Basin water network multi-dimensional benefit evaluation method

Abstract

The invention discloses a drainage basin water network multi-dimensional benefit evaluation method, and relates to the technical field of multi-system association evaluation, and the method comprises the following steps: constructing a water network multi-dimensional benefit evaluation index system, and collecting an index measured value of each index in the water network multi-dimensional benefit evaluation index system corresponding to a research drainage basin; adopting a plurality of weighting methods to carry out weighting on the index measured value to obtain a plurality of groups of weighting results, and adopting a game theory-based combined weighting method to obtain a combined weighting result of the index measured value according to the plurality of groups of weighting results; and calculating the multi-dimensional benefit of the water network according to the combined weighting result and the measured value of the index. According to the water network multi-dimensional benefit evaluation method, the water network multi-dimensional benefit evaluation index system is constructed, and the combined weighting method based on the game theory is combined, so that the problem of inconsistent weighting method preferences in the existing water network multi-dimensional benefit evaluation technology is solved, and the scientificity of water network multi-dimensional benefit evaluation is improved.

Description

Technical Field

[0001] The present application relates to the technical field of multi-system correlation evaluation, and in particular to a multi-dimensional benefit evaluation method for a watershed water network. Background Art

[0002] With the rapid advancement of population growth, urbanization and industrialization, the pressure on water resources and water environment caused by high-intensity human activities is increasing, and the water environment is facing severe challenges. In the context of pursuing high-quality development, the demand for sustainable use of water resources, water environment quality and water ecological health is becoming increasingly prominent. In this context, coordinating the governance of multiple waters, building a water network, and forming a comprehensive engineering system with multiple functions are of great significance to ensuring the sustainable and healthy development of the water ecological environment. Therefore, it is necessary to study the multi-dimensional benefit evaluation of the water network.

[0003] In the prior art, Chinese patent CN117151341A discloses a method, device and electronic equipment for hierarchical evaluation of the adaptability of water network construction. The method first obtains the engineering information of water networks at all levels, and then establishes a corresponding relationship between corresponding water network measures and at least one adaptability for each level of water network based on the engineering information of water networks at all levels. Then, based on the corresponding relationship between water network measures and adaptability of water networks at all levels, the evaluation index information and weight information of water networks at all levels are determined. Then, based on the evaluation index information and weight information of water networks at all levels, the adaptability information of water networks at all levels is determined. Finally, the adaptability of water network construction at all levels is evaluated based on the adaptability information of water networks at all levels.

[0004] When evaluating the multidimensional benefits of water networks, it is necessary to assign weights to various indicators. The existing weighting methods (such as entropy weight method, coefficient of variation method, independence weight method, etc.) often have certain differences in weighting results due to inconsistent weighting preferences, resulting in inconsistent evaluation results, which is not conducive to decision-making. However, the above-mentioned existing technologies have not studied or solved this problem, and the scientificity of the multidimensional benefit evaluation of water networks is relatively poor. Summary of the invention

[0005] The present application provides a method for evaluating the multi-dimensional benefits of a watershed water network, which is used to solve the problems of inconsistent preferences for weighting methods and poor scientificity of the multi-dimensional benefit evaluation of water networks in existing water network multi-dimensional benefit evaluation technologies.

[0006] On the one hand, the present application provides a method for evaluating the multi-dimensional benefits of a watershed network, comprising the following steps:

[0007] Step 1: construct a water network multi-dimensional benefit evaluation index system, and collect the actual measured values ​​of each indicator in the water network multi-dimensional benefit evaluation index system corresponding to the research basin.

[0008] Step 2: Use several weighting methods to weight the actual measured values ​​of the indicators respectively to obtain several groups of weighting results, and use a combined weighting method based on game theory to obtain the combined weighting results of the actual measured values ​​of the indicators according to the several groups of weighting results.

[0009] Step three, calculating the multi-dimensional benefits of the water network based on the combined weighting results and the measured values ​​of the indicators.

[0010] In one possible implementation, in step one, the water network multi-dimensional benefit evaluation index system includes: water resources development and utilization indicators, water environment and water ecological protection indicators, flood control and drainage and water safety indicators, and water network management and intelligence indicators.

[0011] In a possible implementation, in step 2, the weighting method includes: entropy weight method, coefficient of variation method and independence weight method.

[0012] In a possible implementation, in step 2, the combined weighting method based on game theory is used to obtain the combined weighting result of the actual value of the indicator according to several groups of weighting results, including:

[0013] The problem of calculating the combined weighted results is transformed into a problem of coordinating the decisions of several participants, and the optimal game coefficient is solved. The combined weighted results of the actual measured values ​​of the indicators are obtained according to the optimal game coefficient.

[0014] In one possible implementation, step three includes: calculating the benefits of water resource development and utilization, water environment and water ecological protection, flood control and drainage and water safety, water network management and intelligent benefits based on the combined weighted results and the actual measured values ​​of the indicators, and obtaining the multi-dimensional benefits of the water network by weighted summation.

