Irrigation area water and fertilizer coupling regulation and control method based on cross-scale collaborative optimization

Abstract

The invention discloses an irrigation area water and fertilizer coupling regulation and control method based on cross-scale collaborative optimization. The agricultural water and fertilizer management efficiency is improved through a systematic methodology. The method comprises the following steps: firstly, collecting meteorological and agricultural basic data, fitting a water and fertilizer production function by using a least square method, and establishing a relationship between crop yield and water and fertilizer input; on the farmland scale, a moisture balance model and a multi-objective optimization function are constructed, and an optimal irrigation and nitrogen application scheme of each field is solved by adopting NSGA-II and TOPSIS algorithms. In the irrigation domain scale, a water supply scheme with the target of subtracting the water and fertilizer cost from the total output value of crops is optimized, a local water supply boundary is formed, and collaborative iteration of the irrigation domain and the farmland is achieved through a dynamic feedback mechanism. And finally, performing global optimization evaluation on the scale of the irrigation area through a fairness model, and providing decision support for reasonable allocation of water resources. The economic benefit, the resource utilization rate and the environmental influence are comprehensively considered, the scientificity and sustainability of irrigation area water and fertilizer management are effectively improved, and a new thought is provided for intelligent development of modern agriculture.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural water resource management, and specifically relates to a water-fertilizer coupling control method for irrigation areas based on cross-scale collaborative optimization. Background Art

[0002] The optimal allocation of agricultural irrigation water resources is a complex system with multiple levels, scales, and processes. Its internal structure involves tightly coupled relationships between various subsystems and elements. Currently, global water shortages and increasing pressure on food security make agricultural irrigation a crucial component of the national economy. However, traditional irrigation regulation methods often rely on single-level water management or static, phased control, making it difficult to cope with variable climate conditions and complex water and fertilizer response characteristics. While existing technologies have employed crop models to construct relationships between crop yield and water and fertilizer inputs, these approaches are primarily limited to local optima within a single farmland or regional scale, failing to achieve dynamic, coordinated optimization of water resources across scales. Furthermore, they neglect the coupled feedback mechanisms between water, fertilizer, economic, and ecological objectives.

[0003] Existing research has primarily focused on the macroscopic level of irrigation districts, but the precise regulation of water resources at the farmland scale and the multi-scale parameter transmission and feedback mechanisms remain limited. Against this backdrop, a novel irrigation method is urgently needed that can achieve dynamic regulation at three levels, from farmland to irrigation districts, while also addressing multi-objective optimization objectives to meet the practical needs of water conservation, high crop yields, and equitable regional distribution. Summary of the Invention

[0004] In order to solve the technical problems existing in the background technology, the present invention aims to provide an irrigation area water-fertilizer coupling control method based on cross-scale collaborative optimization, construct a three-level model of micro-farmland scale, meso-irrigation area scale and macro-irrigation area scale, and realize global collaborative optimization of water and fertilizer resources and regional economic benefits through parameter transfer and iterative feedback.

[0005] In order to solve the technical problem, the technical solution of the present invention is:

[0006] A method for coupling water and fertilizer control in irrigation areas based on cross-scale collaborative optimization, the method comprising:

[0007] S1: For the collected basic data, the least squares method is used to fit the binary quadratic function model of the relationship between crop yield and water and fertilizer input to obtain the water and fertilizer production function of each crop;

[0008] S2: Using the water-fertilizer production function fitted in S1 and the acquired land and crop classification and field division data, the corresponding water balance formula is used to calculate the daily water layer changes, and the actual crop yield is calculated based on the Stewart model. A multi-objective function is designed and constraints are set to form a complete farmland-scale model structure and constraints.

[0009] S3: Based on the initial availability of surface water and groundwater, the farmland-scale model, and the water-fertilizer production function, the NSGA-II multi-objective optimization algorithm and the TOPSIS optimization method are used to solve the optimal irrigation and nitrogen application strategy for each field throughout the entire growth period. The optimal solution at the farmland scale is output to provide basic input for irrigation area-scale optimization.

[0010] S4: Considering the results at the farmland scale, an irrigation basin economic benefit model is constructed with the goal of maximizing the total output value of crops in the irrigation basin while reducing water and fertilizer costs. Considering the upper limit and priority ratio of regional surface water and groundwater supply, the optimal water supply strategy for each irrigation basin is optimized, a new local water supply boundary is generated and fed back to the farmland scale. After the irrigation basin and farmland models reach equilibrium, the total economic benefit value of the irrigation basin is transmitted to the irrigation district scale.

[0011] S5: Based on the irrigation basin model structure and feedback boundary generated in S4, the irrigation basin layer water supply strategy is continuously optimized, and the updated local water supply boundary is fed back to the farmland model to build a farmland-irrigation basin collaborative optimization closed loop. The model iterates between the two scales until the irrigation basin objective function converges, and outputs the optimal water supply plan, the economic benefits of the irrigation basin layer, and the water supply allocation boundary for the fields.

[0012] S6: Using the irrigation area water supply plan and economic benefits output from S5 as driving parameters of the irrigation district model, combined with the overall constraints of the total water supply in the district and the water allocation ratio of each irrigation area, an irrigation district optimization model that prioritizes both fairness and resource efficiency is constructed. Unit benefit irrigation water consumption and the Gini coefficient are introduced, and a multi-level feedback control mechanism is established from the irrigation district to the irrigation basin and then to the farmland, forming an irrigation district water supply strategy and fairness evaluation model.

[0013] S7: Solve the irrigation district fairness optimization model, feed the obtained new irrigation area water supply boundary back to the irrigation area model, break the original balance between the irrigation area and farmland layer, and restart the collaborative optimization process of S5. This feedback loop is continuously iterated until the irrigation district scale objective function meets the convergence conditions, and finally outputs the global optimal water resource allocation strategy and irrigation district scale water and fertilizer control decision to achieve the overall optimal goal.

[0014] Furthermore, the basic data collected include: meteorological data including daily data of rainfall, maximum temperature, minimum temperature, average relative humidity, average wind speed and sunshine hours; irrigation area data including: planting area of ​​major crops such as rice, corn and soybeans, crop price data, water use cost of different water sources, agricultural planting cost, field leakage, soil moisture content, irrigation amount, nitrogen fertilizer application amount and crop yield; other parameters include crop yield response coefficient and crop coefficient.

[0015] Furthermore, the water and fertilizer production function is:

[0016]

[0017] Where i is the field number; j is the crop type; t is the daily growth time, days (d); n is the different growth periods; Y m,ij is the maximum yield of crop j in field i, kg / ha; SW ijt and GW ijt are the surface water allocation and groundwater allocation for crop j in plot i on day t, mm; a wj 、a nj 、b wj 、b nj 、c j d j are the fitting coefficients of the water and fertilizer productivity functions of the three crops, N ijt is the amount of nitrogen applied to crop j in field i on day t, kg / ha.

