CN112465206B - Water resource optimization configuration and optimization result evaluation feedback method - Google Patents

Water resource optimization configuration and optimization result evaluation feedback method Download PDF

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CN112465206B
CN112465206B CN202011310637.2A CN202011310637A CN112465206B CN 112465206 B CN112465206 B CN 112465206B CN 202011310637 A CN202011310637 A CN 202011310637A CN 112465206 B CN112465206 B CN 112465206B
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潘炜杰
杨志峰
蔡宴朋
肖俊
郭红江
欧阳怡然
谭倩
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Guangdong University of Technology
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Abstract

The invention discloses a water resource optimal allocation and optimal result evaluation feedback method, which comprises the following steps: collecting monitoring data and regional population data; establishing a water resource bearing capacity index system; confirming the index weight of the evaluation model, establishing a correlation function, and constructing a water resource bearing capacity evaluation model; the method comprises the steps of taking the total water supply amount of regional water resources as a core, taking the maximized social and economic benefits and the maximized ecological benefits of unit systems of all departments as optimization targets, taking the water supply amount of each water resource and the water demand amount of each department as constraint conditions, constructing a water resource optimization configuration model, and solving the model to obtain a regional water resource optimization configuration scheme; and converting water consumption data of each department in the water resource optimal allocation scheme into index values through the coefficient of pollutants entering the river of unit water consumption, substituting the index values into a water resource bearing capacity evaluation model for operation, and obtaining a water resource bearing capacity result after optimal allocation. The invention solves the problem that the prediction result of the water resource in the prior art is lack of real-time feedback.

Description

Water resource optimization configuration and optimization result evaluation feedback method
Technical Field
The invention relates to the technical field of water resource optimal configuration, in particular to a water resource optimal configuration and an optimization result evaluation feedback method.
Background
Water resources are important resources on which human beings live, and have great influence on sustainable development of regional socioeconomic and ecological environments. With the rapid development of economic society, the production living standard is continuously improved, the water consumption and the sewage discharge are rapidly increased, and the water saving and water control consciousness is weak, so that the water body is seriously polluted, and the problem of water resource shortage becomes an important restriction factor for the continuous development of the economic society in China. Therefore, research on regional water resource optimal allocation and coordination of the relationship between ecological, living, industrial and agricultural development and water resources are of great significance to regional sustainable development.
Generally, the traditional water resource optimization configuration technology is mainly established on the analysis of historical data, adopts a mathematical programming method, a simulation technology and the like to predict water demand, relates to various factors, and only reflects general change rules by singly adopting some mathematical means. Meanwhile, the existing research on water resource optimization configuration is usually only optimized, a good evaluation system is not provided, the optimization configuration result is fed back, and the prediction result is lack of credibility and use basis. Therefore, the scientific and reasonable evaluation of the bearing capacity of the water resource can provide effective decision guidance for the sustainable utilization of the water resource, and the scientific and reasonable optimization configuration of the water resource can promote the sustainable utilization of the water resource.
Disclosure of Invention
The invention aims to provide a water resource optimal configuration and an optimization result evaluation feedback method, which can be used for further researching the water resource optimal configuration on the basis of the research on the water resource bearing capacity evaluation and can more quickly and effectively realize the sustainable utilization of water resources.
