A method for predicting and controlling irrigation in an agricultural field

Abstract

The application discloses a farmland water demand prediction and irrigation control method. The application belongs to the technical field of farmland intelligent irrigation and water resource allocation. The method comprises the following steps: acquiring historical daily meteorological data, current annual daily rainfall, evaporation, soil moisture content and other basic data of a target farmland (irrigation area), and presetting meteorological data and planting structure of a prediction period; determining crop water demand and drainage capacity according to basic parameters, and correcting water supply parameters; constructing a daily water distribution model and completing model parameter calibration; determining initial conditions, combining dynamic changes of crops, and performing daily prediction by using a water balance model; generating an irrigation or drainage control scheme, and outputting an irrigation schedule in the whole growth period and annual total water demand prediction results and generating the irrigation schedule. The technical scheme can realize agricultural water demand prediction and intelligent irrigation control in an irrigation area, lays a foundation for guaranteeing food safety, and provides a decision basis for regional water resource allocation.

Description

Technical Field

[0001] This application belongs to the field of intelligent irrigation and water resource allocation technology for farmland, specifically relating to a method for predicting farmland water demand and controlling irrigation. Background Technology

[0002] Farmland water demand forecasting and irrigation control are the foundation for efficient agricultural water resource utilization and refined irrigation district management, directly affecting stable and increased crop yields and the ability to solve water waste problems. Existing technologies generally rely on regional empirical quotas or static water balance models to estimate water demand, mainly depending on historical meteorological statistics. They calculate average water demand in segments according to crop growth stages, and then formulate irrigation systems.

[0003] Existing methods have significant drawbacks. The models are static, and the parameters are fixed. Parameters such as crop water requirement coefficients are mostly fixed empirical values, failing to reflect dynamic changes during the growth period. Furthermore, they do not integrate real-time meteorological and forecast data, making daily dynamic prediction difficult. They also suffer from low prediction accuracy and delayed regulation, primarily because existing technologies mostly rely on offline calculations and lack rolling iterative updates. This prevents dynamic optimization of irrigation timing and volume based on future rainfall, leading to over-irrigation, under-irrigation, or untimely drainage, resulting in low water resource utilization. Therefore, existing technologies struggle to accurately predict crop water requirements, necessitating a technical solution capable of accurately generating irrigation regimes. Summary of the Invention

[0004] This application provides a method for predicting farmland water demand and controlling irrigation, aiming to solve the technical problems in existing farmland water demand prediction and irrigation control technologies, such as fixed model parameters, difficulty in reflecting the dynamic changes of crop growth period, failure to integrate the influence of different factors in the actual area, resulting in low accuracy of water demand prediction, lagging irrigation regulation, and waste of water resources.

[0005] In a first aspect, embodiments of this application provide a method for predicting farmland water demand and controlling irrigation, the method comprising: Acquire historical daily meteorological data of the target farmland, daily meteorological data that has occurred in the current year, meteorological forecast data for the future preset period, evaporation, soil moisture, crop type and geological structure characteristics; Based on the aforementioned geological structural characteristics, geological water storage correction parameters are determined; Based on historical daily meteorological data, soil moisture and geological water storage correction parameters, a daily water balance model suitable for the current crop type was constructed, and the model parameters were calibrated. Using the current year's field water conditions as initial conditions, and combining the dynamic changes of crops throughout their growth period, the water balance model is used to predict the field water conditions for a preset future period on a daily basis. When the predicted field moisture conditions deviate from the preset suitable range, an irrigation or drainage control plan is generated. Based on the irrigation or drainage control scheme, output the irrigation system and annual total water demand prediction results for the entire growth period.

[0006] Furthermore, determining the geological water storage correction parameters based on the geological structural characteristics includes: Based on the geological structure characteristics of the farmland area, obtain the stratum dip angle, bedrock burial depth and soil permeability characteristics; Based on the dip angle of the strata, determine whether there is a significant lateral runoff trend; If there is a significant lateral runoff trend, increase the weight of the geological water storage correction parameters; among them, for farmland on the slopes beside mountains, reduce the effective water storage depth in combination with the bedrock burial depth.

[0007] Furthermore, the daily water balance model includes: For paddy fields, a water layer balance relationship is constructed with the change of surface water layer as the core, and a geological water storage correction parameter is introduced into the relationship to deduct lateral seepage loss; For drought-resistant crops, a soil moisture balance relationship is constructed with the change in the planned soil wetting layer water storage as the core, and a geological water storage correction parameter is introduced into this relationship to correct the groundwater recharge.

[0008] Furthermore, the calibration of the model parameters includes: Based on historical daily meteorological data, the water requirement coefficients for each growth stage of crops were determined. Based on soil moisture conditions, the upper and lower limits of soil moisture content and the planned depth of the wetting layer were determined. Based on geological water storage correction parameters, the lateral runoff loss ratio is calibrated to ensure that the error between the historical simulation values ​​and the field measured values ​​meets the preset accuracy.

[0009] Furthermore, the method for determining the dynamic changes of the crop throughout its entire growth period includes: Identify the current growth stage of crops by conducting field leaf age surveys or remote sensing vegetation index inversion; Establish a correlation between growth stages and water requirement coefficients, tighten the irrigation trigger threshold during the booting to heading stage, and relax the drainage trigger threshold during the seedling stage.

[0010] Furthermore, the daily prediction of field moisture conditions for a predetermined future period using the water balance model includes: Using the current field moisture conditions as the initial condition, input the meteorological forecast data for the future preset time period, and calculate the predicted field moisture value on a daily basis; The forecast results for the current day are used as the initial conditions for the forecast of the next period, and the process is repeated in a rolling iteration. When future weather forecasts are updated, the forecasting steps are re-executed based on the new forecast data to achieve dynamic updates of the forecast results.

[0011] Furthermore, the step of generating an irrigation or drainage control plan when the predicted field moisture conditions deviate from the preset suitable range includes: When field moisture is predicted to be below the suitable range, determine the irrigation time and amount; When field moisture is predicted to be above the suitable range, determine the drainage time and drainage volume; The regulation plan is coupled and optimized with future weather forecasts. If heavy rainfall is forecast in the future, the water level in the fields will be lowered in advance.

