Irrigation area planting structure optimization method based on water footprint and water-energy-grain relation

Abstract

The invention discloses an irrigation district planting structure optimization method based on a water footprint and a water-energy-grain relationship, and the method comprises the steps: selecting a typical irrigation district as a crop planting structure optimization target region, determining a planting structure, and collecting and sorting the data of the target region; determining a crop water footprint accounting boundary of the target area based on a water-energy-grain relationship and a water footprint theory, constructing a crop water footprint accounting model, and quantifying water footprint and energy consumption; constructing an irrigation area multi-target crop planting structure optimization model to obtain a crop planting structure optimization scheme; a comprehensive evaluation index system is constructed in combination with social-resource-ecological-economic multi-dimensional system indexes; based on the comprehensive evaluation index system and the TOPSIS evaluation model of the coupling entropy weight method, the planting structure optimization scheme is evaluated, compared and selected, and the optimal planting structure is determined. According to the method, the optimal planting structure scheme with economic and ecological benefits considered can be given, and method reference is provided for efficient resource utilization and agricultural sustainability in related research fields and regions.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of farmland water conservancy engineering, and relates to crop planting structure optimization technology, in particular to a planting structure optimization method for irrigation areas based on water footprint and water-energy-food relationship. BACKGROUND

[0002] An irrigation system is a key carrier for agricultural production and water use, and efficient water use for grain production in an irrigation area plays an important role in ensuring national food security and alleviating regional water shortage. In an irrigation agricultural system, the grain production process relies on the input of various environmental resources such as water, land and energy; at the same time, the acquisition and distribution of water resources consume energy, and the production of energy also cannot do without water, and almost all stages of grain production and supply consume a large amount of water and energy. The adjustment of planting structure based on the efficient use of water resources as the core and the optimization model construction and analysis is one of the important means to improve the agricultural management of the irrigation area. However, the traditional model mostly takes the irrigation water as the object in the quantification of agricultural water and its efficiency, and it is difficult to effectively take into account the green water from natural precipitation and the negative impact of water environment on agricultural production.

[0003] Based on the above situation, the document "Planting structure optimization of pumping irrigation system based on water footprint and water-energy-food relationship" provides a solution, but its scheme is an optimization model developed for the research area, and can only consider four crops for planting structure optimization configuration; in addition, in terms of algorithm, the scheme is limited to the Matlab algorithm, and it is difficult to realize better promotion and application. SUMMARY

[0004] The purpose of the application is to overcome the deficiencies in the prior art, provide a planting structure optimization method for irrigation areas based on water footprint and water-energy-food relationship, and give an optimal planting structure scheme considering economic and ecological benefits, so as to provide a method reference for resource efficient use and agricultural sustainability in related research fields and regions.

[0005] Technical scheme: In order to achieve the above purpose, the application provides a planting structure optimization method for irrigation areas based on water footprint and water-energy-food relationship, comprising the following steps:

[0006] S1: Select a typical irrigation area as the target area for crop planting structure optimization, determine the planting structure, collect and organize the meteorological data and agricultural management measure data of the target area;

[0007] S2: Determine the crop water footprint accounting boundary of the target area based on the water-energy-food relationship and water footprint theory, construct a crop water footprint accounting model, and quantify the water footprint and energy consumption in the whole process of water resource transportation, distribution, consumption and grain output;

[0008] S3: Based on the data obtained in step S2, construct a multi-objective crop planting structure optimization model for the irrigation area to obtain an optimized crop planting structure scheme;

[0009] S4: Construct a comprehensive evaluation index system by combining multi-dimensional system indicators of society, resources, ecology, and economy;

[0010] S5: Based on the constructed comprehensive evaluation index system and the TOPSIS evaluation model of coupled entropy weight method, evaluate and compare the planting structure optimization schemes to determine the optimal planting structure suitable for the sustainable development of the irrigation area.

[0011] Furthermore, the meteorological information in step S1 includes daily maximum temperature, daily minimum temperature, precipitation, relative humidity, average wind speed, and sunshine duration; the agricultural management data includes crop type, crop growth period, crop planting system, crop sown area, and crop coefficient.