In order to realize the task, the invention adopts the following technical scheme:
a water resource optimization configuration and optimization result evaluation feedback method comprises the following steps:
collecting monitoring data and regional population data;
establishing a water resource bearing capacity index system by using the monitoring data and the regional population data;
after standardization and normalization processing are carried out on all index values in a water resource bearing capacity index system, the index weight of an evaluation model is confirmed, a correlation function is established, and a water resource bearing capacity evaluation model is constructed;
obtaining the water supply amount and the water demand amount of each door of each region based on the statistical yearbook and official data;
the method comprises the steps of taking the total water quantity available for regional water resources as a core, meeting the requirements of local industrial, domestic and ecological water use, taking the maximized social and economic benefits and the maximized ecological benefits of unit systems of all departments as optimization targets, taking the water supply quantity available for each water source and the water demand quantity of each department as constraint conditions, constructing a water resource optimization configuration model, and solving the model to obtain a regional water resource optimization configuration scheme;
converting water consumption data of each department in the water resource optimal allocation scheme into each index value through the unit water consumption pollutant river entering coefficient, substituting the index values into a water resource bearing capacity evaluation model for operation, and obtaining a water resource bearing capacity result after optimal allocation;
and comparing the result of the bearing capacity of the water resource before optimization with the result of the bearing capacity of the water resource after optimization, and judging the rationality of the optimization configuration scheme.
Further, the collecting monitoring data and regional population data comprises:
collecting monitoring data of a monitoring station, including water quality data and water quantity data; and collecting relevant data of the population quantity and the area of the region by inquiring the local yearbook or official information of each region.
Further, the water resource bearing capacity index comprises: dissolved oxygen content, inorganic suspended particulate matter, total ammonia content, total nitrogen content.
Further, after the index values in the water resource bearing capacity index system are standardized and normalized, determining the index weight of the evaluation model, establishing a correlation function, and constructing a water resource bearing capacity evaluation model, the method includes:
standardizing and normalizing the index values;
data x obtained by normalization j Substituting the weight coefficient into the following formula to obtain each index weight coefficient: the expression is as follows:
Figure BDA0002789632550000021
Figure BDA0002789632550000022
in the formula, when F j When =0, then H is defined j ＝0；
Figure BDA0002789632550000023
In the formula: f j The proportion of the j index in the total index is; h j Entropy value of j index; w j Entropy weight of j index; n is the index number;
establishing a correlation function, and establishing a water resource bearing capacity evaluation model; determining the relevance degree of each index of the water resource bearing capacity belonging to each reliability grade to obtain a water resource bearing capacity result, wherein the method comprises the following steps:
the relevance of the ith index of the area to the evaluation grade j (j =1,2, \8230;, m) is determined as follows:
Figure BDA0002789632550000031
wherein the distance is as follows:
Figure BDA0002789632550000032
Figure BDA0002789632550000033
calculating the relevance of the area p with respect to the reliability level j:
Figure BDA0002789632550000034
and (3) evaluating the water resource bearing capacity result of the region p:
if K j ＝max K j (p) (j =1,2, \ 8230;, m), then p is rated as belonging to rank j;
in the formula, K j (v i ) Reliability for the ith index of a regionThe degree of association of the level j; ρ (v) i ，V ij ) The distance of the ith index of the area relative to the evaluation grade j; v. of i Data x after normalization processing for evaluation index j ；V ij A magnitude range for each index i with respect to each evaluation level j; v ip Is the total value range of each index i; a is oji 、b oji A magnitude range of the index i with respect to the evaluation level j; a is a pi 、b pi Indicates the total amount value range of the index i; k is j (p) is the degree of association of region p with respect to reliability level j; w is a i Entropy weight W for the jth index j 。
Further, the water resource optimization configuration model includes:
the objective function of the water resource optimization configuration model is as follows:
maximizing the unit economic benefit target of each department:
Figure BDA0002789632550000035
the ecological benefit target of each department unit system is maximized:
Figure BDA0002789632550000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002789632550000042
respectively representing the economic benefit and the system ecological benefit of water resources of all departments in the research area; i represents a sub-study area; j represents different water use departments; t is the study period, I represents the number of study areas, J represents the number of water departments, and T represents the study time range;
Figure BDA0002789632550000043
the quantity of water resources allocated to the subsection j of the sub-research area i in the period t;
Figure BDA0002789632550000044
is a sub-study area of period tWater use Unit profit of Domain i department j, (Yuan/m) 3 )；
Figure BDA0002789632550000045
The unit cost of water utilization of i department j in the sub-research area at the time t; ben ± The economic benefit is achieved; ECO ± The ecological environmental benefit;
Figure BDA0002789632550000046
the content of COD (chemical oxygen demand) in the pollutant of j in the i department of the study area in the period t;
Figure BDA0002789632550000047
is the content percent of pollutants in the sewage discharge of a subregion i department j.
