Method and device for regulating and controlling resources in irrigated area under water-salt constraint

Abstract

The invention discloses an irrigation district resource regulation and control method and device under water-salt constraint, and the method comprises the steps: obtaining the hydro meteorological data and soil and crop data of a target irrigation district, dividing the irrigation district into a plurality of layers in the vertical direction, building a water-salt balance model according to the number of layers, and representing the water-salt dynamic process between different soil layers. The refinement degree of the model in the aspects of simulating irrigation area groundwater supply, evapotranspiration, salt migration and the like is improved, so that the problem of simulation result deviation caused by traditional homogenization treatment is solved. Secondly, price data, water supply data and environment data are further obtained, corresponding objective functions and constraint models are constructed, and then the objective functions and constraint models act together with a water-salt balance model to form a resource optimization model, so that multi-objective cooperative regulation and control can be realized under the condition of considering multiple factors such as economic benefits, water supply safety and ecological environment protection, and the resource optimization efficiency is improved. By combining actual irrigation area execution, it can be ensured that the irrigation system is closer to the crop water requirement rule and the water-salt safety threshold value, and the water resource utilization rate is increased.

Description

Technical Field

[0001] This invention relates to the field of agricultural water resource utilization technology, and in particular to a method and apparatus for regulating irrigation resources under water and salt constraints. Background Technology

[0002] The rational allocation of water resources in irrigation districts can simultaneously produce significant beneficial effects in economic, social, and ecological aspects. First, by optimizing water allocation based on crop water requirements and spatiotemporal distribution, irrigation water utilization efficiency can be significantly improved, and ineffective evaporation and seepage losses reduced, thereby increasing the output efficiency per unit of water resources and enhancing the stability of grain yield. Second, rational allocation helps alleviate groundwater over-extraction, inhibit soil salinization, and promote the sustainable replenishment of surface and groundwater, improving the ecological environment and agricultural production conditions in irrigation districts. Third, by implementing differentiated water allocation, seasonal regulation, and corresponding water pricing and incentive mechanisms, water use conflicts can be reduced, the enthusiasm of agricultural producers for water resource management can be increased, and the efficient operation of irrigation facilities and energy conservation and emission reduction can be promoted.

[0003] Existing methods for irrigation district resource regulation mostly focus on numerical simulations of single processes or single-objective optimization, failing to fully couple the feedback mechanisms between surface and groundwater dynamics, soil water and salt transport, crop water and salt requirements, and engineering regulation measures. This results in insufficient accuracy in simulating short-term fluctuations and long-term cumulative effects. Secondly, model systems often neglect spatiotemporal heterogeneity and scale transformation issues, making it difficult to simultaneously consider the decision-making needs at the field scale and watershed or irrigation district scale. Furthermore, parameter uncertainty, sparse observational data, and weak data assimilation capabilities limit the reliability of model predictions and optimization results. Summary of the Invention

[0004] This invention provides a method and apparatus for regulating irrigation resources under water and salt constraints, so as to improve the utilization rate of irrigation resources.

[0005] To address the aforementioned technical problems, this invention provides a method for resource regulation in irrigation districts under water and salt constraints, comprising:

[0006] Obtain hydrological and meteorological data and soil and crop data of the target irrigation area, divide the target irrigation area into several levels based on the vertical direction, and construct a water-salt balance model of the target irrigation area based on the hydrological and meteorological data, soil and crop data and each level.

[0007] Acquire price data, water supply data, and environmental data; construct an objective function and constraint model based on the price data, water supply data, and environmental data; and construct a resource optimization model based on the objective function and constraint model.

[0008] Based on the resource optimization model and the preset genetic algorithm, a resource regulation scheme is generated, which includes crop planting area, monthly crop irrigation amount and water supply quota.

[0009] Based on the crop planting area, monthly crop irrigation volume, and water supply quota, resource regulation is carried out in the target irrigation area, and a resource regulation report is generated to complete the resource regulation of the target irrigation area.

[0010] This invention acquires hydrological and meteorological data, as well as soil and crop data, of the target irrigation area. It then divides the irrigation area into several layers vertically and establishes a water-salt balance model based on the layered data. This model accurately characterizes the dynamic processes of water and salt between different soil layers, effectively improving the model's precision in simulating groundwater recharge, evapotranspiration, and salt migration within the irrigation area, thus overcoming the problem of simulation bias caused by traditional homogenization processes. Secondly, by further acquiring price, water supply, and environmental data and constructing corresponding objective functions and constraint models, this model, combined with the water-salt balance model, forms a resource optimization model. This model can achieve multi-objective coordinated regulation while considering multiple factors such as economic benefits, water supply security, and ecological environmental protection, thereby improving the scientific rigor and practicality of the solution. Thirdly, based on the resource optimization model and combined with a pre-set genetic algorithm, resource regulation schemes are generated. These schemes can quickly converge to the optimal or near-optimal solution under complex nonlinear constraints, effectively improving the efficiency and stability of scheme generation. Furthermore, by generating control plans that include crop planting area, monthly irrigation volume, and water supply quotas, and implementing these plans in conjunction with actual irrigation districts, it is possible to ensure that the irrigation system more closely aligns with crop water requirements and water-salt safety thresholds, thereby improving water resource utilization and avoiding the adverse effects of salt accumulation on soil and crops. Finally, by outputting standardized resource control reports, the traceability and operability of the control results are guaranteed.

[0011] Furthermore, the irrigation area soil and crop data includes moisture data and salinity data; the process of acquiring hydrological and meteorological data and irrigation area soil and crop data of the target irrigation area, dividing the target irrigation area into several levels based on the vertical direction, and constructing a water-salt balance model of the target irrigation area based on the hydrological and meteorological data, basic irrigation area data, soil and crop data, and each level includes:

[0012] Acquire hydrological and meteorological data, as well as soil and crop data for irrigation areas;

[0013] The target irrigation area is divided into a root zone, a transition zone, and an aquifer based on the vertical direction; the water flow rate from the transition zone to the root zone is equal to the water flow rate from the aquifer to the transition zone.

[0014] Based on the aforementioned hydrological and meteorological data and water data, a root zone water balance model, a transition zone water balance model, and an aquifer water balance model were constructed.

[0015] Based on hydrological and meteorological data and salinity data, a salinity equilibrium model for the root zone, a salinity equilibrium model for the transition zone, and a salinity equilibrium model for the aquifer are constructed; the aquifer salinity equilibrium model includes a salinity equilibrium model for farmland aquifers and a salinity equilibrium model for non-farmland aquifers.

[0016] The water and salt balance model of the target irrigation area is constructed based on the root layer water balance model, transition layer water balance model, aquifer water balance model, root layer salinity balance model, transition layer salinity balance model, and aquifer salinity balance model.

[0017] This invention limits the soil and crop data in the irrigation area to moisture and salinity data, and establishes water balance and salinity balance models based on the root layer, transition layer, and aquifer. The aquifer salinity balance model distinguishes between farmland and non-farmland areas, which can more accurately depict the stratified characteristics and regional differences of water and salt transport in the irrigation area. It can not only reflect the actual water and salt environment in the crop root zone, but also reveal the long-term impact of irrigation on deep water and salt dynamics, thereby improving the accuracy and applicability of water and salt balance simulation and providing more reliable input parameters for subsequent resource optimization and scheduling.

[0018] Furthermore, the water-salt balance model also includes an overall water balance model. After constructing the root zone water balance model, transition zone water balance model, and aquifer water balance model based on the hydrological and meteorological data and water data, it further includes:

[0019] Based on the irrigation area soil and crop data of the target irrigation area, the water interaction relationship between farmland and non-farmland is obtained;

[0020] Based on the water interaction relationship between farmland and non-farmland, a short-term overall water balance model is constructed.

[0021] This invention introduces a global water balance model based on a stratified water balance model, and establishes a monthly-scale global water balance equation through the water interaction between farmland and non-farmland. This model can reflect the water budget characteristics of irrigation districts and the coupling relationship of regional water cycles at a macroscopic level. This method enables the model to not only analyze local water volume changes in the crop root zone and groundwater layer, but also take into account the water resource feedback relationships between different land use units, thus providing a scientific basis for water resource balance assessment and overall optimization at the irrigation district scale.

[0022] Furthermore, the acquisition of price data, water supply data, and environmental data, the construction of an objective function and constraint model based on the price data, water supply data, and environmental data, and the construction of a resource optimization model based on the objective function and constraint model include:

[0023] Acquire price data, water supply data, and environmental data, and construct an objective function based on the price data, water supply data, and environmental data;

[0024] Construct crop constraint models, water supply constraint models, and environmental constraint models;

[0025] A resource optimization model is constructed based on the objective function, crop constraint model, water supply constraint model, and environmental constraint model.