[0012] Furthermore, the water balance model also includes: Obtain the crop leaf area index and divide the crop field water requirement into soil evaporation component and vegetation transpiration component; The vegetation cover influence factor is calculated based on the leaf area index. The weight of the transpiration component is reduced when the vegetation cover is low, and the weight of the transpiration component is increased when the vegetation cover is high.

[0013] Secondly, embodiments of this application provide a farmland water demand prediction and irrigation control device, the device comprising: The data acquisition module is used to acquire historical daily meteorological data of the target farmland, daily meteorological data that has occurred in the current year, meteorological forecast data for the future preset period, evaporation, soil moisture, crop type and geological structure characteristics; The correction parameter determination module is used to determine the geological water storage correction parameters based on the geological structure characteristics. The model configuration module is used to construct a daily water balance model suitable for the current crop type based on historical daily meteorological data, soil moisture and geological water storage correction parameters, and to complete the model parameter calibration. The daily forecasting module is used to forecast the field water conditions for a future preset period using the current year's existing field water conditions as initial conditions, combined with the dynamic changes of crops throughout their growth period, and the water balance model. The control scheme generation module is used to generate irrigation or drainage control schemes when the prediction results deviate from the preset suitable range. The total water demand prediction module is used to output the prediction results of the irrigation system and annual total water demand for the entire growth period based on the irrigation or drainage control scheme.

[0014] Thirdly, embodiments of this application provide an electronic device, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0015] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0016] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0017] The technical solution provided in this application acquires multi-source meteorological, soil, crop, and geological structure characteristic data, introduces geological water storage correction parameters, constructs and calibrates a daily water balance model adapted to different crops, and conducts daily rolling forecasts of field water based on the dynamic changes of crops throughout their entire growth period. This can accurately quantify the impact of geological structure on field water transport, effectively improve the accuracy of farmland water demand forecasting under complex geological conditions, and generate irrigation and drainage control schemes based on dynamic forecast results, while linking with meteorological forecasts to optimize control strategies. This achieves precise matching of irrigation timing and irrigation volume, significantly improves water resource utilization efficiency, ensures stable and increased crop yields, and realizes refined irrigation management in irrigation areas. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the farmland water demand prediction and irrigation control method provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram of the farmland water demand prediction and irrigation control device provided in Embodiment 2 of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in Embodiment 3 of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0020] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0022] The following description, in conjunction with the accompanying drawings, details the farmland water demand prediction and irrigation control method, apparatus, and equipment provided in this application through specific embodiments and application scenarios.

[0023] Example 1 Figure 1 This is a schematic flowchart of the farmland water demand prediction and irrigation control method provided in Embodiment 1 of this application. Figure 1 As shown, the specific steps include the following: S11. Obtain historical daily meteorological data of the target farmland, daily meteorological data that has occurred in the current year, meteorological forecast data for the future preset period, evaporation, soil moisture, crop type and geological structure characteristics; Target farmland refers to any arable land unit that requires water demand forecasting, irrigation planning, and water resource management. The scope can be large or small, such as paddy fields, winter wheat dryland fields, and summer corn irrigated fields within 12 water resource zones of a province. It can also be a contiguous irrigation area, experimental plots, or farmland contracted by farmers.

[0024] Historical daily meteorological data refers to the daily meteorological observation data of the target farmland area that has been continuously measured over the past many years. It includes daily rainfall, E601 evaporation pan evaporation, daily average temperature, sunshine duration, relative humidity, wind speed, etc. For example, the daily measured data of a meteorological station in a certain region of a certain province over the past 10 years.

[0025] The daily meteorological data that has occurred in the current year refers to the daily measured meteorological data from the date of crop sowing to the date of data collection in the current agricultural year. It reflects the actual meteorological conditions of the year in real time. For example, the daily meteorological data during the winter wheat growing season from October 1, 2025 to May 1, 2026.

[0026] Meteorological forecast data for a predetermined period in the future refers to daily meteorological forecast data for a fixed duration in the future, which is usually a short-term forecast and includes daily rainfall probability, rainfall, evaporation, temperature, etc., such as detailed meteorological forecast data for the next 7 days.

[0027] Soil moisture refers to key parameters that characterize the physical and chemical properties and water movement characteristics of target farmland soil, including soil bulk density, field water holding capacity, soil texture, soil permeability coefficient, saturated water content, wilting coefficient, etc. For example, the bulk density of loam is 1.2-1.3 t / m³ and the field water holding capacity is 25%-35%.

[0028] Crop type refers to the types of crops planted in the target farmland, distinguishing between paddy field crops, dryland crops, and irrigated land crops. For example, paddy field crops are rice, dryland crops are winter wheat, and irrigated land crops are summer corn.

[0029] Geological structural characteristics refer to the strata, soil layers, and hydrogeological features of the target farmland area, including strata lithology, soil layer distribution, strata dip angle, bedrock depth, soil permeability characteristics, and groundwater depth range. For example, the strata dip angle of farmland on the piedmont slope is 10°-20° and the bedrock depth is less than 5m.

[0030] Evaporation refers to the process by which water changes from a liquid state to a gaseous state and is lost from the surface of farmland into the atmosphere. It is one of the most significant expenditures of farmland water.

[0031] In this plan, various types of data and characteristic information can be collected and organized through multiple channels and methods, including retrieving historical and current measured meteorological data from the regional meteorological monitoring station database, obtaining future short-term forecast data from the meteorological forecast platform, measuring evaporation and soil moisture through field soil sampling and laboratory testing, determining crop types through field surveys or remote sensing interpretation, and extracting geological structural characteristics through geological survey reports or borehole detection.

[0032] S12. Based on the geological structure characteristics, determine the crop water requirement, drainage volume, and geological water storage correction parameters; Crop water requirement refers to the total amount of water consumed by a crop during its entire growth period from sowing to maturity in order to maintain normal growth and development. Different crops have different water requirements; for example, rice requires much more water than winter wheat. The same crop also varies greatly at different growth stages, with significantly higher water requirements during critical stages such as booting and heading.

[0033] Drainage volume refers to the amount of water that is artificially removed from farmland in order to maintain the field moisture within a suitable range. It mainly occurs when there is too much moisture, such as when the water layer is too deep after heavy rainfall or when the soil moisture content exceeds the upper limit.