[0015] In a possible implementation, step 3 further includes:

[0016] Step 4: Calculate the obstacle degree of each indicator in the water network multidimensional benefit evaluation index system based on the ideal point deviation degree, and identify the obstacle factor indicator in the water network multidimensional benefit evaluation index system according to the obstacle degree.

[0017] In a possible implementation, in step 4, the calculation of the obstacle degree of each indicator in the water network multi-dimensional benefit evaluation index system based on the ideal point deviation degree includes:

[0018] The deviation of the standardized value of each indicator in the water network multi-dimensional benefit evaluation index system from the ideal point is calculated.

[0019] The contribution of each indicator in the water network multi-dimensional benefit evaluation index system to the water network multi-dimensional benefit is calculated.

[0020] The obstacle degree of each indicator in the water network multi-dimensional benefit evaluation index system is calculated according to the deviation degree and the contribution degree.

[0021] A multi-dimensional benefit evaluation method for a watershed water network in this application has the following advantages:

[0022] By constructing a water network multidimensional benefit evaluation index system and combining it with a combined weighting method based on game theory, the problem of inconsistent preferences in weighting methods in existing water network multidimensional benefit evaluation technologies is solved, thereby improving the scientific nature of the water network multidimensional benefit evaluation.

[0023] The proposed water network multi-dimensional benefit evaluation index system includes: water resources development and utilization indicators, water environment and water ecological protection indicators, flood control and drainage and water safety indicators, water network management and intelligent indicators. By constructing indicators from the above four dimensions, the comprehensiveness of the indicators is improved, thereby improving the reliability of the water network multi-dimensional benefit evaluation.

[0024] The proposed weighting methods include entropy weight method, coefficient of variation method and independence weight method, all of which are objective weighting methods, avoiding subjective bias and improving the rationality of the combined weighting method based on game theory.

[0025] The proposed method transforms the problem of calculating the combined weighted empowerment results into the problem of coordinating the decisions of several participants, solves the optimal game coefficient, and obtains the combined weighted empowerment results of the actual measured values ​​of the indicators based on the optimal game coefficient. The vector deviation between the combined weighted empowerment results and the weighted results of each group is minimized through the optimal game coefficient, thereby improving the accuracy of the combined weighted empowerment results.

[0026] The proposed method of calculating the obstacle degree of each indicator in the water network multidimensional benefit evaluation index system based on the ideal point deviation degree, and identifying the obstacle factor indicators in the water network multidimensional benefit evaluation index system according to the obstacle degree, can accurately reflect the indicators that restrict the sustainable development of the regional water network and provide direction for subsequent management. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A flow chart of a method for evaluating the multi-dimensional benefits of a watershed network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] like Figure 1 As shown, the embodiment of the present application provides a method for evaluating the multi-dimensional benefits of a watershed water network, comprising the following steps:

[0031] Step 1: construct a water network multi-dimensional benefit evaluation index system, and collect the actual measured values ​​of each indicator in the water network multi-dimensional benefit evaluation index system corresponding to the research basin.

[0032] Step 2: Use several weighting methods to weight the actual measured values ​​of the indicators respectively to obtain several groups of weighting results, and use a combined weighting method based on game theory to obtain the combined weighting results of the actual measured values ​​of the indicators according to the several groups of weighting results.

[0033] Step three, calculating the multi-dimensional benefits of the water network based on the combined weighting results and the measured values ​​of the indicators.

[0034] Exemplarily, in step one, the water network multi-dimensional benefit evaluation index system includes: water resources development and utilization indicators, water environment and water ecological protection indicators, flood control and drainage and water safety indicators, and water network management and intelligence indicators.

[0035] Specifically, in this embodiment, the water resource development and utilization indicators include: water resource development and utilization rate (E1), water supply guarantee rate (E2), water consumption per 10,000 yuan of GDP (E3), per capita water resource availability (E4), groundwater extraction rate (E5), and irrigation water supply guarantee rate (E6).

[0036] Among them: water resources development and utilization rate (E1) reflects the degree to which water resources in the region are utilized by human activities; water supply guarantee rate (E2) reflects the water supply capacity and stability of water supply projects; water consumption per 10,000 yuan of GDP (E3) reflects the regional water resources utilization efficiency and water-saving potential; per capita available water resources (E4) reflects the geographical and demographic distribution of water resources; groundwater exploitation rate (E5) reflects the utilization degree and sustainability of groundwater resources; irrigation water supply guarantee rate (E6) reflects the ability and reliability of the regional irrigation system to meet the water requirements of crops.

[0037] The calculation method of water resources development and utilization rate (E1) is as follows:

[0038]

[0039] Where: is the water resources development and utilization rate in region j, dimensionless; W 用j is the total water consumption of region j, in m 3 ; W 总j is the total amount of available water resources in region j, in m 3 ;

[0040] The calculation method of water supply guarantee rate (E2) is as follows:

[0041]

[0042] Where: is the water supply guarantee rate of region j, dimensionless; T 足j is the number of days that the actual water supply in area j meets the design standard; T j is the total number of days in the evaluation year.