Further, the constraint conditions of the water resource optimization configuration model are as follows:
condition 1: available water resource quantity constraint:
Figure BDA0002789632550000048
condition 2: water demand restraint:
Figure BDA0002789632550000049
condition 3: non-negative constraints:
Figure BDA00027896325500000410
in the formula, C r {. Is a random chance constraint; eta ± Is the water loss rate in the transport process;
Figure BDA00027896325500000411
is the total available water amount;
Figure BDA00027896325500000412
is a sub-region of the t periodi minimum/maximum water demand for department j, α is the confidence level.
Further, the solving of the model to obtain an optimal configuration scheme of regional water resources includes:
giving different confidence levels alpha, converting fuzzy confidence constraints into typical interval type constraints, and constructing an interval multi-objective optimization model;
constructing a formula of the confidence level alpha;
splitting the interval multi-target optimization model into an upper limit submodel and a lower limit submodel by using an interactive two-step algorithm;
and respectively solving each sub-target to obtain a maximum unit target and a minimum unit target of each department:
Figure BDA0002789632550000051
and with
Figure BDA0002789632550000052
The sub-targets comprise economic benefit targets of all department units and ecological benefit targets of all department unit systems;
constructing a membership function of each sub-target;
and solving the interval multi-target optimization model by adopting a fuzzy membership method to obtain water resource optimization allocation schemes of the research areas corresponding to different confidence levels alpha.
Further, the interval multi-objective optimization model is as follows:
Figure BDA0002789632550000053
in the above-mentioned formula, the compound has the following structure,
Figure BDA0002789632550000054
to represent
Figure BDA0002789632550000055
Assuming a triangular fuzzy number, 1,2,3 represent the minimum, most reliable and maximum possible values, respectively。
Further, the formula for the confidence level α is as follows:
Figure BDA0002789632550000056
further, the membership function of each sub-target is as follows:
Figure BDA0002789632550000057
in the formula (I), the compound is shown in the specification,
Figure BDA0002789632550000058
representing the nth interval form objective function;
solving the interval multi-objective optimization model by adopting a fuzzy membership method, wherein the method comprises the following steps:
maxη ± ＝γ ±
Figure BDA0002789632550000061
Figure BDA0002789632550000062
Figure BDA0002789632550000063
Figure BDA0002789632550000064
in the formula, gamma ± Indicating a degree of satisfaction, ranging from 0 to 1.
Compared with the prior art, the invention has the following technical characteristics:
the method of the invention combines the social proposition of the current water total amount control and the environmental problem of the water quality red line, takes the maximized social and economic benefits and the maximized ecological benefits of each department unit system as optimization targets to obtain a water resource allocation scheme, and solves the problem that the prediction result of the water resource in the prior art is lack of real-time feedback. And respectively inputting the water resource allocation scheme data and the sub-region water resource utilization scheme into a water resource bearing capacity evaluation model to obtain the water resource bearing capacity, overcoming the problem that the traditional solution method and other schemes lack real-time feedback through the quick judgment of the model result, providing a decision for the allocation of the watershed water resources, and achieving the purpose of comprehensively and effectively managing the water resources.
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FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of a solution of a water resource optimization configuration model;
fig. 3 is a schematic diagram of the principle of the present invention.
Detailed Description
Referring to fig. 1 to 3, the present invention provides a water resource optimization configuration and optimization result evaluation feedback method, including the following steps:
step 1, collecting monitoring data and regional population data, comprising:
collecting monitoring data of a monitoring station, including water quality data and water quantity data; and relevant data such as the population quantity of the region, the area of the region and the like are collected by inquiring the local yearbook or official information of each region.
