Multi-criterion decision-making method for improving quality and efficiency of major water conservancy and hydropower engineering

Abstract

The invention relates to a major water conservancy and hydropower engineering quality improvement and efficiency improvement multi-criterion decision-making method, and the method comprises the steps: analyzing quality improvement and efficiency improvement influence factors, and identifying the quality improvement and efficiency improvement influence factors of major water conservancy and hydropower engineering; establishing an evaluation index system, determining a driving target of quality improvement and efficiency improvement of the major water conservancy and hydropower engineering, and constructing a multi-criterion evaluation key index system of quality improvement and efficiency improvement of the major water conservancy and hydropower engineering; according to the method, the technical problem that the quality improvement and efficiency improvement decision of major water conservancy and hydropower engineering is complicated under the multi-element driving-multi-factor synergistic effect is solved, and the conversion of the quality improvement and efficiency improvement decision of the reservoir dam from a single decision to a multi-criterion decision is realized; scientific decision support is provided for quality and efficiency improvement and sustainable development of major water conservancy and hydropower engineering.

Description

Technical Field

[0001] This invention relates to the field of reservoir dam decision-making technology, specifically a multi-criteria decision-making method for improving the quality and efficiency of major water conservancy and hydropower projects. Background Technology

[0002] Existing reservoir dam decision-making technologies and methods mainly focus on single decision-making issues such as reservoir reinforcement, downgrading and decommissioning, dredging, dam heightening, optimized scheduling, and flood control capacity improvement. However, under the multi-driven and multi-factor synergistic effect, the quality and efficiency improvement of major water conservancy and hydropower projects are affected by many social, economic, and ecological environmental factors. Single decision-making methods such as reservoir flood control capacity improvement, reinforcement, and dredging are insufficient to meet the needs of theoretical research and engineering practice for improving the quality and efficiency of major water conservancy and hydropower projects in the new era. Summary of the Invention

[0003] The purpose of this application is to provide a multi-criteria decision-making method for improving the quality and efficiency of major water conservancy and hydropower projects, replacing the traditional single-experience decision-making based on "occurrence-response", thereby realizing the transformation of decision-making for improving the quality and efficiency of major water conservancy and hydropower projects from single-criteria decision-making to multi-criteria decision-making.

[0004] To achieve the above objectives, this application provides the following technical solution:

[0005] This application provides a multi-criteria decision-making method for improving the quality and efficiency of major water conservancy and hydropower projects, including the following steps:

[0006] Analyze the factors influencing quality and efficiency improvement, analyze the behavior, functional changes, operation and maintenance status, public safety and risk prevention and control elements of hydropower projects, and identify the factors influencing quality and efficiency improvement of major water conservancy and hydropower projects;

[0007] Establish an evaluation indicator system, clarify the driving objectives for improving the quality and efficiency of major water conservancy and hydropower projects, and construct a multi-criteria evaluation key indicator system for improving the quality and efficiency of major water conservancy and hydropower projects;

[0008] A multi-criteria decision-making model is constructed, along with a comprehensive development index model and a coupling coordination degree model for improving the quality and efficiency of major water conservancy and hydropower projects. A Cloud-EM-AHP combined weight solution method based on minimum deviation is proposed to solve the weights of the evaluation indicators, and a multi-criteria decision-making model for improving the quality and efficiency of major water conservancy and hydropower projects based on ELECTRE is established.

[0009] The establishment of the evaluation index system specifically involves constructing a key index matrix for the multi-criteria evaluation of the quality and efficiency improvement of major water conservancy and hydropower projects based on the IDAM model and a thorough investigation of the comprehensive benefits and impacts of major domestic and international water conservancy and hydropower projects. In the matrix, each red circle represents a driving factor nested with an influencing factor, and three key indicators are selected to reflect the interrelationship between the driving and influencing factors. The horizontal indicators of the key index matrix characterize the economic, social, and ecological environmental impacts of major water conservancy and hydropower projects, while the vertical indicators characterize the three driving factors of safety, benefits, and risks in improving the quality and efficiency of hydropower projects. SS, SE, and SO represent social safety, economic safety, and ecological safety, respectively; BS, BE, and BO represent social benefits, economic benefits, and ecological benefits, respectively; and RS, RE, and RO represent social risks, economic risks, and ecological risks, respectively. This results in the construction of a key index system for the evaluation of the quality and efficiency improvement of major water conservancy and hydropower projects.

[0010] The specific calculation model for constructing the comprehensive development index model for improving the quality and efficiency of major water conservancy and hydropower projects is as follows:

[0011] ,

[0012] In the formula, CDI k X represents the comprehensive development index of the k-th subsystem in a major water conservancy and hydropower project system. ij Let F(x), G(x), and H(x) represent the standardized value of the j-th indicator in the i-th year. F(x), G(x), and H(x) are the comprehensive development indices of the social, economic, and ecological subsystems involved in the major water conservancy and hydropower project system, respectively, with values ​​ranging from [0, 1].

[0013] The proposed model for the coordination degree of the social-economic-ecosystem coupling of major water conservancy and hydropower projects is as follows:

[0014] ,

[0015] In the formula, CCD and C represent the coupling coordination degree and coupling degree of the major water conservancy and hydropower project system, respectively. The closer the CCD value is to 1, the stronger the interaction between the social, economic and ecological subsystems involved in the major water conservancy and hydropower project system, the better the level of coordinated development among the subsystems, and the more the system as a whole will tend to develop in a new orderly structure. Conversely, when the CCD is close to 0, it means that there is almost no connection between the system elements, and the system will develop towards disorder. T is the weighted comprehensive development index, and α, β and γ represent the weights of the three factors of safety, benefit and risk, respectively. α = β = γ = 1 / 3.

[0016] The proposed Cloud-EM-AHP combined weighting solution method based on minimum deviation is used to solve for the weights of the evaluation indicators.

[0017] Suppose there are m alternative decision-making measures for improving the quality and efficiency of major water conservancy and hydropower projects. Each measure is reflected by n key indicators. The evaluation value of the i-th indicator (i = 1, 2, …, m) of the j-th decision-making measure (j = 1, 2, …, m) is X. ij The corresponding decision matrix is ​​X. Standardizing X yields the standardized decision matrix R, as shown below:

[0018] ,

[0019] In the formula, r ij Let ω represent the evaluation value of the i-th indicator in the j-th decision measure, the number of behavioral decision measures in the decision matrix R, and the number of evaluation indicators. The combined weight vector of each indicator in the j-th decision measure is ω. Cloud-EM-AHP_j The formula is expressed as follows:

[0020] ,

[0021] In the formula, ω i (ω) i > 0) is the Cloud-EM-AHP combined weight based on minimum deviation for the i-th indicator.

