Water resource suitable bearing capacity evaluation method and system considering uncertainty

Abstract

The invention discloses a water resource suitable bearing capacity evaluation method and system considering uncertainty, and belongs to the technical field of water resource management. Constructing a double-layer interval multi-objective planning model, wherein objective functions of the double-layer interval multi-objective planning model comprise an upper-layer objective function and a lower-layer objective function; respectively constructing evaluation functions for the upper-layer objective function and the lower-layer objective function; decomposing the double-layer interval multi-target planning model to obtain two independent interval planning models, respectively constructing a target function lower limit sub-model and a target function upper limit sub-model for each interval planning model, and respectively solving to obtain a lower limit sub-model optimal solution and an upper limit sub-model optimal solution; constructing a membership function, and introducing global satisfaction to establish a satisfaction maximum model; and solving by utilizing the model with the maximum satisfaction degree to obtain a final solution, namely an evaluation result.

Description

Technical Field

[0001] The present invention belongs to the technical field of water resource management, and particularly relates to a method and system for evaluating the suitable carrying capacity of water resources considering uncertainty. Background Art

[0002] In order to coordinate the relationship between economic development and environmental protection, and ensure that limited resources can meet current needs without having a negative impact on subsequent development, it is necessary to ensure the sustainable development of the region. The sustainable utilization of water resources is an extension of the connotation of sustainable development in the field of water resources. The carrying capacity of water resources is the primary constraint for the sustainable utilization of water resources. The development and utilization of water resources should be maintained within the carrying capacity of water resources, and the virtuous cycle of regional water resources should not be damaged to ensure the durability of water resource development and utilization. Scholars often use the social and economic scale that water resources can support to intuitively reflect the constraint of water resource carrying capacity.

[0003] The main influencing factors for the recycling of water resources include water resource management policies, water supply and use technology levels, and economic conditions, specifically including: the natural conditions of regional water resources, the water supply capacity of water conservancy projects, water use efficiency, sewage interception and discharge limits, per capita GDP requirements, industrial structure, etc.

[0004] Among them, the water resource volume at different times in the same region is affected by factors such as upstream inflow and precipitation, showing fluctuations and randomness. Moreover, the loss of water resources during transportation is difficult to accurately measure, resulting in uncertainty. The development of the social and economic system is easily affected by policy regulation, and the development level is difficult to accurately measure. In addition, the water demand of different water use departments in different regions at different times is different and difficult to accurately calculate. For example, in the aspect of crop planting, with the change of planting patterns, the water demand of crops at different growth stages will change; affected by climate, rainfall, evaporation, etc., the same crop will also have different water resource volume requirements.

[0005] Therefore, the suitable carrying capacity of water resources is affected by many factors, and any change in factors may lead to a change in the suitable carrying capacity of water resources. Summary of the Invention

[0006] The present invention provides a method and system for evaluating the suitable carrying capacity of water resources considering uncertainty to solve the technical problems in the above background art.

[0007] The present invention adopts the following technical solutions: A method for evaluating the suitable carrying capacity of water resources considering uncertainty, comprising the following steps: Dividing a specified area into basic calculation units according to the administrative hierarchy system and taking the basic calculation units In Construct a two - layer interval multi - objective programming model with domestic water use, production water use, and ecological water use within a time period as decision variables; The objective function of the two - layer interval multi - objective programming model includes an upper - layer objective function and a lower - layer objective function. The lower - layer objective function is a multi - objective function, and the two - layer interval multi - objective programming model simultaneously satisfies pre - set constraint conditions; Construct evaluation functions for the upper - layer objective function and the lower - layer objective function respectively, so that the values of different objective functions are in the same order of magnitude; Decompose the two - layer interval multi - objective programming model to obtain two independent interval programming models. For each interval programming model, construct a lower - bound sub - model and an upper - bound sub - model of the objective function respectively, and solve them to obtain the optimal solution of the lower - bound sub - model and the optimal solution of the upper - bound sub - model; Construct a membership function based on the optimal solution of the lower - bound sub - model and the optimal solution of the upper - bound sub - model, introduce global satisfaction to establish a maximum - satisfaction model; use the maximum - satisfaction model to solve for the final solution, which is the evaluation result.