[0043] The calculation method of water consumption per 10,000 yuan of GDP (E3) is as follows:

[0044]

[0045] Where: is the water consumption per 10,000 yuan of GDP in region j, in m 3 / 10,000 yuan; W 用j is the total water consumption of region j, in m 3 ; G 总j is the gross domestic product of region j, in ten thousand yuan.

[0046] The calculation method of per capita available water resources (E4) is as follows:

[0047]

[0048] Where: is the per capita available water resources in region j, in m 3 / person; W 总j is the total amount of available water resources in region j, in m 3 ;P 总j is the total population of region j, in persons.

[0049] The calculation method of groundwater extraction rate (E5) is as follows:

[0050]

[0051] Where: is the groundwater extraction rate in region j, dimensionless; W 地开j is the groundwater extraction volume in region j, in m 3 ; W 地总jis the groundwater resources in region j, in m 3 .

[0052] The calculation method of irrigation water supply guarantee rate (E6) is as follows:

[0053]

[0054] Where: is the irrigation water supply guarantee rate of region j, dimensionless; A 有效j is the effective irrigation area of ​​region j, in thousand hectares; A 播种j is the sown area of ​​crops in region j, in thousand hectares.

[0055] Specifically, in this embodiment, the water environment and water ecological protection indicators include: the proportion of excellent water quality in important sections (E7), urban sewage treatment rate (E8), water surface rate (E9), river channel frequency (E10), and ecological flow satisfaction rate of important river control sections (E11).

[0056] Among them: the proportion of good water quality in important sections (E7) reflects the status of water environment quality; the urban sewage treatment rate (E8) reflects the proportion of urban sewage treated before it is fully discharged; the water surface rate (E9) reflects the proportion of regional water area to the total area; the river channel frequency (E10) reflects the degree of development of the river network; the ecological flow satisfaction rate of important river control sections (E11) reflects the frequency or proportion of the actual flow of the river reaching or exceeding the minimum flow required to maintain the health of the ecosystem.

[0057] The calculation method of the proportion of excellent water quality in important sections (E7) is as follows:

[0058]

[0059] Where: is the proportion of good water quality in important sections of region j, dimensionless; FA 达标j is the number of rivers that meet the standards in region j, in units; FA 总j is the total number of rivers in region j, in units.

[0060] The calculation method of urban sewage treatment rate (E8) is as follows:

[0061]

[0062] Where: is the urban sewage treatment rate of region j, dimensionless; S 处理j is the total amount of treated sewage in area j, in tons; S j is the total amount of sewage in area j, in tons.

[0063] The calculation method of water surface rate (E9) is as follows:

[0064]

[0065] Where: is the water surface rate of region j, dimensionless; S sj is the total water surface area of ​​region j, in m 3 , S lj is the total land area of ​​region j, in m 3 .

[0066] The calculation method of river channel frequency (E10) is as follows:

[0067]

[0068] Where: is the frequency of river channels in region j, dimensionless, N j is the number of river channels in region j, in units; A j is the study area of ​​region j, in m 3 .

[0069] The calculation method of the ecological flow satisfaction rate (E11) of important river control sections is as follows:

[0070]

[0071] Where: is the ecological flow satisfaction rate of the important river control section in region j, dimensionless; F j is the ecological flow of the important river control section in region j, in m 3 ; F 目标j is the target ecological flow of the important river control section in region j, in m 3 .

[0072] Specifically, in this embodiment, flood control, drainage and water safety indicators include: embankment compliance rate (E12), disaster prevention emergency plan formulation rate (E13), flood disaster loss rate (E14), and flood control compliance rate (E15).

[0073] Among them: the levee compliance rate (E12) reflects the degree of perfection of urban flood control engineering measures; the disaster prevention emergency plan formulation rate (E13) reflects the region's preparedness and ability to deal with natural disasters and emergencies; the flood disaster loss rate (E14) reflects the impact of flood disasters on the local economy; the flood control compliance rate (E15) reflects the construction level and actual effectiveness of the urban flood control system.

[0074] The calculation method of embankment compliance rate (E12) is as follows:

[0075]

[0076] Where: is the compliance rate of dikes in region j, dimensionless; L 达标j is the length of the dike that meets the standard in area j, in km; L j is the total length of the dike in area j, in km.

[0077] The calculation method of disaster emergency plan formulation rate (E13) is as follows:

[0078]

[0079] Where: is the disaster emergency plan formulation rate of region j, dimensionless; N 已制定j is the number of disaster emergency plans that have been formulated in region j, in units; N j is the number of disaster prevention emergency plans that should be formulated in region j, in units.

[0080] The calculation method of flood disaster loss rate (E14) is as follows:

[0081]

[0082] Where: is the flood disaster loss rate in region j, dimensionless; L 洪j is the amount of flood disaster losses in region j, in ten thousand yuan; G 总j is the gross domestic product of region j, in ten thousand yuan.

