A method for optimizing basin water resource utilization efficiency based on a double-layer decision system

Abstract

The application discloses a kind of based on two-layer decision system's watershed water resource utilization efficiency optimization method, first, construct the watershed water resource optimal allocation model based on two-layer fractional fuzzy programming, the multi-level nature of watershed water resource management system, multi-objective and the fuzzy uncertainty characteristic of agricultural land policy is characterized, realize water resource utilization efficiency optimum;Then, obtain input data by historical database, and obtain the fuzzy distribution of agricultural cultivated land area in combination with cultivated land expansion plan and expected grain yield;Introduce credibility parameter, calculate the corresponding fuzzy variable value under different credibility level, as the input parameter of the watershed water resource optimal allocation model is brought into calculation;Then, according to fractional programming algorithm, introduce new decision variable, and convert the planned model into linear programming model;Finally, coupled with multi-layer iteration and fuzzy satisfaction degree algorithm, solve the overall satisfaction degree of upper and lower layer decision to obtain global optimal solution.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of system management, and particularly relates to a method for optimizing utilization efficiency of water resources in a river basin based on a double-layer decision system. BACKGROUND

[0002] The management of a water resource system in a river basin often involves multiple decision-making layers. For the whole river basin, under the impetus of population growth and economic development, it faces many risks such as water resource shortage, food crisis, and energy supply security. In the decision-making process, the decision-making preferences of different levels of managers need to be considered, while the overall economic development benefits and system safety of the river basin need to be ensured. For example, in the process of water resource system management in a drought-prone river basin, the contradiction between economic development and ecological safety protection needs to be considered. Economic development is the eternal theme of regional comprehensive management, but the vigorous development of industry and agriculture must be accompanied by the large consumption of water resources; for water-deficient areas, slowing down ecological degradation and restoring the ecological environment to some extent requires reducing the consumption of water resources and allocating as much water resources as possible to the ecological system to alleviate ecological problems such as lake shrinkage, wetland degradation, and river cutoff. It can be seen that different decision-makers have different decision-making preferences for system management. Therefore, in the process of water resource system management, the preferences and control strength of managers at different decision-making levels need to be considered.

[0003] With the increasing tension between water supply and demand, how to optimize the utilization efficiency of water resources and improve the sustainability of system management has become a global focus. In the prior art, most traditional water resource planning models determine the most economical solution in the form of maximizing benefits or minimizing costs, and cannot reflect the relationship between water resource utilization and economic development. The amount of resources is only limited in the form of constraints, but this cannot obtain the optimal result. In addition, river basin management involves multiple subsystems such as society, economy, and nature. The changes in population, the development of cities, and the subjective policy bias exacerbate the uncertainty and complexity of water resource management. For example, due to the influence of human estimation errors and policy preferences in the process of statistical land area, the cultivated land area may have fuzzy uncertainty, which brings challenges to the comprehensive management of the river basin.

[0004] Invention objectives

[0005] The purpose of the present application is to overcome the above-mentioned defects existing in the prior art, aiming at the complexity of the decision level in the basin water resource management system, the low efficiency of water resource utilization and the fuzziness of parameter information, researching the actual water resource allocation and water consumption of the agricultural, industrial, municipal and ecological departments, abstracting a mathematical model, constructing a basin water resource utilization efficiency optimization system based on a double-layer decision system, and proposing a basin water resource utilization efficiency optimization method based on a double-layer decision system, so as to quantify the uncertain information in the system management process, balance the contradictions between different decision levels, optimize the marginal benefit of the system, and generate a decision scheme that meets the economic benefit and environmental benefit at the same time. SUMMARY

[0006] The present application provides a basin water resource utilization efficiency optimization method based on a double-layer decision system, comprising the following steps:

[0007] Step A, constructing a basin water resource optimization allocation model based on double-layer fractional fuzzy programming, representing the multi-level nature, multi-objective nature and fuzzy uncertainty characteristics of agricultural land policy of the basin water resource management system, balancing the interest conflicts of different decision levels in the basin, and realizing the optimal utilization efficiency of water resources;

[0008] Step B, obtaining input data through a historical literature database, and combining the cultivated land expansion plan and the expected grain yield to obtain the fuzzy distribution of agricultural cultivated land area; based on the fuzzy possibility degree theory and the quantile algorithm, a credibility parameter is introduced to calculate the corresponding fuzzy variable values at different credibility levels as input parameters of the basin water resource optimization allocation model for calculation;

[0009] Step C, introducing new decision variables according to the fractional programming algorithm, and converting the basin water resource optimization allocation model into a corresponding linear programming model;

[0010] Step D, coupling a multi-layer iterative algorithm and a fuzzy satisfaction degree algorithm, and obtaining the global optimal solution by solving the overall satisfaction degree of the upper and lower layer decisions;

[0011] In the step A, the construction of the basin water resource optimization allocation model based on double-layer fractional fuzzy programming comprises:

[0012] The maximization of the basin water resource utilization efficiency is represented by the upper layer objective function as shown in formula (1):

[0013]

[0014] The upper layer constraint conditions of formula (1) include A01-A09, wherein A01 represents the water demand constraint of each user, and is represented as formula (2):

[0015]

[0016] A02 represents the water availability constraint for each user, as shown in equation (3):

[0017]

[0018] A03 represents the reservoir power generation constraint, as shown in equations (4)-(5):

[0019]

[0020]

[0021] A04 represents the water resource transportation and distribution constraint, as shown in equation (6):

[0022]

[0023] A05 represents the food security constraint, as shown in equation (7):

[0024]

[0025] A06 represents the land resource constraint, as shown in equation (8):

[0026]

[0027] A07 represents the salinity control constraint, as shown in equation (9):

[0028]

[0029] A08 represents the ecosystem coverage constraint, as shown in equation (10):

[0030]

[0031] A09 represents the river ecological water demand constraint, as shown in equation (11):

[0032]

[0033] The maximization of water resource utilization efficiency in each region is represented by the lower objective function, as shown in equation (12):

[0034]

[0035] The lower constraint conditions of equation (12) include A10-A13, wherein A10 represents the water demand constraint for each user, as shown in equation (13):

[0036]

[0037] A11 represents the food security constraint, expressed as in equation (14):

[0038]

[0039] A12 represents the land resource constraint, expressed as in equation (15):

[0040]

[0041] A13 represents the salinity control constraint, expressed as in equation (16):

[0042]