[0008] In a further embodiment, the upper - layer objective function is the maximum population scale function that the regional water resources can support , and its establishment process is as follows: Statistical basic calculation unit In the domestic water consumption during the time period , urban per capita domestic water consumption , and rural per capita domestic water consumption , calculate the population quantity that the water resources of the basic calculation unit can support ; Then, the expression formula of the maximum population scale function is as follows: ; Among them, K represents the total number of basic calculation units in the current region, that is , is a related variable of the population scale that the regional water resources can support.

[0009] In a further embodiment, the lower - layer objective function is the maximum grain yield function that the regional water resources can support , and its establishment process includes: Obtain the agricultural planting water consumption in the basic calculation unit , the water consumption per mu of planting , and the grain yield per mu , calculate the basic calculation unit in Grain yield during a period ; Then, the maximum grain yield function is expressed by the following formula: ; where K represents the total number of basic calculation units in the current area, that is , is a related variable of the grain yield that can be supported by the regional water resources.

[0010] In a further embodiment, the lower-level objective function is the minimum pollution emission amount function of the regional water resources , is a related variable of the COD emission amount, is a related variable of the ammonia nitrogen emission amount; The establishment process of the minimum pollution emission amount function is as follows: Calculate respectively to obtain the COD emission amount of the point source and the ammonia nitrogen emission amount of the basic calculation unit at the time point ; Calculate respectively to obtain the COD emission amount of the non-point source and the ammonia nitrogen emission amount of the basic calculation unit at the time point ; Then, the minimum COD emission amount : ; The minimum ammonia nitrogen emission amount : ; where is the total time period; .

[0011] In a further embodiment, the evaluation function at least includes: the evaluation function about the upper-level objective function and the evaluation function about the lower-level objective function, where is the decision-making objective of the upper-level objective function, is the decision-making objective of the lower-level objective function.

[0012] In a further embodiment, the lower limit sub-model of the objective function is expressed in the following form: ; In the formula, , are respectively the upper limit value and the lower limit value of the interval of the th decision variable, represents the lower limit value of the weight coefficient of the th decision variable, which are respectively the lower limit value and the upper limit value of the conversion coefficient, and are the upper limit value and the lower limit value of the constraint condition parameter, is the total number of positive weight coefficients, is the total number of negative weight coefficients, is the lower limit model of the objective function, and then it is the lower limit sub-model of the objective function, represents being restricted by; The upper limit sub-model of the said objective function is expressed in the following form: ; In the formula, and are respectively the lower limit value and the upper limit value of the lower limit sub-model of the objective function, is the upper limit value of the weight coefficient of the th decision variable, is the upper limit model of the objective function, and then it is the upper limit sub-model of the objective function.

[0013] In a further embodiment, the construction process of the membership function includes the following steps: Establish the membership function of the decision variable to obtain the satisfaction degree of the decision variable ; Based on the optimal solution of the upper limit sub-model, establish the upper layer objective membership function to calculate the satisfaction degree of the upper layer objective : Use the optimal solution of the lower limit sub-model to establish the lower layer objective membership function to calculate the satisfaction degree of the lower layer objective .

[0014] In a further embodiment, the construction process of the maximum satisfaction model is as follows: Based on the satisfaction degree of the decision variable , the satisfaction degree of the upper layer objective and the satisfaction degree of the lower layer objective , calculate the global satisfaction degree : ; Correspondingly, the expression form of the maximum satisfaction model is: ; Among them, are all sets of decision variables.

[0015] In a further embodiment, the constraint conditions at least include: resource constraint conditions, total water consumption control constraint conditions, pollutant discharge constraint conditions, economic constraint conditions, food production constraint conditions, and non - negative constraint conditions; Among them, the economic constraint conditions include: economic level constraint conditions and industrial structure constraint conditions.