[0083] The calculation method of flood control compliance rate (E15) is as follows:

[0084]

[0085] Where: is the flood control compliance rate of region j, dimensionless; N 达标j is the number of water conservancy projects that meet the standards in region j, in units; N j is the total number of water conservancy projects in region j, in units.

[0086] Specifically, in this embodiment, water network management and intelligent indicators include: coverage rate of important river and lake monitoring stations (E16), actual water usage metering rate (E17), and intelligent control rate of major water conservancy projects (E18).

[0087] Among them: the coverage rate of important river and lake monitoring stations (E16) reflects the degree of perfection of the river and lake monitoring network construction; the actual water metering rate (E17) reflects the accuracy and reliability of water metering; and the intelligent control rate of major water conservancy projects (E18) reflects the level of modernization and intelligence of water conservancy projects.

[0088] The calculation method of the coverage rate of important river and lake monitoring stations (E16) is as follows:

[0089]

[0090] Where: is the coverage rate of important river and lake monitoring stations in region j, dimensionless; L 覆j is the length of the river section covered by monitoring stations in region j, in km; L j is the total length of river sections in region j, in km.

[0091] The calculation method of actual water usage rate (E17) is as follows:

[0092]

[0093] Where: is the actual water consumption rate in region j, dimensionless; N 已使用计量器材j is the number of irrigation areas in area j that have used metering equipment, in units; N j is the total number of irrigation districts in region j, in units.

[0094] The calculation method of the intelligent control rate (E18) of major water conservancy projects is as follows:

[0095]

[0096] Where: is the intelligent control rate of major water conservancy projects in region j, dimensionless; N 已实施智能化系统j is the number of water conservancy projects in region j that have implemented intelligent systems, in units; N j is the total number of water conservancy projects in region j, in units.

[0097] Exemplarily, in step 2, the weighting methods include: entropy weight method, coefficient of variation method and independence weight method.

[0098] Specifically, the entropy weight method is an objective weighting method that uses information entropy to calculate the weights of each indicator. According to the basic principles of information theory, information is a measure of the degree of order of a system, while entropy is a measure of the degree of disorder of a system. For a certain indicator, the smaller the information entropy value, the greater the degree of dispersion of the indicator, and the greater the impact of the indicator on the comprehensive evaluation (i.e., weight), and vice versa. The specific calculation steps of the entropy weight method are as follows:

[0099] Construct a normalized numeric matrix.

[0100] For the larger the better indicator, the standardized value is as follows:

[0101]

[0102] For the smaller the better indicator, the standardized value is as follows:

[0103]

[0104] After standardizing the selected indicator data, the evaluation matrix R = 9y ij ) m×n As shown below:

[0105]

[0106] In the formula, y ij (i=1,2,…,m;j=1,2,…,n) is the standardized value of the i-th indicator of the j-th regional sample; x ij is the measured value of the ith indicator of the jth regional sample; n and m represent the total number of regional samples and the total number of indicators, respectively.

[0107] Calculate information entropy E i .

[0108] According to the definition of information entropy in information theory, the following formula is used to calculate the information entropy of the i-th indicator:

[0109]

[0110] Where P ij is the standard value y ij The proportion of all regional samples of the i-th indicator. ij = 0,

[0111] Calculate the entropy weight of the i-th indicator

[0112] According to the entropy weight calculation formula, the entropy weight of the i-th indicator is as follows:

[0113]

[0114] In the formula, the entropy weight satisfy and

[0115] Specifically, the coefficient of variation method is a method of assigning weights based on the degree of change in the actual measured value of an indicator. Indicators with larger changes have larger weights, while indicators with smaller changes have smaller weights. The calculation steps are as follows:

[0116] Calculate the mean and standard deviation.

[0117] On the basis of data standardization, the mean and standard deviation of the i-th indicator are calculated based on the following formula:

[0118]

[0119] In the formula, represents the average value of the i-th indicator, σ i Represents the standard deviation of the ith indicator.

[0120] Calculate the coefficient of variation CV i As shown below:

[0121]

[0122] Calculate the coefficient of variation weight of the i-th indicator

[0123] The coefficient of variation is normalized, and then the coefficient of variation weight of the i-th indicator is calculated as shown in the following formula:

[0124]

[0125] Specifically, the independence weight method is an objective weighting method, and its core idea is to use the strength of collinearity between indicators to determine the weights. The independence weight method aims to quantify the independence of different factors (or indicators) through statistical methods and assign weights to them accordingly. If the correlation between an indicator and other indicators is very strong, it means that the information contained in the indicator has a high degree of repetition with the whole, that is, the information overlap is large, so the weight assigned to the indicator is lower; vice versa. The specific calculation steps of the independence weight method are as follows:

[0126] Calculate the multiple correlation coefficient R as shown below:

[0127]

[0128] In the formula, represents the standard deviation, is obtained through linear regression:

[0129]

[0130] The inverse of the multiple correlation coefficient R is normalized, and then the independence weight of the i-th indicator is calculated as shown in the following formula:

[0131]

[0132] Exemplarily, in step 2, the combined weighting method based on game theory is used to obtain the combined weighting result of the measured value of the indicator according to several groups of weighting results, including:

[0133] The problem of calculating the combined weighted results is transformed into a problem of coordinating the decisions of several participants, and the optimal game coefficient is solved. The combined weighted results of the actual measured values ​​of the indicators are obtained according to the optimal game coefficient.