[0043] In the above equations (1)-(16), f U is the optimal water resources utilization efficiency of the basin, f L is the optimal water resources utilization efficiency of each administrative region; PROAGR, PRODOM, PROIND, and PROECO are the net water allocation benefits of the agricultural sector, the living sector, the industrial sector, and the ecological sector, respectively, and PROSTATE is the net water allocation benefit of each administrative region; WAU and WAL are the total water allocation of the upper and lower model systems, respectively; IW ijt is the surface water allocation of region i and sector j at time t, GW ijt is the groundwater allocation of region i and sector j at time t, EW lt is the water allocation of ecosystem l at time t, HW t is the water-electricity allocation of the basin at time t; EIW ijt is the unit power consumption of surface water allocation at time t, EGW ijt is the unit power consumption of groundwater allocation at time t; BW ijt , and BP t are the unit water allocation benefit and the unit power generation benefit at time t, respectively; θ t is the irrigation water allocation efficiency at time t, LF ijt is the leaching fraction of crop j in region i at time t, QF ijt is the fertilizer application amount of crop j at time t, PF ijt is the unit price of fertilizer, FC ijt is the fixed cost of planting per unit area; is the unit water consumption for power generation, PHC t is the unit cost of water-electricity production; TWI t is the total available surface water of the basin at time t, TWG t is the total available groundwater of the basin at time t; YA ijt is the total population of region i at time t, PO it is the food demand per capita in region i at time t, FD it is the yield per unit area of crop j in region i at time t, AMt Tmax is the maximum arable land area of the basin at time t; WPC ijt HTED is the water requirement of unit planting area of crop i in region j at time t; HTED t HS is the power demand in the basin; HS t EPW is the reservoir storage capacity at time t; EPW t MHP is the reservoir evaporation at time t; MHP nt TLW is the maximum power generation capacity of the reservoir at time t; TLW it GWD is the maximum allowable leaching water quantity in region i at time t; GWD lt ESV is the unit area forest water consumption of wetland l at time t; ESV lt TDI is the unit area ecological service value of wetland l at time t; TDI t TIA is the minimum forest coverage area of the basin at time t; TIA t WIL is the upstream inflow of the basin at time t; WIL t EBW is the river inflow into the lake at time t; EBW t EBW is the minimum ecological water requirement of the river at time t.

[0044] Preferably, in step B, the following steps are included:

[0045] Step B1, based on statistical data, a fuzzy possibility degree theory and a quantile algorithm are used to introduce a credibility parameter a and a triangular fuzzy parameter related to the total amount of agricultural arable land resources, wherein 0≤a≤1, and the triangular fuzzy parameter is AM t , AM t , The constrained fuzzy inequality is converted into a linear inequality, as shown in equation (17):

[0046]

[0047] Step B2, the linearized constraint condition is introduced, and the linear model is reconstructed, specifically including:

[0048] The maximization of basin water resource utilization efficiency is represented by the upper objective function as shown in equation (18):

[0049]

[0050] The upper constraint condition of equation (18) includes B01-B09, wherein B01 represents the water demand constraint of each user, and is represented as shown in equation (19):

[0051]

[0052] B02 represents the available water quantity constraint of each user, and is represented as shown in equation (20):

[0053]

[0054] B03 represents the reservoir power generation constraint, as shown in equations (21)-(22):

[0055]

[0056]

[0057] B04 represents the water resource transmission and distribution constraint, as shown in equation (23):

[0058]

[0059] B05 represents the food security constraint, as shown in equation (24):

[0060]

[0061] B06 represents the land resource constraint, as shown in equation (25):

[0062]

[0063] B07 represents the salinity control constraint, as shown in equation (26):

[0064]

[0065] B08 represents the ecosystem coverage constraint, as shown in equation (27):

[0066]

[0067] B09 represents the river ecological water demand constraint, as shown in equation (28):

[0068]

[0069] The maximization of water resource utilization efficiency in each region is represented by the lower objective function as shown in equation (29):

[0070]

[0071] The lower constraint condition of equation (29) includes B10-B13, wherein B10 represents the water demand constraint of each user, as shown in equation (30):

[0072]

[0073] B11 represents the food security constraint, as shown in equation (31):

[0074]

[0075] B12 represents the land resource constraint, which is expressed as shown in equation (32):

[0076]

[0077] B13 represents the salinity control constraint, which is expressed as shown in equation (33):

[0078]

[0079] The above equations (17)-(33), f U is the optimal water resource utilization efficiency of the basin, fLis the optimal water resource utilization efficiency of each administrative region; PROAGR, PRODOM, PROIND, and PROECO are the net water allocation benefits of the agricultural sector, the living sector, the industrial sector, and the ecological sector, respectively, and PROSTATE is the net water allocation benefit of each administrative region; WAU and WAL are the total water allocation of the upper and lower model systems, respectively; IW ijt is the surface water allocation of region i and sector j at time t, GW ijt is the groundwater allocation of region i and sector j at time t, EW lt is the water allocation of ecosystem l at time t, HW t is the water-electricity allocation of the basin at time t; EIW ijt is the unit power consumption of surface water allocation at time t, EGW ijt is the unit power consumption of groundwater allocation at time t; BW ijt , and BP t are the unit water allocation benefit and the unit power generation benefit at time t, respectively; θ t is the irrigation water allocation efficiency at time t, LF ijt is the leaching fraction of crop j in region i at time t, QF ijt is the fertilizer application amount of crop j at time t, PF ijt is the unit price of fertilizer, FC ijt is the fixed cost of planting per unit area; is the unit water consumption for power generation, PHC t is the unit water-electricity production cost; TWI t is the total available surface water of the basin at time t, TWG t is the total available groundwater of the basin at time t; YA ijt is the total population of region i at time t, PO it is the food demand per capita of region i at time t, FD it is the yield per unit area of crop j in region i at time t, AM t is the maximum arable land area of the basin at time t; WPC ijt is the water requirement per unit planting area of crop j in region i at time t; HTED t is the power demand in the basin, HSt TLW t MHP nt TLW it GWD lt ESV lt TDI t TIA t WIL t EBW t EBW

[0080] Preferably, in step C, the following steps are included:

[0081] Step C1, introducing a parameter r according to the fractional programming algorithm, is represented as shown in equation (34):

[0082]

[0083] The watershed water resources optimal allocation model is converted into a corresponding linear programming model, as shown in equation (35):

[0084]

[0085] The upper constraint condition of equation (35) includes C01-C10, wherein C01 represents a fractional conversion constraint, represented as shown in equation (36):

[0086]

[0087] C02 represents a user water demand constraint, represented as shown in equation (37):

[0088]

[0089] C03 represents a user water availability constraint, represented as shown in equation (38):

[0090]

[0091] C04 represents a reservoir power generation constraint, represented as shown in equations (39)-(40):

[0092]

[0093]