[0018] Furthermore, in step S4, an irrigation area economic benefit model is constructed with the goal of maximizing the total output value of crops in the irrigation area while reducing water and fertilizer costs. The specific objective function is as follows:

[0019] Irrigation net benefit objectives:

[0020] The irrigation net benefit function is selected as the objective function of water resources planning at this scale, aiming to improve the regional agricultural economic benefits from the perspective of irrigation water resources; the expression is as follows:

[0021]

[0022] Where, F is the economic benefit of irrigation, yuan, Y a,j represents the yield of crop j, which is transferred by the first layer model, kg / ha; P j represents the market price of crop j, RMB / ha; A ij represents the planting area of ​​crop j in field i, ha, P gro , P sur , representing the surface water and groundwater prices, respectively, in yuan / m 3 ;GQ ij , SQ ij Represents the water allocation per unit area of ​​crop j in field i, m 3 / ha;P N Indicates the price of nitrogen fertilizer, yuan / kg; C p，j represents the planting cost per unit area of ​​crop j, including the cost of land contracting, seeds, pesticides, tillage, etc., in yuan / ha;

[0023] Constraints:

[0024] Surface water supply constraints:

[0025]

[0026] Where, is the surface water supply in the kth irrigation area, m 3 ;

[0027] Groundwater supply constraints:

[0028]

[0029] Where, is the groundwater supply in the kth irrigation area, m 3 ;

[0030] Surface water priority constraint: To ensure the ecological sustainability of the irrigation area and continue the feedback requirements of the model's superiors, a surface water priority constraint is added. The specific expression is as follows:

[0031] ξ*GQ ij ≤SQ ij

[0032] Where, ξ represents the surface water to groundwater multiplication factor, which is 2.5;

[0033] Food security constraint: This constraint aims to ensure food supply security in the irrigated area while reducing the impact of agricultural irrigation water consumption on agricultural output. It is expressed as:

[0034]

[0035] Among them, WD kj is the target water allocation per unit area for agricultural irrigation of crop j in irrigation area k (m 3 / ha);

[0036] Non-negativity constraint: All decision variables are non-negative, that is:

[0037] GQ ij ≥0,SQ ij ≥0.

[0038] Furthermore, in step S5, the model is iteratively solved until the optimization results of the objective function at two adjacent irrigation scales are satisfied, that is:

[0039]

[0040] Among them, F is the target value of the irrigation area scale, n is the number of model runs, and λ1 is the satisfaction level, which is set to 0.005.

[0041] Furthermore, the step S6 constructs an irrigation district optimization model, including:

[0042] The specific objective function is as follows:

[0043] Optimal fairness:

[0044] Fairness refers to the principle of ensuring the rational allocation of water resources and satisfying the interests of all parties in the process of irrigation water distribution. The model introduces the unit benefit irrigation water consumption embedded in the Gini coefficient as the objective function, which is specifically expressed as follows:

[0045]

[0046] Where, k is the irrigation area number of the irrigation district, and there are 14 irrigation areas in total; and are the surface water and groundwater allocation of the kth irrigation area, m 3 ; F k is the economic benefit of the kth irrigation area, yuan; r and s are 1, 2, ..., k; Q k / F k is the irrigation water consumption per unit benefit of the irrigation area, m 3 / Yuan;

[0047] Constraints:

[0048] Surface water availability:

[0049]

[0050] Where Q sur is the groundwater supply in the irrigation area, m 3 ;

[0051] Groundwater supply:

[0052]

[0053] Where Q gro is the groundwater supply in the irrigation area, m 3 ;

[0054] Surface water priority constraints:

[0055]

[0056] Where, ξ represents the surface water to groundwater multiplication factor, which is 2.5;

[0057] Water demand constraints:

[0058]

[0059] Non-negativity constraint: All decision variables are non-negative, that is

[0060]

[0061] Furthermore, in step S7, the model is iterated repeatedly until two adjacent solution results at the irrigation district scale meet the following conditions, at which point the entire model operation ends;

[0062]

[0063] Among them, Gini is the target yield value at the irrigation district scale, m is the number of model runs, and λ2 is the satisfaction level, which is set to 0.005.

[0064] An irrigation system for water-fertilizer coupling control in an irrigation area based on cross-scale collaborative optimization, the system being configured to execute any of the above methods, the system comprising:

[0065] Data fitting and model development module: Based on the collected basic data, the least squares method is used to fit a binary quadratic function model of the relationship between crop yield and water and fertilizer input to obtain the water and fertilizer production function of each crop;

[0066] Farmland water and yield model construction module: Using the above-fitted water and fertilizer production function and the acquired land and crop classification and field division data, the corresponding water balance formula is used to calculate daily water layer changes, and the actual crop yield is calculated based on the Stewart model. Multi-objective functions are designed and constraints are set to form a complete farmland-scale model structure and constraints;

[0067] Field optimization decision support module: Based on the initial availability of surface water and groundwater, the farmland-scale model, and the water-fertilizer production function, the NSGA-II multi-objective optimization algorithm and the TOPSIS optimization method are used to solve the optimal irrigation and nitrogen application strategy for each field throughout the entire growth period. The optimal solution at the farmland scale is output, providing basic input for irrigation area-scale optimization.

[0068] Irrigation basin economic benefit model construction module: Considering the results at the farmland scale, an irrigation basin economic benefit model is constructed with the goal of maximizing the total output value of crops in the irrigation basin while reducing water and fertilizer costs. Considering the upper limit and priority ratio of regional surface water and groundwater supply, the optimal water supply strategy for each irrigation basin is optimized, a new local water supply boundary is generated and fed back to the farmland scale. After the irrigation basin and farmland models reach equilibrium, the total economic benefit value of the irrigation basin is transmitted to the irrigation district scale.

[0069] Irrigation Basin Water Supply Decision Iterative Module: Based on the generated irrigation basin model structure and feedback boundaries, it continuously optimizes the irrigation basin-level water supply strategy and feeds back the updated local water supply boundaries to the farmland model, establishing a farmland-irrigation basin collaborative optimization closed loop. The model iterates between the two scales until the irrigation basin objective function converges, outputting the optimal water supply plan, the irrigation basin-level economic benefits, and the water supply allocation boundaries for the fields.

[0070] Irrigation District Equity and Resource Optimization Module: This module uses the output irrigation area water supply plan and economic benefits as driving parameters of the irrigation district model. Combined with the overall constraints of the district's total water supply and the water allocation ratio of each irrigation area, it constructs an irrigation district optimization model that prioritizes both fairness and resource efficiency. This module introduces indicators such as unit benefit irrigation water consumption and the Gini coefficient, establishes a multi-level feedback control mechanism from irrigation districts to irrigation basins and then to farmland, and forms an irrigation district water supply strategy and equity evaluation model.

[0071] Iterative optimization and target convergence module: solve the irrigation district fairness optimization model, feed the new irrigation district water supply boundary back to the irrigation district model, break the original balance between the irrigation district and farmland layer, and restart the above-mentioned collaborative optimization process. This feedback loop is continuously iterated until the irrigation district scale objective function meets the convergence conditions, and finally outputs the global optimal water resource allocation strategy and irrigation district scale water and fertilizer control decision to achieve the overall optimal goal.

[0072] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements any one of the above-mentioned irrigation area water-fertilizer coupling control methods based on cross-scale collaborative optimization.

[0073] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any one of the above-mentioned irrigation area water-fertilizer coupling control methods based on cross-scale collaborative optimization.

[0074] Compared with the prior art, the advantages of the present invention are:

[0075] 1. Cross-scale collaborative optimization

[0076] This approach overcomes the limitations of traditional irrigation management, which focuses solely on a single scale (e.g., field plot or irrigation district) by collaboratively optimizing across three scales: farmland, irrigation basin, and irrigation district. By transferring information and adjusting decisions across these scales, it ensures optimal water allocation and fertilization strategies, increasing crop yields while also improving water and fertilizer use efficiency and reducing resource waste.