[0134] Specifically, in game theory, it is assumed that each plan is the result of rational decision-making, and its purpose is to seek the optimal solution for the overall interests of the target system. The final result is not controlled by a single decision maker, but is achieved by all decision makers. It can be attributed to a multi-agent optimization problem, and its basic idea is to find consensus or compromise between different decisions, that is, to minimize the deviation between the final decision and the decision plans of each participant.

[0135] In this embodiment, each individual weighting method is regarded as a participant in the game, and the weight vector obtained each time can be used as a decision of the participant. Therefore, the combined weighting problem can be transformed into a problem of coordinating the decisions of multiple participants, and then the optimal game coefficient is solved by quadratic programming, so as to obtain the vector with the smallest deviation from each group of weight vectors as the combined weight.

[0136] Assume that a total of K methods are used to combine and calculate the indicator weights, then the indicator weight vector is W ik =(w 1k ,w 2k ,…,w ik ), where w ik is the weight value calculated by the kth method for the i-th indicator. The combined weight of the i-th indicator can be expressed by the following formula:

[0137]

[0138] In the formula, α ik is the game coefficient of the kth weighting method of the i-th indicator, and satisfies α ig >0.

[0139] In order to find the optimal weight combination method, the difference between the game theory combination weighted result vector and the weighted result vector of each method is minimized. The optimal game coefficient is solved according to the following formula:

[0140]

[0141] Using the matrix differential properties, the first-order derivative of the above formula is transformed into the following equation:

[0142]

[0143] According to the solution method of the quadratic programming problem, the optimal game coefficient is obtained. After normalizing the optimal game coefficient, it is brought into the combination weight formula of the i-th indicator to obtain the combination weighting result based on game theory.

[0144] Exemplarily, step three includes: calculating the benefits of water resource development and utilization, water environment and water ecological protection, flood control and drainage and water safety, water network management and intelligent benefits based on the combined weighted results and the actual measured values ​​of the indicators, and obtaining the multi-dimensional benefits of the water network by weighted summation.

[0145] Specifically, in this embodiment, the calculation method of defining the benefits of water resource development and utilization (WDI), water environment and water ecological protection benefits (EDI), flood control and water safety benefits (FDI), and water network management and intelligent benefits (MDI) is as follows:

[0146]

[0147] Where: WDI j (t), m W , W i W and They represent the water resources development and utilization benefits of the jth region in year t, the total number of indicators, the combined weight of the i-th indicator, and the standardized value of the i-th indicator in the jth region in year t. j (t), m E , and They represent the water environment and water ecological protection benefits of the jth region in year t, the total number of indicators, the combined weight of the i-th indicator, and the standardized value of the i-th indicator in the jth region in year t. j (t), m F , and They represent the flood control, drainage and water security benefits of the jth region in year t, the total number of indicators, the combined weight of the i-th indicator and the standardized value of the i-th indicator in the jth region in year t. j (t), m M , and They respectively represent the water network management and intelligent benefits of the j-th region in year t, the total number of indicators, the combined weight of the ith indicator and the standardized value of the ith indicator in the j-th region in year t.

[0148] Specifically, in this embodiment, the water network multidimensional benefit index (WBI) is a comprehensive indicator used to characterize the comprehensive state of the multidimensional benefits of the water network. Through this indicator, the overall operation status of the water network can be better understood, providing a scientific basis for the rational development, utilization and protection of water resources.

[0149] For the water network multidimensional benefit index of the jth region in year t It can be expressed as the following formula:

[0150]

[0151] In the formula, α, β, γ, and δ represent the importance weight index of water resources development and utilization benefits, water environment and water ecological protection benefits, flood control and drainage and water safety benefits, and water network management and intelligent benefits, respectively, and meet the requirements of

[0152] The value of the water network multidimensional benefit index is between 0 and 1. When it approaches 1, it means that the multidimensional benefits of the water network are higher, and the comprehensive performance of the water network in multiple dimensions such as water resource development and utilization, water environment and water ecological protection, flood control and drainage, water safety, and water network management and intelligence is better. When it approaches 0, it means that the multidimensional benefits of the water network are lower, and the water network's comprehensive performance in multiple dimensions such as water resources development and utilization, water environment and water ecological protection, flood control and drainage, water safety, and water network management and intelligence is poor.