[0026] This invention acquires price, water supply, and environmental data to construct a resource optimization model that combines an objective function with crop, water supply, and environmental constraints. This model organically integrates economic benefits, resource carrying capacity, and ecological environment requirements. The method ensures that the optimization results not only maximize economic gains but also consider sustainable water supply and ecological security, avoiding local optima or environmental risks caused by a single objective. This significantly improves the comprehensiveness and operability of the control scheme.

[0027] Furthermore, the acquisition of price data, water supply data, and environmental data, and the construction of an objective function based on the price data, water supply data, and environmental data, includes:

[0028] Obtain price data, and construct a price objective function based on the price data, using crop planting area and irrigation volume as decision variables;

[0029] Acquire water supply data, and construct a water supply objective function based on the water supply data and water supply quota as the decision variable;

[0030] Acquire environmental data, and construct an environmental objective function based on the environmental data, using crop planting area as the decision variable;

[0031] A total objective function is constructed based on the aforementioned price objective function, water supply objective function, and environmental objective function.

[0032] This invention achieves multi-objective coordinated optimization by decomposing the price objective function, water supply objective function, and environmental objective function, and unifying them into a unified overall objective function. The price function reflects economic benefits, the water supply function reflects the fairness and utilization rate of resource allocation, and the environmental function constrains soil and ecosystem safety. This avoids the biases that may arise from single-objective optimization, achieves a multi-dimensional balance between economy, resources, and environment, and ensures the scientific rationality of irrigation district regulation schemes.

[0033] Furthermore, the construction of the crop constraint model, water supply constraint model, and environmental constraint model includes:

[0034] A planting ratio constraint model is constructed based on a preset planting area ratio threshold; a crop yield constraint model is constructed based on a preset grain yield threshold; and a crop constraint model is constructed based on the planting ratio constraint model and the crop yield constraint model.

[0035] An irrigation constraint model is constructed based on crop irrigation amount, a maximum water supply constraint model is constructed based on preset total water supply, a water shortage constraint model is constructed based on preset water shortage rate, and a water supply constraint model is constructed based on the irrigation constraint model, the maximum water supply constraint model, and the water shortage constraint model.

[0036] A groundwater level constraint model is constructed based on groundwater level changes, a soil salinity constraint model is constructed based on a preset soil salinity threshold, and an environmental constraint model is constructed based on the groundwater level constraint model and the soil salinity constraint model.

[0037] This invention establishes three constraint models—crops, water supply, and environment—covering multi-dimensional constraints such as planting structure ratio, grain yield, irrigation demand, total water supply, water shortage rate, groundwater level, and soil salinity. This ensures that the optimization results meet the requirements of food security, water resource carrying capacity, and ecological sustainable development. It avoids risks arising from neglecting farmland structure, excessive groundwater extraction, or soil salinization during implementation, significantly improving the stability and practical feasibility of the resource regulation model.

[0038] Secondly, the present invention provides a water-salt constrained irrigation district resource regulation device, comprising: a water-salt balance model construction module, a resource optimization model construction module, a scheme generation module, and a regulation module;

[0039] The water-salt balance model construction module is used to acquire hydrological and meteorological data and soil and crop data of the target irrigation area, divide the target irrigation area into several levels based on the vertical direction, and construct the water-salt balance model of the target irrigation area based on the hydrological and meteorological data, soil and crop data and each level.

[0040] The resource optimization model construction module is used to acquire price data, water supply data, and environmental data, construct an objective function and constraint model based on the price data, water supply data, and environmental data, and construct a resource optimization model based on the objective function and constraint model.

[0041] The scheme generation module is used to generate a resource regulation scheme based on the resource optimization model and a preset genetic algorithm. The resource regulation scheme includes crop planting area, monthly crop irrigation amount and water supply quota.

[0042] The control module is used to control resources in the target irrigation area based on the crop planting area, monthly crop irrigation volume and water supply quota, and generate a resource control report to complete the resource control of the target irrigation area.

[0043] Furthermore, the irrigation area soil and crop data includes moisture data and salinity data; the water-salt balance model construction module is used to acquire hydrological and meteorological data and irrigation area soil and crop data of the target irrigation area, divide the target irrigation area into several levels based on the vertical direction, and construct the water-salt balance model of the target irrigation area based on the hydrological and meteorological data, basic irrigation area data, soil and crop data, and each level, including:

[0044] Acquire hydrological and meteorological data, as well as soil and crop data for irrigation areas;

[0045] The target irrigation area is divided into a root zone, a transition zone, and an aquifer based on the vertical direction; the water flow rate from the transition zone to the root zone is equal to the water flow rate from the aquifer to the transition zone.

[0046] Based on the aforementioned hydrological and meteorological data and water data, a root zone water balance model, a transition zone water balance model, and an aquifer water balance model were constructed.

[0047] Based on hydrological and meteorological data and salinity data, a salinity equilibrium model for the root zone, a salinity equilibrium model for the transition zone, and a salinity equilibrium model for the aquifer are constructed; the aquifer salinity equilibrium model includes a salinity equilibrium model for farmland aquifers and a salinity equilibrium model for non-farmland aquifers.

[0048] The water and salt balance model of the target irrigation area is constructed based on the root layer water balance model, transition layer water balance model, aquifer water balance model, root layer salinity balance model, transition layer salinity balance model, and aquifer salinity balance model.

[0049] Furthermore, the water-salt balance model also includes an overall water balance model. After constructing the root zone water balance model, transition zone water balance model, and aquifer water balance model based on the hydrological and meteorological data and water data, it further includes:

[0050] Based on the irrigation area soil and crop data of the target irrigation area, the water interaction relationship between farmland and non-farmland is obtained;

[0051] Based on the water interaction relationship between farmland and non-farmland, a short-term overall water balance model is constructed.

[0052] Furthermore, the resource optimization model construction module is used to acquire price data, water supply data, and environmental data; construct an objective function and constraint model based on the price data, water supply data, and environmental data; and construct a resource optimization model based on the objective function and constraint model, including:

[0053] Acquire price data, water supply data, and environmental data, and construct an objective function based on the price data, water supply data, and environmental data;

[0054] Construct crop constraint models, water supply constraint models, and environmental constraint models;

[0055] A resource optimization model is constructed based on the objective function, crop constraint model, water supply constraint model, and environmental constraint model. Attached Figure Description

[0056] Figure 1 This is a schematic flowchart of a water-salt constrained irrigation district resource regulation method provided in an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram comparing the simulated and measured values ​​of soil salinity in the root zone of farmland and non-farmland, as described in this embodiment of the invention.

[0058] Figure 3 This is a schematic diagram of the current water allocation in the irrigation area after optimization, provided in an embodiment of the present invention.

[0059] Figure 4 This is a schematic diagram of the optimized monthly irrigation volume for three crops in an irrigation area, provided by an embodiment of the present invention.

[0060] Figure 5 This is a schematic diagram illustrating the optimized changes in groundwater depth in the irrigation area, provided as an embodiment of the present invention.

[0061] Figure 6 This is a schematic diagram of the optimized soil salinity in the irrigation area provided in an embodiment of the present invention;

[0062] Figure 7 This is a schematic diagram showing the proportions of the planting structure in the irrigation area before and after optimization, as provided in an embodiment of the present invention. Detailed Implementation

[0063] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0064] The terms "first" and "second," etc., in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0065] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0066] Example 1

[0067] See Figure 1 , Figure 1 This is a schematic flowchart illustrating a method for regulating irrigation resources under water and salt constraints, provided by an embodiment of the present invention. The method includes steps 101 to 104, as detailed below:

[0068] Step 101: Obtain hydrological and meteorological data and soil and crop data of the target irrigation area, divide the target irrigation area into several levels based on the vertical direction, and construct a water-salt balance model of the target irrigation area based on the hydrological and meteorological data, soil and crop data and each level.

[0069] In this embodiment, the irrigation area soil and crop data includes moisture data and salinity data; the process of acquiring hydrological and meteorological data and irrigation area soil and crop data of the target irrigation area, dividing the target irrigation area into several levels based on the vertical direction, and constructing a water-salt balance model of the target irrigation area based on the hydrological and meteorological data, basic irrigation area data, soil and crop data, and each level includes:

[0070] Acquire hydrological and meteorological data, as well as soil and crop data for irrigation areas;

[0071] The target irrigation area is divided into a root zone, a transition zone, and an aquifer based on the vertical direction; the water flow rate from the transition zone to the root zone is equal to the water flow rate from the aquifer to the transition zone.