Step 2, establishing a water resource bearing capacity index system by using the data collected in the step 1; the water resource bearing capacity index includes: dissolved oxygen content, inorganic suspended particulate matter, total ammonia content, total nitrogen content, and the like.
Step 3, after standardization and normalization processing are carried out on all index values in the water resource bearing capacity index system, the index weight of the evaluation model is confirmed, a correlation function is established, and a water resource bearing capacity evaluation model is constructed; the method comprises the following concrete steps:
3.1 the standardization and normalization processing of each index value, the formula is as follows:
the forward direction index:
Figure BDA0002789632550000071
reverse indexes are as follows:
Figure BDA0002789632550000072
in the formula: x is a radical of a fluorine atom max(j) ，x min(j) The maximum value and the minimum value of the j index value, x * (j) Raw data for evaluation index, x j And normalizing the processed data for the evaluation index.
3.2 confirming the index weight coefficient.
Data x from 3.1 normalization, normalization j And substituted into the following formula to obtain each index weight coefficient. The entropy value reflects the degree of disorder of the system and can be used to measure the amount of useful information provided by the data. The larger the variation degree of a certain index value is, the smaller the entropy value is, the larger the effective information amount provided by the index is, the larger the weight of the index is, and vice versa. The expression is as follows:
Figure BDA0002789632550000073
Figure BDA0002789632550000074
in the formula, when F j When =0, then H is defined j ＝0。
Figure BDA0002789632550000075
In the formula: f j The specific gravity of the j index in the total index is; h j Entropy value of j index; w j Entropy weight of j index; n is the index number.
3.3, establishing a correlation function, and establishing a water resource bearing capacity evaluation model; and determining the relevance degree of each index of the water resource bearing capacity belonging to each reliability grade to obtain a water resource bearing capacity result.
The relevance of the ith index of the area to the evaluation grade j (j =1,2, \8230;, m) is determined as follows:
Figure BDA0002789632550000076
wherein the distance is as follows:
Figure BDA0002789632550000077
Figure BDA0002789632550000081
calculating the relevance of the region p with respect to the reliability level j:
Figure BDA0002789632550000082
and (4) evaluating the water resource bearing capacity result of the region p:
if K j ＝max K j (p) (j =1,2, \8230;, m), then p is rated as belonging to rank j.
In the formula, K j (v i ) The relevance of the ith index of the area with respect to the reliability level j; ρ (v) i ，V ij ) The distance of the ith index of the area relative to the evaluation grade j; v. of i Data x after normalization processing of evaluation index in 3.1 j ；V ij A magnitude range for each index i with respect to each evaluation level j; v ip Is the total value range of each index i; a is a oji 、b oji A magnitude range of the index i with respect to the evaluation level j; a is a pi 、b pi Indicates the total amount value range of the index i; k j (p) is the degree of association of region p with respect to reliability level j; w is a i Entropy weight W for the j index calculated in 3.1 j 。
And (4) outputting a target area water resource bearing capacity result through the water resource bearing capacity evaluation model operation in the step, and comparing the result with the optimized water resource bearing capacity in the subsequent process.
And 4, obtaining the water supply amount and the water demand amount of each department in each region based on the statistical yearbook and the official data.
And 5, taking the total water quantity available by regional water resources as a core, meeting the requirements of local industrial, domestic and ecological water, taking the maximized social and economic benefits and the maximized ecological benefits of unit systems of all departments as optimization targets, taking the water supply quantity available by all water sources, the water demand quantity needed by all the departments and the like as constraint conditions, constructing a water resource optimization configuration model, and solving the model to obtain a regional water resource optimization configuration scheme.