[0153] In a possible embodiment, the classification evaluation criteria for dividing different comprehensive states of multi-dimensional benefits of water networks with 0.1 as the boundary standard are shown in Table 1, which helps decision makers understand the comprehensive benefit state of the water network and further formulate and deploy relevant development plans based on the comprehensive state of multi-dimensional benefits of the water network in the region.

[0154] Table 1 Classification and evaluation criteria for the comprehensive status of multi-dimensional benefits of different water networks

[0155]

[0156] In a possible embodiment, taking the area around Dongting Lake as an example, the influence of 18 basic indicators under 4 categories of indicators in the water network multi-dimensional benefit evaluation index system on the water network is divided into 5 levels from I to V, representing excellent, relatively excellent, qualified, relatively poor, and poor. The standard values ​​of the multi-dimensional benefit evaluation index levels of the water network around Dongting Lake are shown in Table 2, which helps decision makers to further formulate and deploy relevant development plans based on the levels of regional specific indicators.

[0157] Table 2 Standard values ​​of water network multi-dimensional benefit evaluation index grades

[0158] Indicator layer Level I (Excellent) Level II (better) Level III (Pass) Grade IV (poor) V level (poor) Water resources development and utilization rate [25，40] [20，25) [10，20) [5，10) <5 or >40 Water supply security rate ＞98 [95，98] [90，95) [80，90) <80 Water consumption per 10,000 yuan of GDP <50 [50，100) [100，150) [150，200) ＞200 Per capita available water resources ＞900 [667，900] [435,667) [307，435) <307 Groundwater extraction rate <20 [20，40) [40，60) [60，70) ＞70 Irrigation water supply guarantee rate ＞95 [80，95] [65，80) [50，65) <50 Proportion of good water quality in important sections ＞98 [85，98] [75，85) [60，75) <60 Urban sewage treatment rate ＞95 [80，95] [60，80) [40，60) <40 Water surface rate ＞30 [20，30] [10，20) [6，10) <6 River channel frequency ＞1 [0.7，1) [0.5，0.7) [0.2，0.5) <0.2 Ecological flow satisfaction rate of important river control sections ＞98 [90，98] [80，90) [60，80) <60 Dike compliance rate ＞95 [80，95] [75，80) [50，75) <50 Disaster prevention and emergency plan formulation rate ＞80 [70，80] [60，70) [50，60) <50 Flood disaster loss rate [0，1] (1,2.5] (2.5，4] (4,5.5] ＞5.5 Flood control compliance rate [95，100] [80，95] [75，80) [50，75) <50 Coverage rate of important river and lake monitoring stations ＞90 [80，90] [70,80) [60，70) <60 Actual water usage rate [95，100] [80，95] [75，80) [50，75) <50 Intelligent control rate of major water conservancy projects [95，100] [80，95] [75，80) [50，75) <50

[0159] Exemplarily, step three further includes:

[0160] Step 4: Calculate the obstacle degree of each indicator in the water network multidimensional benefit evaluation index system based on the ideal point deviation degree, and identify the obstacle factor indicator in the water network multidimensional benefit evaluation index system according to the obstacle degree.

[0161] Exemplarily, in step 4, the calculation of the obstacle degree of each indicator in the water network multi-dimensional benefit evaluation index system based on the ideal point deviation degree includes:

[0162] The deviation of the standardized value of each indicator in the water network multi-dimensional benefit evaluation index system from the ideal point is calculated.

[0163] The contribution of each indicator in the water network multi-dimensional benefit evaluation index system to the water network multi-dimensional benefit is calculated.

[0164] The obstacle degree of each indicator in the water network multi-dimensional benefit evaluation index system is calculated according to the deviation degree and the contribution degree.

[0165] Specifically, in this embodiment, the standardized value y of the i-th indicator is calculated. i Deviation from the ideal point D i , since the directions of the indicators after maximum-minimum standardization are consistent and satisfy y i ∈[0,1]. Therefore, y i The ideal point is 1. The standardized value y of the i-th indicator i Deviation from the ideal point D i The calculation method is as follows:

[0166] D i =1-y i .

[0167] Calculate the contribution of the i-th indicator to the multi-dimensional benefits of the water network C i In this embodiment, the combined weight value of the indicator is as shown in the following formula:

[0168]

[0169] Calculate the obstacle degree R of the i-th indicator i When, as shown below:

[0170]

[0171] In the formula, R i is the obstacle degree of the ith indicator, and m is the total number of indicators in the system.

[0172] The value of the obstacle degree is between 0 and 1, and R iThe larger the value, the stronger the constraint of the i-th indicator on the multi-dimensional benefits of the water network, and it should be the focus of subsequent management.

[0173] In a possible embodiment, taking the historical measured values ​​of each indicator in the multi-dimensional benefit evaluation index system of the water network in the area around Dongting Lake from 2018 to 2022 as an example, the weights of each indicator are calculated using three weighting methods, namely the entropy weight method, the coefficient of variation method and the independence weight method, and the combined weight value of each indicator is calculated using a combined weighting method based on game theory.