[0016] A water resource suitable carrying capacity assessment system considering uncertainty, which is used to implement the water resource suitable carrying capacity assessment method considering uncertainty as described above, includes: The first module is configured to divide a specified area into basic calculation units according to the administrative hierarchy system , and take the domestic water, production water, and ecological water of the basic calculation unit within the time period as decision variables to construct a two - layer interval multi - objective programming model; The second module is configured to the objective function of the two - layer interval multi - objective programming model includes an upper - layer objective function and a lower - layer objective function, the lower - layer objective function is a multi - objective function, and the two - layer interval multi - objective programming model simultaneously satisfies the pre - set constraint conditions; The third module is configured to construct evaluation functions for the upper - layer objective function and the lower - layer objective function respectively, so that the values of different objective functions are in the same order of magnitude; The fourth module is configured to decompose the two - layer interval multi - objective programming model into two independent interval programming models, construct a lower - bound sub - model and an upper - bound sub - model of the objective function for each interval programming model respectively, and solve to obtain the optimal solution of the lower - bound sub - model and the optimal solution of the upper - bound sub - model; The fifth module is configured to construct a membership function based on the optimal solution of the lower - bound sub - model and the optimal solution of the upper - bound sub - model, introduce global satisfaction to establish a maximum satisfaction model; use the maximum satisfaction model to solve to obtain the final solution, which is the evaluation result.

[0017] Advantages of the present invention: The present invention discloses a method for evaluating the appropriate water resource carrying capacity considering uncertainty, which is realized based on a two-layer interval multi-objective programming model. The two-layer interval multi-objective programming model is an interval hierarchical programming model with two objective layers that considers the uncertainty of parameters such as regional water resource quantity, water use efficiency, sewage discharge coefficient, and economic development. The upper objective is to maximize the population quantity that the regional water resources can carry, and the lower objectives are to maximize the regional grain output and minimize the sewage discharge. There are contradictions between the upper and lower objectives in terms of water resource allocation and the contradictions between social and economic development and environmental protection. The final decision result is a coordinated plan among various layers, which improves the credibility of the calculation result of the population scale that water resources can carry. Description of the Drawings

[0018] Figure 1 is a flowchart for evaluating the appropriate water resource carrying capacity considering uncertainty in Embodiment 1.

[0019] Figure 2 is a graph of the appropriate water resource carrying capacity, its grain output, and pollutant emissions in Scenario 1 of Embodiment 1.

[0020] Figure 3 is a model architecture diagram in Scenario 1 of Embodiment 2. Detailed Embodiment

[0021] The present invention will be further described below with reference to the drawings in the specification and embodiments.

[0022] Embodiment 1 The characterization index of the appropriate water resource carrying capacity is the population quantity. At the same time, considering the important grain-producing areas in coastal cities and the requirements for river ecological environment protection, as well as the uncertainty of the main influencing factors such as the natural conditions of regional water resources, water supply and use technology level, and economic level. Therefore, as Figure 1 shown, this embodiment discloses a method for evaluating the appropriate water resource carrying capacity considering uncertainty, including the following steps: Divide the designated area into basic calculation units according to the administrative hierarchy system , and use the domestic water, production water, and ecological water of the basic calculation unit within the time period as decision variables to construct a two-layer interval multi-objective programming model. It should be noted that in this embodiment, the municipal administrative region is used as the basic calculation unit, and the production water can also be further divided into primary industry water, secondary industry water, and tertiary industry water. Among them, the primary industry water is assumed to be all agricultural planting water, the secondary industry water is industrial water, and the rest is tertiary industry water. When performing model simulation, it is assumed that the monthly water consumption of domestic, secondary industry, and tertiary industry is equal, and only the water consumption of agricultural planting fluctuates with the change of months.