Wherein, the objective function of the water resource optimization configuration model is as follows:
the unit economic benefit target of each department is maximized:
Figure BDA0002789632550000083
the ecological benefit target of each department unit system is maximized:
Figure BDA0002789632550000084
in the formula (I), the compound is shown in the specification,
Figure BDA0002789632550000085
respectively representing the economic benefit and the system ecological benefit of water resources of all departments in the research area; i represents a sub-study area; j represents different water use departments; t is the study period, I indicates the number of study areas, J indicates the number of water departments, and T indicates the study time range;
Figure BDA0002789632550000091
the quantity of water resources allocated to the i department j of the sub-research area in the period t;
Figure BDA0002789632550000092
water use Unit gain for sub-study area i sector j during t period, (Yuan/m) 3 )；
Figure BDA0002789632550000093
The unit cost of water utilization of i department j in the sub-research area at the time t; ben ± The economic benefit is achieved; ECO ± Ecological environmental benefits;
Figure BDA0002789632550000094
the content of COD (chemical oxygen demand) in the pollutant of j in the i department of the study area in the period t;
Figure BDA0002789632550000095
is the content percent of pollutants in the sewage discharge of the sub-area i department j.
The constraint conditions of the water resource optimization configuration model are as follows:
condition 1: available water resource quantity constraint:
Figure BDA0002789632550000096
condition 2: water demand restraint:
Figure BDA0002789632550000097
condition 3: non-negative constraints:
Figure BDA0002789632550000098
in the formula, C r {. Is a random chance constraint; eta ± Is the water loss rate in the transport process;
Figure BDA0002789632550000099
is the total available water quantity (10) 8 m 3 )；
Figure BDA00027896325500000910
Minimum/maximum water demand for sub-region i sector j during period t (10) 8 m 3 ) And α is the confidence level.
Under the constraint conditions, the solution process of the objective function is as follows:
5.1, giving different confidence levels alpha, converting fuzzy confidence constraints into typical interval type constraints, and constructing an interval multi-objective optimization model as follows:
Figure BDA00027896325500000911
Figure BDA0002789632550000101
in the above formula, the first and second carbon atoms are,
Figure BDA0002789632550000102
to represent
Figure BDA0002789632550000103
Assuming a triangular blur number, 1,2,3 represent the minimum possible value, the most reliable value, and the maximum possible value, respectively.
5.2 the formula for the confidence level α is as follows:
Figure BDA0002789632550000104
and 5.3, splitting the interval multi-target optimization model into an upper limit sub-model and a lower limit sub-model by using an interactive two-step algorithm.
5.4 separately solving each sub-target to obtain the maximum unit target and the minimum unit target of each department
Figure BDA0002789632550000105
And
Figure BDA0002789632550000106
the sub-targets comprise economic benefit targets of all department units and ecological benefit targets of all department unit systems.
5.5 construct membership functions for each sub-target as follows:
Figure BDA0002789632550000107
5.6 solving the interval multi-target optimization model by adopting a fuzzy membership method:
maxη ± ＝γ ±
Figure BDA0002789632550000108
Figure BDA0002789632550000109
Figure BDA0002789632550000111
Figure BDA0002789632550000112
in the formula, gamma ± Indicating a degree of satisfaction, ranging from 0 to 1.
5.7 solving based on the above 5.1 to 5.6 to obtain a water resource optimization configuration scheme of the research area:
Figure BDA0002789632550000113
wherein the content of the first and second substances,
Figure BDA0002789632550000114
respectively representing the lower limit and the upper limit of the water resource quantity distributed to the sub-research area i department j in the period t;
5.8, setting different confidence levels alpha, repeating the steps from 5.1 to 5.7 to obtain the water resource optimal allocation scheme of the research area under different confidence levels.
Step 6, optimizing the water resource allocation scheme, wherein the water distribution quantity of each departmentAccording to
Figure BDA0002789632550000115
And (4) converting the coefficient of the pollutants entering the river by unit water consumption into each index value, and substituting the index value into the water resource bearing capacity evaluation model operation in the step (3) to obtain a water resource bearing capacity result after optimal configuration.