[0072] Based on the aforementioned hydrological and meteorological data and water data, a root zone water balance model, a transition zone water balance model, and an aquifer water balance model were constructed.

[0073] Based on hydrological and meteorological data and salinity data, a salinity equilibrium model for the root zone, a salinity equilibrium model for the transition zone, and a salinity equilibrium model for the aquifer are constructed; the aquifer salinity equilibrium model includes a salinity equilibrium model for farmland aquifers and a salinity equilibrium model for non-farmland aquifers.

[0074] The water and salt balance model of the target irrigation area is constructed based on the root layer water balance model, transition layer water balance model, aquifer water balance model, root layer salinity balance model, transition layer salinity balance model, and aquifer salinity balance model.

[0075] In this embodiment, hydrological and meteorological data (precipitation, temperature, radiation, etc.), basic irrigation district data (canal water supply, area composition, aquifer characteristics and water yield, etc.), and soil and crop data (profile water content, soil water holding capacity, salinity profile, crop root depth and water and salt requirements, etc.) are first collected and processed in the target irrigation area on a monthly basis. Based on this, the irrigation area is divided into three layers vertically: root layer, transition layer and aquifer, and water and salt models of each layer are constructed to achieve overall coupling.

[0076] In this embodiment, the root zone water balance model focuses on characterizing the water and salinity balance in the crop root zone. The inputs include precipitation infiltration, effective field irrigation recharge, and capillary upwelling recharge from the transition / ammonia. The outputs include soil evaporation and plant transpiration, crop root water uptake, and downward leaching.

[0077] In this embodiment, the root zone water balance includes three inputs (precipitation infiltration, Yellow River irrigation recharge, and groundwater capillary upwelling) and three outputs (evapotranspiration, crop root upwelling, and deep seepage), where evapotranspiration is composed of soil evaporation and plant transpiration. Irrigation volume is allocated between farmland and non-farmland in the irrigation district according to a certain ratio. Root zone water balance model:

[0078]

[0079] Where, the superscripts j = A, NA represent farmland and non-farmland; ΔW1 is the change in soil moisture content in the root zone, mm; I is the field irrigation water volume per unit area, mm; P is the precipitation, mm; ET a Actual evaporation rate, mm; DP I and DP P CR represents the infiltration rate of irrigation water and precipitation from the root zone to the transition zone, respectively, in mm; CR represents the capillary rise of water from the transition zone to the root zone, in mm; CR F DP represents the capillary upwelling water volume from the transition layer to the root zone during the freezing period, in mm. F The amount of meltwater infiltrating from the root zone to the transition zone during the melting period is expressed in mm.

[0080] In this embodiment, the transition layer serves as the exchange medium between the root layer and the aquifer. The water balance model of the transition layer assumes that the water storage of this layer changes little on a monthly scale, and it is mainly used to balance the leaching flux from top to bottom and the capillary recharge from bottom to top.

[0081] In this embodiment, based on the current situation of shallow groundwater depth in the Hetao Irrigation District and the fact that long-term monitoring results show that the deep soil moisture content is relatively stable, it is assumed that the transition layer does not store or release water, and water is only exchanged between the root layer and groundwater. That is, the water flow from the transition layer to the root layer is equal to the water flow from the aquifer to the transition layer. The water balance model of the transition layer is as follows:

[0082]

[0083] Where ΔW2 represents the change in soil moisture content in the transition layer, in mm; DPS I and DPS P GCR represents the infiltration rate of irrigation water and precipitation from the transition layer to the aquifer, respectively, in mm; GCR represents the capillary rise of water from the aquifer to the transition layer, in mm. F The capillary rise of water from the aquifer to the transition layer during the freezing period, in mm; DPS F The amount of meltwater infiltrating from the transition layer into the aquifer during the melting period is expressed in mm.

[0084] Assuming the soil moisture content in the transition layer remains constant throughout all periods, ΔW2 = 0, and all other water flows are equal, i.e. GCR j =CR j ,

[0085] In this embodiment, the aquifer water balance model focuses on the groundwater level and total water volume balance, describing channel leakage, surface lateral recharge, groundwater extraction, and groundwater exchange between farmland and non-farmland.

[0086] In this embodiment, the aquifer water balance model for farmland and non-farmland is as follows:

[0087]

[0088] Wherein, ΔW3 represents the change in soil moisture content of the aquifer, in mm; μ represents the specific yield of the aquifer; Δh represents the change in groundwater depth, in mm; CS represents the seepage recharge per unit area of ​​the canal system, in mm; D g G represents the groundwater drainage volume per unit area, in mm; GE represents the groundwater extraction volume per unit area, in mm; t RL represents the amount of groundwater exchanged per unit area between farmland and non-farmland, in mm; RL represents the amount of lateral groundwater recharge per unit area, in mm.

[0089] In this embodiment, the root zone salt balance model characterizes the yield response through crop water requirement coefficient and water stress factor, and describes the salt stress and leaching process through dynamic updates of volumetric water content and root zone salt concentration.

[0090] In this embodiment, the salt input to the root zone of farmland and non-farmland in the Hetao Irrigation District is mainly from irrigation water, precipitation, and groundwater upwelling, while the output includes leaching and plant absorption of salt. Based on this, this study does not consider lateral migration of salt in the root zone, thus constructing a root zone salt equilibrium model, as shown below:

[0091]

[0092] Where ΔS1 is the change in salinity of the root zone, in g / m 2 S represents the salt flux per unit area, with the subscript indicating the corresponding water flux process (including salt introduction and leaching from the root zone), consistent with the above notation, in g / m². 2 S C The salt uptake per unit area of ​​crops / plants is taken as 4.5 / 9.0 g / m². 2 .

[0093] In this embodiment, the salt balance model of the transition layer mainly deals with the salt transport and redistribution carried by root zone leaching and aquifer rise.

[0094] In this embodiment, the salt input to the transition zone includes salt carried by root leaching and groundwater evaporation, while the main output pathways are salts from irrigation and precipitation leaching and capillary water migration to the root zone. The salt balance model is as follows:

[0095]

[0096] Where ΔS2 is the change in salt content of the transition layer, in g / m 2 .

[0097] In this embodiment, the aquifer salinity equilibrium model distinguishes between farmland aquifers and non-farmland aquifers, and calculates the amount of salt introduced by channel seepage, capillary rise and lateral inflow, as well as the effects of extraction and lateral outflow on the amount of salt.

[0098] In this embodiment, the salinity equilibrium model of the farmland aquifer is as follows:

[0099]

[0100] Salinity equilibrium model for non-agricultural aquifers:

[0101]

[0102] Wherein, ΔS3 is the change in aquifer salinity, in g / m³. 2 .

[0103] In this embodiment, the three-layer water-salt module operates coupled through a monthly-scale mass conservation relationship. The model output includes the water content, salinity, and corresponding concentration time series of each layer, used to assess the risk of salinity stress and water resource sustainability. In engineering implementation, key parameters (such as capillary rise rate, leaching coefficient, and aquifer specific yield) are calibrated and uncertainty assessed using parameter space random sampling combined with statistical methods to ensure the model's applicability and predictive reliability across different land use units (farmland / non-farmland) in the irrigation area. This embodiment provides physical process constraints and decision support data for subsequent multi-objective resource optimization and regulation schemes built based on this water-salt equilibrium model.

[0104] In this embodiment, the soil and crop data in the irrigation area are limited to moisture and salinity data. A water balance model and a salinity balance model are established based on the root layer, transition layer, and aquifer. The aquifer salinity balance model distinguishes between farmland and non-farmland areas, which can more accurately depict the stratified characteristics and regional differences of water and salt transport in the irrigation area. It can not only reflect the actual water and salt environment in the crop root zone, but also reveal the long-term impact of irrigation on deep water and salt dynamics, thereby improving the accuracy and applicability of water and salt balance simulation and providing more reliable input parameters for subsequent resource optimization and scheduling.

[0105] In this embodiment, the water-salt balance model further includes an overall water balance model. After constructing the root zone water balance model, transition zone water balance model, and aquifer water balance model based on the hydrological and meteorological data and water data, it further includes:

[0106] Based on the irrigation area soil and crop data of the target irrigation area, the water interaction relationship between farmland and non-farmland is obtained;

[0107] Based on the water interaction relationship between farmland and non-farmland, a short-term overall water balance model is constructed.