[0023] Furthermore, the objective function of the bi-level interval multi-objective programming model includes an upper-level objective function and a lower-level objective function. The lower-level objective function is a multi-objective function, and the bi-level interval multi-objective programming model simultaneously satisfies the preset constraint conditions. In other words, the model objectives are divided into upper and lower levels. The upper-level objective is to maximize the population that can be supported by water resources, and the lower-level objectives are to maximize the food production and minimize the amount of pollutants entering the river.

[0024] Construct evaluation functions for the upper-level objective function and the lower-level objective function respectively, so that the values of different objective functions are on the same order of magnitude. Decompose the bi-level interval multi-objective programming model to obtain two independent interval programming models. For each interval programming model, construct a lower bound sub-model and an upper bound sub-model of the objective function respectively, and solve them separately to obtain the optimal solutions of the lower bound sub-model and the upper bound sub-model. Due to the differences between the upper-level objective function and the lower-level objective function, it is unreasonable to directly take the optimal solution of one of the sub-models as the optimal solution of all objectives. Therefore, construct a membership function based on the optimal solutions of the lower bound sub-model and the upper bound sub-model, introduce the global satisfaction degree to establish a maximum satisfaction degree model, and use the maximum satisfaction degree model to solve for the final solution, which is the evaluation result.

[0025] In a further embodiment, the upper-level objective function is the maximum population scale function that can be carried by the regional water resources , and its establishment process is as follows: Statistical basic calculation unit In The domestic water consumption during the time period , urban per capita domestic water consumption And rural per capita domestic water consumption , and the following formula is used to calculate the population that can be carried by the water resources of the basic calculation unit : : ; In the formula, Is the urbanization rate of the basic calculation unit . Then, the expression formula of the maximum population scale function is as follows: ; Among them, K represents the total number of basic calculation units in the current region, that is , Is a related variable of the population scale that can be carried by the regional water resources. Further, if there are three basic calculation units , that is, K = 3, .

[0026] Correspondingly, the lower-layer objective function is the maximum grain yield function that the regional water resources can bear. , and its establishment process includes: Obtain the basic calculation unit In the agricultural planting water consumption during the time period , the water consumption per mu of planting , and the grain yield per mu are calculated to obtain the grain yield of the basic calculation unit in the time period : ; Then, the maximum grain yield function is expressed by the following formula: ; where K represents the total number of basic calculation units in the current region, that is , is a related variable of the grain yield that the regional water resources can bear.

[0027] In a further embodiment, the lower-layer objective function is the minimum pollution emission function of the regional water resources , is a related variable of the COD emission, is a related variable of the ammonia nitrogen emission.

[0028] Due to the different emission characteristics of agricultural pollution from domestic and industrial pollution, agricultural pollution is considered as non-point source pollution, and domestic and industrial pollution are considered as point source pollution. Therefore, when calculating pollutant emissions, the model calculates separately for point sources and non-point sources. Among them, the centralized treated domestic sewage and all industrial sewage will be calculated as point sources; the domestic sewage that has not been centralized treated and the pollutants generated by agricultural planting will be calculated as non-point sources.

[0029] Furthermore, the establishment process of the minimum pollution emission function is as follows: The COD emission and ammonia nitrogen emission of the point source in the basic calculation unit in the time period are calculated by the following formula: ; In the formula, is the domestic water consumption of the basic calculation unit in the time period, is the sewage treatment ratio of domestic water, and are respectively the basic calculation units The water consumption of secondary and tertiary industries during the period, and are respectively the proportion of domestic wastewater and industrial wastewater entering the river, and are respectively the concentrations of treated domestic sewage and industrial sewage, and are respectively the ammonia nitrogen concentrations of treated domestic sewage and industrial sewage; The COD emission amount of the non-point source in the basic calculation unit during the period and the ammonia nitrogen emission amount are calculated using the following formula: : ; In the formula, and are respectively the COD emission coefficients of domestic water and agricultural water, and are respectively the pollutant river entry ratios of domestic water and agricultural water, and are respectively the ammonia nitrogen emission coefficients of domestic water and agricultural water; Then, the minimum COD emission amount : ; The minimum ammonia nitrogen emission amount : ; where is the total period; Finally, .