[0022] The establishment of a multi-criteria decision-making model for improving the quality and efficiency of major water conservancy and hydropower projects based on ELECTRE specifically involves...

[0023] Based on the decision-maker's risk load, and taking into account the decision-maker's preference order and decision data, a threshold function is used to compare different measures in pairs to form a harmony matrix and a disharmony matrix, and then a credibility matrix is ​​constructed. Finally, the decision measures are determined by comparing them with each other and ranking them in order.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] An evaluation index system for improving the quality and efficiency of major water conservancy and hydropower projects was established, and a multi-criteria decision-making model for improving the quality and efficiency of major water conservancy and hydropower projects driven by multiple factors was constructed. This solved the complex technical problem of decision-making for improving the quality and efficiency of major water conservancy and hydropower projects under the synergistic effect of multiple drivers and factors, and realized the transformation of decision-making for improving the quality and efficiency of reservoirs and dams from single decision-making to multi-criteria decision-making, providing scientific decision support for improving the quality and efficiency and sustainable development of major water conservancy and hydropower projects. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart of the method of the present invention;

[0028] Figure 2 This is a diagram illustrating the main objectives and influencing factors for improving the quality and efficiency of major water conservancy and hydropower projects.

[0029] Figure 3 This is a diagram of the comprehensive benefit and impact assessment model for reservoirs and dams based on IDAM, as presented in this invention.

[0030] Figure 4 This invention is a matrix diagram of key indicators for evaluating the quality and efficiency of major water conservancy and hydropower projects based on multiple criteria.

[0031] Figure 5 This is a list of measures for improving the quality and efficiency of major water conservancy and hydropower projects in line with sustainable development, as outlined in this invention.

[0032] Figure 6 This is the key digital feature map of the cloud model of this invention;

[0033] Figure 7 This invention is a flowchart of a multi-criteria decision-making process for major water conservancy and hydropower projects driven by safety, benefits, and risks. Detailed Implementation

[0034] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0035] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0036] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0037] This application provides a multi-criteria decision-making method for improving the quality and efficiency of major water conservancy and hydropower projects, including the following steps:

[0038] Analyze the factors influencing quality and efficiency improvement, analyze the behavior, functional changes, operation and maintenance status, public safety and risk prevention and control elements of hydropower projects, and identify the factors influencing quality and efficiency improvement of major water conservancy and hydropower projects;

[0039] Establish an evaluation indicator system, clarify the driving objectives for improving the quality and efficiency of major water conservancy and hydropower projects, and construct a multi-criteria evaluation key indicator system for improving the quality and efficiency of major water conservancy and hydropower projects;

[0040] A multi-criteria decision-making model is constructed, along with a comprehensive development index model and a coupling coordination degree model for improving the quality and efficiency of major water conservancy and hydropower projects. A Cloud-EM-AHP combined weight solution method based on minimum deviation is proposed to solve the weights of the evaluation indicators, and a multi-criteria decision-making model for improving the quality and efficiency of major water conservancy and hydropower projects based on ELECTRE is established.

[0041] Identification of factors influencing the quality and efficiency improvement of major water conservancy and hydropower projects:

[0042] Based on relevant literature on the decision-making of major domestic and international water conservancy and hydropower projects / reservoirs (hydropower) dams, and combined with attribution studies on the improvement of quality and efficiency of typical projects, the main factors influencing the improvement of quality and efficiency of major water conservancy and hydropower projects are identified. These factors can be summarized into three aspects: social, economic, and ecological environment, and are driven by three major objectives: safety, efficiency, and risk. The main objectives and influencing factors for improving the quality and efficiency of major water conservancy and hydropower projects are as follows: Figure 2 As shown.

[0043] Evaluation index system for improving the quality and efficiency of major water conservancy and hydropower projects

[0044] The main objectives and influencing factors of improving the quality and efficiency of major water conservancy and hydropower projects ( Figure 2As can be seen, the influencing factors are numerous, diverse in dimensions, and involve data with varying dimensions, making them difficult to obtain. Therefore, it is crucial to select key indicators to scientifically and objectively characterize the sustainability of major water conservancy and hydropower projects. The Integrative Dam Assessment Modeling (IDAM) is an effective tool for evaluating the comprehensive benefits and impacts of hydropower projects. It can support smarter and more transparent decision-making during hydropower development. Furthermore, this method is based on the three pillars of sustainable development (social sustainability, economic sustainability, and ecological sustainability) (UNESCO), equitably considering the environmental, technological, social, cost, and benefit aspects of alternative solutions in reservoir and dam decision-making. The IDAM model uses a combination of qualitative and quantitative methods to assign values ​​to the indicators in the model, using the principle of maximizing benefits and minimizing costs. It constructs nine typical evaluation indicators from three dimensions: socio-economic impact, biophysical impact, and geopolitical impact.

[0045] When addressing decision-making issues related to improving the quality and efficiency of major water conservancy and hydropower projects, it is necessary to analyze the potential impacts of each option. The ultimate goal is to maximize positive impacts (reduced risk, lower costs, increased benefits) and minimize negative impacts (increased risk, higher costs, reduced benefits). The evaluation index sector structure and calculation method based on IDAM are as follows: Figure 3 As shown. Figure 3 The sector in the evaluation includes two dimensions: the radial direction measures the objective impact of improving the quality and efficiency of major water conservancy and hydropower projects, while the normal direction represents the decision-maker's subjective assessment of the importance of the indicator. The subjective and objective measures are each assigned a score from 0 to 5, with higher scores indicating greater influence, scope, and importance of the indicator.

[0046] This invention, based on the IDAM model and after a thorough investigation of the comprehensive benefits and impacts of major domestic and international water conservancy and hydropower projects, constructs a multi-criteria evaluation key indicator matrix for improving the quality and efficiency of major water conservancy and hydropower projects. Figure 4 In the matrix, each red circle represents a driving factor (safety, benefit, risk) nested with an influencing factor (social, economic, ecological environment), and three key indicators are selected to reflect the relationship between the driving and influencing factors. Therefore, each row and each column of the matrix has nine key indicators, and the entire matrix has a total of 27 key indicators.