[0034] Geological water storage correction parameters refer to correction coefficients used to quantify the impact of geological structures on farmland water transport. They mainly reflect the differences in lateral seepage loss or groundwater recharge caused by stratum dip angle, bedrock burial depth, and soil permeability characteristics. The values ​​vary with geological conditions; for example, the values ​​increase when lateral seepage is severe on sloping farmland and decrease when the values ​​decrease in plain areas.

[0035] Specifically, the acquired geological structural characteristics can be quantitatively analyzed, and combined with indicators such as stratum dip angle, bedrock burial depth, and soil permeability characteristics, geological water storage correction parameters adapted to the actual geological conditions of the target farmland can be calculated through preset calculation formulas or empirical relationships.

[0036] S13. Based on historical daily meteorological data, soil moisture and geological water storage correction parameters, construct a daily water balance model suitable for the current crop type and complete the model parameter calibration. Among them, the daily water balance model refers to a mathematical model built on the principle of farmland water balance with the natural day as the minimum time step. It is used to describe the dynamic relationship between farmland water income, expenditure and storage changes. It is divided into paddy field water layer balance model and dryland crop soil water balance model. The paddy field model focuses on the changes in the field surface water layer, while the dryland crop model focuses on the changes in the planned wetting layer water storage in the soil.

[0037] Model parameter calibration refers to the process of adjusting the values ​​of unknown parameters in the model so that the error between the model simulation results and the actual field observation data meets the preset accuracy requirements. The purpose is to improve the model's simulation accuracy of actual farmland moisture changes.

[0038] This technical solution, based on the core principle of farmland water balance, integrates historical daily meteorological data, soil moisture, and geological water storage correction parameters. It differentiates between paddy fields and dryland crops, establishing corresponding daily water balance mathematical models for each. The paddy field model incorporates geological water storage correction parameters to deduct lateral seepage losses, while the dryland crop model incorporates these parameters to correct groundwater recharge. Then, using historical daily meteorological data and field-measured moisture data, optimization algorithms such as trial and error and least squares are employed to repeatedly adjust parameters in the model, including the crop water requirement coefficient, upper and lower limits of soil moisture content, and the proportion of lateral runoff loss, until the error between the model's historical simulated values ​​and the field-measured values ​​is less than a preset threshold.

[0039] S14. Using the current year's field water conditions as initial conditions, and combining the dynamic changes of crops throughout their growth period, the water balance model is used to predict the field water conditions for a future preset period on a daily basis. Among them, field moisture status refers to the core indicator reflecting the water storage status of farmland. In the context of paddy fields, it specifically refers to the depth of the water layer on the field surface, while in the context of dryland crops, it specifically refers to the soil moisture content within the planned wetting layer. For example, the suitable water layer depth for rice during the greening stage is 30-50 mm, and the suitable soil moisture content for winter wheat during the jointing stage is 90%-140% of the field water holding capacity.

[0040] The entire growth period refers to the complete growth cycle of a crop from sowing and emergence to maturity and harvest. Different crops have different stages. For rice, it is divided into the flooding stage, greening stage, tillering stage, jointing stage, booting stage, heading stage, milk stage, and yellowing stage. Winter wheat can be divided into the sowing stage, emergence stage, tillering stage, overwintering stage, greening stage, tillering stage, jointing stage, booting stage, heading stage, flowering stage, grain filling stage, and maturity stage.

[0041] Specifically, it can continuously calculate the daily changes in field water conditions within a preset time period, using natural days as the unit, to achieve refined prediction of dynamic water changes.

[0042] Then, using the measured field water conditions of the current year as the initial input conditions for the model, and combining the water consumption patterns and growth dynamics of different growth stages throughout the crop's entire growth period, the daily water balance model with completed parameter calibration is run to calculate the predicted field water values ​​for the future preset time period on a daily basis.

[0043] S15. When the predicted field moisture conditions deviate from the preset suitable range, generate an irrigation or drainage control plan. The preset suitable range refers to the reasonable range of field water required for normal growth of crops at different growth stages. In the case of paddy fields, it is the upper and lower limits of the water depth on the field surface, and in the case of dryland crops, it is the upper and lower limits of the soil moisture content. For example, the suitable water layer range for rice tillering stage is 10-50mm, and the suitable soil moisture content range for summer maize jointing stage is 50%-130% of the field water holding capacity.

[0044] Irrigation or drainage control plans refer to specific water control plans formulated when field moisture deviates from the suitable range. They include key information such as irrigation time, single irrigation volume, drainage time, and single drainage volume, and are used to guide actual irrigation and drainage operations in the field.

[0045] This scheme can compare and analyze the field water conditions predicted daily with the preset suitable range for the corresponding growth period. When the predicted value is lower than the lower limit of the suitable range, a corresponding irrigation plan is formulated; when the predicted value is higher than the upper limit of the suitable range, a corresponding drainage plan is formulated, thus forming a complete irrigation or drainage control scheme.

[0046] S16. Based on the irrigation or drainage control scheme, output the irrigation system and annual total water demand prediction results for the entire growth period.

[0047] A full-growth-cycle irrigation system refers to a comprehensive plan for all irrigation and drainage operations during the entire growth period of crops, from sowing to maturity. It includes the irrigation dates, irrigation volumes, drainage dates, drainage volumes, and water level / soil moisture control standards for each growth period, and is a core technical document for farmland irrigation management.

[0048] Annual water demand refers to the total amount of irrigation water required for crops throughout their entire growth period. It is usually measured in cubic meters per mu (a Chinese unit of area, approximately 0.165 acres) of cultivated land. For example, the annual water demand for rice throughout its entire growth period in a certain province is approximately 300 cubic meters per mu.

[0049] This plan summarizes and integrates the irrigation or drainage control plans generated daily throughout the entire growth period, compiles them into a standardized irrigation system document for the entire growth period, and calculates the total irrigation volume throughout the entire growth period to obtain and output the annual total water demand forecast.