[0030] In a further embodiment, the constraint conditions at least include: resource constraint conditions, total water consumption control constraint conditions, pollutant emission constraint conditions, economic constraint conditions, food production constraint conditions, and non-negativity constraint conditions; among them, the economic constraint conditions include: economic level constraint conditions and industrial structure constraint conditions.

[0031] Furthermore, the definition of the resource constraint condition is that the regional water consumption is not greater than the maximum water volume that water conservancy facilities can provide, expressed as: ; where is the local maximum water supply volume of the basic calculation unit during the period, the ecological water consumption of the basic calculation unit during the period: , Represents the basic calculation unit The per capita ecological environment water consumption during the time period, is the population quantity that the water resources can support. is the basic calculation unit The maximum external water supply for a single water supply target during the time period, is the basic calculation unit The maximum external water supply for multiple water supply targets during the time period.

[0032] The constraint condition for total water consumption control is that the water consumption of the social and economic development of each calculation unit is not greater than the water consumption control limit value, and it is further expressed as: , where is the basic calculation unit total water consumption control value.

[0033] The constraint condition for pollutant emissions is that the pollutant emissions generated by the social and economic development of each calculation unit are not greater than the total pollutant discharge limit, and it is further expressed as: , where , is the basic calculation unit red line value of pollutant absorption (CON); , where ; is the basic calculation unit red line value of pollutant absorption (AN).

[0034] The further economic level constraint condition is expressed as: , where is the total GDP of the basic calculation unit , is the basic calculation unit total number of people, represents the minimum requirement of the per capita GDP of the basic calculation unit .

[0035] The industrial structure constraint condition is expressed as: ; , are respectively k water consumption per 10,000 yuan of added value of the secondary industry and water consumption per 10,000 yuan of added value of the tertiary industry of the 3 unit, in m is the basic calculation unit lower limit value of the sum of the proportions of the secondary industry and the tertiary industry.

[0036] The food production constraint condition is the basic calculation unit The food production should be greater than the existing food production.

[0037] The non - negative constraint condition means that the values of all variables in the model are greater than or equal to 0.

[0038] In another embodiment, the evaluation function at least includes: the evaluation function regarding the upper - layer objective function and the evaluation function regarding the lower - layer objective function ; Among them, the expression form of the evaluation function regarding the upper - layer objective function is as follows: ; ; represents the decision - making objective of the upper - layer objective function, 、 are both upper - layer objective functions, represents the upper - layer objective function The total number of, ; represents the upper - layer objective function The total number of, , are both weight coefficients, 、 represents the regularization factor; represents the set of decision variables, 、 are both decision - variable weight coefficients, 、 are preset constants, 、 refer to the number of decision variables; The expression form of the evaluation function regarding the lower - layer objective function is as follows: ; ; In the formula, represents the decision - making objective of the lower - layer objective function, 、 are both lower - layer objective functions, is the lower - layer objective function The total number of, , is the number of the lower - layer objective function The number of, is the lower - layer objective function The total quantity, ; are all weight coefficients, , represent the regularization factor, , represent the decision variable weight coefficients, , are preset constants, , refers to the number of decision variables.

[0039] Furthermore, considering that the decision-making objectives of the upper-level objective function and the lower-level objective function are different, the bilevel interval multi-objective programming model is decomposed into two independent interval programming models. Then, the fuzzy linear programming algorithm (IFLP) is used to solve each single-objective interval programming model obtained by decomposition.