[0047] from Figure 4As can be seen, the horizontal indicators characterize the economic, social, and ecological environmental impacts of major water conservancy and hydropower projects, while the vertical indicators characterize the three driving factors of safety, benefits, and risks in improving the quality and efficiency of hydropower projects. SS, SE, and SO represent social security, economic security, and ecological security, respectively; BS, BE, and BO represent social benefits, economic benefits, and ecological benefits, respectively; and RS, RE, and RO represent social risks, economic risks, and ecological risks, respectively. Therefore, a key indicator system for evaluating the quality and efficiency improvement of major water conservancy and hydropower projects is constructed, as shown in Table 1 below.

[0048] Table 1 Evaluation Index System for Improving the Quality and Efficiency of Major Water Conservancy and Hydropower Projects

[0049]

[0050] Multi-criteria decision-making model for improving the quality and efficiency of major water conservancy and hydropower projects

[0051] Comprehensive Development Index Model and Coupling Coordination Degree Model for Improving the Quality and Efficiency of Major Water Conservancy and Hydropower Projects

[0052] This invention constructs a Comprehensive Development Index (CDI) model for improving the quality and efficiency of major water conservancy and hydropower projects. This model quantitatively explores the overall development of major water conservancy and hydropower project systems and the development trends of their related social, economic, and ecological subsystems. It is used to verify the rationality of the evaluation index system and the correlation between the indicators. The CDI is used to quantitatively assess the development trend of the interaction and positive mutual influence among the social, economic, and ecological subsystems of major water conservancy and hydropower project systems. The calculation model is shown below:

[0053]

[0054] In the formula, CDI k X represents the comprehensive development index of the k-th subsystem within a major water conservancy and hydropower project system. ij Let F(x), G(x), and H(x) represent the standardized value of the j-th indicator in the i-th year. F(x), G(x), and H(x) are the comprehensive development indices of the social, economic, and ecological subsystems involved in the major water conservancy and hydropower project system, respectively, with values ​​ranging from [0, 1].

[0055] Coupling, a concept originating from physics, measures the phenomenon where two or more entities interact and influence each other to ultimately unite. The degree of coupling is an indicator used to measure the strength of this interaction between systems; the stronger the mutual influence, the greater the coupling, and the systems will gradually exhibit a consistent development trend. Coupling coordination reflects the overall function of the system and the synergistic development effect among its subsystems. Only when the interconnected subsystems achieve benign resonance can the system as a whole achieve a synergistic effect. Based on sustainable development theory, coupling coordination theory, and a comprehensive development index model, this study constructs a socio-economic-ecosystem coupling coordination model for major water conservancy and hydropower projects as follows:

[0056]

[0057] In the formula, CCD and C represent the coupling coordination degree and coupling degree of a major water conservancy and hydropower project system, respectively. The closer the CCD value is to 1, the stronger the interaction between the social, economic, and ecological subsystems involved in the major water conservancy and hydropower project system, the better the coordinated development level among the subsystems, and the more the system as a whole will tend towards a new ordered structure. Conversely, when the CCD approaches 0, it indicates that there is almost no correlation between the system elements, and the system will develop towards disorder. T is a weighted comprehensive development index, and α, β, and γ represent the weights of the three factors of safety, benefit, and risk, respectively. Given that the three factors all play an irreplaceable and important role in the coupled and coordinated development of major water conservancy and hydropower project systems, in this study, α = β = γ = 1 / 3.

[0058] Construction of a list of measures to improve the quality and efficiency of major water conservancy and hydropower projects

[0059] According to the multi-criteria evaluation index system for improving the quality and efficiency of major water conservancy and hydropower projects ( Figure 4 (Table 1) Based on literature review and interviews with industry experts engaged in teaching, research, and engineering practice related to water conservancy and hydropower engineering, this study has compiled a list of measures to improve the quality and efficiency of major water conservancy and hydropower projects for sustainable development, such as... Figure 5 As shown.

[0060] (1) Improved flood control capacity. With the increasing frequency of extreme weather events and the increase in flood volume in the basin in recent years, the existing flood control system is unable to meet the needs of the basin's socio-economic development. As a key link in the basin's flood control engineering system, major water conservancy and hydropower projects play an important role in improving the basin's disaster prevention and mitigation capabilities and alleviating flood pressure by tapping the flood control potential of existing projects. Improved flood control capacity mainly includes increasing the flood control capacity of reservoirs by raising dams and dredging; for projects with insufficient sediment discharge and flood discharge capacity, adding sediment discharge and flood discharge structures and inlet gates to enhance outflow capacity; improving hydrological forecasting and scheduling levels, and setting necessary flood control capacity for reservoirs mainly used for power generation, water supply and other water resource utilization.

[0061] (2) Dam reinforcement and safety improvement. After years of operation, the reservoir's structure has aged, and its performance has deteriorated. Coupled with the impact of natural disasters such as extreme precipitation, floods exceeding standard levels, and biological erosion under the background of climate change, the risk of engineering failure and even destruction remains constant. Furthermore, the uncertainty of the magnitude and frequency of natural water inflow has increased, and the contact surface between the dam and the water body is constantly changing, frequently experiencing rapid shifts between drought and flood. During the flood season, it may be impacted by extreme floods, challenging the stability and erosion resistance of the project. Therefore, dam reinforcement and safety improvement is the most direct and effective engineering measure. The main reinforcement and safety improvement measures include expanding or adding spillway structures to increase flood discharge capacity and reservoir flood control capacity; and enhancing the seepage prevention capacity of the dam foundation through methods such as curtain grouting, concrete anti-seepage walls, and geomembrane anti-seepage.