[0174] The weight calculation results of each indicator corresponding to the multi-dimensional benefit evaluation index system of water network are shown in Table 3:

[0175] Table 3 Calculation results of weights of various indicators in the water network multidimensional benefit evaluation index system

[0176]

[0177] According to Table 3, there are differences in the weight coefficient results calculated by different methods. This is mainly because: the entropy weight method is a method of calculating weights based on the basic theory of informatics and the information content of each indicator in the system; while the coefficient of variation method and the independence weight method are methods of calculating weights based on statistical theory, taking into account the degree of statistical change of each indicator in the system. Different methods have different calculation principles and focuses, so this application uses a combined weighting method based on game theory. This method combines the calculation results of the three weighting methods to obtain the optimal combined weight value, which is conducive to overcoming the error influence of different methods on the calculation results, and providing objective and reasonable indicator weight values ​​for the multi-dimensional benefit evaluation of the water network in the area around Dongting Lake.

[0178] Among the water resources development and utilization indicators, the water supply guarantee rate (E2), as an important indicator to measure the stability of the water supply system and the water supply capacity of the water supply project, has a weight of 0.0197 and has a dominant impact on the multi-dimensional benefits of the water network in the Dongting Lake area. This shows that the water supply guarantee issue is an important factor affecting the multi-dimensional benefits of the water network.

[0179] Among the water environment and water ecological protection indicators, the ecological flow satisfaction rate of important river control sections (E11) has a weight of 0.0224, which is absolutely dominant. The ecological flow satisfaction rate of important river control sections is an important indicator to measure the ecological health of rivers and the effectiveness of water resources management. It reflects the degree of compliance between the actual ecological flow of the river control section and the set ecological flow target. In addition, the proportion of good water quality in important sections (E7), as the most direct reflection of the water environment quality, has a weight of 0.0197 and also plays an important role in the multi-dimensional benefit evaluation of water networks.

[0180] Among the flood control and water safety indicators, the weight of flood control compliance rate (E15) is 0.0213. The levee compliance rate reflects the construction level of a region's flood control system, is an important guarantee for water safety, and is also one of the important factors in the multi-dimensional benefit evaluation of the water network.

[0181] Among the water network management and intelligence indicators, the coverage rate of important river and lake monitoring stations (E16) has a weight of 0.0239. The coverage rate of important river and lake monitoring stations reflects the monitoring capacity and level of water resources management and ecological protection. It is an important indicator of water network management and intelligence level, and is also one of the important factors in the multi-dimensional benefit evaluation of water networks.

[0182] Combined with the above discussion, it can be seen that in the evaluation of the multidimensional benefits of water networks, the indicators with the largest weights in each system are usually indicators that represent the ability to support the sustainable development of social economy and ecological environment. For example, the water supply guarantee rate (E2) represents water resources indicators, the ecological flow satisfaction rate of important river control sections (E11) and the proportion of excellent water quality in important sections (E7) represent water environment indicators, the flood control compliance rate (E15) represents water safety indicators, and the coverage rate of important river and lake monitoring stations (E16) represents water management indicators. These indicators all have a large weight. Therefore, ensuring the basic needs of the economy and society for water resources, water environment, water safety and water management is the key to improving the multidimensional benefits of water networks.

[0183] In a possible embodiment, taking the historical measured values ​​of each indicator in the water network multi-dimensional benefit evaluation index system of the present application corresponding to the area around Dongting Lake in 2021 as an example, the multi-dimensional benefits of the water network of each indicator are calculated based on the combined weighting results and the measured values ​​of the indicators.

[0184] The evaluation results of the multi-dimensional benefits of water networks corresponding to each indicator of the multi-dimensional benefits evaluation index system of water networks are shown in Table 4:

[0185] Table 4 Evaluation results of water network multidimensional benefit of each indicator in the water network multidimensional benefit evaluation index system

[0186]

[0187] According to Table 4, the multi-dimensional benefit evaluation level of each indicator of the water network in the area around Dongting Lake is at a relatively good level or above, and the multi-dimensional benefit evaluation level of most indicators of the water network is at a relatively good level or excellent level. There are 9 indicators at the excellent level, namely, water resources development and utilization rate, water supply guarantee rate, per capita water resources available, groundwater exploitation rate, irrigation water supply guarantee rate, proportion of excellent water quality in important sections, urban sewage treatment rate, disaster prevention emergency plan formulation rate, and coverage rate of important river and lake monitoring stations. There are 5 indicators at the relatively good level, namely, water surface rate, ecological flow satisfaction rate of important river control sections, flood control compliance rate, actual water metering rate, and intelligent control rate of major water conservancy projects. There are 3 indicators at the passing level, namely, water consumption per 10,000 yuan of GDP, river channel frequency, and flood disaster loss rate. There is 1 indicator at the poor level, which is the embankment compliance rate.