[0077] 2. Intelligent and data-driven decision support

[0078] This method incorporates the NSGA-II and TOPSIS multi-objective optimization algorithms, combining water and fertilizer production functions with field data to enable intelligent, data-driven decision-making. These algorithms not only consider the balance between crop yield and water and fertilizer inputs but also find the optimal solution across multiple objectives, achieving comprehensive optimization of water resources, fertilizer use efficiency, and crop production.

[0079] 3. Dynamic feedback and real-time adjustment

[0080] Through a dynamic feedback mechanism, the system responds in real time to changes in crop growth stages and water availability. Feedback between irrigation areas and farmland ensures flexible resource allocation, enabling timely adjustments to irrigation and fertilization plans based on actual conditions, thus ensuring the system's dynamic adaptability and sustainability.

[0081] 4. Multi-dimensional optimization goals and comprehensive benefits

[0082] This invention not only focuses on crop yield but also incorporates multiple optimization objectives, including water efficiency, greywater footprint, economic benefits, and fairness. By comprehensively considering these factors, it is possible to meet agricultural production needs while minimizing negative environmental impacts and promoting sustainable agricultural development.

[0083] 5. Improve water resource utilization efficiency and save costs

[0084] Through precise water and fertilizer regulation, this method can effectively reduce over-irrigation and over-fertilization, lowering water waste and the cost of excessive fertilizer use. Furthermore, optimizing water allocation across irrigation areas and districts can reduce irrigation system operating costs and improve the overall benefits of water resources.

[0085] 6. Strong applicability and broad application prospects

[0086] This method is suitable for areas with different types of crops and different irrigation modes. It can be customized according to specific agricultural production conditions and water sources. It has good flexibility and scalability and can be widely used globally. It is especially suitable for areas with water shortages and the needs of efficient agricultural production.

[0087] In summary, the present invention not only improves the efficiency of water and fertilizer regulation in irrigation areas, but also provides strong technical support for sustainable agricultural development, and helps to cope with multiple challenges facing global agricultural production, such as water resource shortages and environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 , Schematic diagram of the structure of the cross-scale water-fertilizer coupling control system of the present invention. DETAILED DESCRIPTION

[0089] The specific implementation of the present invention is described below in conjunction with examples:

[0090] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0091] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0092] Example 1:

[0093] Figure 1 This is a schematic diagram of the cross-scale water-fertilizer coupling control system of the present invention, illustrating the data transmission and dynamic iterative feedback relationship between the farmland scale, irrigation area scale, and irrigation district scale. This embodiment provides an irrigation district water-fertilizer coupling control method based on cross-scale collaborative optimization, specifically including:

[0094] S1: Basic data collection: Meteorological data include daily data such as rainfall, maximum temperature, minimum temperature, average relative humidity, average wind speed, and sunshine hours; irrigation area data include the planting area of ​​major crops such as rice, corn, and soybeans, crop price data, water use costs of different water sources, agricultural planting costs, field leakage, soil moisture content, irrigation volume, nitrogen fertilizer application amount, and crop yield; other parameters include crop yield response coefficient and crop coefficient.

[0095] S2: Fit the water and fertilizer production function, using the least squares method to fit a quadratic function to express the relationship between irrigation amount, nitrogen application amount and crop yield. The water and fertilizer production function is:

[0096]

[0097] Where i is the field number; j represents the crop type; t represents the daily growth time, days (d); and n represents different growth periods. m,ij is the maximum yield of crop j in field i, kg / ha; SW ijt and GW ijt are the surface water allocation and groundwater allocation for crop j in plot i on day t, mm; a wj 、a nj 、b wj 、b nj 、c j d j are the fitting coefficients of the water and fertilizer productivity functions of the three crops, N ijt is the amount of nitrogen applied to crop j in field i on day t, kg / ha.

[0098] S3: Construct a farmland-scale model: (1) Calculate daily water layer changes for different crops, paddy fields, and dry fields using the corresponding water balance formula, and calculate actual crop yields based on the Stewart model; (2) Construct a multi-objective function: maximize irrigation water utilization efficiency and minimize gray water footprint while maintaining crop yields; (3) Set constraints, including upper and lower limits on crop water requirements, soil moisture content limits, surface water and groundwater availability, and drainage and fertilization time constraints;

[0099] (1) Production target

[0100] The yield target is to maximize the yield per unit area on a specific area of ​​land through optimizing planting management, resource allocation, and environmental control. By coupling the Stewart crop water production function model with nitrogen fertilizer and considering the differences in the daily water balance equations for paddy fields and dry fields, the daily surface and groundwater irrigation rates and nitrogen fertilizer application rates for different crops during different growth periods are determined to achieve the goal of maximizing the yield of farmland crops during their growth period. The specific calculation formula is as follows:

[0101]

[0102] ET m,ijt =K c,ijt ×K s,ijt ×ET 0,ijt

[0103]

[0104] Where Y a,ij Y represents the yield of crop j in field i, kg / ha, determined using the Stewart water production function; m,ij is the maximum yield of crop j in field i, kg / ha; ET a ET m The actual evaporation and potential evaporation (ET) of crops during the whole growth period under water shortage and sufficient water supply are respectively m Calculated using the single crop coefficient method), mm or 10m 3 / ha, of which ET a,ijn ET m,ijn are the sum of the crop j in the nth growth period of the field i; K c,ijt K is the crop water requirement coefficient of crop j in field i on day t, dimensionless; s,ijt K is the water stress constant of crop j in field i on day t, dimensionless; y is the crop yield response coefficient, dimensionless.

[0105] Actual evapotranspiration (ET) in the Stewart model a,ijnThe water balance method is used for calculation, and hydrological processes such as precipitation, evaporation, runoff, infiltration, and drainage will cause the actual evapotranspiration of crops to change accordingly. This study lists the water balance equations for paddy fields and dry fields respectively.

[0106] Daily paddy field irrigation water requirement: The formula can be expressed as follows:

[0107] h ijt =h ij(t-1) +P t +SW ijt +GW ijt -ET a,ijt -S ijt -D ijt

[0108] Where h ijt is the water layer depth of crop j in plot i on day t, mm; h ij(t-1) is the water layer depth of crop j in plot i on day t-1, mm; P t is the effective rainfall on day t, mm; SW ijt and GW ijt are the surface water allocation and groundwater allocation for crop j in plot i on day t, mm; ET a,ijt is the actual evapotranspiration of the jth crop in plot i on day t, that is, the actual water consumption, mm; S ijt D is the leakage amount of crop j in plot i on day t, mm; ijt is the drainage volume of crop j in field i on day t, in mm.

[0109] Daily scale dryland irrigation water requirement: For dryland, its water balance process can be expressed by the following formula:

[0110] P t +SW ijt +GW ijt +H ij(t-1) -ET a,ijt =H ijt +Q ijt +(Q up -Q uc ) ijt

[0111] In the formula, some parameters are the same as above. ij(t-1) With H ijt are the soil moisture content of crop j in field i at the beginning and end of the period, mm; Q ijt is the surface runoff (i.e., drainage) generated by rainfall in the equilibrium period t for crop j in plot i, mm; (Q up -Q uc ) ijtIt is the difference between the precipitation infiltration and the capillary rise of crop j in field i during this period, mm.

[0112] The soil moisture content can be expressed as follows:

[0113] H ijt =1000γ ijt Z ijt (θ ijt -θ ij,w )

[0114] Where, γ ijt is the soil dry bulk density of crop j in plot i on day t, g / cm 3 ; Z ijt The planned wet layer depth for crop j in field i on day t, m; θ ijt is the soil moisture content of crop j in plot i on day t (the soil moisture content mentioned in this paper refers to mass moisture content), %; θ ij,w is the lower limit of soil moisture content, %, among which the minimum moisture content should be greater than the wilting moisture content to prevent inhibition of crop growth.