[0040] Correspondingly, the lower bound sub-model of the objective function is expressed in the following form: ; In the formula, , are respectively the upper bound value and the lower bound value of the th decision variable, represents the lower bound value of the weight coefficient of the th decision variable, are respectively the lower bound value and the upper bound value of the conversion coefficient, , are the upper bound value and the lower bound value of the constraint condition parameter, is the total number of positive weight coefficients, is the total number of negative weight coefficients, is the lower bound model of the objective function, then is the lower bound sub-model of the objective function, represents subject to; The upper bound sub-model of the objective function is expressed in the following form: ; In the formula, and are respectively the lower bound value and the upper bound value of the lower bound sub-model of the objective function, is the upper bound value of the weight coefficient of the th decision variable, is the upper bound model of the objective function, then is the upper bound sub-model of the objective function.

[0041] Furthermore, the construction process of the membership function includes the following steps: Establish the decision variable Membership function: ; In the formula, represents the satisfaction degree of the decision variable . is the optimal solution of the upper bound sub-model corresponding decision variable, obtained by solving the upper bound sub-model of the objective function . is the given fluctuation range of the decision variable interval, that is ; Based on the optimal solution of the upper bound sub-model, the upper-level objective membership function in the following form is established: ; In the formula, represents the upper-level objective satisfaction degree, represents the highest tolerance of the upper-level objective; Using the optimal solution of the lower bound sub-model, the lower-level objective membership function in the following form is established: ; In the formula represents the lower-level objective satisfaction degree, is the optimal solution of the lower bound sub-model obtained by solving the lower bound sub-model of the objective function . represents the highest tolerance of the lower-level objective.

[0042] Finally, in order to simultaneously satisfy the satisfaction degrees of the decision variable, the upper-level objective, and the lower-level objective, the global satisfaction degree is introduced to establish the maximum satisfaction degree model. The specific process is as follows: Based on the satisfaction degree of the decision variable , the upper-level objective satisfaction degree and the lower-level objective satisfaction degree , calculate the global satisfaction degree : ; Correspondingly, the expression form of the maximum satisfaction degree model is: ; where are all decision variable sets.

[0043] This embodiment takes the water diversion project in the coastal area of a certain province as an example. The water diversion project in the coastal area of this province mainly includes the project of diverting the Yangtze River water northward, the project of diverting the Yangtze River water eastward, and the project of self-flowing diversion of the Yangtze River water in Tongnan. Among them, the areas north and south of the old Yellow River in the project of diverting the Yangtze River water northward, the middle and west lines of the project of diverting the Yangtze River water eastward, and the water-receiving areas of the project of self-flowing diversion of the Yangtze River water in Tongnan are relatively independent. The east line (Taidong River-Tongyu River) of the project of diverting the Yangtze River water eastward runs through the three coastal cities. According to the topological relationship of the geographical locations of the three coastal cities in this province, the current situation of water supply and use, and water conservancy facilities, the regional water resources system is generalized. Taking the municipal administrative region as the water use unit, the specific water use units include City One, City Two, and City Three.

[0044] Due to the different ranges of input of model parameters in each city, there are also differences in the ranges of the appropriate water resources carrying capacity, its grain output, and pollutant emissions. As Figure 2 shown, in Scenario 1, the appropriate water resources carrying capacity in the coastal area of a certain province is [16.2435, 20.5603] million people, which can support the actual population of 19.03 million in a certain year. However, there are differences in the support situations of each municipal administrative unit. As shown in Table 1, the appropriate water resources carrying capacity of City One is [5.9468, 6.8373] million people, which is less than the current actual population of 7.31 million; while the actual population numbers of City Two and City Three are within the range of the appropriate water resources carrying capacity. In this plan, the grain output of City Two is the largest, reaching [7.4748, 9.1019] million tons, and those of City One and City Three are relatively close; the COD emissions of the three cities show a decreasing trend from south to north, and the ammonia nitrogen emissions are the largest in City Two, and those of City One and City Three are close, as shown in Table 1.