[0108] In this embodiment, after completing the construction and monthly simulation of the layered water balance model of the root zone, transition zone, and aquifer, the local layered processes are further coupled with regional-scale groundwater dynamics to construct an overall groundwater balance model to reflect the water interaction between farmland and non-farmland and the overall water level response. Specifically, the root zone model focuses on characterizing fluxes such as infiltration, irrigation recharge, capillary rise, evapotranspiration, and downward leaching in the crop root zone, with soil moisture and crop water requirements as inputs; the transition zone model, as an exchange buffer zone between the root zone and the aquifer, is mainly used to balance upward leaching and downward capillary rise, and usually takes small or near-zero assumptions about monthly water storage changes in order to transfer water flux to the aquifer through interlayer exchange; the aquifer model, with groundwater depth and aquifer specific yield as the core, describes the processes of channel leakage, lateral recharge, groundwater extraction, and groundwater exchange between farmland and non-farmland. Based on this three-layer model, using data such as soil profiles of irrigation areas, crop water and salt requirements, and land use patterns, the model first estimates key fluxes such as infiltration, recharge, extraction, and evapotranspiration in farmland and non-farmland within each monthly step. From this, it derives the water interaction relationships between the two land units, including uneven recharge due to irrigation differences, upward recharge caused by capillary rise, and the combined impact of surface and canal leakage on the aquifer. To ensure regional water conservation and water level consistency, the model forces consistent groundwater depth changes in farmland and non-farmland at the overall scale, meaning that both types of units exhibit the same direction and amplitude of water level response within the same time step. Simultaneously, this shared water level change is converted into an increase or decrease in water volume per unit area through aquifer yield, and then allocated and balanced according to the area ratio of farmland and non-farmland, thus forming an overall groundwater budget description with a monthly step. This overall equilibrium module can be used to assess the impact of short-term (monthly) groundwater level fluctuations on root zone water and salt stress, as well as to predict long-term (multi-month / yearly) groundwater evolution trends. It also serves as the dynamic process basis for groundwater constraints in resource optimization models, providing physically consistent and operable decision support data for water resource allocation and water-salt co-management at the irrigation district scale.

[0109] In this embodiment, based on the interaction between farmland and non-farmland groundwater, the change in groundwater is always equal to Δh = Δh. A =Δh NA Establish a global groundwater equilibrium model with a monthly time step. The variables for farmland and non-farmland should satisfy the following:

[0110] Δh=αΔh A +(1-α)Δh NA (8)

[0111]

[0112] Among them, A A and A NAThe areas of farmland and non-farmland are respectively, in meters. 2 α represents the proportion of farmland area.

[0113] In this embodiment, a global water balance model is introduced based on the stratified water balance model. A monthly-scale global water balance equation is established through the water interaction between farmland and non-farmland, reflecting the water budget characteristics of the irrigation district and the coupling relationship of the regional water cycle at a macroscopic level. This method enables the model to analyze not only local water volume changes in the crop root zone and groundwater layer, but also the water resource feedback relationships between different land use units, thus providing a scientific basis for water resource balance assessment and overall optimization at the irrigation district scale.

[0114] Step 102: Obtain price data, water supply data, and environmental data; construct an objective function and constraint model based on the price data, water supply data, and environmental data; and construct a resource optimization model based on the objective function and constraint model.

[0115] In this embodiment, the steps of acquiring price data, water supply data, and environmental data, constructing an objective function and constraint model based on the price data, water supply data, and environmental data, and constructing a resource optimization model based on the objective function and constraint model include:

[0116] Acquire price data, water supply data, and environmental data, and construct an objective function based on the price data, water supply data, and environmental data;

[0117] Construct crop constraint models, water supply constraint models, and environmental constraint models;

[0118] A resource optimization model is constructed based on the objective function, crop constraint model, water supply constraint model, and environmental constraint model.

[0119] In this embodiment, price data (including crop market prices, planting costs, and irrigation water prices), water supply data (including available water from each water source, transmission and distribution capacity, and historical water supply records), and environmental data (including soil salinity and nitrogen baselines, ecological water demand indicators, and environmental quality thresholds) are acquired and preprocessed. This data is then integrated with the operational interface of the constructed water-salt balance model as process constraint inputs during optimization. The price objective function, constructed based on price data, uses crop planting area and irrigation volume as decision variables to measure regional agricultural output and net income. The water supply objective function, constructed based on water supply data, uses water supply quotas as decision variables to measure the efficiency and fairness of water allocation (e.g., water use efficiency, sectoral water shortage rate, etc.). The environmental objective function, constructed based on environmental data, uses crop planting structure as a decision variable to measure ecological and environmental costs (e.g., soil salinization risk, total nitrogen load, etc.).

[0120] In this embodiment, a constraint model is constructed based on the prescribed engineering and management requirements: crop constraints include the lower / upper limit of the planting area ratio, the minimum grain yield guarantee, and the minimum irrigation amount during the crop growing season; water supply constraints include the maximum available water volume of each water source, the overall water supply balance, and the maximum water shortage rate of each water-using sector; environmental constraints include the safe range of groundwater level, soil salinity, and nitrogen thresholds. The above objective function and constraint model are coupled with the water-salt balance process model to form a multi-objective resource optimization model with crop planting area, monthly irrigation amount, and water supply quota as decision variables. To solve this model, this embodiment uses a genetic algorithm based on non-dominated sorting to generate a Pareto solution set, and selects representative strategies in the evaluation stage by combining decision-maker preferences or weighted methods.

[0121] In this embodiment, the optimization process includes calculating the monthly water-salt response using a water-salt balance model for each candidate solution to verify constraint feasibility, and handling constraint violations through penalty functions or feasibility-first strategies. The final output includes a multi-objective optimal solution (Pareto front), the corresponding water-salt time-series response and constraint satisfaction table for each solution, and the generation of a zoned planting structure, monthly irrigation plan and water supply quota, and a standardized resource regulation report for implementation. This embodiment also suggests conducting scenario analysis and sensitivity analysis on key uncertain parameters to verify the robustness of the solution and provide decision-makers with risk warnings and alternative measures.

[0122] In this embodiment, by acquiring price, water supply, and environmental data, a resource optimization model is constructed that combines the objective function with crop constraints, water supply constraints, and environmental constraints. This model organically integrates economic benefits, resource carrying capacity, and ecological environment requirements. The method ensures that the optimization results not only maximize economic gains but also consider sustainable water supply and ecological security, avoiding local optima or environmental risks caused by a single objective. This significantly improves the comprehensiveness and operability of the regulation scheme.

[0123] In this embodiment, the acquisition of price data, water supply data, and environmental data, and the construction of an objective function based on the price data, water supply data, and environmental data, includes:

[0124] Obtain price data, and construct a price objective function based on the price data, using crop planting area and irrigation volume as decision variables;

[0125] Acquire water supply data, and construct a water supply objective function based on the water supply data and water supply quota as the decision variable;

[0126] Acquire environmental data, and construct an environmental objective function based on the environmental data, using crop planting area as the decision variable;

[0127] A total objective function is constructed based on the aforementioned price objective function, water supply objective function, and environmental objective function.

[0128] In this embodiment, price objective function, water supply objective function and environmental objective function are constructed based on measured and statistical data, and the three are used as evaluation vectors for multi-objective optimization.

[0129] In this embodiment, the price objective function uses the regional crop planting area and monthly irrigation volume as decision variables, and is constructed by subtracting planting costs and irrigation water fees from crop output value. Price parameters such as crop unit price, unit cost, and irrigation water price are obtained from market surveys or statistical yearbooks, and are expressed in yuan, kg, and m³. 3 The same unit input is used; the output can be derived from the output response curve of the water-salt balance model under different irrigation conditions. The proportion of Yellow River water φ and the canal system utilization efficiency η are used to embed the source of irrigation supply and the transmission and distribution loss into the economic calculation, so that the price objective function can simultaneously reflect the comprehensive benefits of output value, cost and water resource utilization cost.

[0130] In this embodiment, the water supply objective function uses the quota of each water source to each water user as the decision variable, and is characterized by the departmental water shortage rate or water allocation fairness and water supply efficiency indicators. The water demand, the available amount of each water source and the transmission and distribution capacity are provided by the water conservancy authorities and water supply scheduling data. The objective function aims to minimize the overall water shortage rate and balance the water supply distribution pressure among different sectors (domestic, agricultural, industrial and ecological).

[0131] In this embodiment, the environmental objective function uses crop planting area as the decision variable. The total nitrogen load of agriculture in the irrigation area is calculated by estimating the nitrogen content per unit area of ​​fertilizer input and straw return to the field, as well as the loss coefficient of the crop area. Environmental data (soil salinity baseline, nitrogen loss coefficient, environmental threshold, etc.) are obtained from environmental monitoring and soil surveys, so that the environmental objective can quantitatively reflect the contribution of agricultural production to soil-water pollution and salinization risk.

