.50, 0.30, 0.20) Factor J is slightly more important than factor i. (0.40, 0.45, 0.15) Factor j is significantly more important than factor i. (0.30, 0.60, 0.10) Factor J is much more important than factor i. (0.20, 0.75, 0.05) Factor j is more extremely important than factor i. (0.10, 0.90, 0.00) Step S2 specifically includes: inviting evaluation experts from diverse backgrounds, such as riverside residents, water conservancy management departments, and researchers, to perform pairwise comparisons of the three-layer elements using intuitionistic fuzzy numbers, thereby establishing an intuitionistic fuzzy judgment matrix R; the intuitionistic fuzzy numbers are represented as (μ, ν, π), where μ represents membership degree, ν represents non-membership degree, and π represents hesitation degree. The formula for calculating the intuitionistic fuzzy judgment matrix R is as follows: (1); In equation (1), i and j are different indices, i = 1, 2, ..., n, j = 1, 2, ..., n, and n is the number of elements in the layer; μ ij The membership degree represents the degree to which the expert considers indicator i to be more important than indicator j; ν ij Indicates non-membership degree; μij ,ν ij ∈[0,1] and μ ij +ν ij ≤1; π ij For the degree of hesitation of experts, π ij =1-μ ij -ν ij ; Specifically, taking the fuzzy judgment of the criterion layer relative to the target layer as an example, it involves comparing the importance of C1-C6 with respect to the target layer G pairwise to obtain the intuitionistic fuzzy judgment matrix C of the criterion layer. i (i=1,2,3, representing coastal residents, management departments, and research scholars, respectively): (2); (3); (4); Step S3: Based on the intuitionistic fuzzy judgment matrix R, the local weights of each layer element are calculated using the intuitionistic fuzzy hierarchical analysis method, and the global weight vector W of each evaluation index relative to the overall goal is finally synthesized through hierarchical single sorting and consistency test. Step S3 specifically includes: Step S31: For the intuitionistic fuzzy judgment matrix constructed by each expert, the group opinions are aggregated using the intuitionistic fuzzy weighted average operator to obtain the comprehensive intuitionistic fuzzy judgment matrix; Step S32: Based on the comprehensive intuitionistic fuzzy judgment matrix, calculate the intuitionistic fuzzy weight of each element using the intuitionistic fuzzy geometric mean method; for example, for the criterion layer C, its intuitionistic fuzzy weight w i Calculated using the following formula: (5); The calculated weights are approximately (0.35, 0.55) for C1 and (0.28, 0.62) for C2. Step S33: Defuzzify the intuitionistic fuzzy weights to obtain precise weight values, and perform normalization processing; calculate the score S(w) for each intuitionistic fuzzy weight. i S(w1) = μ - ν, S(w2) = 0.35 - 0.55 = -0.20, S(w3) = 0.28 - 0.62 = -0.34; Normalize the scores (to make their sum equal to 1) to obtain the precise weight vector of the criterion layer, for example, W. C = [0.30, 0.25, 0.20, 0.15, 0.10]; Step S34: Perform a consistency test under intuitionistic fuzzy conditions. If the test fails, feedback is provided to experts to adjust the judgment matrix. Due to limitations in decision-makers' knowledge and experience, as well as the influence of subjective and objective factors such as the complexity of decision-making, the judgment matrices given by decision-makers vary considerably. Therefore, it is necessary to study the compatibility and consistency of the judgment matrices: by constructing a product-type consistency matrix. Furthermore, the similarity between the two matrices is quantified using a distance metric formula to define a consistency test criterion. If R fails the consistency test (e.g., >0.1), only the matrix parameters need to be adjusted, and the decision-maker does not need to re-score. Step S35: Repeat the above process to calculate the weight of each indicator P relative to its corresponding criterion C, and then multiply it by the criterion layer weights to obtain the global weight of each indicator P relative to the overall objective G, ultimately obtaining the global weight vector W. Assume the decision-maker... l The intuitive fuzzy judgment matrix given based on the relative importance of attributes i and j is as follows: (6); In equation (6), μ ij (l) and ν ij (l) Let i and j represent the relative importance of the decision-maker l based on attributes i and j, respectively. The weights of the indicators at this level are calculated using the following formula: (7); The weights obtained from equation (7) are still in the form of an intuitionistic fuzzy set. A scoring function H(w) is then introduced. i Converting it to a single weight and processing it yields the global weights of the indicator layer: (8); The results of the local weight calculation for the criterion layer are shown in Table 3: Table 3 Coastal residents 0.2161 0.1890 0.1435 0.1094 0.1546 0.1874 Management Department 0.2128 0.1859 0.1369 0.1027 0.1893 0.1724 Researchers 0.1890 0.1890 0.1411 0.1077 0.1759 0.1973 Overall weight 0.2060 0.1880 0.1405 0.1066 0.1732 0.1857 The global weight results of the indicator layer are shown in Table 4: Table 4
[0026] Step S4: Invite experts to transform the actual performance of each underlying indicator into a unified intuitive fuzzy evaluation value, forming an evaluation value matrix Q; Step S4 specifically includes: For quantitative indicators, the measured values are mapped to intuitive fuzzy numbers through a preset membership function; For qualitative indicators, evaluation is conducted by experts or the public using intuitive fuzzy numbers.