[0115] Effective precipitation: Refer to the calculation formula of the United States Department of Agriculture:

[0116]

[0117] Where, P t is the effective precipitation, mm; P is the rainfall on the tth day, mm.

[0118] (2) Irrigation water utilization rate target

[0119] Irrigation water use efficiency is the ratio of crop yield to irrigation water volume. This objective function optimizes and adjusts crop water and fertilizer resources by maximizing water use efficiency, aiming to improve water use efficiency and achieve effective water conservation. The formula is:

[0120]

[0121] Where, F W，ij represents the irrigation water use efficiency of crop j in field i, kg / m 3 .

[0122] (3) Greywater footprint target

[0123] The greywater footprint is a measure of water pollution, referring to the volume of freshwater required to dilute pollutants to meet standards. It can be used to assess the degree of water pollution from a water volume perspective and help evaluate the impact of water pollution on water scarcity. The specific formula is as follows:

[0124]

[0125] Where, Fgrey，ij represents the grey water footprint of crop j in field i during its growth period, m 3 / kg; α represents the fertilizer leaching rate, nitrogen is 0.1; C max It indicates the maximum permissible concentration of pollutants in water, which is 0.01kg / m 3 ; C min It represents the natural background concentration of pollutants in the receiving water body, which is 0kg / m 3 .

[0126] (4) Constraints

[0127] Crop water requirement constraint: To ensure normal crop growth, the daily water requirement of the crop must be between its minimum and maximum water requirements. This constraint is expressed as

[0128] ET min,ijt ≤ET a,ijt ≤ET m,ijt

[0129] Where, ET m,ijt and ET min,ijt are the upper and lower limits of water requirements of crop j in field i on day t.

[0130] Constraint on available surface water: Due to the limited total amount of river water resources in the irrigation area, the amount of surface water allocated to fields at different growth stages should not exceed the amount of surface water that can be allocated at that growth stage, and the amount of surface water that can be allocated during the entire growth period should not exceed the total amount of surface water that can be exploited. This constraint is expressed as

[0131]

[0132] Where, SQ is the surface water allocable amount for crop j in field i on day t; ij is the surface water available for crop j in field i, mm.

[0133] Constraint on available groundwater volume: Similar to the constraint on available surface water volume, the amount of groundwater allocated to the field at different growth stages should not exceed the amount of groundwater available for distribution at that growth stage, and the amount of groundwater available for distribution during the entire growth period should not exceed the amount of groundwater available for extraction. This constraint is expressed as

[0134]

[0135] Where, GQ is the allocable amount of groundwater for crop j in field i on day t, mm; ij is the amount of groundwater available for crop j in field i, mm.

[0136] Surface water priority constraint: To alleviate the serious overexploitation of groundwater in the irrigation area, a surface water priority constraint is set for the model, aiming to adjust the water use structure in the irrigation area. This constraint is expressed as

[0137] ξ*GW ijt ≤SW ijt

[0138] In the formula, ξ represents the surface water - groundwater ratio factor, and its value is 2.5.

[0139] Drainage constraint: When the water depth on the field surface is greater than the maximum water storage layer depth, drainage is required. This constraint aims to prevent crops from being overly flooded and waterlogged. This constraint is expressed as

[0140]

[0141] In the formula, k t is the surface water storage layer depth, in mm; k i，max is the maximum surface water storage layer depth, in mm.

[0142] Nitrogen application rate constraint: Due to the particularity of crop fertilization, except for the necessary basal fertilizer at the initial growth stage, the model sets the application of nitrogen fertilizer at specific growth stages, and the nitrogen application rate in other growth stages is set to 0. At the same time, the total nitrogen application amount during the growth period does not exceed the upper limit that the crop can tolerate for nitrogen fertilizer. This constraint is expressed as:

[0143] When p < t < q, N ijt >0

[0144]

[0145] In the formula, p and q represent the fertilization dates, which are determined according to the historical nitrogen fertilizer application dates in the irrigation area, and N ij represents the total nitrogen application amount of the j - th crop during the growth period of a single farmland.

[0146] Field surface water depth constraint: To ensure the normal growth of rice, this constraint condition is expressed as:

[0147] h ijt,min ≤h ijt ≤h ijt,max

[0148] Soil moisture content constraint: To ensure the normal growth of dry - field crops such as corn and soybeans, the soil moisture content should be controlled to vary between the allowable maximum moisture content and the allowable minimum moisture content. This constraint condition is expressed as:

[0149] θ w ≤θ ijt ≤θ ijt,max

[0150] In the formula, θ ijt,maxare the upper limits of soil moisture content, taken as field capacity, in %.

[0151] Non-negativity constraint: All decision variables are non-negative, that is

[0152] GW ijt ≥0,SW ijt ≥0,N ijt ≥0

[0153] S4: Farmland scale model solution: First, we surveyed the irrigation district administration for water and fertilizer data and fitted the water and fertilizer production functions of the three crops, that is, the relationship between maximum yield and irrigation water and nitrogen application (Y m,j (SW ijt +GW ijt ,N ijt Using the initial available water values ​​SQ and GQ of each plot as constraint boundaries, the NSGA-II and TOPSIS optimization methods were combined to determine the objective function value and decision variable results for each plot during its growth period. The optimal solution was then output.

[0154] S5: Construct an irrigation basin scale model: Optimize the optimal water supply plan for each irrigation basin and form a new local water supply boundary, which is fed back to the farmland scale model. When the farmland scale and irrigation basin scale reach the optimal balance, the total economic benefits of the irrigation basin at this time are transferred to the irrigation district scale. The specific objective function is as follows:

[0155] (1) Net benefit target of irrigation

[0156] Promoting the sustainable development of regional agricultural economy is an important task of agricultural production in irrigated areas. Therefore, this study selected the irrigation net benefit function as the objective function of water resources planning at this scale, aiming to improve the regional agricultural economic benefits from the perspective of irrigation water resources. The expression is as follows

[0157]

[0158] Where, F is the economic benefit of irrigation, yuan. a,j represents the yield of crop j, which is transferred by the first layer model, kg / ha; P j represents the market price of crop j, RMB / ha; A ij represents the planting area of ​​crop j in field i, ha, P gro , P sur , representing the surface water and groundwater prices, respectively, in yuan / m 3 ;GQ ij , SQ ij Represents the water allocation per unit area of ​​crop j in field i, m 3 / ha;P N Indicates the price of nitrogen fertilizer, yuan / kg; C p，jIt represents the planting cost per unit area of ​​crop j, including the cost of land contracting, seeds, pesticides, tillage, etc., in Yuan / ha.

[0159] (2) Constraints

[0160] Surface water supply constraints:

[0161]

[0162] Where, is the surface water supply in the kth irrigation area, m 3 .

[0163] Groundwater supply constraints:

[0164]

[0165] Where, is the groundwater supply in the kth irrigation area, m 3 .

[0166] Surface water priority constraint: To ensure the ecological sustainability of the irrigation area and continue the feedback requirements of the model's superiors, a surface water priority constraint is added. The specific expression is as follows:

[0167] ξ*GQ ij ≤SQ ij

[0168] Where ξ represents the surface water to groundwater multiplication factor, which is set to 2.5.

[0169] Food security constraint: This constraint aims to ensure food supply security in the irrigated area while reducing the impact of agricultural irrigation water consumption on agricultural output. It is expressed as

[0170]

[0171] Among them, WD kj is the target water allocation per unit area for agricultural irrigation of crop j in irrigation area k (m 3 / ha).