[0132] In this embodiment, the three objective functions of price, water supply, and environment are used as multi-objective vector input optimizers (or weighted and synthesized into a single objective according to the decision-maker's preferences when needed), and coupled with the water-salt balance model and constraint model. During the genetic algorithm solution process, each candidate decision vector calls the process model to obtain responses such as yield, groundwater, and salinity, so as to ensure that the objective calculation has physical process support and meets engineering and ecological constraints, forming a Pareto optimal solution set or weighted optimal solution that can be used for decision-making.

[0133] In this embodiment, by decomposing the price objective function, water supply objective function, and environmental objective function, and unifying them into a unified overall objective function, multi-objective coordinated optimization can be achieved. The price function reflects economic benefits, the water supply function reflects the fairness and utilization rate of resource allocation, and the environmental function constrains soil and ecosystem safety. This avoids the biases that may arise from single-objective optimization, achieves a multi-dimensional balance between economy, resources, and environment, and ensures the scientific rationality of the irrigation district regulation scheme.

[0134] In this embodiment, price data, water supply data, and environmental data are used to construct the objective function:

[0135]

[0136] Where F1 is the price objective function, in yuan; i = 1 to 5 are the partition numbers; m = 1 to 3 are the crop numbers, representing wheat, corn, and sunflower respectively; t = 1 to 12 are the months; B im Let hm represent the planting area of ​​crop m within partition i. 2 ; The market price per unit of crop m is yuan / kg; O im The yield per unit area of ​​crop m within zone i under insufficient irrigation, in kg / hm². 2 ;P Water The price for agricultural irrigation water is 0.526 yuan / m³. 3 W imt For the irrigation amount of crop m in month t, m is the amount of water used in the i-th crop. 3 / hm 2 ; η represents the ratio of Yellow River water to total irrigation water; η is the water utilization efficiency of the canal system. The planting cost per unit area of ​​crop m, in yuan / hm² 2 The crop planting area B in this objective function im and irrigation volume W imt One variable is the decision variable, and the others are constants.

[0137]

[0138] Wherein, minF2 is the objective function for water supply, F2 is the total water shortage rate, and k = 1 to 4 are the water-using sector numbers, representing domestic, agricultural, industrial, and ecological water use, respectively; α k The water shortage rate for each department; l = 1 to 6 are the water source numbers, representing Yellow River water, groundwater, reclaimed water, irrigation area runoff, naoer water, and reservoir water, respectively; Let m be the water demand of water user k. 3 ; The amount of water supplied from water source l to water user k, m 3 The water supply quota in this objective function These are decision variables.

[0139]

[0140] Wherein, minF3 is the environmental objective function, where F3 is the total nitrogen load generated by agricultural planting in the irrigation area; N m1 The total nitrogen content in fertilizer m (pure nitrogen) is expressed in kg / hm². 2 N m2 The nitrogen content of crop straw returned to the field (m) is expressed in kg / hm². 2 ; Let B be the loss coefficient of crop planting area m. The crop planting area B is the objective function. im One variable is the decision variable, and the rest are constants.

[0141] In this embodiment, the construction of the crop constraint model, water supply constraint model, and environmental constraint model includes:

[0142] A planting ratio constraint model is constructed based on a preset planting area ratio threshold; a crop yield constraint model is constructed based on a preset grain yield threshold; and a crop constraint model is constructed based on the planting ratio constraint model and the crop yield constraint model.

[0143] An irrigation constraint model is constructed based on crop irrigation amount, a maximum water supply constraint model is constructed based on preset total water supply, a water shortage constraint model is constructed based on preset water shortage rate, and a water supply constraint model is constructed based on the irrigation constraint model, the maximum water supply constraint model, and the water shortage constraint model.

[0144] A groundwater level constraint model is constructed based on groundwater level changes, a soil salinity constraint model is constructed based on a preset soil salinity threshold, and an environmental constraint model is constructed based on the groundwater level constraint model and the soil salinity constraint model.

[0145] In this embodiment, parameters such as crop planting ratio thresholds, minimum guaranteed grain yield, minimum water requirement for each crop growth period, maximum available water supply from each water source, and maximum allowable water shortage rate for each water user are first determined based on historical statistical data, farming systems, and local management regulations (for example, the lower limit of crop planting ratio per field is 2%, and the upper limit is 90%; the total planting ratio range of the irrigation area is 80%–100%; the lower limit and upper limit of safe groundwater depth are 1.0m and 2.5m, respectively). These thresholds are then used to construct three types of constraint models.

[0146] In this embodiment, the crop constraint model consists of planting ratio constraints and yield guarantee constraints. The former ensures that the planting structure of a single crop and the overall irrigation area is within the range allowed by experience and regulations, while the latter ensures food security with the minimum social / regional food demand as the bottom line. At the same time, the minimum irrigation amount during the crop growth period is included in the model to constrain irrigation decisions for each month and avoid loss of control during the growth period due to insufficient irrigation.

[0147] In this embodiment, the planting ratio constraint model is as follows: based on the planting experience requirements of irrigation districts, the planting area ratio of each crop in each irrigation area shall not be less than 2% and not more than 90%; the total planting area ratio of each irrigation area shall not be less than 80% and not more than 100%.

[0148]

[0149] in, Let hm be the current total crop planting area of ​​irrigation area i. 2 .

[0150] In this embodiment, the crop yield constraint model states that to ensure local food production needs are met, the total yield of each crop must not be less than the minimum social demand.

[0151]

[0152] in, This represents the minimum requirement for the total yield of crop m, expressed in kg.

[0153] In this embodiment, the water supply constraint model includes irrigation quota constraints, maximum capacity constraints of water supply sources, and sectoral water shortage rate constraints, which are used to limit the amount of irrigation water allocated to agriculture to be no less than the crop growth requirements and no more than the allocable capacity of the water supply source. The upper limit of the water shortage rate of each sector is used to measure and control the risks of water supply fairness and social stability.

[0154] In this embodiment, the irrigation constraint model states that the monthly irrigation amount for different crops should not be less than the minimum irrigation amount required to ensure their normal growth and development.

[0155]

[0156] in, This represents the minimum water requirement for crop m to maintain its growth and development in month t.

[0157] In this embodiment, the maximum water supply constraint model states that for all water sources, the total water supply to water-using sectors does not exceed their maximum allocable capacity.

[0158]

[0159] Among them, W l S,max The maximum available water volume of water source l is m 3 .

[0160] In this embodiment, the water shortage constraint model satisfies the following conditions for the entire irrigation district: domestic, agricultural, industrial, and ecological water shortage rates, respectively:

[0161]

[0162] in, This represents the maximum water shortage rate for all departments within the irrigation district.

[0163] In this embodiment, the environmental constraint model is based on groundwater level and soil salinity threshold. The former prevents soil salinization and root zone diseases by limiting the monthly groundwater depth to a safe range, while the latter limits the planting area and irrigation intensity with salt stress index or soil salinity threshold, thereby simultaneously constraining ecological safety during the optimization process.

[0164] In this embodiment, the groundwater level constraint model is as follows: To prevent soil salinization and ensure normal crop development, the groundwater depth in different months should meet the following requirements:

[0165] h t,min ≤h it ≤h t,max (twenty one)

[0166] Where h t,min The value is 1.0m, h t,max The value is 2.5m.

[0167] In this embodiment, the soil salinity constraint model is used: the impact of soil salinity on crop yield is usually quantified by the salinity stress index. To ensure normal crop growth and food yield security, soil salinity in different months should meet the following requirements:

[0168] SC it <0.3g / 100g, i=1~5, t=4~9 (22)

[0169] In this embodiment, in addition to the planting area, other decision variables should also satisfy the non-negativity constraint:

[0170] W imt ≥0, i=1~5 (23)

[0171]

[0172] In this embodiment, to achieve the aforementioned constraints in the numerical solution, key constraints such as food security and water supply capacity are set as hard feasibility conditions in the optimizer, ensuring their satisfaction is prioritized. Soft constraints related to society and the environment can be handled using hierarchical penalty functions or feasibility-first strategies. The satisfaction level and residual margin of each constraint are output in the solution evaluation to facilitate decision-makers' considerations. Furthermore, constraint parameters should be updated regularly based on monitoring data and scenario analysis, and a feedback loop should be formed after optimization through monitoring and control reports to ensure the timeliness and policy compatibility of the constraint model, thereby achieving irrigation district resource regulation that balances production security, water supply security, and ecological protection.