[0027] Step S5: Using the five-element semi-partial subtraction set-pair potential model, the global weight vector W and the evaluation value matrix Q are combined to obtain the comprehensive evaluation value and corresponding level of the river and lake; Step S5 specifically includes: Step S51: Divide the effectiveness of the Happy Rivers and Lakes construction into 5 evaluation levels; specifically, divide the effectiveness of the Happy Rivers and Lakes construction into 5 evaluation levels (levels V, IV, III, II, and I correspond to very unhappy, unhappy, basically happy, happy, and very happy, respectively), as shown in Table 5: Table 5 Level I (Very Happy) The river system is in a highly harmonious state. Safety and stability are robust, the ecosystem is healthy and intact, the landscape is beautiful and pleasant, cultural heritage is revitalized, smart governance is efficient, and the public is highly satisfied. The relationship between humans and water has reached an ideal state of positive interaction, serving as a model for others. Level II (Happiness) The river system is in a well-coordinated state. There are no obvious shortcomings in any dimension, and its basic functions such as safety, ecology, landscape, culture, and intelligence are being well performed, resulting in high public satisfaction. The relationship between humans and water is basically harmonious, with a solid foundation for continued improvement. Level III (Basic Happiness) The river system is basically up to standard. Key safety and ecological bottom lines have been maintained, but some dimensions are lacking, limiting its functionality and resulting in generally low public satisfaction. The relationship between humans and water is at a critical equilibrium, requiring targeted strengthening of construction and management. Level IV (Unhappy) The river system exhibits significant defects or hidden dangers. There are prominent problems at the fundamental level, with declining service functions and low public satisfaction. Tension has arisen in the relationship between humans and water, necessitating systematic governance and restoration. Level V (Very unhappy) The river system is severely imbalanced or degraded. Safety risks are prominent, ecological damage is significant, service functions have been largely lost, and public discontent is strong. The relationship between humans and water is severely conflicted, urgently requiring comprehensive management. Step S52: Set pair analysis studies the intrinsic relationship between each element of the system and the whole system, and establishes a system structure model that determines the degree of determinism and uncertainty of a certain attribute between two related sets; Step S52 specifically includes: based on the difference coefficients I1, I2, and I3 of the indicator sample values and the correlation components of the level 2, level 3, and level 4 evaluation standards, as well as the opposition coefficient J, the sameness component a, the opposition component c, and the difference components b1, b2, and b3, the expression for the quinary correlation coefficient μ is obtained: (9); In equation (9), a b1, b2, and b3 are the same degree components, c is the opposite degree component, I1, I2, and I3 are the difference coefficients of the component relating the indicator sample value to the level 2, level 3, and level 4 evaluation standards, respectively, and J is the opposite coefficient. Step S53: Calculate the correlation coefficient μ of the index values for the sample of happy river and lake construction in the given area. ijk Specifically, it means: based on the sample value x of index j of evaluation sample i. ij With evaluation rating standards s kj Calculate μ ij1 μ ij2 μ ij3 μ ij4 and μ ij5 The calculation formula is as follows: (10); (11); (12); (13); (14); In equations (10) to (14), the indicators whose sample values increase (decrease) as the evaluation level k decreases are positive (negative) indicators; S 0j S 5jThe endpoint thresholds for levels 5 and 1, respectively; S kj To evaluate the critical value between level k and level k+1 of indicator j, where k = 1, 2, 3, 4; the resulting correlation coefficient μ of the indicator value is... ijk The graph illustrates this as follows: Figure 2 As shown; Step S54: Evaluate the correlation coefficient μ of the indicator values of the sample. ijk The corresponding relative membership degree μ ijk * And