[0050] The technical solution provided in this embodiment integrates multi-source meteorological, soil, crop, and geological structure characteristic data, introduces geological water storage correction parameters to construct and calibrate a daily water balance model adapted to different crops, and combines the dynamic changes of crops throughout their entire growth period to achieve daily dynamic prediction of field water. This can accurately quantify the impact of geological structure differences on farmland water transport, overcome the shortcomings of traditional static quota models such as low accuracy and poor adaptability, and achieve an upgrade from static estimation to dynamic and refined prediction. This provides reliable technical support for irrigation districts to formulate scientific and precise irrigation systems and carry out efficient water resource management.

[0051] In one embodiment, optionally, determining the geological water storage correction parameters based on the geological structural characteristics includes: Based on the geological structure characteristics of the farmland area, obtain the stratum dip angle, bedrock burial depth and soil permeability characteristics; Based on the dip angle of the strata, determine whether there is a significant lateral runoff trend; If there is a significant lateral runoff trend, increase the weight of the geological water storage correction parameters; among them, for farmland on the slopes beside mountains, reduce the effective water storage depth in combination with the bedrock burial depth.

[0052] The dip angle of the strata refers to the angle between the strata in the farmland area and the horizontal plane. It is used to characterize the degree of inclination of the land. The larger the angle, the steeper the slope of the land and the stronger the lateral flow of water. For example, the dip angle of the strata in sloping farmland is usually greater than 5°, while the dip angle of the strata in plain areas is close to 0°.

[0053] Bedrock depth refers to the vertical distance from the bottom of the surface soil layer to the top of the hard bedrock underground. It determines the size of the effective water storage space of the soil. The shallower the depth, the smaller the effective water storage space, and the easier it is for water to seep out and be lost. For example, the bedrock depth of farmland on the side slope of a mountain is often less than 3m.

[0054] Soil permeability refers to the soil's ability to allow water to seep downwards and laterally. It is determined by soil texture, porosity, and other factors, and is classified into strong permeability, medium permeability, and weak permeability. For example, sandy soil has strong permeability, while clay soil has weak permeability.

[0055] Lateral runoff tendency refers to the tendency of farmland water to flow laterally along the sloping soil layer and out of the field. It easily causes water loss in the field and reduces water use efficiency. The greater the slope and the stronger the soil permeability, the more significant the lateral runoff tendency.

[0056] The weight of geological water storage correction parameters refers to the proportion of the influence of geological water storage correction parameters in the calculation results of water balance models. The higher the weight, the greater the influence of geological structure on water transport.

[0057] Sloping farmland refers to farmland located on the side of a hillside with a certain slope. Unlike flat plain farmland, it is prone to lateral water seepage and soil erosion.

[0058] Effective water storage depth refers to the thickness of the soil layer that can effectively store field water for crop absorption and utilization. It is limited by the depth of bedrock burial; the smaller the bedrock burial depth, the smaller the effective water storage depth.

[0059] Specifically, the dip angle and bedrock depth of farmland areas can be measured through geological surveys, field drilling, and soil profile excavation. Combined with soil texture testing results, the soil permeability grade is determined. The measured dip angle is compared with a preset threshold. If the dip angle is greater than the preset threshold (e.g., 5°), a significant lateral runoff trend is identified; otherwise, no significant lateral runoff trend is identified. After identifying a significant lateral runoff trend, its weight is increased by increasing the value of the geological water storage correction parameter. For farmland on hillside slopes, the effective soil water storage depth is calculated using a preset formula, based on bedrock depth data, to quantify the limiting effect of geological conditions on water storage.

[0060] This technical solution refines geological structural characteristic indicators, accurately identifies lateral runoff trends, and differentiates and adjusts the weights of correction parameters. At the same time, it reduces the effective water storage depth by combining bedrock burial depth with sloping farmland. It can accurately quantify the differences in water leakage and storage under different geological conditions, avoid parameter deviations caused by neglecting geological differences in traditional methods, further improve the accuracy and pertinence of geological water storage correction parameters, and enhance the model's adaptability to farmland with complex geological conditions.

[0061] In one embodiment, optionally, the daily water balance model includes: For paddy fields, a water layer balance relationship is constructed with the change of surface water layer as the core, and a geological water storage correction parameter is introduced into the relationship to deduct lateral seepage loss; For drought-resistant crops, a soil moisture balance relationship is constructed with the change in the planned soil wetting layer water storage as the core, and a geological water storage correction parameter is introduced into this relationship to correct the groundwater recharge.

[0062] Paddy fields refer to cultivated land that stores water year-round and grows aquatic crops such as rice. The fields maintain a certain depth of surface water, and water movement is mainly through horizontal flow and vertical seepage, such as paddy fields in the Lixiahe suburbs of a certain province.

[0063] Changes in paddy field surface water layer refer to the increase or decrease in the depth of the paddy field surface water layer over time. It is affected by factors such as rainfall, evaporation, seepage, irrigation, and drainage, and is a core monitoring indicator for paddy field water balance.

[0064] The water balance relationship refers to the mathematical relationship that describes the dynamic changes in the water depth of paddy fields, reflecting the quantitative relationship between rainfall, evaporation, seepage, irrigation, drainage and changes in water depth.

[0065] Lateral seepage loss refers to the amount of water lost from paddy fields as it flows laterally out of the field along the sloping soil layer. It is distinct from vertical seepage loss and is significantly affected by the dip angle of the strata and the soil permeability characteristics.

[0066] Dryland crops are crops that do not require long-term water storage during their growth process and rely on soil moisture for growth. There is no long-term water layer in the field, and water is stored in the planned moist layer of the soil, such as winter wheat and summer corn.

[0067] The planned wetting layer of soil refers to the soil layer where crop roots are mainly distributed and can absorb and utilize water. Its depth varies with the crop growth stage. The planned wetting layer depth can reach 550 mm during the jointing stage of winter wheat.

[0068] Soil moisture balance refers to the mathematical formula describing the dynamic changes in the planned wetting layer water storage of dryland crop soils, reflecting the quantitative relationship between rainfall, evaporation, transpiration, groundwater recharge, irrigation, drainage and changes in soil water storage.

[0069] Groundwater recharge refers to the amount of water from underground aquifers that replenishes the planned wetting layer of the soil. It is affected by groundwater depth, soil permeability, and geological structure, and is an important source of water income for dryland crops.