[0062] (3) Downgrading and Scrapping. Downgrading and scrapping is an inevitable stage in the entire life cycle of reservoirs and dams. As the service life of the project increases, aging and disrepair intensify, and the high-quality development of the social economy places increasingly higher demands on the safety of water conservancy infrastructure, projects that have lost their function, suffered a sharp decline in benefits, are seriously damaged, and lack the conditions for reinforcement can be downgraded and scrapped according to actual needs. In addition, for the sake of project safety and to enhance the connectivity of river and lake systems, reservoir decommissioning can improve the longitudinal connectivity of river and lake water bodies, restore the upstream and downstream migration channels of fish, reconstruct the habitats of aquatic plants and animals, and thus restore the natural flow pattern of rivers and aquatic habitats. Complete decommissioning of a reservoir requires the demolition of the dam and all ancillary structures. Partial decommissioning of a reservoir means the decommissioning of some engineering facilities, while retaining the remaining buildings or ancillary facilities to continue to play a role.

[0063] (4) Dredging. The accumulation of sediments such as silt in the reservoir weakens its flood control function, reduces its water supply capacity and benefits, threatens project safety, and increases ecological and environmental risks. Mechanical dredging and sediment discharge scheduling can enhance the storage capacity of existing reservoirs, extend their service life, and reduce the additional economic and ecological pressures brought by newly built reservoirs. Mechanical dredging uses dredgers and other machinery to treat the silt deposits in the reservoir through suction, excavation, discharge, and transportation, which can quickly increase reservoir capacity and reduce the probability of dam failure. For reservoirs where siltation does not severely affect project safety, upstream soil and water conservation, silt reduction scheduling at the reservoir tail during drawdown periods, and silt reduction dredging projects can effectively reduce the loss of effective reservoir capacity due to siltation.

[0064] (5) Enhanced Monitoring Capabilities. A comprehensive monitoring system will be established to strengthen monitoring of dam surface deformation, spillway slope deformation, and dam seepage. Based on the acquisition of massive amounts of data, big data and artificial intelligence technologies will be used to deeply mine the monitoring data, enabling timely and accurate understanding of the dam facility's operational status, and subsequently conducting engineering safety status assessments, decision analysis, and scheduling management. Key measures to enhance monitoring capabilities include constructing reservoir rainfall and water level monitoring and dam safety monitoring facilities, developing intelligent monitoring and sensing equipment for water engineering safety, and utilizing new safety monitoring technologies such as BeiDou and unmanned aerial vehicles (UAVs).

[0065] (6) Adjustment of operational functions. Service functions and operational modes are reduced, added, or optimized based on the actual conditions of the project. For example, at the beginning of the People's Republic of China, the main purpose of reservoirs was to solve agricultural irrigation problems. With the development of advanced irrigation technology and the improvement of agricultural water use efficiency, reservoirs that were originally mainly for irrigation have gradually developed into comprehensive water conservancy projects. Secondly, changes in water demand structure force adjustments to reservoir functions. For example, with the increasing demand for safe drinking water and ecological water use, coupled with the growing emphasis on the green and sustainable development of water conservancy projects, the improvement of urban river and lake water environments has received increasing attention, and reservoir functions are shifting towards ecological environmental protection. Furthermore, for reservoirs with severely polluted areas, a series of comprehensive treatment projects, such as ecological water replenishment scheduling, dredging, soil and water conservation, and water source purification, are implemented to restore the function of drinking water sources.

[0066] (7) Optimize reservoir scheduling and management. Reservoir scheduling refers to, under the premise of ensuring project safety, integrating basin hydrological and meteorological data, grasping water and rainfall characteristics, accurately predicting and receiving changes in inflow, discharging floodwaters during the high-water season and replenishing water during the low-water season, adjusting the operation mode of reservoir spillway structures and hydropower station units, giving full play to the reservoir's regulation and storage function, and maximizing the comprehensive benefits of the reservoir. Currently, the main reservoir scheduling measures adopted include flood control compensation scheduling, pressure reduction scheduling for small and medium floods, pre-flood drawdown, pre-flood storage scheduling, flood tail interception, dynamic control of operating water level during the flood season, ecological scheduling, water replenishment scheduling, and saltwater intrusion suppression scheduling. Among these, implementing refined ecological scheduling helps improve the water quality of reservoirs and downstream rivers, reduce hydraulic residence time, and increase water flow velocity, thereby avoiding algal blooms.

[0067] A method for solving the combined weights of Cloud-EM-AHP based on minimum deviation.

[0068] Due to the complexity, fuzziness, and multi-layered nature of multi-criteria decision-making problems in water conservancy and hydropower projects / reservoirs and dams, determining the weights of indicators is a crucial step, as differences in weighting results directly affect the accuracy and rationality of the evaluation results. Subjective weighting methods can leverage the rich experience of experts in the research field to grasp indicator weights, making the results more consistent with the decision-maker's understanding of the actual engineering problem. However, they also suffer from over-reliance on decision-makers' preferences and strong subjectivity, making it difficult to reflect the data information of the indicators. Objective weighting methods mainly determine weights through data measurement, resulting in stronger objectivity and theoretical advantages. However, they fail to incorporate expert knowledge and practical experience, leading to poor interpretability of the weighting results and a tendency for discrepancies between the results and reality. Combined weighting methods are a common practice in current research and practice, using multiple weighting methods in combination to leverage their strengths and minimize information loss. This chapter combines subjective and objective methods such as the Analytic Hierarchy Process (AHP), cloud models, and entropy weighting to combine weights for indicators, reducing the randomness and uncertainty of weights, overcoming the limitations of single methods, and enhancing the authenticity, objectivity, and interpretability of indicator weights.

[0069] (1) Numerical characteristics of cloud models and cloud generator algorithm

[0070] The cloud model theory and methods, based on probability theory and fuzzy mathematics, have been widely applied in research across natural and social sciences, including information prediction, risk warning, and sustainability assessment. According to the cloud model concept, assuming the universe of discourse X = {x} is a general set containing a fuzzy set Z, then for any element x, there exists a random number μ(x) with a stable tendency; μ(x) is called the membership degree of x with respect to Z. If the factors in X = {x} are of simple order, X can be considered a fundamental variable, and the distribution of membership degree μ(x) on X is called the membership cloud. Conversely, if the factors in X = {x} do not conform to the principle of simple order, then according to a certain principle k, X can be projected onto another universe of discourse with simple order, X'. If there is one and only one x' corresponding to x in X', then X' is considered a fundamental variable, and the distribution of μ(x) on X' is called the membership cloud with respect to X'. A small change in μ(x) at a certain point will not have a significant impact on the overall characteristics of the membership cloud. The study focuses more on the overall characteristics reflected by the shape of the membership cloud and the changing patterns reflected by the membership degree.