[0012] Furthermore, the determination of the crop water footprint accounting boundary in step S2 includes crop type, growth period, and planting structure. Dividing the three processes of water resource transport and distribution, consumption, and food production using the crop water footprint accounting boundary facilitates the determination of the time scale and the construction of a water footprint accounting model at the corresponding scale.

[0013] Furthermore, in the crop water footprint accounting model of step S2, the crop water footprint includes blue water footprint, green water footprint, and gray water footprint; the water footprint includes crop production water footprint and crop consumption water footprint; and energy consumption includes electricity consumption and electricity productivity.

[0014] Furthermore, the crop water footprint accounting model in step S2 is expressed as follows:

[0015] (1)

[0016] (2)

[0017] (3)

[0018] (4)

[0019] (5)

[0020] Where: CWF blue CWF green and CWF grey These are blue water footprints, green water footprints, and gray water footprints; ET c and P e These are crop evaporation and effective precipitation, respectively. c and P eThe WF value was calculated using the CropWat model combined with the crop water requirement method. grey The grey water footprint per unit crop yield; α represents the proportion of chemical substances used that enter freshwater, i.e., leaching and runoff loss rates; AR represents the amount of nitrogen fertilizer applied in the field; c max and c nat These represent the maximum acceptable concentration and the natural concentration of the nutrient in the receiving water body, respectively; Y represents crop yield per unit area; and OP represents crop yield.

[0021] Furthermore, the calculation formulas for the crop production water footprint and the crop consumption water footprint in step S2 are shown in equations (6)-(9):

[0022] (6)

[0023] (7)

[0024] (8)

[0025] (9)

[0026] In the formula: WEP is the water footprint of crop production; WFC is the water footprint of crop consumption; GDV and NGO are the food demand of the target area and the national food output, respectively; RPC is the per capita food availability; P and NP are the population of the target area and the country, respectively.

[0027] The formulas for calculating electricity consumption are shown in equations (10)-(12):

[0028] (10)

[0029] (11)

[0030] (12)

[0031] In the formula: EEC is the power consumption; PO is the power of the water pump motor; t is the water intake time, which is determined by the water demand and the water pump parameters; k is the water pump number; IWW is the total pumping capacity of the pumping station; β is the water pump efficiency; Q is the water pump flow rate; IE is the irrigation efficiency;

[0032] Electricity productivity is the ratio of crop yield to electricity consumption, and the calculation formula is shown in equation (13):

[0033] (13)

[0034] Further, the step S3 multi-objective crop planting structure optimization model of the irrigation area takes the crop sowing area as the decision variable, takes the increase of the economic net benefit of single water as the optimization target of the economic benefit level, takes the reduction of the carbon footprint of the irrigation water taking process as the optimization target of the ecological benefit level, takes the blue water resource pressure constraint, the electric energy resource pressure constraint, the food safety constraint, the sowing area constraint and the non-negative constraint as the limiting conditions; the model solving method is NSGA-II, and multiple sets of optimization schemes, i.e., combinations satisfying the two optimization targets, are obtained.

[0035] Further, in the step S3 multi-objective crop planting structure optimization model of the irrigation area:

[0036] The expression of the multi-objective optimization equation is as shown in formula (14):

[0037] (14)

[0038] In the formula, F1 represents the economic net benefit of single water; i is the crop number; R i and C i are the crop unit area income and cost, respectively; A i is the crop sowing area; CWF i is the crop water footprint; F2 represents the carbon footprint of the irrigation water taking process; E f is the emission factor; AEC i is the unit area electricity consumption;

[0039] The calculation formula of AEC i is as shown in formula (15):

[0040] (15)

[0041] In the formula, j is the year number, and EEC ij is the electricity consumption of the crop in different years.