[0070] For paddy field scenarios, the focus can be on changes in surface water depth, integrating factors such as rainfall, evaporation, vertical seepage, irrigation, and drainage to establish a mathematical relationship for water balance. Geological water storage correction parameters can then be incorporated into this relationship to calculate and deduct lateral seepage losses. For dryland crop scenarios, the focus can be on changes in the planned wetting layer water storage, integrating factors such as rainfall, soil evaporation, vegetation transpiration, groundwater recharge, irrigation, and drainage to establish a mathematical relationship for soil moisture balance. Geological water storage correction parameters can then be incorporated into this relationship to correct the calculated groundwater recharge.

[0071] This technical solution distinguishes the different water transport characteristics of paddy fields and dryland crops, constructs differentiated water balance relationships, and introduces geological water storage correction parameters to correct lateral seepage loss and groundwater recharge, which can accurately match the water movement patterns of different crops, avoid simulation bias caused by using a uniform model, significantly improve the accuracy of field water simulation calculations for paddy fields and dryland crops, and enhance the model's scene adaptability.

[0072] In one embodiment, optionally, the completion of model parameter calibration includes: Based on historical daily meteorological data, the water requirement coefficients for each growth stage of crops were determined. Based on soil moisture conditions, the upper and lower limits of soil moisture content and the planned depth of the wetting layer were determined. Based on geological water storage correction parameters, the lateral runoff loss ratio is calibrated to ensure that the error between the historical simulation values ​​and the field measured values ​​meets the preset accuracy.

[0073] Among them, the water requirement coefficient of each growth stage of crops refers to the field water requirement ratio corresponding to the unit evaporation of crops at different growth stages, reflecting the difference in water consumption intensity of crops. The values ​​are different for different growth stages. For example, the water requirement coefficient of rice during the booting stage is 1.16 and the water requirement coefficient of rice during the yellow ripening stage is 1.26.

[0074] The upper and lower limits of soil moisture content refer to the maximum and minimum soil moisture content required for normal crop growth. The upper limit is the moisture content corresponding to field capacity, and the lower limit is the moisture content at which the crop wilts. For example, the upper limit of soil moisture content during the jointing stage of winter wheat is 140% of field capacity, and the lower limit is 90%.

[0075] Lateral runoff loss ratio refers to the proportion of water lost through lateral seepage in farmland to the total water expenditure, quantifying the degree of water loss caused by lateral runoff. It is affected by geological structure characteristics, with a high proportion in sloping farmland and a low proportion in plains.

[0076] Preset accuracy refers to the maximum allowable error threshold between the model simulation results and the field measurement results. It is set according to the accuracy requirements of irrigation management, such as a relative error of less than 10%.

[0077] Specifically, based on historical daily meteorological data and combined with measured water consumption data of crops at each growth stage, the water demand coefficient can be adjusted to match the crop water demand calculated by the model with the measured water consumption; based on soil moisture and combined with field soil moisture observation data, the upper and lower limits of soil moisture content and the planned wetting layer depth can be adjusted to fit the actual soil moisture storage capacity; based on geological water storage correction parameters and combined with measured seepage data under different geological conditions such as sloping farmland and plains, the lateral runoff loss ratio can be adjusted, and the parameters can be iteratively optimized until the error between the historical simulation value of the model and the measured value in the field is less than the preset accuracy.

[0078] This technical solution targets and optimizes model parameters related to crops, soil, and geology across different dimensions. By iteratively optimizing parameter values ​​using historical measured data, it eliminates the subjective bias of empirical parameters, ensuring that model parameters closely match the actual crop growth, soil characteristics, and geological conditions of the target farmland. This effectively reduces model simulation errors, improves the model's fit to historical moisture changes, and provides an accurate and reliable model foundation for subsequent dynamic predictions.

[0079] In one embodiment, optionally, the method for determining the dynamic changes of the crop throughout its entire growth period includes: Identify the current growth stage of crops by conducting field leaf age surveys or remote sensing vegetation index inversion; Establish a correlation between growth stages and water requirement coefficients, tighten the irrigation trigger threshold during the booting to heading stage, and relax the drainage trigger threshold during the seedling stage.

[0080] Field leaf age survey refers to the method of judging the growth process of crops by observing the number and morphology of crop leaves in the field. It is a common means of identifying the growth stage in agricultural production. For example, rice enters the late tillering stage when it reaches 10 leaves.

[0081] Remote sensing vegetation index refers to an index that reflects crop growth status and vegetation cover, calculated from satellite remote sensing images. The commonly used index is NDVI (Normalized Difference Vegetation Index). The higher the value, the more vigorous the crop growth.

[0082] Inversion refers to a technical method that uses remote sensing vegetation index data, combined with crop growth patterns, to estimate the current growth stage of a crop, thereby achieving large-scale, non-contact identification of growth stages.

[0083] The correspondence between growth stages and water requirement coefficients refers to a pre-established matching table or mathematical relationship between different growth stages of crops and their corresponding water requirement coefficients. For example, the water requirement coefficient for the rice greening stage is 0.98, and the water requirement coefficient for the heading stage is 1.34.

[0084] The irrigation trigger threshold refers to the critical value of field moisture that triggers irrigation operations. For paddy fields, it is the lower limit of water depth, and for dryland crops, it is the lower limit of soil moisture content. The tighter the threshold, the easier it is to trigger irrigation.

[0085] The drainage trigger threshold refers to the critical value of field moisture that triggers drainage operations. For paddy fields, it is the upper limit of water depth, and for dryland crops, it is the upper limit of soil moisture content. The wider the threshold, the more difficult it is to trigger drainage.

[0086] The booting stage to the heading stage is a critical water-sensitive period for crop growth. During this time, crops have a high water requirement and are sensitive to water stress. Water shortage can easily lead to yield reduction. For example, the booting stage to the heading stage of rice is the core stage of yield formation.

[0087] The seedling stage refers to the early growth stage of crops after sowing and emergence. At this time, the crop plants are small, consume little water, are highly resistant to waterlogging, and are less sensitive to excessive water.

[0088] Specifically, by conducting field surveys of crop leaf age and growth morphology, or by calculating NDVI from satellite remote sensing images, and combining this with the crop's growth cycle patterns, the specific growth stage of the crop can be determined. For example, winter wheat can be identified as currently in the jointing stage. Based on experimental data or agricultural literature, a one-to-one correspondence between each crop growth stage and its water requirement coefficient can be established. For the booting to heading stage, the irrigation trigger threshold can be appropriately increased (lowering the lower limit), allowing irrigation to be triggered when the moisture level is slightly low, ensuring water supply during this sensitive period. For the seedling stage, the drainage trigger threshold can be appropriately increased (upper limit), allowing slightly higher field moisture levels and avoiding frequent drainage that wastes water resources.