[0172] Non-negativity constraint: All decision variables are non-negative, that is

[0173] GQ ij ≥0,SQ ij ≥0

[0174] S6: Solve the irrigation basin scale model: The initial optimization results of the yield per unit area and the total nitrogen application rate during the growing period at the farmland scale are used as the driving parameters of this layer model. The decision variable results of this layer are fed back to the farmland scale as a new coordination variable, namely the water supply of each field, to achieve the synergistic linkage between the farmland scale and irrigation basin scale models. Repeat the above process and iterate the model until the optimization results of the objective function at the irrigation basin scale are satisfied twice, that is,

[0175]

[0176] Among them, F is the target value of the irrigation area scale, n is the number of model runs, and λ1 is the satisfaction level, which is set to 0.005.

[0177] S7: Construct an irrigation district scale model: (1) Based on the optimization of the irrigation area scale, use the unit benefit irrigation water consumption and Gini coefficient indicators to evaluate the fairness of water resources in the entire area; (2) Set the total water supply and the proportion of water resources used in each irrigation area as global constraints; (3) Through top-down and bottom-up two-way feedback, adjust the water supply boundaries at the irrigation area and farmland scales until the global objective function reaches the convergence standard. The specific objective function is as follows:

[0178] (1) Optimal fairness

[0179] Fairness refers to the principle of ensuring the rational allocation of water resources and satisfying the interests of all parties during the irrigation water distribution process. In irrigation districts, to ensure that each irrigation area has fair access to irrigation water and avoid the imbalance of water resource allocation caused by uncertain factors that may damage regional irrigation water rights and interests, the model introduces "unit benefit irrigation water consumption" embedded in the Gini coefficient as the objective function, aiming to characterize the amount of irrigation water consumed to achieve unit benefit in the irrigation district, so as to ensure the fairness of water resource allocation. The specific expression is as follows:

[0180]

[0181] Where, k is the irrigation area number of the irrigation district, and there are 14 irrigation areas in total; and are the surface water and groundwater allocation of the kth irrigation area, m 3 ; F k is the economic benefit of the kth irrigation area, yuan; r and s are 1, 2, ..., k; Q k / F k is the irrigation water consumption per unit benefit of the irrigation area, m 3 / Yuan.

[0182] (2) Constraints

[0183] Surface water availability:

[0184]

[0185] Where Q sur is the groundwater supply in the irrigation area, m 3 .

[0186] Groundwater supply:

[0187]

[0188] Where Q gro is the groundwater supply in the irrigation area, m 3 .

[0189] Surface water priority constraints:

[0190]

[0191] Where ξ represents the surface water to groundwater multiplication factor, which is set to 2.5.

[0192] Water demand constraints:

[0193]

[0194] Non-negativity constraint: All decision variables are non-negative, that is

[0195]

[0196] S8: Solve the irrigation district-scale model. When step S6 terminates, the economic benefit values ​​for each irrigation district obtained at the current irrigation district scale are used as the driving parameters of the current model layer. The decision variables of this layer are fed back to the irrigation district scale as a new coordination variable, namely the water supply at each irrigation district scale. At this point, the balance between the farmland scale and the irrigation district scale is broken, and the model returns to step S6 to continue. This process is repeated, and the model is iterated until two consecutive solutions at the irrigation district scale meet the following conditions. At this point, the entire model run ends.

[0197]

[0198] Among them, Gini is the target yield value at the irrigation district scale, m is the number of model runs, and λ2 is the satisfaction level, which is set to 0.005.

[0199] This method achieves a global balance between water, fertilizer, economic, and ecological objectives by constructing a three-level collaborative optimization model for farmland, irrigation basins, and irrigation districts, and employing dynamic feedback iterative solutions. This method enables precise regulation of water resources in large agricultural irrigation areas under conditions of climate change and uncertainty, providing strong technical support and theoretical foundation for sustainable regional agricultural development.

[0200] Example 2:

[0201] This embodiment provides a terminal device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a water-fertilizer coupling control method for an irrigation area based on cross-scale collaborative optimization, including the following steps:

[0202] S1: Basic data collection: Meteorological data include daily data such as rainfall, maximum temperature, minimum temperature, average relative humidity, average wind speed, and sunshine hours; irrigation area data include the planting area of ​​major crops such as rice, corn, and soybeans, crop price data, water use costs of different water sources, agricultural planting costs, field leakage, soil moisture content, irrigation volume, nitrogen fertilizer application amount, and crop yield; other parameters include crop yield response coefficient and crop coefficient.

[0203] S2: Fit the water and fertilizer production function, using the least squares method to fit a quadratic function to express the relationship between irrigation amount, nitrogen application amount and crop yield. The water and fertilizer production function is:

[0204]

[0205] Where i is the field number; j represents the crop type; t represents the daily growth time, days (d); and n represents different growth periods. m,ij is the maximum yield of crop j in field i, kg / ha; SW ijt and GW ijt are the surface water allocation and groundwater allocation for crop j in plot i on day t, mm; a wj 、a nj 、b wj 、b nj 、c j d j are the fitting coefficients of the water and fertilizer productivity functions of the three crops, N ijtis the amount of nitrogen applied to crop j in field i on day t, kg / ha.

[0206] S3: Construct a farmland-scale model: (1) Calculate daily water layer changes for different crops, paddy fields, and dry fields using the corresponding water balance formula, and calculate actual crop yields based on the Stewart model; (2) Construct a multi-objective function: maximize irrigation water utilization efficiency and minimize gray water footprint while maintaining crop yields; (3) Set constraints, including upper and lower limits on crop water requirements, soil moisture content limits, surface water and groundwater availability, and drainage and fertilization time constraints;

[0207] S4: Farmland scale model solution: First, we surveyed the irrigation district administration for water and fertilizer data and fitted the water and fertilizer production functions of the three crops, that is, the relationship between maximum yield and irrigation water and nitrogen application (Y m,j (SW ijt +GW ijt ,N ijt Using the initial available water values ​​SQ and GQ of each plot as constraint boundaries, the NSGA-II and TOPSIS optimization methods were combined to determine the objective function value and decision variable results for each plot during its growth period. The optimal solution was then output.

[0208] S5: Construct an irrigation basin scale model: (1) After summarizing the farmland scale output data, construct an irrigation basin economic benefit model with the goal of "total crop output value of the irrigation basin - water and fertilizer cost"; (2) Introduce the upper limit and priority use ratio of surface water and groundwater supply in the region, optimize the optimal water supply plan for each irrigation basin and form a new local water supply boundary, and feed it back to the farmland scale model; (3) When the farmland scale and irrigation basin scale reach the optimal balance, the total economic benefit of the irrigation basin at this time will be transferred to the irrigation district scale.

[0209] S6: Solve the irrigation basin scale model: The initial optimization results of the yield per unit area and the total nitrogen application rate during the growing period at the farmland scale are used as the driving parameters of this layer model. The decision variable results of this layer are fed back to the farmland scale as a new coordination variable, namely the water supply of each field, to achieve the synergistic linkage between the farmland scale and irrigation basin scale models. Repeat the above process and iterate the model until the optimization results of the objective function at the irrigation basin scale are satisfied twice, that is,

[0210]

[0211] Among them, F is the target value of the irrigation area scale, n is the number of model runs, and λ1 is the satisfaction level, which is set to 0.005.