Step 7, comparing the result of the bearing capacity of the water resource before optimization with the result of the bearing capacity of the water resource after optimization, and judging the rationality of the optimization configuration scheme; obtaining timely feedback information; if the water resource optimization configuration scheme is not reasonable enough, the water resource optimization configuration scheme is adjusted in time.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (6)

1. A water resource optimal configuration and optimization result evaluation feedback method is characterized by comprising the following steps:
collecting monitoring data and regional population data;
establishing a water resource bearing capacity index system by using the monitoring data and the regional population data;
after standardization and normalization processing are carried out on all index values in a water resource bearing capacity index system, the index weight of an evaluation model is confirmed, a correlation function is established, and a water resource bearing capacity evaluation model is constructed;
obtaining the water supply amount and the water demand amount of each department in each region based on the statistical yearbook and official data;
the method comprises the steps of taking the total water quantity available for regional water resources as a core, meeting the requirements of local industrial, domestic and ecological water use, taking the maximized social and economic benefits and the maximized ecological benefits of unit systems of all departments as optimization targets, taking the water supply quantity available for each water source and the water demand quantity of each department as constraint conditions, constructing a water resource optimization configuration model, and solving the model to obtain a regional water resource optimization configuration scheme;
converting water consumption data of each department in the water resource optimal configuration scheme into index values through the coefficient of pollutants entering the river of unit water consumption, substituting the index values into a water resource bearing capacity evaluation model for operation, and obtaining a water resource bearing capacity result after optimal configuration;
comparing the result of the bearing capacity of the water resource before optimization with the result of the bearing capacity of the water resource after optimization, and judging the rationality of the optimization configuration scheme;
the water resource optimal configuration model comprises:
the objective function of the water resource optimization configuration model is as follows:
maximizing the unit economic benefit target of each department:
Figure FDA0003751323790000011
the ecological benefit target of each department unit system is maximized:
Figure FDA0003751323790000012
in the formula (I), the compound is shown in the specification,
Figure FDA0003751323790000013
f 1 ± respectively representing the economic benefit and the system ecological benefit of water resources of all departments in the research area; i represents a sub-study area; j represents different water use departments; t is the study period, I indicates the number of study areas, J indicates the number of water departments, and T indicates the study time range;
Figure FDA0003751323790000014
the quantity of water resources allocated to the department j of the sub-research area i in the period t;
Figure FDA0003751323790000015
the water utilization unit profit of the i department j of the sub-research area in the period t is as follows: yuan per square meter;
Figure FDA0003751323790000021
the unit cost of water utilization of the i department j of the sub-research area in the period t; ben ± The economic benefit is achieved; ECO ± The ecological environmental benefit;
Figure FDA0003751323790000022
researching the content of Chemical Oxygen Demand (COD) in the pollutants of j in the section i in the area for the period t;
Figure FDA0003751323790000023
the content of pollutants in the sewage discharge of the department j of the subregion i;
the constraint conditions of the water resource optimization configuration model are as follows:
condition 1: available water resource quantity constraint:
Figure FDA0003751323790000024
condition 2: water demand restraint:
Figure FDA0003751323790000025
condition 3: non-negative constraints:
Figure FDA0003751323790000026
in the formula, C r {. Is a random chance constraint; eta ± Water loss rate during transportation;
Figure FDA0003751323790000027
total available water amount;
Figure FDA0003751323790000028
is the minimum/maximum water demand of the sub-area i department j in the period t, and alpha is a confidence level;
the method for solving the model to obtain the regional water resource optimization configuration scheme comprises the following steps:
giving different confidence levels alpha, converting fuzzy confidence constraints into typical interval type constraints, and constructing an interval multi-objective optimization model;
constructing a formula of the confidence level alpha;
splitting the interval multi-target optimization model into an upper limit sub-model and a lower limit sub-model by using an interactive two-step algorithm;
respectively solving each sub-target to obtain the maximum unit target and the minimum unit target of each department
Figure FDA0003751323790000029
And
Figure FDA00037513237900000210
the sub-targets comprise economic benefit targets of all department units and ecological benefit targets of all department unit systems;
constructing a membership function of each sub-target;
solving the interval multi-target optimization model by adopting a fuzzy membership method to obtain water resource optimization configuration schemes of research areas corresponding to different confidence levels alpha;
the interval multi-objective optimization model is as follows:
Figure FDA0003751323790000031
in the above formula, the first and second carbon atoms are,
Figure FDA0003751323790000032
to represent
Figure FDA0003751323790000033
Assuming a triangular blur number, 1,2,3 represent the minimum possible value, the most reliable value, and the maximum possible value, respectively.