[0089] This technical solution accurately identifies crop growth stages by combining field surveys with remote sensing inversion, establishes a dynamically matched water demand coefficient relationship, and differentiates the control thresholds for key growth periods and seedling stages. It can match the water demand characteristics of different growth stages of crops, ensure sufficient water during critical and sensitive periods, reduce ineffective drainage during the seedling stage, balance stable crop yields with water conservation, and improve the scientific and targeted nature of irrigation control.

[0090] In one embodiment, optionally, the step of using the water balance model to predict the field moisture status for a preset future period on a daily basis includes: Using the current field moisture conditions as the initial condition, input the meteorological forecast data for the future preset time period, and calculate the predicted field moisture value on a daily basis; The forecast results for the current day are used as the initial conditions for the forecast of the next period, and the process is repeated in a rolling iteration. When future weather forecasts are updated, the forecasting steps are re-executed based on the new forecast data to achieve dynamic updates of the forecast results.

[0091] Current field moisture status refers to the actual field moisture state measured at the start of the prediction. For paddy fields, it is the actual water depth on the field surface, and for dryland crops, it is the actual soil moisture content. This is the initial baseline data for the prediction.

[0092] Meteorological forecast data refers to the predicted data of daily rainfall, evaporation, temperature, etc. for a predetermined period of time in the future, which provides the driving conditions for the model's future water income and expenditure.

[0093] Field moisture forecast refers to the field moisture status value calculated by the model on a future day. For paddy fields, it is the water depth forecast, and for dryland crops, it is the soil moisture content forecast.

[0094] Rolling iteration refers to the process of using the previous day's forecast as the initial condition for the next day's calculation, and then pushing forward and calculating continuously day by day to achieve dynamic and continuous simulation of moisture changes.

[0095] Weather forecast updates refer to the release of new short-term forecast data by meteorological departments to update existing forecast results. Updates are usually made once a day.

[0096] Dynamic updates refer to re-executing the forecasting process based on the latest weather forecast data, correcting the original forecast results, and making the forecasts more in line with the latest weather changes.

[0097] In this scheme, the current measured field moisture condition can be used as the initial input to the model. Weather forecast data for a preset future period is then input into the calibrated water balance model. The model calculates the predicted field moisture value daily, using natural days as the step size. The predicted field moisture value calculated for each day is used as the initial condition for the model calculation on the next day, and so on, progressively advancing the prediction for the entire preset period to achieve rolling iterative calculation. After obtaining updated weather forecast data, the original prediction result is discarded, and the model is re-run with the current field moisture condition as the initial condition, substituting the new forecast data to complete the daily prediction, thus achieving dynamic updating of the prediction results.

[0098] This technical solution combines daily rolling iterative forecasting with dynamic updates of weather forecasts, enabling real-time tracking of the impact of weather changes on field water. It avoids the shortcomings of traditional static forecasting, which cannot respond to weather fluctuations, and achieves dynamic, continuous, and accurate forecasting of field water. This improves the timeliness and accuracy of forecast results, providing a timely and reliable basis for adjusting irrigation control plans in real time.

[0099] In one embodiment, optionally, generating an irrigation or drainage control scheme when the predicted field moisture conditions deviate from a preset suitable range includes: When field moisture is predicted to be below the suitable range, determine the irrigation time and amount; When field moisture is predicted to be above the suitable range, determine the drainage time and drainage volume; The regulation plan is coupled and optimized with future weather forecasts. If heavy rainfall is forecast in the future, the water level in the fields will be lowered in advance.

[0100] The irrigation time refers to the specific date and time period for carrying out irrigation operations. Usually, times with low evaporation, such as early morning or evening, are chosen to reduce water wastage.

[0101] Irrigation volume refers to the amount of water added to the field in a single irrigation operation. For paddy fields, it is the increase in water depth after irrigation, and for dryland crops, it is the amount of water required to reach the appropriate upper limit. The unit is millimeters or cubic meters per mu.

[0102] Drainage time refers to the specific date and time period during which drainage operations are carried out, usually before rainfall or when the moisture content in the fields is too high.

[0103] Drainage volume refers to the amount of water discharged from the field in a single drainage operation. For paddy fields, it is the decrease in water depth after drainage; for dryland crops, it is the amount of excess soil moisture discharged. The unit is millimeters or cubic meters per acre.

[0104] Coupling optimization refers to combining irrigation and drainage control schemes with future weather forecast data, comprehensively analyzing and adjusting control strategies to adapt the schemes to future weather changes.

[0105] Heavy rainfall refers to a rainfall event in which the predicted rainfall amount reaches or exceeds a preset threshold within a predetermined time period, such as a daily rainfall of more than 50 millimeters, which can easily lead to waterlogging and flooding in fields.

[0106] Field water storage level refers to the equivalent water level corresponding to the depth of the water layer on the surface of paddy fields or the water storage capacity of dryland crops. Lowering it in advance can reserve water storage space and avoid waterlogging caused by heavy rainfall.

[0107] When field moisture is predicted to be below the suitable range, the optimal irrigation time is determined by combining the crop's water requirements during its growth period with evaporation patterns. Simultaneously, the amount of irrigation water required to replenish the current moisture level to the suitable upper limit is calculated. Conversely, when field moisture is predicted to be above the suitable range, the optimal drainage time is determined, and the amount of drainage required to reduce the current moisture level to the suitable upper limit is calculated. Furthermore, the preliminary irrigation and drainage control plan can be correlated with future weather forecast data. If heavy rainfall is forecast, the plan can be adjusted in advance, lowering the field water level before rainfall to reserve water storage space and prevent waterlogging caused by heavy rainfall, thus optimizing the rationality and resilience of the control plan.