[0094] C05 represents water resource delivery constraint, expressed as formula (41):

[0095]

[0096] C06 represents food security constraint, expressed as formula (42):

[0097]

[0098] C07 represents land resource constraint, expressed as formula (43):

[0099]

[0100] C08 represents salinity control constraint, expressed as formula (44):

[0101]

[0102] C09 represents ecosystem coverage constraint, expressed as formula (45):

[0103]

[0104] C10 represents river ecological water demand constraint, expressed as formula (46):

[0105]

[0106] Maximizing water resource utilization efficiency of each region is expressed as lower target function formula (47):

[0107]

[0108] Lower constraint conditions of formula (47) include C11-C15, wherein C11 represents fractional conversion constraint, expressed as formula (48):

[0109]

[0110] C12 represents water demand constraint of each user, expressed as formula (49):

[0111]

[0112] C13 represents food security constraint, expressed as formula (50):

[0113]

[0114] C14 represents land resource constraint, expressed as formula (51):

[0115]

[0116] C15 represents the salinity control constraint, expressed as in equation (52):

[0117]

[0118] In the above equations (34)-(52), f U is the optimal water resource utilization efficiency of the basin, f L is the optimal water resource utilization efficiency of each administrative region; PROAGR, PRODOM, PROIND, and PROECO are the net water allocation benefits of the agricultural sector, the domestic sector, the industrial sector, and the ecological sector, respectively, and PROSTATE is the net water allocation benefit of each administrative region; WAU and WAL are the total water allocation of the upper and lower model systems, respectively; is a new fractional decision variable; EIW ijt is the unit power consumption of surface water allocation at time t, EGW ijt is the unit power consumption of groundwater allocation at time t; BW ijt , BP t are the unit water allocation benefit and the unit power generation benefit at time t, respectively; λ t is the irrigation water allocation efficiency at time t, LF ijt is the leaching fraction of crop j in region i at time t, QF ijt is the fertilizer application amount of crop j at time t, PF ijt is the unit price of fertilizer, FC ijt is the fixed cost per unit area of planting; is the unit water consumption for power generation, PHC t is the unit cost of water and electricity production; TWI t is the total available surface water of the basin at time t, TWG t is the total available groundwater of the basin at time t; YA ijt is the total population of region i at time t, PO it is the per capita food demand of region i at time t, FD it is the yield per unit area of crop j in region i at time t, AM t is the maximum arable land area of the basin at time t; WPC ijt is the water requirement per unit planting area of crop j in region i at time t; HTED t is the power demand within the basin, HS t is the reservoir storage at time t, EPW t is the evaporation of the reservoir at time t, MHP nt is the maximum power generation capacity of the reservoir at time t; TLW it is the maximum allowable leaching water quantity in region i at time t, GWD lt is the unit area of forest water consumption of wetland l at time t, ESV ltis the ecological service value per unit area of the wetland L at time t; TDI t is the minimum forest coverage area of the watershed at time t; TIA t is the upstream inflow of the watershed at time t; WIL t is the river inflow into the lake at time t; EBW t is the minimum ecological water demand of the river at time t.

[0119] Preferably, the step D comprises the following steps:

[0120] Step D1, respectively solving the optimal solution of the upper and lower sub-models, and constructing the satisfaction membership function of the system objective and decision variable based on the fuzzy satisfaction degree algorithm:

[0121]

[0122] Introducing the satisfaction degree λ, the global optimal solution can be obtained by solving the overall satisfaction degree of the upper and lower decision-making;

[0123] Wherein, the overall satisfaction degree optimal is represented as the objective function as shown in formula (53):

[0124] Max f = λ (53);

[0125] The constraint conditions of formula (53) include D01-D12, wherein D01 represents the fractional conversion constraint, which is represented as shown in formula (54):

[0126]

[0127] D02 represents the water demand constraint of each user, which is represented as shown in formula (55):

[0128]

[0129] D03 represents the available water quantity constraint of each user, which is represented as shown in formula (56):

[0130]

[0131] D04 represents the reservoir power generation constraint, which is represented as shown in formula (57)-(58):

[0132]

[0133]

[0134] D05 represents the water resource transportation and distribution constraint, which is represented as shown in formula (59):

[0135]

[0136] D06 represents the food security constraint, expressed as in equation (60):

[0137]

[0138] D07 represents the land resource constraint, expressed as in equation (61):

[0139]

[0140] D08 represents the salinity control constraint, expressed as in equation (62):

[0141]

[0142] D09 represents the ecosystem coverage constraint, expressed as in equation (63):

[0143]

[0144] D10 represents the river ecological water demand constraint, expressed as in equation (64):

[0145]

[0146] D11 represents the objective function satisfaction constraint, expressed as in equations (65)-(66):

[0147] μ(f L )≥λ (65),

[0148] μ(f U )≥λ (66),

[0149] D12 represents the decision variable satisfaction constraint, expressed as in equations (67)-(72):

[0150]

[0151]

[0152]

[0153]

[0154]

[0155] 0<λ<1 (72),

[0156] In the above equations (53)-(72), f U is the optimal water resource utilization efficiency of the basin, f LThe optimal water resource utilization efficiency is represented by PROAGR, PRODOM, PROIND, and PROECO, which represent the net water distribution benefits for the agricultural, residential, industrial, and ecological sectors, respectively. PROSTATE represents the net water distribution benefit for each administrative region. WAU and WAL represent the total water distribution volume of the upper and lower level model systems, respectively. For the new fractional decision variables; EIW ijt EGW is the unit power consumption of surface water during period t. ijt The unit power consumption of groundwater during period t; BW ijt BP t These represent the unit water distribution efficiency and unit power generation efficiency for period t, respectively; θ t For irrigation water distribution efficiency during period t, LF ijt QF represents the leaching fraction of crop j in region i during period t. ijt For the amount of fertilizer applied to crop j during period t, PF ijt FC is the unit price of fertilizer. ijt Fixed costs for planting per unit area; PHC is the electricity generated per unit of water consumed. t Unit hydropower production cost; TWI t The total available surface water in the basin during period t, TWG t YA represents the total available groundwater volume in the basin during period t. ijt PO represents the total population of region i during period t. it For the food demand per unit population in region i during period t, FD it For the yield per unit area of ​​crop j in region i during period t, AM t The maximum arable land area in the watershed during period t; WPC ijt Water requirement per unit planting area of ​​crop j in region i during period t; HTED t To meet the electricity demand within the basin, HS t Let t be the reservoir storage capacity, EPW t Let MHP be the reservoir evaporation rate during period t. nt The maximum power generation capacity of the reservoir during period t; TLW it The maximum allowable rinsing water volume for region i during period t, GWD lt Water consumption per unit area of ​​forest land in wetland l during period t, ESV lt The ecosystem service value per unit area of ​​wetland l during period t; TDI t The minimum forest cover area in the watershed during period t; TIA t For the upstream water inflow during period t, WIL t For the river inflow into the lake during period t, EBW t The minimum ecological water demand of the river channel at time t; if the optimal solution of the transformed model is... The original model variable IW ijt * = IW ijt × r, GW nt * = GW nt · r, HW t * = HW t · r, HS t * = HS t · r, BRIEF DESCRIPTION OF DRAWINGS

[0157] Figure 1 is the framework diagram of the watershed water resource utilization efficiency optimization method based on the double-layer decision system described in the present application.