[0042] Further, in the limiting conditions of the step S3 multi-objective crop planting structure optimization model of the irrigation area:

[0043] The blue water resource pressure constraint is:

[0044] The blue water resource cannot exceed the farmland irrigation water quantity, and is expressed as shown in formula (16)-(17):

[0045] (16)

[0046] (17)

[0047] AWR = AWF + AWR blue,i blue,ij

[0048] Electric energy resource pressure constraint:

[0049] The electric energy resource pressure constraint is set as the sum of the electric energy consumed by each crop after optimization cannot exceed the maximum electricity consumption. The electric energy resource is the electric energy consumed by irrigation. The electric energy resource pressure constraint is expressed as shown in equation (18):

[0050] (18)

[0051] EEC = EEC + EEC

[0052] Food security constraint:

[0053] The food security constraint is set as the sum of the yields of each crop cannot be lower than the minimum demand of the target area. The food security constraint is expressed as shown in equation (19):

[0054] (19)

[0055] GDV = GDV + GDV

[0056] Sowing area constraint:

[0057] The sowing area constraint is set as the sum of the sowing areas of each crop in the same growth period cannot exceed the cultivated land area. The food security constraint is expressed as shown in equation (20):

[0058] (20)

[0059] TA = TA + TA

[0060] Non-negative constraint:

[0061] The non-negative constraint is set as each decision variable is greater than 0. The non-negative constraint is expressed as shown in equation (21):

[0062] (21)

[0063] ​​Further, the first layer of the comprehensive evaluation index system in the step S4 is a system layer, including a social system, a resource system, an ecological system and an economic system; the second layer is an index layer, which is composed of related indexes of each system layer, specifically, crop water productivity and virtual water output represent the social system index; blue water utilization rate and electric energy productivity are taken as the resource system index; crop carbon footprint and grey water footprint are taken as the ecological system index; single water economic net benefit and economic net benefit are taken as the economic system index;

[0064] The crop water productivity is the ratio of crop yield and crop water footprint, and the calculation formula is shown as formula (22):

[0065] (22)

[0066] In the formula, CWP is the crop water productivity;

[0067] The virtual water output is represented as shown in formula (23)-(24):

[0068] (23)

[0069] (24)

[0070] In the formula, GEV is the grain output, and VWE is the virtual water output;

[0071] The blue water utilization rate is calculated by the ratio of blue water footprint and crop water footprint, and the calculation formula is shown as formula (25):

[0072] (25)

[0073] The economic net benefit is calculated according to formula (26):

[0074] (26)

[0075] Further, in the step S5, based on the comprehensive evaluation index system, the evaluation index values under different planting structure optimization schemes are calculated, the TOPSIS evaluation model is established by means of the coupling entropy weight method, the relative closeness of different schemes is calculated, the various planting structure optimization schemes are sorted according to the value of the relative closeness, the scheme with the highest relative closeness is selected as the optimal scheme, and the optimal planting structure suitable for sustainable development of the irrigation area is determined.

[0076] The method of the application considers the influence of crop carbon footprint, grey water footprint and virtual water output on the irrigation system, and provides a planting structure planning basis for similar regions.

[0077] Beneficial Effects: Compared with existing technologies, this invention addresses the entire process of water resource transportation, consumption, and grain production in irrigation systems. By combining the advantages of crop water footprint and water-energy-food relationships, it expands valuable perspectives for scientific research and management practices in optimizing regional crop planting structures, providing methodological references for resource-efficient utilization and agricultural sustainability in related research fields and regions. The method of this invention primarily uses optimization models, expands the range of crop types covered, and is not limited to Matlab algorithms, enhancing the universality of both regional applications and the algorithm, thus facilitating widespread application. Attached Figure Description

[0078] Figure 1 This is a flowchart of the method of the present invention;

[0079] Figure 2 A schematic diagram of the crop planting structure optimization model for irrigation areas;

[0080] Figure 3 A comprehensive evaluation index system for optimizing crop planting structure. Detailed Implementation

[0081] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0082] Example 1:

[0083] like Figure 1 As shown, this embodiment provides a method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship, including the following steps:

[0084] Step 1: In this embodiment, the irrigated area of ​​Lianshui County, Huai'an City, Jiangsu Province, is selected as the study area, with 2006, 2009, 2012, 2015, and 2018 as the analysis years. The main crops planted in the study area are rice, wheat, corn, and soybeans. Meteorological information and agricultural management data for the study area are collected and organized. Meteorological information includes daily maximum and minimum temperatures, precipitation, relative humidity, average wind speed, and sunshine duration. Agricultural management data includes crop type, crop growth period, crop planting system, crop sown area, and crop coefficient.