[0071] The normal membership cloud model combines the uncertainty in fuzzy problems with the randomness in membership degrees, thereby achieving the conversion between qualitative and quantitative relationships. Its distribution rule is generally expressed by three key numerical features: expectation Ex, entropy En, and hyperentropy He, denoted as N. 3 (Ex, En, He). With Figure 6For example, the horizontal axis represents the uncertainty measurement range x, and the vertical axis represents the membership degree μ(x), whose value represents the probability that a sample point can represent a qualitative concept, used to reflect fuzziness and randomness. Here, the expectation Ex is the sample mean, which expresses the qualitative concept of the event by transforming the uncertainty of the event into certainty. In the cloud model diagram, Ex reflects the distribution of cloud droplets in the universe of discourse, representing the centroid value within the cloud coverage area (…). Figure 6 a~ Figure 6 b). Entropy En represents the range of values ​​for cloud droplets, used to measure the uncertainty of fuzzy concepts, in contrast to... Figure 6 a and Figure 6 As can be seen from c, the larger En is, the wider the distribution range of cloud droplets (horizontal axis), the greater the conceptual ambiguity, and the more difficult it is to quantify qualitative concepts. Hyperentropy He is a measure of the uncertainty of entropy, determined by the ambiguity and randomness of entropy, and can reflect the thickness and dispersion of clouds. (Comparison) Figure 6 a and Figure 6 As can be seen from d, the smaller He is, the lower the degree of uncertainty dispersion, the easier it is to reach a consensus on qualitative concepts, and the easier it is to be accepted by most decision-makers.

[0072] The cloud model describes the uncertainty of events through Membership Clouds Generators (MCGs). MCGs are divided into forward cloud generators and reverse cloud generators. The forward cloud generator is based on a known normal membership cloud N. 3 Under the condition of (Ex, En, He), two-dimensional points satisfying the normal distribution law of subordinate clouds are generated, denoted as ξ(x, μ), thus converting qualitative indicators into quantitative values. The inverse cloud generator, on the other hand, is based on the known distribution of a certain number of cloud droplets ξ(x, μ) in subordinate clouds. It uses statistical analysis, pattern recognition technology, and visualization technology to solve for the three numerical feature values ​​Ex, En, and He of the normal subordinate cloud, thus realizing the conversion from quantitative values ​​to qualitative concepts. The calculation steps of the inverse cloud generator are: ① Determine the cloud model sample points x i (i = 1, 2, …, n); ② Calculate the sample mean, first-order sample central moment CM, and sample variance S. 2 ③ Calculate the numerical feature N 3 (Ex, En, He). The inverse cloud generator is represented by the following model:

[0073]

[0074] (2) Cloud Model - Entropy Weight Method (Cloud-EM) Model

[0075] In contrast to information, which characterizes the orderliness of a system, "entropy" measures the degree of disorder (uncertain information) within a system. The magnitude of information entropy reflects the dispersion of an indicator; the smaller the information entropy, the greater the amount of information provided by the indicator, the higher its frequency of occurrence, and thus its greater role in the comprehensive evaluation, resulting in a higher weight. Based on the concept of entropy, the entropy weight method (EM) determines the weight of an indicator by calculating its dispersion, objectively reflecting the amount of information in the data and avoiding errors caused by subjective factors in weight calculation. Parameters with higher frequency of occurrence have greater influence and contribution. Given the different dimensions and influences of the original data, the original data is first processed to be dimensionless to ensure consistency and comparability. This study uses a non-zero transformation range standardization method to maintain the variation range of each indicator data between 0.01 and 1. The specific formula is as follows:

[0076]

[0077] In the formula, X ij + and X ij - These are indices representing positive and negative attributes, respectively. ij X ij 、max{x ij} and min{x ij} represents the original value, standardized value, maximum value, and minimum value of the data sample for the j-th indicator (j = 1, 2, …, n) under the i-th criterion (i = 1, 2, …, m).

[0078]

[0079] In the formula, ω EM_j (0 ≤ ω) EM_j ≤ 1) is the weight of the j-th evaluation index among the quality improvement and efficiency enhancement measures for major water conservancy and hydropower projects obtained using the EM method; p ij (0 ≤ p) ij ≤ 1) is the proportion of the j-th evaluation indicator in the i-th criterion layer; e j (0 ≤ e) j ≤ 1) is the information entropy of the j-th evaluation index; 1 / lnm (1 / lnm > 0) is the information entropy coefficient.

[0080] Traditional EM model calculations are simple, but they rely excessively on objective data, resulting in poor accuracy and reliability in ensuring the calculation results. For example, when the average value of the raw data for certain indicators is the same, their calculated EM weight values ​​will also be the same, making it difficult to reflect the actual situation. However, in real-world decision-making problems, the patterns of each index are not entirely the same; that is, the cloud entropy parameter in the cloud model varies drastically, requiring full utilization of the entropy influence in the cloud model. Therefore, this study combines the cloud model with the EM model, constructing a combined weighting method based on the cloud model-entropy weight method (Cloud-EM) (referred to as the cloud entropy method) to process the numerical values ​​in the cloud model. This aims to improve the efficiency of model processing, better reflect the importance of each indicator, and effectively handle indicator differences, thus making the weight distribution more scientific and reasonable. Given n indicators (column vectors) and m experts (row vectors), the key parameters of the cloud model for the j-th indicator are calculated based on the traditional EM model and a cloud model inverse cloud generator. The specific calculation formula is as follows:

[0081]

[0082]

[0083] In the formula, ω Cloud-EM_j (0 ≤ ω) Cloud-EM_j ≤ 1) represents the weight of the j-th evaluation index in the quality improvement and efficiency enhancement measures for major water conservancy and hydropower projects calculated based on the Cloud-EM method. When the cloud entropy En j When En is ≠ 0, it indicates a significant divergence of opinion among experts regarding this indicator, and its weight should be reduced. Conversely, if En... j A smaller value indicates less disagreement among experts regarding this indicator, and its weight should be increased in this case. When En... j When the value is 0, it indicates that the decision-making experts have the same score for this indicator.