[0158] Figure 2 is the water resource utilization efficiency and water resource allocation result of the four embodiments of the present application.

[0159] Figure 3 is the water resource allocation mode and lake inflow in different periods DETAILED DESCRIPTION

[0160] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0161] Those skilled in the art should understand that the step numbers used in the present application are only for the convenience of description, and are not limited to the execution sequence of the steps. The terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless otherwise clearly indicated by the context, the singular forms "a", "an" and "the" are intended to include the plural forms. The terms "comprise" and "include" indicate the presence of described features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or sets thereof. The term "and / or" refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0162] Figure 1is a developed system framework diagram, first, according to the natural resources of the basin, social and economic conditions and water demand, set the basic data, build a double-layer fuzzy programming model, and introduce fuzzy credibility algorithm, quantile algorithm, multi-layer iterative algorithm and fuzzy satisfaction algorithm to solve the model, so as to deal with the complexity and uncertainty of the basin water resources management system, and finally output the water resources allocation scheme of the double-layer basin system under uncertain conditions.

[0163] The specific implementation process includes:

[0164] Step A, constructing a basin water resources optimal allocation model based on double-layer fuzzy programming, representing the multi-level, multi-objective and fuzzy uncertainty characteristics of agricultural land policy of the basin water resources management system, balancing the interest conflicts of different decision-making layers in the basin, and realizing the optimal efficiency of water resources utilization;

[0165] Step B, obtaining input data through historical literature database, and combining with the farmland expansion plan and expected grain yield to obtain the fuzzy distribution of agricultural farmland area; based on fuzzy possibility degree theory and quantile algorithm, introducing credibility parameters, calculating the corresponding fuzzy variable values under different credibility levels as input parameters of the basin water resources optimal allocation model;

[0166] Step C, according to the fractional programming algorithm, introducing new decision variables, converting the basin water resources optimal allocation model into a corresponding linear programming model;

[0167] Step D, coupling multi-layer iterative algorithm and fuzzy satisfaction algorithm, and obtaining the global optimal solution by solving the overall satisfaction of upper and lower layer decision-making;

[0168] In the step A, the construction of the basin water resources optimal allocation model based on double-layer fuzzy programming includes:

[0169] The maximization of basin water resources utilization efficiency is represented by the upper layer objective function as shown in formula (1):

[0170]

[0171] The upper layer constraint conditions of formula (1) include A01-A09, wherein A01 represents the water demand constraint of each user, which is represented as formula (2):

[0172]

[0173] A02 represents the available water quantity constraint of each user, which is represented as formula (3):

[0174]

[0175] A03 represents the reservoir power generation constraint, and is expressed as shown in equations (4)-(5):

[0176]

[0177]

[0178] A04 represents the water resource transmission and distribution constraint, and is expressed as shown in equation (6):

[0179]

[0180] A05 represents the food security constraint, and is expressed as shown in equation (7):

[0181]

[0182] A06 represents the land resource constraint, and is expressed as shown in equation (8):

[0183]

[0184] A07 represents the salinity control constraint, and is expressed as shown in equation (9):

[0185]

[0186] A08 represents the ecosystem coverage constraint, and is expressed as shown in equation (10):

[0187]

[0188] A09 represents the river ecological water demand constraint, and is expressed as shown in equation (11):

[0189]

[0190] The maximization of water resource utilization efficiency in each region is expressed as shown in equation (12):

[0191]

[0192] The lower constraint condition of equation (12) includes A10-A13, wherein A10 represents the water demand constraint of each user, and is expressed as shown in equation (13):

[0193]

[0194] A11 represents the food security constraint, and is expressed as shown in equation (14):

[0195]

[0196] A12 represents the land resource constraint, and is expressed as shown in equation (15):

[0197]

[0198] A13 represents the salinity control constraint, expressed as equation (16):

[0199]

[0200] In the above equations (1)-(16), f U is the optimal water resources utilization efficiency of the basin, f L is the optimal water resources utilization efficiency of each administrative region; PROAGR, PRODOM, PROIND, and PROECO are the net water allocation benefits of the agricultural sector, the domestic sector, the industrial sector, and the ecological sector, respectively, and PROSTATE is the net water allocation benefit of each administrative region; WAU and WAL are the total water allocations of the upper and lower model systems, respectively; IW ijt is the surface water allocation of region i and sector j at time t, GW ijt is the groundwater allocation of region i and sector j at time t, EW lt is the water allocation of ecosystem l at time t, HW t is the water-electricity allocation of the basin at time t; EIW ijt is the unit power consumption of surface water allocation at time t, EGW ijt is the unit power consumption of groundwater allocation at time t; BW ijt , and BP t are the unit water allocation benefit and the unit power generation benefit at time t, respectively; θ t is the irrigation water allocation efficiency at time t, LF ijt is the leaching fraction of crop j in region i at time t, QF ijt is the fertilizer application amount of crop j at time t, PF ijt is the unit price of fertilizer, FC ijt is the fixed cost per unit area of planting; is the unit water consumption for power generation, PHC t is the unit cost of water-electricity production; TWI t is the total available surface water of the basin at time t, TWG t is the total available groundwater of the basin at time t; YA ijt is the total population of region i at time t, PO it is the food demand per capita of region i at time t, FD it is the yield per unit area of crop j in region i at time t, AM t is the maximum arable land area of the basin at time t; WPC ijt is the water requirement per unit planting area of crop j in region i at time t; HTED t is the power demand in the basin, HS tLet t be the reservoir storage capacity, EPW t Let MHP be the reservoir evaporation rate during period t. nt The maximum power generation capacity of the reservoir during period t; TLW it The maximum allowable rinsing water volume for region i during period t, GWD lt Water consumption per unit area of ​​forest land in wetland l during period t, ESV lt The ecosystem service value per unit area of ​​wetland l during period t; TDI t The minimum forest cover area in the watershed during period t; TIA t For the upstream water inflow during period t, WIL t For the river inflow into the lake during period t, EBW t The minimum ecological water demand of the river channel at time t. Step B includes the following steps:

[0201] Step B1: Based on statistical data, using fuzzy probability theory and quantile algorithm, a confidence parameter α and a triangular fuzzy parameter related to the total amount of agricultural arable land resources are introduced, where 0≤α≤1. The triangular fuzzy parameters are respectively... AM t AM t , The fuzzy inequalities of the constraints are transformed into linear inequalities, as shown in equation (17):

[0202]

[0203] Step B2: Introduce the linearized constraints and reconstruct the linear model, specifically including:

[0204] The objective function for maximizing water resource utilization efficiency in the basin is expressed as shown in equation (18):

[0205]

[0206] The upper-level constraints of equation (18) include B01 to B09, where B01 represents the water demand constraints of each user, as shown in equation (19):

[0207]

[0208] B02 represents the available water volume constraint for each user, as shown in equation (20):

[0209]

[0210] B03 characterizes the reservoir power generation constraint, as shown in equations (21)-(22):

[0211]

[0212]

[0213] B04 represents water resource delivery constraint, expressed as formula (23):

[0214]

[0215] B05 represents food security constraint, expressed as formula (24):

[0216]

[0217] B06 represents land resource constraint, expressed as formula (25):

[0218]

[0219] B07 represents salinity control constraint, expressed as formula (26):

[0220]

[0221] B08 represents ecosystem coverage constraint, expressed as formula (27):

[0222]

[0223] B09 represents river ecological water demand constraint, expressed as formula (28):

[0224]

[0225] Maximizing water resource utilization efficiency of each region is expressed as lower target function formula (29):

[0226]

[0227] The lower constraint condition of formula (29) includes B10-B13, wherein B10 represents user water demand constraint, expressed as formula (30):

[0228]

[0229] B11 represents food security constraint, expressed as formula (31):

[0230]

[0231] B12 represents land resource constraint, expressed as formula (32):

[0232]

[0233] B13 represents salinity control constraint, expressed as formula (33):

[0234]

[0235] f U f L f ijt f ijt f lt f t f ijt f ijt f ijt f t f t f ijt f ijt f ijt f ijt f f t f t f t f ijt f it f it f t f ijt f t f t f t f nt f it f ltESV is the unit area forest land water consumption of wetland l at t period lt TDI is the unit area ecological service value of wetland l at t period t TIA is the minimum forest land coverage area of the basin at t period t WIL is the upstream water inflow of the basin at t period t EBW is the river inflow into the lake at t period t EBW is the river inflow into the lake at t period

[0236] The step C includes the following steps:

[0237] Step C1, introducing according to the fractional programming algorithm, introducing parameter r, represented as shown in equation (34):

[0238]

[0239] The watershed water resources optimal allocation model is converted into a corresponding linear programming model, as shown in equation (35):

[0240]

[0241] The upper constraint condition of equation (35) includes C01-C10, wherein C01 represents the fractional conversion constraint, represented as shown in equation (36):

[0242]

[0243] C02 represents the water demand constraint of each user, represented as shown in equation (37):

[0244]

[0245] C03 represents the available water quantity constraint of each user, represented as shown in equation (38):

[0246]

[0247] C04 represents the reservoir power generation constraint, represented as shown in equations (39)-(40):

[0248]

[0249]

[0250] C05 represents the water resources transportation constraint, represented as shown in equation (41):

[0251]

[0252] C06 represents the food security constraint, represented as shown in equation (42):

[0253]

[0254] C07 represents a land resource constraint, expressed as shown in equation (43):

[0255]

[0256] C08 represents a salinity control constraint, expressed as shown in equation (44):

[0257]

[0258] C09 represents an ecosystem coverage constraint, expressed as shown in equation (45):

[0259]

[0260] C10 represents a river ecological water demand constraint, expressed as shown in equation (46):

[0261]

[0262] Maximizing water resource utilization efficiency in each region is expressed as a lower-level objective function as shown in equation (47):

[0263]

[0264] The lower-level constraints of equation (47) include C11-C15, wherein C11 represents a fractional conversion constraint, expressed as shown in equation (48):

[0265]

[0266] C12 represents a user water demand constraint, expressed as shown in equation (49):

[0267]

[0268] C13 represents a food security constraint, expressed as shown in equation (50):

[0269]

[0270] C14 represents a land resource constraint, expressed as shown in equation (51):

[0271]

[0272] C15 represents a salinity control constraint, expressed as shown in equation (52):

[0273]

[0274] In the above equations (34)-(52), fU f is the optimal water resources utilization efficiency of the basin L f is the optimal water resources utilization efficiency of each administrative region; PROAGR, PRODOM, PROIND, PROECO are the net water allocation benefits of the agricultural sector, the living sector, the industrial sector, and the ecological sector, respectively, and PROSTATE is the net water allocation benefit of each administrative region; WAU and WAL are the total water allocation of the upper and lower model systems, respectively; is a new fractional decision variable; EIW ijt EGW is the unit power consumption of surface water allocation at time t ijt BW is the unit power consumption of groundwater allocation at time t ijt , BP t are the unit water allocation benefit and the unit power generation benefit at time t, respectively; θ t is the irrigation water allocation efficiency at time t ijt is the leaching fraction of crop j in region i at time t ijt is the fertilizer application amount of crop j at time t ijt is the unit price of fertilizer ijt is the fixed cost of planting per unit area is the unit water consumption power generation, PHC t is the unit water and power production cost; TWI t TWG is the total available surface water of the basin at time t t is the total available groundwater of the basin at time t ijt YA is the total population of region i at time t it is the per capita food demand of region i at time t it AM is the unit area yield of crop j in region i at time t t is the maximum arable land area of the basin at time t ijt is the water demand per unit area of crop j in region i at time t t HS is the power demand in the basin t EPW is the reservoir storage at time t t MHP is the evaporation of the reservoir at time t nt TLW is the maximum power generation capacity of the reservoir at time t it GWD is the maximum allowable leaching water in region i at time t lt ESV is the unit area forest water consumption of wetland l at time t lt TDI is the unit area ecological service value of wetland l at time t t TIA is the minimum forest coverage area of the basin at time t t WIL is the upstream inflow of the basin at time t t EBW is the river inflow into the lake at time t tThe minimum ecological water demand of the river channel for t period.