[0173] In this embodiment, by establishing three types of constraint models—crops, water supply, and environment—covering multi-dimensional constraints such as planting structure ratio, grain yield, irrigation demand, total water supply, water shortage rate, groundwater level, and soil salinity, the optimization results can be ensured to meet the requirements of food security, water resource carrying capacity, and ecological sustainable development. This avoids risks arising from neglecting farmland structure, excessive groundwater extraction, or soil salinization during implementation, significantly improving the stability and practical feasibility of the resource regulation model.

[0174] Step 103: Generate a resource regulation scheme based on the resource optimization model and the preset genetic algorithm. The resource regulation scheme includes crop planting area, monthly crop irrigation amount and water supply quota.

[0175] In this embodiment, a resource optimization model is coupled with a pre-defined genetic algorithm to generate a resource regulation scheme that includes the planting area of ​​crops in each region, monthly irrigation volume, and water supply quotas for each source. Specifically, the decision variable vector is first encoded into chromosomes of genetic operators, typically including the planting area of ​​each region and crop, the monthly irrigation volume sequence, and the water supply quota matrix. Then, several candidate solutions are initialized according to a population-generation evolutionary paradigm (historical scheme perturbation or heuristic generation can be used to improve initial feasibility). During the fitness evaluation phase, each candidate chromosome is decoded, and a calibrated water-salt balance model is used for monthly simulations to obtain yield, groundwater depth, soil salinity, and various flux time series. Then, the economic, social, and environmental objective function values ​​are calculated, and all constraints (planting ratio, minimum growing season irrigation volume, water supply capacity, water shortage rate, groundwater level, and soil salinity thresholds, etc.) are tested. Candidate solutions that violate hard constraints are repaired or have their fitness reduced according to a penalty function. For soft constraints, a tiered penalty or feasibility-first strategy is used to maintain search diversity. Genetic manipulation employs a combination of non-dominated sorting and crowding preservation (NSGA style) along with crossover, mutation, and elite retention strategies to advance population evolution until a preset number of generations or convergence criterion is reached. After evolution, a Pareto optimal solution set is output, and several representative schemes are selected through clustering, knee point identification, or weighted decision-making methods. For each scheme, implementable regulatory details are generated, including a regional crop structure table, a monthly irrigation plan, a water use sector and water supply source quota table, and comparative evaluations of key indicators (water use efficiency, overall water shortage rate, groundwater response, soil salinity changes, and nitrogen load, etc.).

[0176] Step 104: Based on the crop planting area, monthly crop irrigation amount and water supply quota, perform resource regulation on the target irrigation area and generate a resource regulation report to complete the resource regulation of the target irrigation area.

[0177] In this embodiment, after obtaining the crop planting area, monthly irrigation volume, and water supply quotas generated based on the optimization model, this implementation step transforms the above-mentioned decision-making outputs into executable field and scheduling instructions to complete the regulation and control of irrigation district resources. Specifically: the crop planting area of ​​each zone is distributed to farmers and agricultural management units as an annual planting plan; monthly / time-period irrigation water allocation plans are generated based on the monthly irrigation volume and allocated to each canal and irrigation district pumping station by the canal system scheduling center; the outflow and distribution priority of each water supply source (Yellow River water, groundwater, reclaimed water, etc.) are adjusted according to the water supply quota; at the same time, the irrigation schedule and pumping plan are input into the field control system to drive the pumping stations, gates, and distribution equipment, and, when necessary, coordinate with the water users for graded water allocation and rotational irrigation. During implementation, the control measures are checked and their compliance verified in real time by combining on-site monitoring (groundwater level, soil moisture and conductivity, canal flow and water quality, etc.) and telemetry data. Any deviations or anomalies (such as groundwater level exceeding limits, soil salinity increases, or water shortages) trigger emergency adjustment measures and record the events. The final resource control report includes: a summary of the control plan (zoning planting structure, monthly irrigation plan, water supply quota table), implementation progress and execution records, monthly / annual water and salt balance and key indicator comparisons (water use efficiency, overall water shortage rate, groundwater level changes, soil salinity and nitrogen load, etc.), constraint satisfaction and breaches, risk assessment and response recommendations, and subsequent model recalibration and plan revision recommendations. This report serves as both a basis for management decision-making and accountability, and a feedback loop between the optimized model and on-site observations. It is used to periodically update water and salt balance model parameters, adjust constraint thresholds, and rerun optimization when necessary to maintain the timeliness and robustness of the control plan.

[0178] In this embodiment, to enhance the engineering applicability of the solution, scenario testing and sensitivity analysis (e.g., drought, water supply constraints, or price fluctuations) are performed on representative solutions. The final selected solutions, their constraint satisfaction, risk warnings, and key points for implementation monitoring are compiled into a standardized resource regulation report for decision-making reference.

[0179] In one embodiment, hydrological and meteorological data (precipitation, temperature, sunshine and potential evapotranspiration, etc.), basic irrigation data (canal diversion and leakage, aquifer parameters, water use statistics, etc.), and soil and crop data (profile water content, soil salinity profile, soil water holding curve, crop root depth and water and salt requirements, etc.) of the target irrigation area are collected and processed on a monthly basis at the irrigation district scale. Based on the data, the irrigation district is vertically divided into a root layer, a transition layer, and an aquifer, and a lumped three-layer coupled water and salt balance model of farmland and non-farmland is established (described in S1). The model is run in four time periods (growing season: May–August; autumn irrigation season: September–November; freezing season: December–February of the following year; thawing season: March–April) to simulate the water and salt balance of each layer and the water and salt interaction between farmland and non-farmland. Model calibration and validation show that the groundwater equilibrium sub-model exhibits good performance during both the calibration and validation periods (groundwater depth simulation RMSE = 0.19–0.24 m, R...). 2 =0.65–0.79), the fitting accuracy of soil salinity simulation differs between farmland and non-farmland: please refer to Figure 2 The salinity of the root zone in farmland was 0.11–0.18 g / 100g, with an RMSE of 0.033, while in non-farmland it was 0.32–0.53 g / 100g, with an RMSE of 0.037. This is related to the completeness of farmland observation data and the spatial heterogeneity of non-farmland data. These results validate that the water-salt balance model based on vertical stratification and distinguishing between farmland and non-farmland can provide reliable process constraints and response predictions for subsequent optimization.

[0180] Secondly, this embodiment constructs a price objective function based on market price data, irrigation water prices, and agricultural costs, using crop planting area and monthly irrigation volume as decision variables; it constructs a water supply objective function based on available water supply and distribution capacity data, using water supply quotas as decision variables; and it constructs an environmental objective function based on environmental monitoring (such as soil salinity and nitrogen indicators), using crop planting area as a decision variable. The constraint model includes constraints on crop planting ratio and minimum yield (food security), minimum irrigation volume during crop growth, maximum availability of each water source and maximum water shortage rate for each sector, as well as environmental constraints such as safe groundwater depth and soil salinity thresholds (corresponding to the constraint structures of claims 4–6). In implementation, water sources are classified into six categories as listed in the claims: Yellow River water, groundwater, irrigation runoff, reclaimed water, swamp water, and reservoir water. Water allocation rules and the available quantity of each source are incorporated into the water supply constraint model to ensure that the model solution considers both resource availability and sectoral fairness. Please refer to [link / reference]. Figure 3In the current water use structure of irrigation districts, agriculture is the primary water user, accounting for 86.7% to 89.5% of total water consumption under different economic and ecological objectives. Studies have found that as economic development goals are elevated, the proportion of agricultural water use gradually increases, while ecological water use decreases accordingly. This phenomenon indicates that under conditions of limited water supply, competition between agricultural and ecological water use becomes more intense. Under the premise of meeting the lower limit of ecological water demand, more water resources are allocated to irrigated agriculture to achieve greater crop yields and economic benefits.

[0181] Furthermore, given the large proportion of agricultural water consumption in this region, an in-depth analysis of irrigation amounts for various crops in the irrigation district was conducted to further optimize water resource allocation. Based on current conditions, the monthly irrigation amounts for wheat, corn, and sunflower in each irrigated area were calculated. The analysis results show that... Figure 4 After optimization, the monthly irrigation amounts for three crops in the Hetao Irrigation District were analyzed. Wheat, as a high water-consuming crop, consumed significantly more water than corn and sunflower. Sunflower, as the main economic crop in the Hetao Irrigation District, exhibited low water consumption and high economic benefits, making it the preferred crop for the district's agricultural sector. Regarding the timing of irrigation, the irrigation amounts in the fields where all three crops were grown peaked during the autumn irrigation period, with an average irrigation amount of 1284 m³. 3 / hm2, this is mainly to meet the agricultural needs of salt suppression and moisture retention. Looking at the entire crop growth period, the net irrigation quota for wheat, corn, and sunflower is 2656m2 respectively. 3 / hm2、2000m 3 / hm2 and 1642m 3 / hm2, this result is basically consistent with the actual observation data, verifying the reliability of the model.