[0084] (3) Analytic Hierarchy Process (AHP)

[0085] The analytic hierarchy process (AHP) is a multi-criteria analysis method that combines qualitative and quantitative approaches. It is widely used to solve complex decision problems with multiple objectives or unstructured characteristics. AHP treats the research object as a hierarchical system, where the weights of elements at each level directly or indirectly affect the final result. AHP first decomposes the decision problem into different hierarchical structures in the order of overall objective, sub-objectives at each level, evaluation criteria, and specific alternatives. It then constructs and solves the eigenvectors of the judgment matrix to obtain the priority weights of each element at each level relative to the elements at the previous level. Finally, it recursively sums these weights to obtain the final weights of each alternative relative to the overall objective. The alternative with the highest weight is the optimal solution. The basic steps of AHP are as follows:

[0086] ① Hierarchical structure model establishment

[0087] Establishing a hierarchical model with dominance relationships can decompose complex decision-making problems into several elements, forming several levels such as the goal layer, criterion layer, and bottom layer. The upper-level elements, as criteria, exert a dominant effect on the lower-level elements. Among them, the goal layer is the predetermined goal or ideal result of the complex problem, the criterion layer contains the intermediate links involved in achieving the goal, and the bottom layer contains the various decision measures and solutions that can be selected to achieve the goal.

[0088] ② Determine the matrix construction

[0089] Hierarchical structures are used to reflect the relationships between factors, but from the decision-maker's perspective, the weight of each criterion in the objective measurement is not the same. Suppose we want to compare n factors X = {x1, …, x}. n To determine the magnitude of the influence of a certain factor Z, the n factors are compared pairwise using the nine-level scale scoring method proposed by Saaty (1987). A corresponding judgment matrix is ​​then established based on the comparison results, i.e., two factors x are selected each time. i and x j , with a ij x represents i and x j The ratio of the effects of factor Z to the total influence of factor Z then exists as a. ij = 1 / a ji (a) ij > 0; a ii = 1; i = 1, 2, …, n; j = 1, 2, …, n), and the elements on the diagonal of this judgment matrix are all 1s, and the elements on both sides of the diagonal are reciprocals of each other. This can be represented by the matrix as follows:

[0090]

[0091] In the formula, A is the judgment matrix, which is based on the nine-level scale scoring method for a.i and a j By performing pairwise comparisons, we can obtain a. ij value.

[0092] ③ Hierarchical single sorting and consistency check

[0093] Let the largest eigenvalue of the judgment matrix A be λ. max The corresponding normalized feature vector is W = (w1, w2, …, w n ) T Then a ij = w i / w j (∀i, j, k = 1, 2, …, n). If the comparison results are completely consistent, then the matrix elements should satisfy a ij ‧a jk = a ik At this time, a ij and a ji This is called a consistency matrix. After normalization, the ranking weights of corresponding elements at the same level relative to the factors at the previous level constitute the hierarchical single ranking. The judgment matrices A and W are modeled as follows:

[0094]

[0095] In the formula, ω AHP_j (0 ≤ ω) AHP_j ≤ 1) is the weight of the j-th evaluation index in the quality improvement and efficiency enhancement measures of major water conservancy and hydropower projects calculated based on the AHP method.

[0096] Since the characteristic roots continuously depend on a ij , leading to λ max The greater the value of λ, the more severe the inconsistency of A becomes. max The more difficult it is for the corresponding standardized feature vector to truly reflect X = {x1, …, x} n The proportion of influence of a certain factor Z. Based on the AHP principle and calculation method, it can be determined by "if λ max The formula `= n` is used to test whether A is a consistent matrix. The formula is as follows:

[0097]

[0098] In the formula, CI is the consistency index. When CI < 0.10, the judgment matrix is ​​considered to have acceptable consistency, and decisions can be made according to the results represented by the combined weight vector. When CI = 0, it indicates complete consistency. When CI > 0.1, the judgment matrix does not meet the consistency requirements and needs to be appropriately modified until satisfactory consistency is achieved. Considering that randomness may cause consistency deviations, when checking whether the judgment matrix has satisfactory consistency, CI and the random consistency index RI also need to be compared to calculate the consistency test coefficient CR. Generally speaking, when CR < 0.1, the judgment matrix passes the consistency test; otherwise, it does not have satisfactory consistency. The formula for calculating CR is as follows:

[0099]

[0100] ④ Overall hierarchical ranking and consistency check

[0101] Let hierarchy A contain A1, …, A m There are m factors in total, with hierarchical total ranking weights of a1, …, a m The B hierarchy includes B1, ..., B n There are n factors in total, and they are related to A. j The hierarchical single ranking weights are b respectively 1j , …, b nj (When B) i With A j When there is no correlation, b ij = 0). Then, based on Table 2, the weights b1, …, b of each factor in layer B with respect to the overall goal are calculated. n .

[0102] Table 2. Calculation Method for Overall Hierarchical Ranking

[0103]

[0104] In practical decision-making, inconsistencies at different levels can accumulate, leading to significant inconsistencies in the final result. Therefore, it is necessary to perform consistency checks on the overall hierarchical ranking layer by layer from high to low, obtaining the single-rank consistency index CI(j) and the random consistency index RI(j), and then calculating the random consistency ratio CR' of the overall ranking at level B. When CR' < 0.1, the overall hierarchical ranking result is considered to have satisfactory consistency and is accepted. The formula for CR' is as follows:

[0105]

[0106] (4) Weight calculation method based on cloud model-entropy weight-analysis hierarchy

[0107] This study constructs an evaluation index system for improving the quality and efficiency of major water conservancy and hydropower projects, which contains numerous factors with significant differences among them. To better facilitate cross-comparison and fully utilize other parameters, this section constructs a Cloud-EM-AHP combined weighting solution method based on minimum deviation. This method assigns weights to each index in the decision-making model for improving the quality and efficiency of major water conservancy and hydropower projects, minimizing the deviation of the decision results under combined weighting and improving the model's processing efficiency.

[0108] Suppose there are m alternative decision-making measures for improving the quality and efficiency of major water conservancy and hydropower projects. Each measure is reflected by n key indicators. The evaluation value of the i-th indicator (i = 1, 2, …, m) of the j-th decision-making measure (j = 1, 2, …, m) is X. ij The corresponding decision matrix is ​​X. Standardizing X yields the standardized decision matrix R, as shown below:

[0109]

[0110] In the formula, r ij Let be the evaluation value of the i-th indicator in the j-th decision measure. The number of behavioral decision measures in the decision matrix R is represented by the number of evaluation indicators. The combined weight vector of each indicator in the j-th decision measure is ω. Cloud-EM-AHP_j The formula is expressed as follows:

[0111]

[0112] In the formula, ω i (ω) i > 0) is the Cloud-EM-AHP combined weight based on minimum deviation for the i-th indicator.