the relative membership degree υ ijk * After normalization, the five-element relational component υ of the happiness river and lake index value is obtained. ijk ; Relative membership degree μ ijk * The calculation formula is as follows: (15); Normalizing formula (15) yields the five-element correlation coefficient components of the Happiness River and Lake Index, calculated as follows: (16); Step S55: Using the five-element relational component υ of the Happy Rivers and Lakes Index value. ijk Calculate the correlation coefficient μ of the evaluation index value ij And obtain the quinary contact number μ of the evaluation sample. i ; Evaluation index value correlation number μ ij The calculation formula is as follows: (17); The quinary contact coefficient μ of the evaluation sample can be obtained from formula (17). i The calculation formula is as follows: (18); Step S56: Conduct a comprehensive evaluation of the evaluation samples, using the five-element connection coefficient μ of the evaluation samples. i Calculate the grade of the evaluation sample. The grade of the evaluation sample is calculated using the following formula: (19); In equation (19), μ i ∈[-1,1];y i Let yi be the evaluation level of sample i, where yi∈[1,5]; Step S6: By introducing an obstacle degree model, identify the key factors that restrict the construction of happy rivers and lakes, and propose optimization suggestions.
[0028] Step S6 specifically includes: Obstacles O for each indicator iSort by size from largest to smallest, identify the top-ranking indicators as key constraint factors; Obstacle degree O i Calculated using the following formula: (20); In equation (20), μ i r and μ j r The scores for indicators i and j reflect the actual performance level of the indicators; ω i and ω j These are the global weights for indicators i and j, respectively; (1-μ) i r ) and (1-μ j r This indicates the gap between the indicator and the ideal state; Based on the criterion layer dimensions to which the key constraint factors belong and their specific connotations, and in combination with the actual situation of rivers and lakes, targeted optimization suggestions are output.
[0029] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for evaluating the happiness of rivers and lakes based on intuitionistic fuzzy hierarchical analysis, characterized in that, include: Establish a multi-level evaluation indicator system that includes the target layer, criterion layer, and indicator layer; Multiple subjects are invited to use intuitionistic fuzzy numbers to compare the importance of each element in the same level relative to the level above, and an intuitionistic fuzzy judgment matrix R is constructed. Based on the intuitionistic fuzzy judgment matrix R, the local weights of each layer element are calculated using the intuitionistic fuzzy hierarchical analysis method, and the global weight vector W of each evaluation index relative to the overall goal is finally synthesized through hierarchical single sorting and consistency test. Obtain the evaluation value matrix Q; Using a five-element semi-partial subtraction set-pair potential model, the global weight vector W and the evaluation value matrix Q are aggregated to obtain the comprehensive evaluation value and corresponding level of the river and lake.
2. The happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis as described in claim 1, characterized in that, Based on the intuitionistic fuzzy judgment matrix R, the local weights of each layer's elements are calculated using the intuitionistic fuzzy hierarchical analysis method. Through hierarchical single sorting and consistency checks, the global weight vector W of each evaluation index relative to the overall objective is finally synthesized, including: For each expert's constructed intuitionistic fuzzy judgment matrix, the intuitionistic fuzzy weighted average operator is used to aggregate group opinions to obtain a comprehensive intuitionistic fuzzy judgment matrix. Based on the aforementioned comprehensive intuitionistic fuzzy judgment matrix, the intuitionistic fuzzy weight of each element is calculated using the intuitionistic fuzzy geometric average method. The intuitive fuzzy weights are defuzzified to obtain accurate weight values, and then normalized. Perform a consistency check in an intuitively ambiguous environment; if the check fails, provide feedback to the experts to adjust the judgment matrix.