[0085] Step 2: Based on the water-energy-food relationship and water footprint theory, determine the water footprint accounting boundaries for four crops in the target area, construct water footprint accounting models for different crops, and determine and quantify the water footprint and energy consumption of the entire process of water resource transportation, consumption and food production.

[0086] The crop water footprint accounting boundary determination includes crop type, growth period and planting structure. By dividing the water resource transportation, consumption and food output into three processes through the crop water footprint accounting boundary division, the time scale is determined, and the water footprint accounting model of corresponding scale is constructed.

[0087] The crop water footprint includes blue water footprint, green water footprint and grey water footprint; the water footprint includes crop production water footprint and crop consumption water footprint; the energy consumption includes electricity consumption and electricity production rate.

[0088] The calculation formula of the crop water footprint CWF is as follows:

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] In the formula, CWF blue , CWF green and CWF grey are blue water footprint, green water footprint and grey water footprint respectively, and the unit is m 3 ; ET c and P e are crop evaporation and effective precipitation respectively, and the unit is mm, ET c and P e are obtained by using the crop water requirement method of the Cropwat model; WF grey is the grey water footprint of unit yield of crops, and the unit is m 3 / t; α represents the proportion of chemicals entering fresh water, i.e. leaching and runoff loss rate, and the nitrogen is 0.1; AR is the field nitrogen fertilizer application rate, and the unit is kg / hm 2 ; c max and c nat are the maximum acceptable concentration and natural concentration of the nutrient in the receiving water body respectively, and the unit is mg / L; Y is the yield of crops, and the unit is t / hm 2 ; OP is the yield of crops, and the unit is kg.

[0095] The calculation formula of the crop production water footprint and the crop consumption water footprint is as follows:

[0096]

[0097]

[0098]

[0099]

[0100] Where WEP is the water footprint of crop production, unit is m 3 / kg; WFC is the water footprint of crop consumption, unit is m 3 ; GDV and NGO are the target area food demand and the national food production, unit is kg; RPC is the per capita food possession, unit is kg / person; P and NP are the population of the target area and the country, respectively.

[0101] The calculation formula of electricity consumption is as follows:

[0102]

[0103]

[0104]

[0105] Where EEC is the electricity consumption, unit is kWh; PO is the water pump motor power, unit is kW; t is the water inlet time, determined by water demand and water pump parameters, unit is h; k is the water pump number; IWW is the total pumping capacity of the pumping station, unit is m 3 ; β is the water pump efficiency, dimensionless; Q is the water pump flow, unit is m 3 / s; IE is the irrigation efficiency, dimensionless.

[0106] The electricity production rate is the ratio of crop yield to electricity consumption, unit is kg / kWh, and the calculation formula is as follows:

[0107]

[0108] Step 3: Constructing the multi-objective crop planting structure optimization model of irrigation area, obtaining the crop planting structure optimization scheme by means of NSGA-II algorithm, and the principle of optimization model is as Figure 2 shown; wherein the single water economic net benefit and the carbon footprint of irrigation water taking process represent the economic and ecological benefits of the irrigation area, respectively, and a multi-objective optimization model is constructed. The expression of multi-objective optimization equation is as follows:

[0109]

[0110]

[0111] Where F1 represents the single water economic net benefit, unit is yuan / m 3 ; i is the crop number, in this embodiment, i = 1-4 respectively corresponds to rice, wheat, corn and soybean; R i and Ci These are the crop income and cost per unit area, expressed in yuan / hm². 2 A i This refers to the crop planting area, expressed in hectares (hm²). 2 CWF i Crop water footprint, in meters (m). 3 F2 represents the carbon footprint of irrigation water intake, in tons; 960 is the emission factor; AEC i Electricity consumption per unit area, expressed in kWh / hm² 2 EEC ij The electricity consumption of crops in different years is expressed in kWh.