[0113] In practical applications, if a subjective or objective weighting method is used to assign weights to the indicators in a quality-improvement and efficiency-enhancing decision-making measure, the indicator weight vector of the plan or measure can be obtained as follows:

[0114]

[0115] In the formula, μ k Let be the index weight vector obtained using the k-th weighting method. Based on this, assuming s weighting methods are used to assign weights to the indicators, then the linear combination coefficient α corresponding to the k-th method is... k The calculation model is shown below:

[0116]

[0117] To minimize the discrepancy between the index weights calculated by a single subjective and objective weighting method and the combined weighting method, this study introduces the following deviation function:

[0118]

[0119] In the formula, f j (μ k Let be the deviation function obtained by applying the k-th weighting method to the j-th quality improvement and efficiency enhancement decision measure. Based on this, a single-objective optimization model for the deviation function is established, as shown below:

[0120]

[0121] Construct a Lagrangian function with ω and λ as parameters, based on the necessary condition for the existence of extrema ( , Find the partial derivatives as shown below:

[0122]

[0123] To effectively combine subjective and objective weighting methods, fully utilize indicator information, and minimize the weight bias obtained by the combined weighting method, a linear combined weight α is further established. k The optimal model is shown below:

[0124]

[0125] Construct the Lagrangian function with ω and λ as parameters, as shown below:

[0126]

[0127] Based on the necessary condition for the existence of extrema, find α respectively. k The partial derivatives with λ are shown below:

[0128]

[0129]

[0130] By combining formulas (18) and (23), the weights μ of each decision-making measure indicator can be obtained. k And the combined weight ω. The subjective weighting method used in this study is AHP, and the objective weighting method is Cloud-EM. Let the combined weighting coefficient α = (α1, α2) be the combined weighting coefficient. T (α1 + α2 = 1), thus obtaining the joint system of equations as shown below:

[0131]

[0132] In summary, the Cloud-EM-AHP combined weighting method based on minimum deviation avoids the problems of overly even and dispersed weight distribution in single weighting methods, and can effectively improve the weight differences between indicators and increase the accuracy of indicator weight calculation. At the same time, this method adjusts the weight distribution based on the inverse cloud model, making the weight ranking more accurate, objective, and consistent with common perceptions.

[0133] Safety-Benefit-Risk Driven ELECTRE Multi-Criterion Decision-Making Model for Improving the Quality and Efficiency of Major Water Conservancy and Hydropower Projects

[0134] Improving the quality and efficiency of major water conservancy and hydropower projects involves multi-criteria decision-making. However, due to the complexity and uncertainty of objective phenomena, as well as the ambiguity and subjectivity of human thinking, some decision information is difficult to express precisely numerically and can only be given in the form of interval numbers. In this case, the ELECTRE-III method can consider the uncertainty of decision data, concretize abstract decision-making thinking, and use hierarchical priority relationships to solve multi-attribute evaluation and decision-making problems. The core idea of ​​the ELECTRE-III method is to construct a weaker order relationship, that is, a hierarchy above the hierarchy. This relationship is based on the following assumption: X(x i ∈X) is a given set of measures, {y ij Let} represent the preference order and attribute matrix of a given decision-maker. When the decision-maker believes x i ≥ x k When, then x is called i The level is higher than x k This hierarchy of priorities is based on the decision-maker's willingness to take on x. i ≥ x k Based on the resulting risk consequences, ELECTRE-III, using the decision-maker's risk load as a basis, integrates the decision-maker's preference order and decision data, uses a threshold function to compare different measures pairwise, forming harmony and disharmony matrices, and then constructs a credibility matrix. Finally, it determines the decision measures by ranking them sequentially through mutual comparison. The multi-criteria decision-making process for major water conservancy and hydropower projects driven by safety, benefit, and risk is as follows: Figure 7 As shown.

[0135] (1) Threshold determination

[0136] Assume there are m alternative measures constituting a measure set A, n indicators constituting an indicator system set G, and W is the set of evaluation indicator weights (Equation 5-25). The indicator weights are calculated using the Cloud-EM-AHP combined weight solution method constructed in this study. Define g... j (a i ) for measure a i Evaluation criteria g j Let g j (ai Without loss of generality, then for measure (a) i , a k Introducing an indifference threshold q j Strict priority threshold p j , rejection threshold v j Three thresholds are used. Among them, q... j Indicates when measure a i and a k In criterion g j The difference in attribute values ​​is no greater than q. j At that time, it was believed that a i and a k In criterion g j The following is indistinguishable. j Indicates when a i and a k In criterion g j The difference in attribute values ​​under is greater than p j At that time, a i Strictly superior to a in terms of criteria k v j Indicates when a i The attribute value is lower than a k The attribute value reaches or exceeds v j At that time, a is no longer considered i Overall, it is at a higher level than A. k The three thresholds mentioned above are determined by the decision-maker, and for any attribute j, generally v must be satisfied. j ≥ p j ≥ q j ≥ 0, v j = 3p j .

[0137]

[0138] (2) Calculation of local harmony and disharmony indices

[0139] For measures a and b satisfying (a, b)∈A, ELECTRE-III first distinguishes between indifference and strict preference, then uses a preference threshold P ≥ Q, and introduces a "hesitation zone" (weak preference) as a buffer between preference and indifference to expand the model, thus obtaining the following dual-threshold model:

[0140]

[0141] For all j∈J, the local harmony index c j The formula for calculating (a, b) is as follows:

[0142]

[0143] In the formula, c j (a, b) is a local harmony index, which refers to the degree to which measure a is superior to measure b in terms of attribute j; g j (a) is the evaluation criterion g for a. j g j (b) is the evaluation criterion g for b. j ;p j For strict priority thresholds; q j The threshold value is set to indifference. Based on this, the calculation model for the global harmony index c(a, b) is as follows:

[0144]

[0145] In the formula, w j Representation criterion g j The relative importance weights.