[0182] In this embodiment, decision variables (regional crop planting area, monthly crop irrigation amount, and water supply quota) are encoded as chromosome vectors for a genetic algorithm, and a non-dominated sorting genetic algorithm (NSGA) is used to solve the multi-objective problem. During the fitness evaluation phase, each candidate scheme uses the calibrated water-salt balance model to perform monthly simulations, obtaining process responses such as yield, groundwater depth, soil salinity at each layer, and nitrogen load. These responses are used to calculate the three objectives of price, water supply, and environment, and to verify the satisfaction of constraints. Representative schemes are selected from the Pareto solution set obtained through genetic evolution, and a regional planting structure table, a monthly irrigation plan, and a water supply quota table are output as the candidate set for regulatory decisions.

[0183] Finally, as Figure 5The optimized model accurately depicts the annual variation pattern of groundwater depth in the Hetao Irrigation District, characterized by "two rises and two falls." These fluctuations (peaks in April and October, troughs in June and January) are primarily driven by irrigation activities and can be categorized as follows: water level rise during the thawing period in March; water level rise due to irrigation during the growing season from April to June; water level drop to its lowest non-freezing level during the autumn irrigation evaporation period from July to September; water level surge to its annual peak during the autumn irrigation period in October; and water level gradually decreases during the freezing period from December to February. The simulation results highly match the actual water and salt regulation needs of the irrigation district, validating the model's reliability. Future efforts should focus on strengthening irrigation management to optimize water resource utilization efficiency and ecological sustainability. Figure 6 The optimized soil salinity values ​​in the irrigation area were concentrated between 0.1g / 100g and 0.18g / 100g, ensuring that the soil salinity was maintained within the non-salinization range. This effectively verified the scientific nature and effectiveness of the constraints and objectives of the water resource optimization and regulation model in the Hetao Irrigation Area.

[0184] After optimization, the total crop planting area in the Hetao Irrigation District is 812,800 hectares. 2 The crop planting areas in each irrigation area from west to east are 90,058.5 hm2, 192,349.5 hm2, 140,238 hm2, 268,174.4 hm2, and 121,960.8 hm2, respectively. This indicates that the optimized scheme achieves the optimal adjustment of the planting structure while meeting the planting area constraints. Figure 7 From the optimized planting ratio, the crop planting structure of each irrigation area in the Hetao Irrigation District shows significant diversity and regional characteristics, fully reflecting the principle of agricultural layout adapted to local conditions.

[0185] like Figure 7In the five irrigation districts, sunflower, as the main economic crop, already had the highest planting proportion before optimization, accounting for 45% in the Jiefangzha irrigation district, 41% in the Yongji irrigation district, 54% in the Yichang irrigation district, 77% in the Urad irrigation district, and 72% in the Ulanbuhe irrigation district, thus occupying the majority of the planting area. After optimization, which prioritizes economic benefits, the proportions of wheat, corn, and sunflower in the Ulanbuhe irrigation district were 0.06, 0.6, and 0.39, respectively; in the Jiefangzha irrigation district, 0.1, 0.06, and 0.89; in the Yongji irrigation district, 0.15, 0.04, and 0.86; in the Yichang irrigation district, 0.03, 0.12, and 0.89; and in the Urad irrigation district, 0.02, 0.6, and 0.42, respectively. The proportion of sunflower cultivation in the Jiefangzha, Yongji, and Yichang irrigation areas has further increased, rising by 117.1%, 59.2%, and 15.6% respectively. The Jiefangzha irrigation area saw the fastest growth, while the Yichang irrigation area experienced a smaller increase, primarily due to the already large existing sunflower population there. This increase in sunflower cultivation is mainly attributed to the low water consumption and high economic value of sunflowers. Under the premise of meeting ecological and social constraints, increasing the sunflower cultivation proportion can not only significantly increase the economic benefits of the irrigation areas but also reduce agricultural water demand, alleviate water resource pressure, and thus promote the sustainable development of agriculture in the irrigation areas. Furthermore, although the proportion of sunflower cultivation is high, its yield per unit area is lower than that of corn. Therefore, appropriately increasing the corn cultivation proportion and optimizing the planting structure in the Ulan Buh and Urad irrigation areas can not only improve land use efficiency but also further promote the efficient development of the irrigation area's economy.

[0186] This invention provides a water-salt constrained irrigation district resource regulation device, comprising: a water-salt balance model construction module, a resource optimization model construction module, a scheme generation module, and a regulation module;

[0187] The water-salt balance model construction module is used to acquire hydrological and meteorological data and soil and crop data of the target irrigation area, divide the target irrigation area into several levels based on the vertical direction, and construct the water-salt balance model of the target irrigation area based on the hydrological and meteorological data, soil and crop data and each level.

[0188] The resource optimization model construction module is used to acquire price data, water supply data, and environmental data, construct an objective function and constraint model based on the price data, water supply data, and environmental data, and construct a resource optimization model based on the objective function and constraint model.

[0189] The scheme generation module is used to generate a resource regulation scheme based on the resource optimization model and a preset genetic algorithm. The resource regulation scheme includes crop planting area, monthly crop irrigation amount and water supply quota.

[0190] The control module is used to control resources in the target irrigation area based on the crop planting area, monthly crop irrigation volume and water supply quota, and generate a resource control report to complete the resource control of the target irrigation area.

[0191] In this embodiment, the irrigation area soil and crop data includes moisture data and salinity data; the water-salt balance model construction module is used to acquire hydrological and meteorological data and irrigation area soil and crop data of the target irrigation area, divide the target irrigation area into several levels based on the vertical direction, and construct the water-salt balance model of the target irrigation area based on the hydrological and meteorological data, basic irrigation area data, soil and crop data, and each level, including:

[0192] Acquire hydrological and meteorological data, as well as soil and crop data for irrigation areas;

[0193] The target irrigation area is divided into a root zone, a transition zone, and an aquifer based on the vertical direction; the water flow rate from the transition zone to the root zone is equal to the water flow rate from the aquifer to the transition zone.

[0194] Based on the aforementioned hydrological and meteorological data and water data, a root zone water balance model, a transition zone water balance model, and an aquifer water balance model were constructed.

[0195] Based on hydrological and meteorological data and salinity data, a salinity equilibrium model for the root zone, a salinity equilibrium model for the transition zone, and a salinity equilibrium model for the aquifer are constructed; the aquifer salinity equilibrium model includes a salinity equilibrium model for farmland aquifers and a salinity equilibrium model for non-farmland aquifers.

[0196] The water and salt balance model of the target irrigation area is constructed based on the root layer water balance model, transition layer water balance model, aquifer water balance model, root layer salinity balance model, transition layer salinity balance model, and aquifer salinity balance model.

[0197] In this embodiment, the water-salt balance model further includes an overall water balance model. After constructing the root zone water balance model, transition zone water balance model, and aquifer water balance model based on the hydrological and meteorological data and water data, it further includes:

[0198] Based on the irrigation area soil and crop data of the target irrigation area, the water interaction relationship between farmland and non-farmland is obtained;

[0199] Based on the water interaction relationship between farmland and non-farmland, a short-term overall water balance model is constructed.

[0200] In this embodiment, the resource optimization model construction module is used to acquire price data, water supply data, and environmental data; construct an objective function and constraint model based on the price data, water supply data, and environmental data; and construct a resource optimization model based on the objective function and constraint model, including:

[0201] Acquire price data, water supply data, and environmental data, and construct an objective function based on the price data, water supply data, and environmental data;

[0202] Construct crop constraint models, water supply constraint models, and environmental constraint models;

[0203] A resource optimization model is constructed based on the objective function, crop constraint model, water supply constraint model, and environmental constraint model.

[0204] In this embodiment of the invention, a terminal device is also provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-described method for regulating irrigation district resources under water and salt constraints.

[0205] In this embodiment of the invention, a computer-readable storage medium is also provided, which includes a stored computer program, wherein the computer program controls the device where the computer-readable storage medium is located to execute the above-described water and salt constraint-based irrigation district resource regulation method when it is running.

[0206] For example, a computer program can be divided into one or more modules, one or more of which are stored in memory and executed by a processor to perform the present invention. The one or more modules can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0207] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor, memory, and display. Those skilled in the art will understand that the above components are merely examples of terminal devices and do not constitute a limitation on the terminal device. It may include more or fewer components, or combinations of certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0208] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device through various interfaces and lines.