[0045] Table 1 Embodiment 2 Taking the Scenario 1 given in the embodiment as an example, as Figure 3 shown, this embodiment discloses an evaluation system for the appropriate water resources carrying capacity considering uncertainty, including: The first module is configured to divide the specified area into basic calculation units according to the administrative hierarchy system , and take the domestic water, production water, and ecological water of the basic calculation unit within the time period as decision variables to construct a two-layer interval multi-objective programming model; The second module is configured to the objective function of the two-layer interval multi-objective programming model includes an upper-layer objective function and a lower-layer objective function, the lower-layer objective function is a multi-objective function, and the two-layer interval multi-objective programming model simultaneously satisfies the preset constraint conditions; The third module is configured to construct evaluation functions for the upper-layer objective function and the lower-layer objective function respectively, so that the values of different objective functions are at the same order of magnitude; The fourth module is configured to decompose the two-layer interval multi-objective programming model into two independent interval programming models, construct a lower-bound sub-model and an upper-bound sub-model of the objective function for each interval programming model respectively, and solve them respectively to obtain the optimal solution of the lower-bound sub-model and the optimal solution of the upper-bound sub-model; The fifth module is configured to construct a membership function based on the optimal solution of the lower-bound sub-model and the optimal solution of the upper-bound sub-model, introduce the global satisfaction degree to establish a maximum satisfaction degree model; use the maximum satisfaction degree model to solve and obtain the final solution, which is the evaluation result.

Claims

1. A method for assessing the suitable carrying capacity of water resources considering uncertainty, characterized in that: The following steps are involved: Divide the designated area into basic calculation units according to the administrative hierarchy system , the basic computing unit exist The domestic water use, production water use and ecological water use in a period are used as decision variables to build a two-level interval multi-objective programming model; The objective function of the double-layer interval multi-objective programming model includes an upper-layer objective function and a lower-layer objective function, the lower-layer objective function is a multi-objective function, and the double-layer interval multi-objective programming model satisfies the preset constraints at the same time; Construct evaluation functions for the upper and lower objective functions respectively, so that the values ​​of different objective functions are in the same order of magnitude; Decomposing the two-layer interval multi-objective programming model into two independent interval programming models, constructing a lower limit sub-model of the objective function and an upper limit sub-model of the objective function for each interval programming model, and solving the lower limit sub-model and the upper limit sub-model to obtain the optimal solution respectively; A membership function is constructed based on the optimal solution of the lower limit sub-model and the optimal solution of the upper limit sub-model, and the global satisfaction is introduced to establish a maximum satisfaction model; the final solution is obtained using the maximum satisfaction model, which is the evaluation result.

2. A method for assessing the suitable carrying capacity of water resources considering uncertainty according to claim 1, characterized in that: The upper objective function is the maximum population scale function that regional water resources can carry , and its establishment process is as follows: Statistical Basic Computing Unit exist Domestic water consumption during the period , Urban per capita domestic water consumption and rural per capita domestic water consumption , calculate the basic computing unit The number of people that can be supported by water resources ; Then, the maximum population size function The expression formula is: ; Among them, K represents the total number of basic computing units in the current area, that is, , It is a variable related to the population size that regional water resources can support.

3. A method for assessing the suitable carrying capacity of water resources considering uncertainty according to claim 1, characterized in that: The lower objective function is the maximum grain production function that the regional water resources can carry. , the establishment process includes: Get the basic computing unit exist Agricultural water consumption during the period , Average water consumption per mu for planting , grain yield per mu , calculate the basic computing unit exist Food production during the period ; Then, the maximum grain production function is It is expressed using the following formula: ; Among them, K represents the total number of basic computing units in the current area, that is, , It is a variable related to the grain production that can be carried by regional water resources.