[0146] For all j∈J, the local disharmony index d j (a, b) represents the degree to which measure a is inferior to b in terms of attribute j. d j The formula for calculating (a, b) is as follows:

[0147]

[0148] (3) The assignment level is higher than the relation judgment level.

[0149] Based on c(a, b) and d j Given (a, b), a credibility score S(a, b) can be calculated for a > b (a is better than b), which is used to represent the measure of the conclusion that "measure a is generally of a higher level than measure b". The calculation model for the credibility score S(a, b) is as follows:

[0150]

[0151] (4) Credibility calculation and measure ranking

[0152] Consistency confidence S calculated using the net value method + (a i Inconsistent reliability S - (a i ) and net credibility δ ai Then, we can explore the priority relationships, and the calculation model is as follows:

[0153]

[0154] In the formula, S + (a i ) and S - (a i ) represent measures a respectivelyi The degree to which it is superior to or inferior to all other measures. δ ai The priority score is determined by the level of the measure, δ. ai The higher the satisfaction level of a measure, the better the measure.

[0155] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A multi-criteria decision-making method for improving the quality and efficiency of major water conservancy and hydropower projects, characterized in that, Includes the following steps: Analyze the factors influencing quality and efficiency improvement, analyze the behavior, functional changes, operation and maintenance status, public safety and risk prevention and control elements of hydropower projects, and identify the factors influencing quality and efficiency improvement of major water conservancy and hydropower projects; Establish an evaluation indicator system, clarify the driving objectives for improving the quality and efficiency of major water conservancy and hydropower projects, and construct a multi-criteria evaluation key indicator system for improving the quality and efficiency of major water conservancy and hydropower projects; A multi-criteria decision-making model is constructed, along with a comprehensive development index model and a coupling coordination degree model for improving the quality and efficiency of major water conservancy and hydropower projects. A Cloud-EM-AHP combined weight solution method based on minimum deviation is proposed to solve the weights of the evaluation indicators, and a multi-criteria decision-making model for improving the quality and efficiency of major water conservancy and hydropower projects based on ELECTRE is established.

2. The multi-criteria decision-making method for improving the quality and efficiency of major water conservancy and hydropower projects according to claim 1, characterized in that, The establishment of the evaluation index system specifically involves constructing a key index matrix for the multi-criteria evaluation of the quality and efficiency improvement of major water conservancy and hydropower projects based on the IDAM model and a thorough investigation of the comprehensive benefits and impacts of major domestic and international water conservancy and hydropower projects. In the matrix, each red circle represents a driving factor nested with an influencing factor, and three key indicators are selected to reflect the interrelationship between the driving and influencing factors. The horizontal indicators of the key index matrix characterize the economic, social, and ecological environmental impacts of major water conservancy and hydropower projects, while the vertical indicators characterize the three driving factors of safety, benefits, and risks in improving the quality and efficiency of hydropower projects. SS, SE, and SO represent social safety, economic safety, and ecological safety, respectively; BS, BE, and BO represent social benefits, economic benefits, and ecological benefits, respectively; and RS, RE, and RO represent social risks, economic risks, and ecological risks, respectively. This results in the construction of a key index system for the evaluation of the quality and efficiency improvement of major water conservancy and hydropower projects.

3. The multi-criteria decision-making method for improving the quality and efficiency of major water conservancy and hydropower projects according to claim 1, characterized in that, The specific calculation model for constructing the comprehensive development index model for improving the quality and efficiency of major water conservancy and hydropower projects is as follows: ， In the formula, CDI k X represents the comprehensive development index of the k-th subsystem in a major water conservancy and hydropower project system. ij Let F(x), G(x), and H(x) represent the standardized value of the j-th indicator in the i-th year. F(x), G(x), and H(x) are the comprehensive development indices of the social, economic, and ecological subsystems involved in the major water conservancy and hydropower project system, respectively, with values ​​ranging from [0, 1].

4. The multi-criteria decision-making method for improving the quality and efficiency of major water conservancy and hydropower projects according to claim 3, characterized in that, The proposed model for the coordination degree of the social-economic-ecosystem coupling of major water conservancy and hydropower projects is as follows: ， In the formula, CCD and C represent the coupling coordination degree and coupling degree of the major water conservancy and hydropower project system, respectively. The closer the CCD value is to 1, the stronger the interaction between the social, economic and ecological subsystems involved in the major water conservancy and hydropower project system, the better the level of coordinated development among the subsystems, and the more the system as a whole will tend to develop in a new orderly structure. Conversely, when the CCD is close to 0, it means that there is almost no connection between the system elements, and the system will develop towards disorder. T is the weighted comprehensive development index, and α, β and γ represent the weights of the three factors of safety, benefit and risk, respectively. α = β = γ = 1 / 3.

5. A multi-criteria decision-making method for improving the quality and efficiency of major water conservancy and hydropower projects according to claim 3, characterized in that, The proposed Cloud-EM-AHP combined weighting solution method based on minimum deviation is used to solve for the weights of the evaluation indicators. Suppose there are m alternative decision-making measures for improving the quality and efficiency of major water conservancy and hydropower projects. Each measure is reflected by n key indicators. The evaluation value of the i-th indicator (i = 1, 2, …, m) of the j-th decision-making measure (j = 1, 2, …, m) is X. ij The corresponding decision matrix is ​​X. Standardizing X yields the standardized decision matrix R, as shown below: ， In the formula, r ij Let ω represent the evaluation value of the i-th indicator in the j-th decision measure, the number of behavioral decision measures in the decision matrix R, and the number of evaluation indicators. The combined weight vector of each indicator in the j-th decision measure is ω. Cloud-EM-AHP_j The formula is expressed as follows: ， In the formula, ω i (ω) i > 0) is the Cloud-EM-AHP combined weight based on minimum deviation for the i-th indicator.

6. The multi-criteria decision-making method for improving the quality and efficiency of major water conservancy and hydropower projects according to claim 1, characterized in that, The establishment of a multi-criteria decision-making model for improving the quality and efficiency of major water conservancy and hydropower projects based on ELECTRE specifically involves... Based on the decision-maker's risk load, and taking into account the decision-maker's preference order and decision data, a threshold function is used to compare different measures in pairs to form a harmony matrix and a disharmony matrix, and then a credibility matrix is ​​constructed. Finally, the decision measures are determined by comparing them with each other and ranking them in order.

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