3. The happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis as described in claim 1, characterized in that, The invitation of multiple subjects uses intuitionistic fuzzy numbers to compare the importance of each element within the same level relative to the level above, constructing an intuitionistic fuzzy judgment matrix R, including: Evaluation experts from diverse backgrounds, including riverside residents, water conservancy management departments, and researchers, were invited to use intuitionistic fuzzy numbers to perform pairwise comparisons of the three-layer elements and establish an intuitionistic fuzzy judgment matrix R. The intuitionistic fuzzy numbers are represented as (μ, ν, π), where μ represents the degree of membership, ν represents the degree of non-membership, and π represents the degree of hesitation.
4. The happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis as described in claim 1, characterized in that, The establishment of a multi-level evaluation index system comprising a target layer, a criterion layer, and an indicator layer includes: The target layer is the "Comprehensive Evaluation Score of Happy Rivers and Lakes", and the criteria layer includes C1 Safe and Tranquil River Index, C2 Healthy River Index, C3 Beautiful River Index, C4 Cultural River Index, C5 Intelligent River Index and C6 Harmonious River Index. The index layer consists of multiple lower-level parameter indicators under each index of the criteria layer.
5. The happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis as described in claim 1, characterized in that, Also includes: By introducing an obstacle degree model, key factors restricting the construction of happy rivers and lakes are identified, and optimization suggestions are proposed.
6. The happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis as described in claim 5, characterized in that, By introducing an obstacle degree model, key factors restricting the construction of happy rivers and lakes are identified, and optimization suggestions are proposed, including: Obstacles O for each indicator i Sort the indicators from largest to smallest and identify the top-ranked indicators as key constraint factors. Based on the criterion layer dimensions to which the key constraint factors belong and their specific connotations, and in combination with the actual situation of rivers and lakes, targeted optimization suggestions are output.
7. The happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis as described in claim 1, characterized in that, The invited experts will transform the actual performance of each underlying indicator into a unified intuitive fuzzy evaluation value, forming an evaluation value matrix Q, including: For quantitative indicators, the measured values are mapped to intuitive fuzzy numbers through a preset membership function; For qualitative indicators, evaluation is conducted by experts or the public using intuitive fuzzy numbers.
8. The happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis as described in claim 1, characterized in that, The method of using a five-element semi-partial subtraction set-pair potential model to aggregate the global weight vector W and the evaluation value matrix Q to obtain the comprehensive evaluation value and corresponding level of the river or lake includes: The effectiveness of the construction of happy rivers and lakes is divided into 5 evaluation levels; Set pair analysis studies the intrinsic relationship between each element of a system and the whole system, and establishes a system structure model of the degree of deterministic and uncertain relationship between two related sets that conform to a certain attribute. Calculate the correlation coefficient μ of the index values for a sample of happy river and lake construction in a given region. ijk ; The correlation coefficient μ of the evaluation sample index value ijk The corresponding relative membership degree υ ijk * And the relative membership degree υ ijk * After normalization, the five-element relational component υ of the happiness river and lake index value is obtained. ijk ; Using the five-element relational component of the Happy Rivers and Lakes index value υ ijk Calculate the correlation coefficient μ of the evaluation index value ij And obtain the quinary contact number μ of the evaluation sample. i ; A comprehensive evaluation of the assessment sample is conducted, using the five-element relationship coefficient μ of the assessment sample. i Calculate the rating of the evaluation sample.
9. The happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis as described in claim 8, characterized in that, The correlation coefficient μ of the index values for the construction of happy rivers and lakes in a given region is calculated. ijk ,include: Based on the sample value x of index j of evaluation sample i ij With evaluation rating standards s kj Calculate μ ij1 μ ij2 μ ij3 μ ij4 and μ ij5 .
10. The happiness evaluation method for rivers and lakes based on intuitionistic fuzzy hierarchical analysis as described in claim 8, characterized in that, The set pair analysis studies the intrinsic relationships between the elements of the system and the entire system, and establishes a system structure model of the degree of deterministic and uncertain relationships between two related sets that conform to a certain attribute, including: Based on the difference coefficients I1, I2, and I3 of the indicator sample values and the correlation components of the level 2, level 3, and level 4 evaluation standards, as well as the opposition coefficient J, the sameness component a, the opposition component c, and the difference components b1, b2, and b3, the expression for the quinary correlation coefficient μ is obtained.