[0275] The step D comprises the following steps:

[0276] The step D1 comprises the following steps:

[0277]

[0278] The satisfaction degree λ is introduced, and the global optimal solution is obtained by solving the overall satisfaction degree of the upper and lower layer decisions;

[0279] The overall satisfaction degree is represented as the objective function as shown in equation (53):

[0280] Max f = λ (53);

[0281] The constraint conditions of equation (53) comprise D01-D12, wherein D01 represents the fractional conversion constraint, and is represented as shown in equation (54):

[0282]

[0283] D02 represents the water demand constraint of each user, and is represented as shown in equation (55):

[0284]

[0285] D03 represents the available water quantity constraint of each user, and is represented as shown in equation (56):

[0286]

[0287] D04 represents the power generation constraint of the reservoir, and is represented as shown in equations (57)-(58):

[0288]

[0289]

[0290] D05 represents the water resource transportation and distribution constraint, and is represented as shown in equation (59):

[0291]

[0292] D06 represents the food safety constraint, and is represented as shown in equation (60):

[0293]

[0294] D07 represents the land resource constraint, and is represented as shown in equation (61):

[0295]

[0296] D08 represents a salinity control constraint, expressed as shown in equation (62):

[0297]

[0298] D09 represents an ecosystem coverage constraint, expressed as shown in equation (63):

[0299]

[0300] D10 represents a river ecological water demand constraint, expressed as shown in equation (64):

[0301]

[0302] D11 represents a target function satisfaction constraint, expressed as shown in equations (65)-(66):

[0303] μ(f L )≥λ (65),

[0304] μ(f U )≥λ (66),

[0305] D12 represents a decision variable satisfaction constraint, expressed as shown in equations (67)-(72):

[0306]

[0307]

[0308]

[0309]

[0310]

[0311] 0<λ<1 (72),

[0312] In the above equations (53)-(72), f U is the optimal water resource utilization efficiency of the basin, f L is the optimal water resource utilization efficiency of each administrative region; PROAGR, PRODOM, PROIND, and PROECO are the net water distribution benefits of the agricultural sector, the living sector, the industrial sector, and the ecological sector, respectively, and PROSTATE is the net water distribution benefit of each administrative region; WAU and WAL are the total water distribution amounts of the upper and lower model systems, respectively; is a new fractional decision variable; EIW ijt is the unit power consumption of surface water distribution at time t, and EGW ijtThe unit power consumption of groundwater during period t; BW ijt BP t These represent the unit water distribution efficiency and unit power generation efficiency for period t, respectively; λ t For irrigation water distribution efficiency during period t, LF ijt QF represents the leaching fraction of crop j in region i during period t. ijt For the amount of fertilizer applied to crop j during period t, PF ijt FC is the unit price of fertilizer. ijt Fixed costs for planting per unit area; PHC is the electricity generated per unit of water consumed. t Unit hydropower production cost; TWI t The total available surface water in the basin during period t, TWG t YA represents the total available groundwater volume in the basin during period t. ijt PO represents the total population of region i during period t. it For the food demand per unit population in region i during period t, FD it For the yield per unit area of ​​crop j in region i during period t, AM t The maximum arable land area in the watershed during period t; WPC ijt Water requirement per unit planting area of ​​crop j in region i during period t; HTED t To meet the electricity demand within the basin, HS t Let t be the reservoir storage capacity, EPW t Let MHP be the reservoir evaporation rate during period t. nt The maximum power generation capacity of the reservoir during period t; TLW it For region i during period t, the maximum allowable rinsing water volume is GWD. lt Water consumption per unit area of ​​forest land in wetland l during period t, ESV lt The ecosystem service value per unit area of ​​wetland l during period t; TDI t The minimum forest cover area in the watershed during period t; TIA t For the upstream water inflow during period t, WIL t For the river inflow into the lake during period t, EBW t The minimum ecological water demand of the river channel at time t; if the optimal solution of the transformed model is... If obtained, then the original model variable IW ijt * =IW ijt ·r,GW nt * =GW nt ·r, HW t * =HW t ·r,HS t * =HS t·r,

[0313] Embodiment

[0314] Figure 2 is a water resources allocation scheme of a river basin in four scenarios, Figure 3 is a water resources allocation mode and water inflow in different periods. The water resources allocation system of the river basin is specifically considered in six planning periods, two decision layers, four administrative regions, three agricultural departments, three other water use departments, one water and electricity department, and three ecological departments. The implementation process is as follows:

[0315] Step A: Taking the maximization of the overall water resources utilization efficiency of the river basin as the upper target and the maximization of the water resources utilization efficiency of each administrative region as the lower target, fully considering the constraints of water demand of each user, available water resources, irrigable cultivated land area, power production, food security, salinity control, and ecological system coverage, a double-layer fractional fuzzy programming-based optimal allocation model of the water resources of the river basin is constructed. The upper target function is shown in formula (1) as follows:

[0316]

[0317] The constraint conditions of formula (1) are A01-A09, which are shown in formulas (2)-(11) as follows.

[0318] The lower target function is shown in formula (12) as follows:

[0319]

[0320] The constraint conditions of formula (12) are A10-A13, which are shown in formulas (13)-(16) as follows.

[0321] Step B: Collect and process data. The input data are obtained by consulting plans, yearbooks, literature, and expert consultation, and the original data are processed into continuous statistical data required by the model by using the interpolation method, and the fuzzy discrete distribution of the cultivated land area is generated.

[0322] Step C: Select four credibility levels (i.e. α = 0.5, 0.6, 0.8, 0.9) of the fuzzy distribution of the cultivated land area as test scenarios, and use software programming to calculate the model under different credibility levels. The results include the water allocation efficiency of the river basin system, the agricultural planting structure, the ecological land mode, and the water resources allocation scheme. The following table is the input data of the cultivated land area under four credibility levels

[0323] Table 1 is the input data of the cultivated land area scenarios in this embodiment:

[0324] Table 1 Input data of cultivated land area (unit: hectare)

[0325]

[0326] Step D: According to the calculation results, arrange the water resource allocation plan for each user, and adjust the water use mode according to the water distribution scheme under different scenarios to achieve the optimal water resource utilization efficiency.