[0212] S7: Construct an irrigation district scale model: (1) Based on the optimization of the irrigation basin scale, use the unit benefit irrigation water consumption and Gini coefficient indicators to evaluate the fairness of water resources in the entire district; (2) Set the total water supply and the proportion of water resources used in each irrigation basin as global constraints; (3) Through top-down and bottom-up two-way feedback, adjust the water supply boundaries at the irrigation basin and farmland scales until the global objective function reaches the convergence standard.

[0213] S8: Solve the irrigation district-scale model. When step S6 terminates, the economic benefit values ​​for each irrigation district obtained at the current irrigation district scale are used as the driving parameters of the current model layer. The decision variables of this layer are fed back to the irrigation district scale as a new coordination variable, namely the water supply at each irrigation district scale. At this point, the balance between the farmland scale and the irrigation district scale is broken, and the model returns to step S6 to continue. This process is repeated, and the model is iterated until two consecutive solutions at the irrigation district scale meet the following conditions. At this point, the entire model run ends.

[0214]

[0215] Among them, Gini is the target yield value at the irrigation district scale, m is the number of model runs, and λ2 is the satisfaction level, which is set to 0.005.

[0216] Example 3:

[0217] This embodiment provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0218] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the above embodiment regarding a method for coupling water and fertilizer control in an irrigation area based on cross-scale collaborative optimization. The processor may load and execute the following steps:

[0219] S1: Basic data collection: Meteorological data include daily data such as rainfall, maximum temperature, minimum temperature, average relative humidity, average wind speed, and sunshine hours; irrigation area data include the planting area of ​​major crops such as rice, corn, and soybeans, crop price data, water use costs of different water sources, agricultural planting costs, field leakage, soil moisture content, irrigation volume, nitrogen fertilizer application amount, and crop yield; other parameters include crop yield response coefficient and crop coefficient.

[0220] S2: Fit the water and fertilizer production function, using the least squares method to fit a quadratic function to express the relationship between irrigation amount, nitrogen application amount and crop yield. The water and fertilizer production function is:

[0221]

[0222] Where i is the field number; j represents the crop type; t represents the daily growth time, days (d); and n represents different growth periods. m,ij is the maximum yield of crop j in field i, kg / ha; SW ijt and GW ijt are the surface water allocation and groundwater allocation for crop j in plot i on day t, mm; a wj 、a nj 、b wj 、b nj 、c j d j are the fitting coefficients of the water and fertilizer productivity functions of the three crops, N ijt is the amount of nitrogen applied to crop j in field i on day t, kg / ha.

[0223] S3: Construct a farmland-scale model: (1) Calculate daily water layer changes for different crops, paddy fields, and dry fields using the corresponding water balance formula, and calculate actual crop yields based on the Stewart model; (2) Construct a multi-objective function: maximize irrigation water utilization efficiency and minimize gray water footprint while maintaining crop yields; (3) Set constraints, including upper and lower limits on crop water requirements, soil moisture content limits, surface water and groundwater availability, and drainage and fertilization time constraints;

[0224] S4: Farmland scale model solution: First, we surveyed the irrigation district administration for water and fertilizer data and fitted the water and fertilizer production functions of the three crops, that is, the relationship between maximum yield and irrigation water and nitrogen application (Y m,j (SW ijt +GW ijt ,N ijt Using the initial available water values ​​SQ and GQ of each plot as constraint boundaries, the NSGA-II and TOPSIS optimization methods were combined to determine the objective function value and decision variable results for each plot during its growth period. The optimal solution was then output.

[0225] S5: Construct an irrigation basin scale model: (1) After summarizing the farmland scale output data, construct an irrigation basin economic benefit model with the goal of "total crop output value of the irrigation basin - water and fertilizer cost"; (2) Introduce the upper limit and priority use ratio of surface water and groundwater supply in the region, optimize the optimal water supply plan for each irrigation basin and form a new local water supply boundary, and feed it back to the farmland scale model; (3) When the farmland scale and irrigation basin scale reach the optimal balance, the total economic benefit of the irrigation basin at this time will be transferred to the irrigation district scale.

[0226] S6: Solve the irrigation basin scale model: The initial optimization results of the yield per unit area and the total nitrogen application rate during the growing period at the farmland scale are used as the driving parameters of this layer model. The decision variable results of this layer are fed back to the farmland scale as a new coordination variable, namely the water supply of each field, to achieve the synergistic linkage between the farmland scale and irrigation basin scale models. Repeat the above process and iterate the model until the optimization results of the objective function at the irrigation basin scale are satisfied twice, that is,

[0227]

[0228] Among them, F is the target value of the irrigation area scale, n is the number of model runs, and λ1 is the satisfaction level, which is set to 0.005.

[0229] S7: Construct an irrigation district scale model: (1) Based on the optimization of the irrigation basin scale, use the unit benefit irrigation water consumption and Gini coefficient indicators to evaluate the fairness of water resources in the entire district; (2) Set the total water supply and the proportion of water resources used in each irrigation basin as global constraints; (3) Through top-down and bottom-up two-way feedback, adjust the water supply boundaries at the irrigation basin and farmland scales until the global objective function reaches the convergence standard.

[0230] S8: Solve the irrigation district-scale model. When step S6 terminates, the economic benefit values ​​for each irrigation district obtained at the current irrigation district scale are used as the driving parameters of the current model layer. The decision variables of this layer are fed back to the irrigation district scale as a new coordination variable, namely the water supply at each irrigation district scale. At this point, the balance between the farmland scale and the irrigation district scale is broken, and the model returns to step S6 to continue. This process is repeated, and the model is iterated until two consecutive solutions at the irrigation district scale meet the following conditions. At this point, the entire model run ends.

[0231]

[0232] Among them, Gini is the target yield value at the irrigation district scale, m is the number of model runs, and λ2 is the satisfaction level, which is set to 0.005.

[0233] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0234] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0235] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0236] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0237] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

[0238] Many other changes and modifications can be made without departing from the spirit and scope of the present invention. It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A method for coupling water and fertilizer control in irrigation areas based on cross-scale collaborative optimization, characterized in that: The method comprises: S1: For the collected basic data, the least squares method is used to fit the binary quadratic function model of the relationship between crop yield and water and fertilizer input to obtain the water and fertilizer production function of each crop; S2: Using the water-fertilizer production function fitted in S1 and the acquired land and crop classification and field division data, the corresponding water balance formula is used to calculate the daily water layer changes, and the actual crop yield is calculated based on the Stewart model. A multi-objective function is designed and constraints are set to form a complete farmland-scale model structure and constraints. S3: Based on the initial availability of surface water and groundwater, the farmland-scale model, and the water-fertilizer production function, the NSGA-II multi-objective optimization algorithm and the TOPSIS optimization method are used to solve the optimal irrigation and nitrogen application strategy for each field throughout the entire growth period. The optimal solution at the farmland scale is output to provide basic input for irrigation area-scale optimization. S4: Considering the results at the farmland scale, an irrigation basin economic benefit model is constructed with the goal of maximizing the total output value of crops in the irrigation basin while reducing water and fertilizer costs. Considering the upper limit and priority ratio of regional surface water and groundwater supply, the optimal water supply strategy for each irrigation basin is optimized, a new local water supply boundary is generated and fed back to the farmland scale. After the irrigation basin and farmland models reach equilibrium, the total economic benefit value of the irrigation basin is transmitted to the irrigation district scale. S5: Based on the irrigation basin model structure and feedback boundary generated in S4, the irrigation basin layer water supply strategy is continuously optimized, and the updated local water supply boundary is fed back to the farmland model to build a farmland-irrigation basin collaborative optimization closed loop. The model iterates between the two scales until the irrigation basin objective function converges, and outputs the optimal water supply plan, the economic benefits of the irrigation basin layer, and the water supply allocation boundary for the fields. S6: Using the irrigation area water supply plan and economic benefits output from S5 as driving parameters of the irrigation district model, combined with the overall constraints of the total water supply in the district and the water allocation ratio of each irrigation area, an irrigation district optimization model that prioritizes both fairness and resource efficiency is constructed. Unit benefit irrigation water consumption and the Gini coefficient are introduced, and a multi-level feedback control mechanism is established from the irrigation district to the irrigation basin and then to the farmland, forming an irrigation district water supply strategy and fairness evaluation model. S7: Solve the irrigation district fairness optimization model, feed the obtained new irrigation area water supply boundary back to the irrigation area model, break the original balance between the irrigation area and farmland layer, and restart the collaborative optimization process of S5. This feedback loop is continuously iterated until the irrigation district scale objective function meets the convergence conditions, and finally outputs the global optimal water resource allocation strategy and irrigation district scale water and fertilizer control decision to achieve the overall optimal goal.