[0108] This technical solution, by accurately determining the timing and volume of irrigation and drainage, and combining it with meteorological forecasts to optimize the control scheme, enables precise and proactive irrigation and drainage operations. This avoids water waste or waterlogging risks caused by blind irrigation and drainage, improves the adaptability of the control scheme to meteorological changes, ensures that field moisture is always within a suitable range, and further improves water resource utilization efficiency and crop growth stability.

[0109] In one embodiment, optionally, the water balance model further includes: Obtain the crop leaf area index and divide the crop field water requirement into soil evaporation component and vegetation transpiration component; The vegetation cover influence factor is calculated based on the leaf area index. The weight of the transpiration component is reduced when the vegetation cover is low, and the weight of the transpiration component is increased when the vegetation cover is high.

[0110] Leaf Area Index (LAI) is the ratio of the total area of ​​crop leaves per unit land area to the land area. It reflects the degree of crop vegetation growth. The higher the value, the more leaves there are and the higher the coverage. For example, the LAI of rice is 2-3 during the tillering stage and can reach 5-6 during the heading stage.

[0111] Crop field water requirement refers to the total amount of water consumed in the field during crop growth. It consists of two parts: soil evaporation and vegetation transpiration, and is the core expenditure item of the water balance model.

[0112] Soil evaporation refers to the amount of water lost through evaporation from the soil surface in the field. It is related to soil moisture content, meteorological conditions, and vegetation cover. The lower the vegetation cover, the stronger the soil evaporation.

[0113] Vegetation transpiration refers to the amount of water lost through stomata from crop leaves. It is related to crop growth status, leaf area index, and meteorological conditions. The higher the leaf area index, the stronger the transpiration.

[0114] The vegetation cover impact factor is a coefficient calculated based on the leaf area index that characterizes the impact of vegetation cover on transpiration. It is used to dynamically adjust the proportion of transpiration in total water demand.

[0115] Low vegetation cover refers to the state of crops in the seedling stage and early growth stage, when the leaf area index is small and there is a lot of bare soil in the field. At this time, transpiration is weak and soil evaporation is strong.

[0116] High vegetation cover refers to the state during the vigorous growth period of crops, when the leaf area index is large and the field is basically covered by vegetation. At this time, transpiration is strong and soil evaporation is weak.

[0117] This scheme obtains crop leaf area index through field measurements or remote sensing inversion. Based on the farmland water demand mechanism, the crop field water demand in the model is decomposed into soil evaporation and vegetation transpiration components, and the water consumption of each component is calculated separately. Then, based on the obtained leaf area index, the vegetation cover influence factor is calculated using a preset formula. When the vegetation cover is low, the weight of the transpiration component in the total water demand is reduced, and the weight of the soil evaporation component is increased. When the vegetation cover is high, the weight of the transpiration component is increased, and the weight of the soil evaporation component is decreased, dynamically matching the actual water consumption composition.

[0118] This technical solution introduces dynamic decomposition of leaf area index and adjusts the weights of soil evaporation and vegetation transpiration components, which can accurately reflect the differences in water consumption composition at different growth stages of crops. It overcomes the shortcomings of traditional methods that fix the proportion of water demand, making the model's water demand calculation fit the actual field water consumption pattern, further improving the accuracy of field water simulation and prediction, and providing a more scientific basis for irrigation system formulation.

[0119] To enable those skilled in the art to better understand this solution, this application also provides a preferred embodiment.

[0120] This calculation of agricultural water demand uses daily rainfall and evaporation data to calculate the water demand process of paddy fields, dryland crops (winter wheat), and irrigated land (summer corn) on a ten-day basis, and obtains the total water demand per mu per year (i.e., annual quota, unit: 10,000 cubic meters / mu).

[0121] I. Principles for Calculating Water Requirements of Paddy Fields (i.e., Rice) The water demand of paddy fields is divided into irrigation water during the paddy field soaking period and irrigation water during other periods.

[0122] 1. Water requirement during paddy field flooding period The formula for calculating irrigation water during the paddy field flooding period is as follows.

[0123] ; —The required water depth in the paddy field during rice transplanting is generally 30~50mm, but in many places it is actually much greater. The system asks the user to input this information.

[0124] --The seepage rate during the flooding period is generally 2~7mm / day, as specified in the table below for each zone.

[0125] — The evaporation intensity of the field surface is obtained by multiplying the measured "E601 evaporation dish data" by the evaporation conversion factor, which is shown in the table below.

[0126]

[0127] —The number of days spent soaking in the fields is generally 3 to 5 days in a certain province.

[0128] —The total rainfall during the flooding period was calculated based on daily rainfall measurements.

[0129] 2. Other water requirements during the growing season (irrigation and drainage). During other growth stages of rice, it is necessary to ensure that the amount of water in the field fluctuates within a certain range. When it is lower than h min时则Irrigation is required when the temperature is above h. p时则 Drainage is required. The water balance formula for the time period is as follows (leakage is not considered here).

[0130] ; — Depth of the surface water layer at the beginning of the time period, in mm.

[0131] —Depth of water layer on the field surface at the end of the time period, in mm.

[0132] P – Rainfall, in mm.

[0133] d — Discharge volume, in mm.

[0134] m – Irrigation volume, unit mm.

[0135] WC – Field water consumption, calculated as the measured evaporation rate of the “E601 evaporation dish” × evaporation conversion factor × water demand factor, in mm.

[0136] Irrigation conditions: If the farmland moisture level at the beginning of the period is at the upper limit of the suitable water layer (paddy field) (h) max After a period of consumption, the water level on the field surface drops to the lower limit of the suitable water level (h). min If there is no rainfall at this time, irrigation is required, and the irrigation quota is: h max -h min .

[0137] Drainage conditions: If, after rainfall, the water depth in the paddy field reaches the maximum flood-resistant depth H... p Then it is necessary to drain the water to H p .

[0138] The suitable upper and lower limits for rice at different stages and the preliminary determination of the rice growth period are as follows.

[0139]

[0140]

[0141] 1. According to relevant regulations, the rice growth period is divided into seven stages.

[0142] 2. The timeframes for the seven stages currently use data compiled and analyzed from irrigation experiments on a certain crop, which requires further research and improvement.

[0143] II. Principles for calculating water requirements of dryland crops (winter wheat or summer maize); Compared to paddy fields, dryland crop water requirements calculations involve less paddy field soaking and more groundwater recharge. For dryland crops, the change in water storage within the planned wetting layer (H) of the soil at any time t during the entire growth period can be represented by the following water balance equation.