[0327] The results show that among the four scenarios, the maximum water resource utilization efficiency and the minimum water distribution in the basin occur in the scenario of α = 0.9, which are 0.63 $ / m 3 and 671.38 × 10 12 m 3 respectively. Overall, as the credibility of system decision increases, the water resource utilization efficiency will increase, and vice versa. This is mainly because as α increases, the policy satisfaction of cultivated land area increases, thereby expanding the decision space and more conducive to the optimization of water resource utilization efficiency. From the proportion of water distribution of each user, agriculture is the main water-consuming user in the basin, and agricultural water will increase from 28.8 × 10 9 m 3 at the beginning of the planning to 50.5 × 10 9 m 3 at the end of the planning. Ecological water distribution will rise over time, and at t = 6, ecological water distribution will reach 17.5% to 21.3%, and the maximum increase in river ecological water volume will be 30.0%. From the land use mode, economic crops account for the largest proportion of total cultivated land area. From t = 1 to t = 6, as the irrigation efficiency level improves year by year, the proportion of economic crops will increase from 18.6% to 31.6%, and the area of cereal crops will decrease by an average of 12.3%. By the end of the planning, the area of cereal crops will be controlled below 850.3 × 10 6 hectares. This is because after meeting the minimum area requirement of food crops, due to the high profit and low water demand of economic crops, their water will be guaranteed first. Grassland accounts for the largest proportion of all types of ecological land. From the beginning of the planning to the end, the grassland area will increase from 1.6 × 10 9 hectares to 2.1 × 10 9 hectares, and the area of forest and wetland will decrease over time. This may be because the water demand of grassland is low, and it has high ecological tourism and soil conservation value. With the continuous rise of ecological restoration targets, appropriately expanding the planning area of grassland during the planning period will help the system save water resources and achieve higher ecological service value.

[0328] In summary, in the established double-layer decision-making system of water resources in the basin, the water resource utilization efficiency of the basin and each administrative region can be significantly improved, the interest relationship between different decision-making layers can be effectively balanced, and the water resource demand of each user can be competitively satisfied.

[0329] The double-layer fuzzy planning model developed in the application is applied to the management of water resources in a basin, aiming to balance the interest conflicts between the whole basin and different decision-makers in each administrative region, optimize the water resource utilization efficiency, and provide a trade-off analysis between the system satisfaction and marginal benefit, so as to generate a flexible decision-making scheme for managers.

[0330] Compared with the existing optimization allocation method of water resources in a basin, the application has the following advantages:

[0331] (1) Most of the traditional water resource allocation methods determine the most economic solution in the form of maximizing the benefit or minimizing the cost, and cannot reflect the relationship between water resource utilization and economic development. The application introduces the fractional planning technology, which can not only overcome the subjective error problem of weight factor setting in the traditional multi-objective planning, but also optimize the water resource utilization efficiency of the basin system and balance the economic development and resource protection.

[0332] (2) The traditional allocation method mainly aims at a single decision-making layer, ignores the interaction and influence between different decision-makers, and cannot balance the contradiction problems between different stakeholders from the overall point of view. Influenced by the dynamic nature of social and economic conditions and the limitation of human cognition, the parameter uncertainty in the management of water resource system cannot be ignored. The application couples the double-layer planning technology with the fuzzy credibility technology, which can not only deal with the fuzzy uncertainty of the cultivated land area caused by policy changes and social development, but also consider the decision-making needs of the decision-makers at the basin level and the subordinate administrative region level, balance the contradiction problems between different level decision-makers, and realize the coordinated development of the whole basin to the local.

[0333] The application has the following beneficial effects:

[0334] This invention combines bilevel programming, fractional programming, and fuzzy reliability programming to generate a bilevel fractional fuzzy programming model, which addresses the multi-level, multi-objective, and uncertain problems in integrated watershed water resources management. The proposed model not only effectively reflects the parameter fuzziness caused by policy subjectivity in water resources management systems, improving the flexibility and stability of watershed water resources development and utilization decisions, but also balances the conflicts of interest among managers at different levels within the watershed, alleviates the contradiction between watershed economic production and resource protection, improves system water use efficiency, and helps decision-makers achieve a trade-off between risk aversion and marginal benefits, thereby realizing the overall coordinated and sustainable development of the watershed.

Claims

1. A method for optimizing watershed water resource utilization efficiency based on a two-level decision-making system, characterized in that, Includes the following steps: Step A: Construct a watershed water resources optimization allocation model based on two-level fractional fuzzy programming to characterize the multi-level, multi-objective nature of the watershed water resources management system and the fuzzy uncertainty of agricultural land use policies, balance the conflicting interests of different decision-making levels within the watershed, and achieve optimal water resources utilization efficiency. Step B: Obtain input data through historical document databases and combine it with farmland expansion plans and expected grain output to obtain the fuzzy distribution of agricultural farmland area; Based on the fuzzy probability theory and quantile algorithm, a confidence parameter is introduced to calculate the corresponding fuzzy variable values ​​under different confidence levels, which are then used as input parameters for the watershed water resources optimization allocation model. Step C: Based on the fractional programming algorithm, introduce new decision variables to transform the watershed water resources optimization allocation model into a corresponding linear programming model; Step D: Couple the multi-level iterative algorithm and the fuzzy satisfaction algorithm to obtain the global optimal solution by solving the overall satisfaction of the upper and lower level decisions; In step A, constructing the watershed water resources optimization allocation model based on bi-level fractional fuzzy programming includes: Maximizing the efficiency of water resource utilization in the basin can be represented by the upper-level objective function as shown in equation (1): （1）； The upper-level constraints of equation (1) include A01~A09, where A01 represents the water demand constraints of each user, as shown in equation (2): （2）， A02 represents the available water volume constraint for each user, as shown in equation (3): （3）， A03 characterizes the reservoir power generation constraint, as shown in equations (4)-(5): （4）， （5）， A04 characterizes the water resource transport and distribution constraints, as shown in equation (6): （6）， A05 represents the food security constraint, as shown in equation (7): （7）， A06 represents land resource constraints, as shown in equation (8): （8）， A07 characterizes the salinity control constraint, as shown in equation (9): （9）， A08 characterizes the ecosystem cover constraint, as shown in equation (10): （10）， A09 represents the ecological water demand constraint of the river channel, as shown in equation (11): （11）， The maximization of water resource utilization efficiency in each region can be expressed by the lower-level objective function as shown in equation (12): （12）； The lower-level constraints of equation (12) include A10 to A13, where A10 represents the water demand constraints of each user, as shown in equation (13): （13）， A11 represents the food security constraint, as shown in equation (14): （14）， A12 represents land resource constraints, as shown in equation (15): （15）， A13 characterizes the salinity control constraint, as shown in equation (16): （16）， In the above equations (1)-(16), f U For optimal water resource utilization efficiency in the basin, f L The optimal water resource utilization efficiency is represented by PROAGR, PRODOM, PROIND, and PROECO, which represent the net water distribution benefits for the agricultural, residential, industrial, and ecological sectors, respectively. PROSTATE represents the net water distribution benefit for each administrative region. WAU and WAL represent the total water distribution volume of the upper and lower level model systems, respectively. For the surface water distribution of region i and department j during period t, For the groundwater distribution volume of region i and sector j during period t, The water distribution of the ecosystem during period t. The water volume allocated for hydropower in the basin during period t; The unit electricity consumption for surface water during period t. The unit power consumption of groundwater during period t; , These represent the unit water distribution efficiency and unit power generation efficiency during period t, respectively. The irrigation water distribution efficiency during period t. The leaching fraction of crop j in region i during period t. The amount of fertilizer applied to crop j during period t. This is the unit price of fertilizer. Fixed costs for planting per unit area; Electricity generated per unit of water consumed The cost per unit of hydropower production; The total usable surface water volume in the basin during period t. The total available groundwater volume in the basin during period t; Let t represent the total population of region i during period t. The food demand per unit population in region i during period t. For period t, the yield per unit area of ​​crop j in region i. The maximum arable land area in the watershed during period t; Water requirement per unit planting area of ​​crop j in region i during period t; To meet the electricity demand within the basin, Let t be the reservoir's water storage capacity at time t. Let t be the evaporation rate of the reservoir during period t. This represents the maximum power generation capacity of the reservoir during period t. The maximum allowable rinsing water volume for region i during period t. Let t be the water consumption per unit area of ​​forest land in wetland l during period t. The ecological service value per unit area of ​​wetland l during period t; The minimum forest coverage area in the watershed during period t; The upstream water inflow during period t. The amount of water flowing into the lake during period t. This represents the minimum ecological water demand of the river channel during period t.