2. The method for coupling water and fertilizer control in irrigation areas based on cross-scale collaborative optimization according to claim 1 is characterized in that: The water and fertilizer production function is: Where i is the field number; j is the crop type; t is the daily growth time, days (d); n is the different growth periods; Y m,ij is the maximum yield of crop j in field i, kg / ha; SW ijt and GW ijt are the surface water allocation and groundwater allocation for crop j in plot i on day t, mm; a wj 、a nj 、b wj 、b nj 、c j d j are the fitting coefficients of the water and fertilizer productivity functions of the three crops, N ijt is the amount of nitrogen applied to crop j in field i on day t, kg / ha.

3. The method for coupling water and fertilizer control in irrigation areas based on cross-scale collaborative optimization according to claim 1 is characterized in that: The step S2 constructs a farmland scale model, including: (1) Production target The yield target refers to maximizing yield per unit area on a specific area of ​​land by optimizing planting management, resource allocation, and environmental control measures. By coupling the Stewart crop water production function model with nitrogen fertilizer and considering the differences in the daily water balance equations for paddy fields and dry fields, the daily surface and groundwater irrigation rates and nitrogen fertilizer application rates for different crops during different growth periods are determined to achieve the goal of maximizing the yield of farmland crops during their growth period. The specific calculation formula is as follows: AND m,ijt =K c,ijt ×K s,ijt ×AND 0,ijt Where Y a,ij Y represents the yield of crop j in field i, kg / ha, determined using the Stewart water production function; m,ij is the maximum yield of crop j in field i, kg / ha; ET a ET m The actual evaporation and potential evaporation (ET) of crops during the whole growth period under water shortage and sufficient water supply are respectively m Calculated using the single crop coefficient method), mm or 10m 3 / ha, of which ET a,ijn ET m,ijn are the sum of the crop j in the nth growth period of the field i; K c,ijt K is the crop water requirement coefficient of crop j in field i on day t, dimensionless; s,ijt K is the water stress constant of crop j in field i on day t, dimensionless; y is the crop yield response coefficient, dimensionless; Daily paddy field irrigation water requirement: The formula is as follows: h ijt =h ij(t-1) +P t + SW ijt +GW ijt -ET a,ijt -S ijt -D ijt Where h ijt is the water layer depth of crop j in plot i on day t, mm; h ij(t-1) is the water layer depth of crop j in plot i on day t-1, mm; P t is the effective rainfall on day t, mm; SW ijt and GW ijt are the surface water allocation and groundwater allocation for crop j in plot i on day t, mm; ET a,ijt is the actual evapotranspiration of the jth crop in plot i on day t, that is, the actual water consumption, mm; S ijt D is the leakage amount of crop j in plot i on day t, mm; ijt is the drainage volume of crop j in field i on day t, mm; Daily scale dryland irrigation water requirement: For dryland, its water balance process can be expressed by the following formula: P t +SW ijt +GW ijt +H ij(t-1) -ET a,ijt =H ijt +Q ijt +(Q up -Q uc ) ijt Where H ij(t-1) With H ijt are the soil moisture content of crop j in field i at the beginning and end of the period, mm; Q ijt is the surface runoff or drainage volume generated by rainfall in the balance period t of crop j in field i, mm; (Q up -Q uc ) ijt is the difference between the precipitation infiltration and capillary rise of crop j in plot i during this period, mm; The soil moisture content is expressed as follows: H ijt =1000g ijt Z ijt (i ijt -θ ij,w ) Where, γ ijt is the soil dry bulk density of crop j in plot i on day t, g / cm 3 ; Z ijt The planned wet layer depth for crop j in field i on day t, m; θ ijt is the soil moisture content of crop j in plot i on day t, i.e., mass moisture content, %; θ ij,w is the lower limit of soil moisture content, %, where the minimum moisture content should be greater than the wilting moisture content to prevent inhibition of crop growth; Effective precipitation: Refer to the calculation formula of the United States Department of Agriculture: Where, P t is the effective precipitation, mm; P is the rainfall on day t, mm; (2) Irrigation water utilization rate target Irrigation water use efficiency is the ratio of crop yield to irrigation water volume. This objective function optimizes and adjusts crop water and fertilizer resources from the perspective of maximizing water use efficiency, aiming to improve water use efficiency and achieve effective water conservation. The formula is: Where, F W，ij represents the irrigation water use efficiency of crop j in field i, kg / m 3 ; (3) Greywater footprint target The grey water footprint is a measure of water pollution, referring to the volume of fresh water required to dilute pollutants to meet standards. It can be used to assess the degree of water pollution from a water volume perspective and helps to assess the impact of water pollution on water scarcity. The specific formula is as follows: Where, F grey，ij represents the grey water footprint of crop j in field i during its growth period, m 3 / kg; α represents the fertilizer leaching rate, nitrogen is 0.1; C max It indicates the maximum permissible concentration of pollutants in water, which is 0.01kg / m 3 ; C min It represents the natural background concentration of pollutants in the receiving water body, which is 0kg / m 3 ; (4) Constraints Crop water requirement constraint: This constraint is expressed as: AND min,ijt ≤AND a,ijt ≤AND m,ijt Where, ET m,ijt and ET min,ijt are the upper and lower limits of water requirements of crop j in field i on day t; Constraint on available surface water: This constraint is expressed as: Where, SQ is the surface water allocable amount for crop j in field i on day t; ij is the surface water available for crop j in field i, mm; Constraint on available groundwater volume: This constraint is expressed as: Where, GQ is the allocable amount of groundwater for crop j in field i on day t, mm; ij is the amount of groundwater available for crop j in field i, mm; Surface water priority constraint: This constraint is expressed as: ξ*GW ijt ≤SW ijt Where, ξ represents the surface water to groundwater multiplication factor, which is 2.5; Drainage constraint: This constraint is expressed as: Where k t is the depth of the surface aquifer, mm; k i，max is the maximum depth of the surface aquifer, mm; Nitrogen application rate constraint: This constraint is expressed as: When p < t < q, then N ijt > 0 Where p and q represent the date of fertilization, which is determined based on the historical date of nitrogen fertilizer application in the irrigation area. ij represents the total nitrogen application amount of the jth crop during the growth period of a single farmland; Field surface water depth constraint: This constraint is expressed as: h ijt,min ≤h ijt ≤h ijt,max Soil moisture constraint: This constraint is expressed as: i w ≤θ ijt ≤θ ijt,max Where θ ijt,max are the upper limit of soil moisture content, taken as field capacity, %; Non-negativity constraint: All decision variables are non-negative, that is: GW ijt ≥0, SW ijt ≥0,N ijt ≥0。 4. The method for coupling water and fertilizer control in irrigation areas based on cross-scale collaborative optimization according to claim 1 is characterized in that: In step S4, an irrigation area economic benefit model is constructed with the goal of maximizing the total output value of crops in the irrigation area while reducing water and fertilizer costs. The specific objective function is as follows: Irrigation net benefit objectives: The irrigation net benefit function is selected as the objective function of water resources planning at this scale, aiming to improve the regional agricultural economic benefits from the perspective of irrigation water resources; the expression is as follows: Where, F is the economic benefit of irrigation, yuan, Y a,j represents the yield of crop j, which is transferred by the first layer model, kg / ha; P j represents the market price of crop j, RMB / ha; A ij represents the planting area of ​​crop j in field i, ha, P gro , P sur , representing the surface water and groundwater prices, respectively, in yuan / m 3 ;GQ ij , SQ ij Represents the water allocation per unit area of ​​crop j in field i, m 3 / ha;P N Indicates the price of nitrogen fertilizer, yuan / kg; C p，j represents the planting cost per unit area of ​​crop j, including the cost of land contracting, seeds, pesticides, tillage, etc., in yuan / ha; Constraints: Surface water supply constraints: Where, is the surface water supply in the kth irrigation area, m 3 ; Groundwater supply constraints: Where, is the groundwater supply in the kth irrigation area, m 3 ; Surface water priority constraint: To ensure the ecological sustainability of the irrigation area and continue the feedback requirements of the model's superiors, a surface water priority constraint is added. The specific expression is as follows: ξ*GQ ij ≤SQ ij Where, ξ represents the surface water to groundwater multiplication factor, which is 2.5; Food security constraint: This constraint aims to ensure food supply security in the irrigated area while reducing the impact of agricultural irrigation water consumption on agricultural output. It is expressed as: Among them, WD kj is the target water allocation per unit area for agricultural irrigation of crop j in irrigation area k (m 3 / ha); Non-negativity constraint: All decision variables are non-negative, that is: GQ ij ≥0,SQ ij ≥0。 5. The method for coupling water and fertilizer control in irrigation areas based on cross-scale collaborative optimization according to claim 1 is characterized in that: In step S5, the model is iteratively solved until the optimization results of the objective function at two adjacent irrigation scales are satisfied, that is: Among them, F is the target value of the irrigation area scale, n is the number of model runs, and λ1 is the satisfaction level, which is set to 0.