4. The method for evaluating the suitable carrying capacity of water resources considering uncertainty according to claim 1 is characterized in that: The lower objective function is the minimum pollution discharge function of regional water resources ,in, is the relevant variable of COD emission, is the variable related to ammonia nitrogen emission; The minimum pollution emission function The establishment process is as follows: Calculate the basic computing units separately exist COD emissions from point sources during the period and ammonia nitrogen emissions ; Calculate the basic computing units separately exist COD emissions from non-point sources during the period and ammonia nitrogen emissions ; Then, the minimum COD emission : ; Minimum ammonia nitrogen emission : ;in, is the total period; 。 5. The method for evaluating the suitable carrying capacity of water resources considering uncertainty according to claim 1 is characterized in that: The evaluation function at least includes: Evaluation function of the upper objective function and the evaluation function of the underlying objective function ,in, is the decision target of the upper objective function, is the decision target of the lower layer objective function.

6. A method for assessing the suitable carrying capacity of water resources considering uncertainty according to claim 1, characterized in that: The objective function lower limit submodel is expressed in the following form: ； In the formula, , Respectively The upper and lower limits of the interval for the decision variables, Indicates The lower limit of the weight coefficient of the decision variable, are the lower and upper limits of the conversion coefficient, respectively. , are the upper and lower limits of the constraint parameters, is the total number of positive weight coefficients, is the total number of negative weight coefficients, is the lower limit model of the objective function, Then it is the lower limit sub-model of the objective function, Indicates that it is limited to; The upper limit submodel of the objective function is expressed in the following form: ； In the formula, and They are the lower limit submodels of the objective function Lower and upper limits, It is The upper limit of the weight coefficient of the decision variables, is the upper limit model of the objective function, It is the upper limit sub-model of the objective function.

7. The method for evaluating the water resources suitable carrying capacity considering uncertainty according to claim 1, characterized in that: The construction process of the membership function includes the following steps: Create decision variables The membership function of the decision variable Satisfaction ; Based on the optimal solution of the upper sub-model, the upper target membership function is established to calculate the upper target Satisfaction : The lower-level target membership function is established by using the optimal solution of the lower-limit submodel to calculate the lower-level target Satisfaction .

8. A method for assessing the suitable carrying capacity of water resources taking into account uncertainty according to claim 7, characterized in that: The construction process of the maximum satisfaction model is as follows: Based on the decision variables Satisfaction , upper level goals Satisfaction and lower level targets Satisfaction , calculate the global satisfaction : ； Correspondingly, the expression of the maximum satisfaction model is: ； in, are all sets of decision variables.

9. The method for evaluating the suitable carrying capacity of water resources considering uncertainty according to claim 1 is characterized in that: The constraints include at least: resource constraints, total water use control constraints, pollutant emission constraints, economic constraints, food production constraints and non-negative constraints; The economic constraints include economic level constraints and industrial structure constraints.

10. A water resources suitable carrying capacity assessment system considering uncertainty, used to implement the water resources suitable carrying capacity assessment method considering uncertainty as claimed in any one of claims 1 to 9, characterized in that: include: The first module is set to divide the designated area into basic calculation units according to the administrative hierarchy system. , the basic computing unit exist The domestic water use, production water use and ecological water use in a period are used as decision variables to build a two-level interval multi-objective programming model; The second module is set to the objective function of the double-layer interval multi-objective programming model, which includes an upper objective function and a lower objective function, wherein the lower objective function is a multi-objective function, and the double-layer interval multi-objective programming model satisfies the preset constraints at the same time; The third module is set to construct evaluation functions for the upper objective function and the lower objective function respectively, so that the values ​​of different objective functions are in the same order of magnitude; The fourth module is configured to decompose the two-layer interval multi-objective programming model into two independent interval programming models, construct a lower limit sub-model of the objective function and an upper limit sub-model of the objective function for each interval programming model, and respectively solve the optimal solution of the lower limit sub-model and the optimal solution of the upper limit sub-model; The fifth module is configured to construct a membership function based on the optimal solution of the lower limit sub-model and the optimal solution of the upper limit sub-model, introduce global satisfaction to establish a maximum satisfaction model; and use the maximum satisfaction model to solve and obtain the final solution, which is the evaluation result.

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