[0188] Specifically, many indicators such as water resource development and utilization rate, water supply guarantee rate, and per capita available water resources are at an excellent level, reflecting the efficient use and reasonable allocation of water resources in the region, while also ensuring the water demand for residents' lives and economic and social development. Indicators such as water consumption per 10,000 yuan of GDP, river channel frequency, and flood disaster loss rate are at a passing level, indicating that more attention should be paid to water resource conservation and utilization and river ecological restoration. The levee compliance rate is at a poor level, indicating that levee construction should be strengthened, and early warning and prevention measures should still be strengthened in response to extreme weather and natural disasters.

[0189] The embodiment of the present application solves the problem of inconsistent preferences of weighting methods in existing water network multidimensional benefit evaluation technologies by constructing a water network multidimensional benefit evaluation index system and combining it with a combined weighting method based on game theory, thereby improving the scientific nature of the water network multidimensional benefit evaluation.

[0190] The proposed water network multi-dimensional benefit evaluation index system includes: water resources development and utilization indicators, water environment and water ecological protection indicators, flood control and drainage and water safety indicators, water network management and intelligent indicators. By constructing indicators from the above four dimensions, the comprehensiveness of the indicators is improved, thereby improving the reliability of the water network multi-dimensional benefit evaluation.

[0191] The proposed weighting methods include entropy weight method, coefficient of variation method and independence weight method, all of which are objective weighting methods, avoiding subjective bias and improving the rationality of the combined weighting method based on game theory.

[0192] The proposed method transforms the problem of calculating the combined weighted empowerment results into the problem of coordinating the decisions of several participants, solves the optimal game coefficient, and obtains the combined weighted empowerment results of the actual measured values ​​of the indicators based on the optimal game coefficient. The vector deviation between the combined weighted empowerment results and the weighted results of each group is minimized through the optimal game coefficient, thereby improving the accuracy of the combined weighted empowerment results.

[0193] The proposed method of calculating the obstacle degree of each indicator in the water network multidimensional benefit evaluation index system based on the ideal point deviation degree, and identifying the obstacle factor indicators in the water network multidimensional benefit evaluation index system according to the obstacle degree, can accurately reflect the indicators that restrict the sustainable development of the regional water network and provide direction for subsequent management.

[0194] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0195] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A multi-dimensional benefit evaluation method for a river basin water network, characterized in that: The following steps are involved: Step 1: construct a water network multidimensional benefit evaluation index system, and collect the measured values ​​of each indicator in the water network multidimensional benefit evaluation index system corresponding to the research basin; Step 2: using several weighting methods to weight the measured values ​​of the indicators respectively, obtaining several groups of weighting results, and using a combined weighting method based on game theory to obtain the combined weighting results of the measured values ​​of the indicators according to the several groups of weighting results; Step three, calculating the multi-dimensional benefits of the water network based on the combined weighting results and the measured values ​​of the indicators.

2. A method for evaluating the multi-dimensional benefits of a watershed network according to claim 1, characterized in that: In step one, the water network multi-dimensional benefit evaluation index system includes: water resources development and utilization indicators, water environment and water ecological protection indicators, flood control and drainage and water safety indicators, and water network management and intelligence indicators.

3. A method for evaluating the multi-dimensional benefits of a watershed network according to claim 1, characterized in that: In step 2, the weighting methods include: entropy weight method, coefficient of variation method and independence weight method.

4. A method for evaluating the multi-dimensional benefits of a watershed network according to claim 1, characterized in that: In step 2, the combined weighting method based on game theory is used to obtain the combined weighting results of the measured values ​​of the indicators according to several groups of weighting results, including: The problem of calculating the combined weighted results is transformed into a problem of coordinating the decisions of several participants, and the optimal game coefficient is solved. The combined weighted results of the actual measured values ​​of the indicators are obtained according to the optimal game coefficient.

5. The multi-dimensional benefit evaluation method of a watershed network according to claim 1 is characterized in that: Step three includes: calculating the benefits of water resource development and utilization, water environment and water ecological protection, flood control and drainage and water safety, water network management and intelligent benefits based on the combined weighted results and the actual measured values ​​of the indicators, and obtaining the multi-dimensional benefits of the water network by weighted summation.

6. A method for evaluating the multi-dimensional benefits of a watershed network according to claim 1, characterized in that: After step 3, it also includes: Step 4: Calculate the obstacle degree of each indicator in the water network multidimensional benefit evaluation index system based on the ideal point deviation degree, and identify the obstacle factor indicator in the water network multidimensional benefit evaluation index system according to the obstacle degree.

7. A method for evaluating the multi-dimensional benefits of a watershed network according to claim 6, characterized in that: In step 4, the obstacle degree of each indicator in the water network multi-dimensional benefit evaluation index system is calculated based on the ideal point deviation degree, including: Calculate the deviation of the standardized value of each indicator in the water network multi-dimensional benefit evaluation index system from the ideal point; Calculating the contribution of each indicator in the water network multi-dimensional benefit evaluation index system to the water network multi-dimensional benefit; The obstacle degree of each indicator in the water network multi-dimensional benefit evaluation index system is calculated according to the deviation degree and the contribution degree.

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