005.

6. The method for coupling water and fertilizer control in irrigation areas based on cross-scale collaborative optimization according to claim 1 is characterized in that: The step S6 of constructing an irrigation district optimization model includes: The specific objective function is as follows: Optimal fairness: Fairness refers to the principle of ensuring the rational allocation of water resources and satisfying the interests of all parties in the process of irrigation water distribution. The model introduces the unit benefit irrigation water consumption embedded in the Gini coefficient as the objective function, which is specifically expressed as follows: Where, k is the irrigation area number of the irrigation district, and there are 14 irrigation areas in total; and are the surface water and groundwater allocation of the kth irrigation area, m 3 ; F k is the economic benefit of the kth irrigation area, yuan; r and s are 1, 2, ..., k; Q k / F k is the irrigation water consumption per unit benefit of the irrigation area, m 3 / Yuan; Constraints: Surface water availability: Where Q sur is the groundwater supply in the irrigation area, m 3 ; Groundwater supply: Where Q gro is the groundwater supply in the irrigation area, m 3 ; Surface water priority constraints: Where, ξ represents the surface water to groundwater multiplication factor, which is 2.5; Water demand constraints: Non-negativity constraint: All decision variables are non-negative, that is 7. The method for coupling water and fertilizer control in irrigation areas based on cross-scale collaborative optimization according to claim 1 is characterized in that: In step S7, the model is iterated repeatedly until two adjacent solution results at the irrigation district scale meet the following conditions, at which point the entire model operation ends; Among them, Gini is the target yield value at the irrigation district scale, m is the number of model runs, and λ2 is the satisfaction level, which is set to 0.

005.

8. An irrigation system for water-fertilizer coupling control in irrigation areas based on cross-scale collaborative optimization, characterized by: The system is used to perform the method according to any one of claims 1 to 7, and the system includes: Data fitting and model development module: Based on the collected basic data, the least squares method is used to fit a binary quadratic function model of the relationship between crop yield and water and fertilizer input to obtain the water and fertilizer production function of each crop; Farmland water and yield model construction module: Using the above-fitted water and fertilizer production function and the acquired land and crop classification and field division data, the corresponding water balance formula is used to calculate daily water layer changes, and the actual crop yield is calculated based on the Stewart model. Multi-objective functions are designed and constraints are set to form a complete farmland-scale model structure and constraints; Field optimization decision support module: Based on the initial availability of surface water and groundwater, the farmland-scale model, and the water-fertilizer production function, the NSGA-II multi-objective optimization algorithm and the TOPSIS optimization method are used to solve the optimal irrigation and nitrogen application strategy for each field throughout the entire growth period. The optimal solution at the farmland scale is output, providing basic input for irrigation area-scale optimization. Irrigation basin economic benefit model construction module: Considering the results at the farmland scale, an irrigation basin economic benefit model is constructed with the goal of maximizing the total output value of crops in the irrigation basin while reducing water and fertilizer costs. Considering the upper limit and priority ratio of regional surface water and groundwater supply, the optimal water supply strategy for each irrigation basin is optimized, a new local water supply boundary is generated and fed back to the farmland scale. After the irrigation basin and farmland models reach equilibrium, the total economic benefit value of the irrigation basin is transmitted to the irrigation district scale. Irrigation Basin Water Supply Decision Iterative Module: Based on the generated irrigation basin model structure and feedback boundaries, it continuously optimizes the irrigation basin-level water supply strategy and feeds back the updated local water supply boundaries to the farmland model, establishing a farmland-irrigation basin collaborative optimization closed loop. The model iterates between the two scales until the irrigation basin objective function converges, outputting the optimal water supply plan, the irrigation basin-level economic benefits, and the water supply allocation boundaries for the fields. Irrigation District Equity and Resource Optimization Module: This module uses the output irrigation area water supply plan and economic benefits as driving parameters of the irrigation district model. Combined with the overall constraints of the district's total water supply and the water allocation ratio of each irrigation area, it constructs an irrigation district optimization model that prioritizes both fairness and resource efficiency. This module introduces indicators such as unit benefit irrigation water consumption and the Gini coefficient, establishes a multi-level feedback control mechanism from irrigation districts to irrigation basins and then to farmland, and forms an irrigation district water supply strategy and equity evaluation model. Iterative optimization and target convergence module: solve the irrigation district fairness optimization model, feed the new irrigation district water supply boundary back to the irrigation district model, break the original balance between the irrigation district and farmland layer, and restart the above-mentioned collaborative optimization process. This feedback loop is continuously iterated until the irrigation district scale objective function meets the convergence conditions, and finally outputs the global optimal water resource allocation strategy and irrigation district scale water and fertilizer control decision to achieve the overall optimal goal.

9. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an irrigation area water-fertilizer coupling control method based on cross-scale collaborative optimization according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for irrigation area water-fertilizer coupling control based on cross-scale collaborative optimization according to any one of claims 1 to 7.

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