2. The method for optimizing configuration of water resources and evaluating and feeding back an optimization result according to claim 1, wherein the collecting monitoring data and regional population data comprises:
collecting monitoring data of a monitoring station, including water quality data and water quantity data; and collecting relevant data of the population quantity and the area of the region by inquiring the local yearbook or official information of each region.
3. The method for optimizing configuration of water resources and evaluating and feeding back an optimization result according to claim 1, wherein the water resource bearing capacity index comprises: dissolved oxygen content, inorganic suspended particulate matter, total ammonia content, total nitrogen content.
4. The method for optimizing water resource configuration and feeding back an evaluation result of an optimization result according to claim 1, wherein the step of confirming an evaluation model index weight and establishing a correlation function to construct a water resource bearing capacity evaluation model after standardizing and normalizing index values in a water resource bearing capacity index system comprises the steps of:
standardizing and normalizing the index values;
data x obtained by normalization j Substituting the weight coefficient into the following formula to obtain each index weight coefficient: the expression is as follows:
Figure FDA0003751323790000034
Figure FDA0003751323790000035
in the formula (II)F j When =0, then H is defined j ＝0；
Figure FDA0003751323790000041
In the formula: f j The specific gravity of the j index in the total index is; h j Entropy value of j index; w j Entropy weight of j index; n is the index number;
establishing a correlation function, and establishing a water resource bearing capacity evaluation model; determining the relevance degree of each index of the water resource bearing capacity belonging to each reliability grade to obtain a water resource bearing capacity result, wherein the method comprises the following steps:
the relevance of the ith index of the area to the evaluation grade j (j =1,2, \8230;, m) is determined as follows:
Figure FDA0003751323790000042
wherein the distance is as follows:
Figure FDA0003751323790000043
Figure FDA0003751323790000044
calculating the relevance of the area p with respect to the reliability level j:
Figure FDA0003751323790000045
and (3) evaluating the water resource bearing capacity result of the region p:
if K is j ＝maxK j (p) (j =1,2, \8230;, m), then p is rated as belonging to rank j;
in the formula, K j (v i ) Reliability rating for ith index of regionThe degree of association of j; ρ (v) i ,V ij ) The distance of the ith index of the area relative to the evaluation grade j; v. of i Data x after normalization processing for evaluation index j ；V ij A magnitude range for each index i with respect to each evaluation level j; v ip Is the total value range of each index i; a is oji 、b oji A magnitude range of the index i with respect to the evaluation level j; a is a pi 、b pi Indicates the total amount value range of the index i; k is j (p) is the degree of association of region p with respect to reliability level j; w is a i Entropy weight W for the jth index j 。
5. The method for optimizing configuration of water resources and evaluating and feeding back an optimization result according to claim 1, wherein the confidence level α is expressed by the following formula:
Figure FDA0003751323790000051
6. the method of claim 1, wherein the membership function of each sub-goal is as follows:
Figure FDA0003751323790000052
in the formula (I), the compound is shown in the specification,
Figure FDA0003751323790000053
representing the nth interval form objective function;
solving the interval multi-target optimization model by adopting a fuzzy membership method, wherein the method comprises the following steps:
maxη ± ＝γ ±
Figure FDA0003751323790000054
Figure FDA0003751323790000055
Figure FDA0003751323790000056
Figure FDA0003751323790000057
in the formula, gamma ± Indicating a degree of satisfaction, ranging from 0 to 1.
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