[0112] The multi-objective crop planting structure optimization model for irrigation districts is constrained by blue water resource pressure, electricity resource pressure, food security, sown area, and non-negativity constraints. Specifically, the blue water resource constraint stipulates that the total blue water footprint of crops must not exceed the farmland irrigation water consumption; the electricity resource constraint stipulates that the sum of electricity consumption for irrigation of each crop after optimization must not exceed the maximum electricity consumption; the food security constraint stipulates that the sum of yields of each crop must not be lower than the minimum demand of the target area; the sown area constraint stipulates that the sum of sown areas of each crop within the same growth period must not exceed the cultivated land area; and the non-negativity constraint stipulates that all decision variables are greater than 0. The expressions for these constraints are as follows:

[0113]

[0114] In the formula: AWR represents the amount of water used for farmland irrigation, in cubic meters (m³). 3 ;maxAWR is the multi-year maximum value of AWR; AWF blue,i The blue water footprint per unit area for a specific crop, expressed in m³ / hm². 2 EEC represents irrigation electricity consumption in kWh; maxEEC is the multi-year maximum value of EEC; GDV represents the target area's food demand in kg; maxGDV is the multi-year maximum value of GDV; TA represents arable land area in hm². 2 maxTA is its multi-year maximum value.

[0115] Among them, AWF blue,i The expression is as follows:

[0116]

[0117] Where: CWF blue,ij The blue water footprint of a specific crop in a specific year, in meters (m). 3 .

[0118] The whole optimization model is built by MATLAB. The model takes the crop planting structure of irrigation area in Lianshui County in 2006 as the starting state of the optimization model, and the data to be loaded include crop planting structure, irrigation water quantity, irrigation electricity quantity, food demand quantity and cultivated land area, and various planting structure combinations represented by different optimization schemes are obtained.

[0119] Step 4: Based on the social-resource-ecological-economic multi-dimensional system index, a comprehensive evaluation index system is constructed, and the index system diagram is shown in Figure 3 The top layer of the evaluation index system is the target layer, i.e. different optimization schemes. The second layer is the system layer, including the social system, the resource system, the ecological system and the economic system. The third layer is the index layer, which is composed of related indexes of each system layer. The crop water productivity and virtual water output are taken as the social system indexes; the blue water utilization rate and the electricity production rate are taken as the resource system indexes; the crop carbon footprint and the grey water footprint are taken as the ecological system indexes; and the single water economic net benefit and the economic net benefit are taken as the economic system indexes in the evaluation index system.

[0120] The crop water productivity is the ratio of crop yield to crop water footprint, and the calculation formula is as follows:

[0121]

[0122] In the formula, CWP is the crop water productivity, the unit is kg / m 3 .

[0123] The calculation formula of virtual water output is as follows:

[0124]

[0125]

[0126] In the formula, GEV is the food output, the unit is kg; VWE is the virtual water output, the unit is m 3 .

[0127] The blue water utilization rate is calculated by the ratio of blue water footprint to crop water footprint, and the calculation formula is as follows:

[0128]

[0129] The calculation formula of economic net benefit is as follows:

[0130]

[0131] Step 5: Based on the constructed comprehensive evaluation index system and the TOPSIS evaluation model of coupled entropy weight method, the relative closeness of different optimization schemes is calculated, the planting structure optimization schemes are evaluated and compared, and the optimal planting structure suitable for the sustainable development of the irrigation area is determined. The specific implementation steps of the TOPSIS evaluation model of coupled entropy weight method include:

[0132] (1) Calculate the related indexes of the comprehensive evaluation system, and construct the index matrix to be evaluated;

[0133] (2) Normalize the standard matrix;

[0134] (3) Calculate the weight of each index by entropy weight method, and get the weight matrix;

[0135] (4) Multiply the normalized standard matrix and the weight matrix to get the matrix z mn , get the Euclidean distance (optimal solution distance d m + and the worst solution distance d m - ) and the relative closeness (S m ), the calculation formula is as follows:

[0136]

[0137]

[0138]

[0139] In the formula: z mn is the evaluation index, z n + is the maximum value of the nth evaluation index, z n - is the minimum value of the nth evaluation index.