2. The method for optimizing watershed water resource utilization efficiency based on a two-level decision-making system according to claim 1, characterized in that, Step B includes the following steps: Step B1: Based on statistical data, using fuzzy probability theory and quantile algorithm, a confidence parameter α and a triangular fuzzy number related to the total amount of agricultural arable land resources are introduced, where 0 ≤ α ≤ 1. The parameters of the triangular fuzzy number are as follows: The fuzzy inequalities of the constraints are transformed into linear inequalities, as shown in equation (17): （17）； Step B2: Introduce the linearized constraints and reconstruct the linear model, specifically including: The objective function for maximizing the efficiency of water resource utilization in the basin is expressed as shown in equation (18): （18）； The upper-level constraints of equation (18) include B01 to B09, where B01 represents the water demand constraints of each user, as shown in equation (19): （19）， B02 represents the available water volume constraint for each user, as shown in equation (20): （20）， B03 characterizes the reservoir power generation constraint, as shown in equations (21)-(22): （21）， （22）， B04 characterizes the water resource transport and distribution constraints, as shown in equation (23): （23）， B05 represents food security constraints, as shown in equation (24): （24）， B06 represents land resource constraints, as shown in equation (25): （25）， B07 characterizes the salinity control constraint, as shown in equation (26): （26）， B08 represents the ecosystem cover constraint, expressed as shown in equation (27): （27）， B09 represents the ecological water demand constraint of the river channel, as shown in equation (28): （28）； The maximization of water resource utilization efficiency in each region can be expressed by the lower-level objective function as shown in equation (29): （29）； The lower-level constraints of equation (29) include B10~B13, where B10 represents the water demand constraints of each user, as shown in equation (30): （30）， B11 represents the food security constraint, as shown in equation (31): （31）， B12 represents the land resource constraint, as shown in equation (32): （32）， B13 characterizes the salinity control constraint, as shown in equation (33): （33）。 3. The method for optimizing watershed water resource utilization efficiency based on a two-level decision-making system according to claim 2, characterized in that, Step C includes the following steps: Step C1: Based on the fractional programming algorithm, introduce the parameter r, as shown in equation (34): (34), The watershed water resources optimization allocation model is transformed into a corresponding linear programming model, as shown in equation (35): （35）； The upper-level constraints of equation (35) include C01~C10, where C01 represents the fractional transformation constraint, as shown in equation (36): （36）， C02 represents the water demand constraint of each user, as shown in equation (37): （37）， C03 represents the available water volume constraint for each user, as shown in equation (38): （38）， C04 characterizes the reservoir power generation constraint, as shown in equations (39)-(40): （39）， （40）， C05 characterizes water resource transport and distribution constraints, as shown in equation (41): （41）， C06 represents the food security constraint, as shown in equation (42): （42）， C07 represents land resource constraints, as shown in equation (43): （43）， C08 characterizes the salinity control constraint, as shown in equation (44): （44）， C09 characterizes the ecosystem cover constraint, as shown in equation (45): （45）， C10 characterizes the ecological water demand constraint of the river channel, as shown in equation (46): （46）； The maximization of water resource utilization efficiency in each region can be expressed by the lower-level objective function as shown in equation (47): （47）； The lower-level constraints of equation (47) include C11 to C15, where C11 represents the fractional transformation constraint, as shown in equation (48): （48）， C12 represents the water demand constraint for each user, as shown in equation (49): （49）， C13 represents the food security constraint, expressed as shown in equation (50): （50）， C14 represents the land resource constraint, as shown in equation (51): （51）， C15 characterizes the salinity control constraint, as shown in equation (52): （52）， In the above equations (34)-(52), , , , , These are the new fractional decision variables.

4. The method for optimizing watershed water resource utilization efficiency based on a two-level decision-making system according to claim 3, characterized in that, Step D includes the following steps: Step D1: Solve for the optimal solutions of the upper and lower sub-models respectively, and construct the satisfaction membership functions of the system objective and decision variables based on the fuzzy satisfaction algorithm: ； Introducing satisfaction The global optimal solution can be obtained by solving the overall satisfaction of the upper and lower level decisions; The overall satisfaction is optimally represented by the objective function as shown in equation (53): （53）； The constraints in equation (53) include D01~D12, where D01 represents the fractional transformation constraint, as shown in equation (54): （54）， D02 represents the water demand constraint for each user, as shown in equation (55): （55）， D03 represents the available water volume constraint for each user, as shown in equation (56): （56）， D04 characterizes the reservoir power generation constraint, as shown in equations (57)-(58): （57）， （58）， D05 characterizes the water resource transport and distribution constraints, as shown in equation (59): （59）， D06 represents the food security constraint, as shown in equation (60): （60）， D07 represents land resource constraints, as shown in equation (61): （61）， D08 characterizes the salinity control constraint, as shown in equation (62): （62）， D09 characterizes the ecosystem cover constraint, as shown in equation (63): （63）， D10 characterizes the ecological water demand constraint of the river channel, as shown in equation (64): （64）， D11 represents the objective function satisfaction constraint, expressed as shown in equations (65)-(66): （65）， （66）， D12 represents the satisfaction constraint of the decision variable, as shown in equations (67)-(72): （67）， （68）， （69）， （70）， （71）， （72）， In the above equations (53)-(72), if the optimal solution of the transformed model is... , , , , If obtained, then the original model variables , , , , .

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