[0209] Memory can be used to store computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as sound playback, text conversion, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as audio data, text message data, etc.). In addition, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0210] Among them, if the module for regulating irrigation district resources under water and salt constraints is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Those skilled in the art can understand and implement this without any inventive effort.

[0211] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for resource regulation in irrigation districts under water-salt constraints, characterized in that, include: Obtain hydrological and meteorological data and soil and crop data of the target irrigation area, divide the target irrigation area into several levels based on the vertical direction, and construct a water-salt balance model of the target irrigation area based on the hydrological and meteorological data, soil and crop data and each level. Acquire price data, water supply data, and environmental data; construct an objective function and constraint model based on the price data, water supply data, and environmental data; and construct a resource optimization model based on the objective function and constraint model. Based on the resource optimization model and the preset genetic algorithm, a resource regulation scheme is generated, which includes crop planting area, monthly crop irrigation amount and water supply quota. Based on the crop planting area, monthly crop irrigation volume, and water supply quota, resource regulation is carried out in the target irrigation area, and a resource regulation report is generated to complete the resource regulation of the target irrigation area.

2. The method for resource regulation in irrigation districts under water and salt constraints as described in claim 1, characterized in that, The irrigation area soil and crop data includes moisture data and salinity data; the acquisition of hydrological and meteorological data and irrigation area soil and crop data of the target irrigation area, dividing the target irrigation area into several levels based on the vertical direction, and constructing a water-salt balance model of the target irrigation area based on the hydrological and meteorological data, basic irrigation area data, soil and crop data, and each level, includes: Acquire hydrological and meteorological data, as well as soil and crop data for irrigation areas; The target irrigation area is divided into a root zone, a transition zone, and an aquifer based on the vertical direction; the water flow rate from the transition zone to the root zone is equal to the water flow rate from the aquifer to the transition zone. Based on the aforementioned hydrological and meteorological data and water data, a root zone water balance model, a transition zone water balance model, and an aquifer water balance model were constructed. Based on hydrological and meteorological data and salinity data, a salinity equilibrium model for the root zone, a salinity equilibrium model for the transition zone, and a salinity equilibrium model for the aquifer are constructed; the aquifer salinity equilibrium model includes a salinity equilibrium model for farmland aquifers and a salinity equilibrium model for non-farmland aquifers. The water and salt balance model of the target irrigation area is constructed based on the root layer water balance model, transition layer water balance model, aquifer water balance model, root layer salinity balance model, transition layer salinity balance model, and aquifer salinity balance model.

3. The method for resource regulation in irrigation areas under water and salt constraints as described in claim 2, characterized in that, The water-salt balance model also includes an overall water balance model. After constructing the root zone water balance model, transition zone water balance model, and aquifer water balance model based on the hydrological and meteorological data and water data, it further includes: Based on the irrigation area soil and crop data of the target irrigation area, the water interaction relationship between farmland and non-farmland is obtained; Based on the water interaction relationship between farmland and non-farmland, a short-term overall water balance model is constructed.

4. The method for resource regulation in irrigation districts under water and salt constraints as described in claim 3, characterized in that, The process of acquiring price data, water supply data, and environmental data, constructing an objective function and constraint model based on the price data, water supply data, and environmental data, and constructing a resource optimization model based on the objective function and constraint model includes: Acquire price data, water supply data, and environmental data, and construct an objective function based on the price data, water supply data, and environmental data; Construct crop constraint models, water supply constraint models, and environmental constraint models; A resource optimization model is constructed based on the objective function, crop constraint model, water supply constraint model, and environmental constraint model.

5. A method for resource regulation in irrigation districts under water and salt constraints as described in claim 4, characterized in that, The acquisition of price data, water supply data, and environmental data, and the construction of an objective function based on the price data, water supply data, and environmental data, include: Obtain price data, and construct a price objective function based on the price data, using crop planting area and irrigation volume as decision variables; Acquire water supply data, and construct a water supply objective function based on the water supply data and water supply quota as the decision variable; Acquire environmental data, and construct an environmental objective function based on the environmental data, using crop planting area as the decision variable; A total objective function is constructed based on the aforementioned price objective function, water supply objective function, and environmental objective function.

6. The method for resource regulation in irrigation districts under water and salt constraints as described in claim 4, characterized in that, The construction of crop constraint model, water supply constraint model and environmental constraint model includes: A planting ratio constraint model is constructed based on a preset planting area ratio threshold; a crop yield constraint model is constructed based on a preset grain yield threshold; and a crop constraint model is constructed based on the planting ratio constraint model and the crop yield constraint model. An irrigation constraint model is constructed based on crop irrigation amount, a maximum water supply constraint model is constructed based on preset total water supply, a water shortage constraint model is constructed based on preset water shortage rate, and a water supply constraint model is constructed based on the irrigation constraint model, the maximum water supply constraint model, and the water shortage constraint model. A groundwater level constraint model is constructed based on groundwater level changes, a soil salinity constraint model is constructed based on a preset soil salinity threshold, and an environmental constraint model is constructed based on the groundwater level constraint model and the soil salinity constraint model.

7. A resource regulation device for irrigation areas under water and salt constraints, characterized in that, include: The system includes modules for constructing water-salt balance models, resource optimization models, scheme generation, and regulation. The water-salt balance model construction module is used to acquire hydrological and meteorological data and soil and crop data of the target irrigation area, divide the target irrigation area into several levels based on the vertical direction, and construct the water-salt balance model of the target irrigation area based on the hydrological and meteorological data, soil and crop data and each level. The resource optimization model construction module is used to acquire price data, water supply data, and environmental data, construct an objective function and constraint model based on the price data, water supply data, and environmental data, and construct a resource optimization model based on the objective function and constraint model. The scheme generation module is used to generate a resource regulation scheme based on the resource optimization model and a preset genetic algorithm. The resource regulation scheme includes crop planting area, monthly crop irrigation amount and water supply quota. The control module is used to control resources in the target irrigation area based on the crop planting area, monthly crop irrigation volume and water supply quota, and generate a resource control report to complete the resource control of the target irrigation area.

8. A water- and salt-constrained irrigation district resource regulation device as described in claim 7, characterized in that, The irrigation area soil and crop data includes moisture data and salinity data; the water-salt balance model construction module is used to acquire hydrological and meteorological data and irrigation area soil and crop data of the target irrigation area, divide the target irrigation area into several levels based on the vertical direction, and construct the water-salt balance model of the target irrigation area based on the hydrological and meteorological data, basic irrigation area data, soil and crop data, and each level, including: Acquire hydrological and meteorological data, as well as soil and crop data for irrigation areas; The target irrigation area is divided into a root zone, a transition zone, and an aquifer based on the vertical direction; the water flow rate from the transition zone to the root zone is equal to the water flow rate from the aquifer to the transition zone. Based on the aforementioned hydrological and meteorological data and water data, a root zone water balance model, a transition zone water balance model, and an aquifer water balance model were constructed. Based on hydrological and meteorological data and salinity data, a salinity equilibrium model for the root zone, a salinity equilibrium model for the transition zone, and a salinity equilibrium model for the aquifer are constructed; the aquifer salinity equilibrium model includes a salinity equilibrium model for farmland aquifers and a salinity equilibrium model for non-farmland aquifers. The water and salt balance model of the target irrigation area is constructed based on the root layer water balance model, transition layer water balance model, aquifer water balance model, root layer salinity balance model, transition layer salinity balance model, and aquifer salinity balance model.

9. A resource regulation device for irrigation areas under water and salt constraints as described in claim 8, characterized in that, The water-salt balance model also includes an overall water balance model. After constructing the root zone water balance model, transition zone water balance model, and aquifer water balance model based on the hydrological and meteorological data and water data, it further includes: Based on the irrigation area soil and crop data of the target irrigation area, the water interaction relationship between farmland and non-farmland is obtained; Based on the water interaction relationship between farmland and non-farmland, a short-term overall water balance model is constructed.

10. A water- and salt-constrained irrigation district resource regulation device as described in claim 9, characterized in that, The resource optimization model construction module is used to acquire price data, water supply data, and environmental data; construct an objective function and constraint model based on the price data, water supply data, and environmental data; and construct a resource optimization model based on the objective function and constraint model, including: Acquire price data, water supply data, and environmental data, and construct an objective function based on the price data, water supply data, and environmental data; Construct crop constraint models, water supply constraint models, and environmental constraint models; A resource optimization model is constructed based on the objective function, crop constraint model, water supply constraint model, and environmental constraint model.

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