[0140] The larger the relative closeness is, the better the comprehensive evaluation of the corresponding scheme is. According to the value of the relative closeness, the schemes are sorted, and the scheme with the highest relative closeness is selected as the optimal scheme.

Claims

1. A method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship, characterized in that, Includes the following steps: S1: Select typical irrigation areas as target areas for crop planting structure optimization, determine the planting structure, and collect and organize meteorological data and agricultural management data of the target areas; S2: Based on the water-energy-food relationship and water footprint theory, determine the boundary of crop water footprint accounting for the target area, construct a crop water footprint accounting model, and quantify the water footprint and energy consumption of the entire process of water resource transportation, consumption and food production. S3: Based on the data obtained in step S2, construct a multi-objective crop planting structure optimization model for the irrigation area to obtain an optimized crop planting structure scheme; S4: Construct a comprehensive evaluation index system by combining multi-dimensional system indicators of society, resources, ecology, and economy; S5: Based on the constructed comprehensive evaluation index system and the TOPSIS evaluation model of coupled entropy weight method, evaluate and compare the planting structure optimization schemes to determine the optimal planting structure.

2. The method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship as described in claim 1, characterized in that, The meteorological information in step S1 includes daily maximum temperature, daily minimum temperature, precipitation, relative humidity, average wind speed, and sunshine duration; the agricultural management data includes crop type, crop growth period, crop planting system, crop sown area, and crop coefficient.

3. The method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship as described in claim 1, characterized in that, In the crop water footprint accounting model of step S2, the crop water footprint includes blue water footprint, green water footprint, and gray water footprint; the water footprint includes crop production water footprint and crop consumption water footprint; and energy consumption includes electricity consumption and electricity productivity.

4. The method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship as described in claim 3, characterized in that, The crop water footprint accounting model in step S2 is expressed as follows: （1）； （2）； （3）； （4）； （5）； Where: CWF blue CWF green and CWF grey These are blue water footprints, green water footprints, and gray water footprints; ET c and P e These are crop evaporation and effective precipitation, respectively. c and P e The WF value was calculated using the CropWat model combined with the crop water requirement method. grey The grey water footprint per unit crop yield; α represents the proportion of chemical substances used that enter freshwater, i.e., leaching and runoff loss rates; AR represents the amount of nitrogen fertilizer applied in the field; c max and c nat These represent the maximum acceptable concentration and the natural concentration of the nutrient in the receiving water body, respectively; Y represents crop yield per unit area; and OP represents crop yield.

5. The method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship as described in claim 4, characterized in that, The calculation formulas for crop production water footprint and crop consumption water footprint in step S2 are shown in equations (6)-(9): （6）； （7）； （8）； （9）； In the formula: WEP is the water footprint of crop production; WFC is the water footprint of crop consumption; GDV and NGO are the food demand of the target area and the national food output, respectively; RPC is the per capita food availability; P and NP are the population of the target area and the country, respectively. The formulas for calculating electricity consumption are shown in equations (10)-(12): （10）； （11）； （12）； In the formula: EEC is the power consumption; PO is the power of the water pump motor; t is the water intake time, which is determined by the water demand and the water pump parameters; k is the water pump number; IWW is the total pumping capacity of the pumping station; β is the water pump efficiency; Q is the water pump flow rate; IE is the irrigation efficiency; Electricity productivity is the ratio of crop yield to electricity consumption, and the calculation formula is shown in equation (13): （13）。 6. The method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship as described in claim 5, characterized in that, In step S3, the multi-objective crop planting structure optimization model for irrigation districts uses crop planting area as the decision variable, improves the net economic benefit per unit of water as the optimization objective at the economic benefit level, reduces the carbon footprint of irrigation water intake as the optimization objective at the ecological benefit level, and is constrained by blue water resource pressure, electricity resource pressure, food security, planting area, and non-negativity constraints. The model is solved using NSGA-II, resulting in multiple sets of optimization schemes, which are combinations that satisfy the two optimization objectives.

7. The method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship as described in claim 6, characterized in that, In the multi-objective crop planting structure optimization model of the irrigation area in step S3: The expression for the multi-objective optimization equation is shown in equation (14): (14)； In the formula: F1 represents the net economic benefit per unit volume of water; i is the crop number; R i and C i These are the income and cost per unit area of ​​crops, respectively; A i Crop planting area; CWF i F2 represents the crop water footprint; F2 represents the carbon footprint of irrigation water intake; E f For emission factors; AEC i Electricity consumption per unit area; AEC i The calculation formula is shown in equation (15): (15)； In the formula: j is the year number, EEC ij This represents the electricity consumption of crops in different years.

8. The method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship as described in claim 7, characterized in that, The constraints of the multi-objective crop planting structure optimization model in the irrigation area in step S3 are: Blue water resource pressure constraints: Blue water resources are defined as the total blue water footprint of crops not exceeding the amount of water used for farmland irrigation, as shown in equations (16)-(17): (16)； (17)； Where: AWR is the amount of water used for farmland irrigation; maxAWR is the multi-year maximum value of AWR; AWF blue,i Blue water footprint per unit area for specific crops; CWF blue,ij The blue water footprint of a specific crop in a specific year; Electricity resource pressure constraints: The power resource pressure constraint is set to ensure that the sum of the power consumption of each crop irrigation after optimization does not exceed the maximum power consumption. Power resources are the power consumed for irrigation. The power resource pressure constraint is expressed as shown in equation (18): (18)； In the formula: EEC represents the electricity consumption for irrigation; maxEEC represents the multi-year maximum value of EEC; Food security constraints: The food security constraint is set as follows: the sum of the yields of all crops must not be lower than the minimum demand of the target area. The food security constraint is expressed as shown in Equation (19): (19)； Where: GDV is the grain demand of the target area; maxGDV is the multi-year maximum value of GDV; Planting area constraints: The sowing area constraint is set as follows: the sum of the sowing areas of all crops in the same growth period shall not exceed the cultivated land area. The food security constraint is expressed as shown in Equation (20): (20)； In the formula: TA is the cultivated land area, and maxTA is its multi-year maximum value; Nonnegativity constraint: The nonnegativity constraint is set to ensure that all decision variables are greater than 0, and is expressed as shown in equation (21): (21)。 9. A method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship as described in claim 8, characterized in that, The first layer of the comprehensive evaluation index system in step S4 is the system layer, which includes social system, resource system, ecosystem and economic system; the second layer is the index layer, which consists of relevant indicators of each system layer. Specifically, crop water productivity and virtual water output are used to represent social system indicators; blue water utilization and electricity productivity are used as resource system indicators; crop carbon footprint and grey water footprint are used as ecosystem indicators; and net economic benefits per unit of water and net economic benefits are used as economic system indicators. Crop water productivity is the ratio of crop yield to crop water footprint, and the calculation formula is shown in equation (22): （22）； In the formula: CWP is crop water productivity; The virtual water output is represented as shown in equations (23)-(24): （23）； （24）； In the formula: GEV is the grain output; VWE is the virtual water output; Blue water utilization rate is calculated by the ratio of blue water footprint to crop water footprint, and the calculation formula is shown in equation (25): （25）； The net economic benefit is calculated according to formula (26): (26)。 10. A method for optimizing the planting structure of irrigation areas based on water footprint and water-energy-food relationship as described in claim 9, characterized in that, In step S5, based on the comprehensive evaluation index system, the evaluation index values ​​under different planting structure optimization schemes are calculated. The TOPSIS evaluation model is established using the coupled entropy weight method to calculate the relative closeness of different schemes. According to the value of the relative closeness, the planting structure optimization schemes are ranked, and the scheme with the highest relative closeness is selected as the optimal scheme to determine the optimal planting structure suitable for the sustainable development of the irrigation area.

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