[0144] ; Where: W0, Wt — the planned water storage in the soil wetting layer at the beginning of the time period and at any time t; Wr — The amount of water added due to the planned increase in wetting layer. This item does not apply if the planned wetting layer does not change during the period. P0 – Effective rainfall stored in the planned wetting layer of the soil. P0 is generally calculated as rainfall × coefficient α. The selection of α is based on experience and is not very accurate. See the following reference books.

[0145] α—Rainfall infiltration coefficient, whose value is related to factors such as rainfall amount, rainfall intensity, rainfall duration, soil properties, ground cover, and topography. Generally, when the rainfall amount is less than 5 mm, α is 0; when the rainfall amount is between 5 and 50 mm, α is approximately 1.0 to 0.8; and when the rainfall amount is greater than 50 mm, α is 0.7 to 0.8.

[0146] K – Groundwater recharge, which is also subject to experience and is closely related to groundwater depth. Refer to the table below or use a regression equation. calculate.

[0147] M - Irrigation volume ET – Crop field water requirement, calculated as the measured evaporation rate of “E601 evaporation dish” × evaporation conversion factor × water requirement factor, in mm. The water requirement factor for winter wheat is shown in the table below.

[0148] Irrigation conditions: Water volume is below the lower limit of soil moisture content × soil dry bulk density × planned wetting layer depth; Irrigation volume = (upper limit of soil moisture content - lower limit of soil moisture content) × soil dry bulk density × planned wetting layer depth. Drainage conditions: Water volume exceeding the upper limit of soil moisture content × soil dry bulk density × planned wetting layer depth shall be drained to the upper limit of soil moisture content × soil dry bulk density × planned wetting layer depth. The parameters are shown in the table below:

[0149]

[0150]

[0151] Note: 1. The main dryland crops in a certain province during the summer include corn, soybeans, and peanuts. Among them, corn has the highest water consumption, and summer corn is used as a representative of the water requirements of summer dryland crops.

[0152] 2. The growth period of summer maize and winter wheat should not overlap. 3. The start date shall not be earlier than June 1 and at the latest September 30.

[0153]

[0154]

[0155] This embodiment also provides a specific scenario application example of farmland located on a hillside. The current area has a stratum dip angle of about 12°, a bedrock depth of 2.5m, a soil texture of loam, a soil bulk density of 1.2t / m³, a field water holding capacity of 28%, a groundwater depth of 1.5 to 2.0m, and obvious lateral seepage.

[0156] 1. Acquisition of data from multiple sources; Historical daily meteorological data: daily rainfall, evaporation of E601 evaporation pan, temperature, and sunshine in Ganyu District over the past 10 years; Meteorological data already available that year: Daily weather measurements from the sowing of winter wheat this year to the present day; 7-day weather forecast data: detailed forecasts of rainfall, evaporation, and temperature; Soil moisture: soil bulk density, field water holding capacity, saturated water content, wilting coefficient, and permeability coefficient; Crop type: Winter wheat (dryland) + rice (paddy field) rotation; Geological structural characteristics: strata dip angle, bedrock depth, soil permeability characteristics, and groundwater depth range.

[0157] 2. Determination of geological water storage correction parameters; According to geological survey data, the strata dip angle in this embodiment is 12° > 5°, indicating a significant lateral runoff trend; the bedrock is buried at a depth of 2.5m, and the effective water storage depth is reduced to 1.8m due to the farmland on the side slope of the mountain.

[0158] According to the method of this invention, the geological water storage correction parameter is calculated to be 0.28, which is used for subsequent model deduction of lateral leakage and correction of groundwater recharge.

[0159] 3. Construct and calibrate a daily water balance model; 3.1 Paddy Field (Rice) Model Using the depth of the water layer on the field surface as the state variable: ; in: ; The water requirement coefficients for rice growth stages are as follows: greening stage 0.98, tillering stage 1.04, booting stage 1.16, heading stage 1.34, and ripening stage 1.26. =Geological water storage correction parameter × Leakage baseline value; Introducing LAI (Leaf Area Index), WC is broken down into soil evaporation component + vegetation transpiration component: transpiration weighting is 30% during seedling stage, 60% during jointing stage, and 85% during heading stage.

[0160] 3.2 Dryland crop (winter wheat) model; Using the planned moisture content of the soil wetting layer as a state variable: ； Effective rainfall; =Geological water storage correction parameter × Groundwater recharge baseline value; ; Water requirement coefficients for winter wheat: jointing stage 1.31, booting stage 1.83, grain filling stage 1.83; Similarly, LAI is used to split the evaporation / transpiration weights.

[0161] 3.3 Parameter calibration; Calibration using historical data from multiple years: Rice: Water level 30-50mm during the greening stage, 10-50mm during tillering, and 10-40mm during heading; Winter wheat: Moisture content at the jointing stage: upper limit 140%, lower limit 90%; The lateral runoff loss ratio is set at 18%; The model error is controlled within 8%, meeting the preset accuracy.

[0162] 4. Dynamic identification and daily rolling forecasting throughout the entire reproductive period; 4.1 Dynamic identification of the reproductive period; Winter wheat: Identified by field leaf age survey and NDVI (Normalized Difference Vegetation Index), it is currently in the jointing stage; Rice: According to the growth period of a certain province: June 15 greening, June 26 tillering, July 21 jointing, August 2 booting, August 18 heading, August 31 milk stage, September 15 yellow stage.

[0163] 4.2 Daily rolling forecast; Initial conditions: Current measured soil moisture content / surface water layer; Input: Rainfall and evaporation forecast for the next 7 days; The forecast result for the current day serves as the starting point for the next day, and the forecast is iterated daily. The forecast is updated daily, dynamically updating the moisture levels for the next 7 days.

[0164] 5. Optimization of dynamic threshold and forecast coupling; 5.1 Dynamic moisture threshold; Rice: water level 10-50mm during tillering stage, tightened to 30-60mm during booting stage, and relaxed to dryness during yellow ripening stage; Winter wheat: 90%–140% during the jointing stage, and relaxed to 45%–100% during the seedling stage.

[0165] 5.2 Forecast Coupling Optimization; The forecast predicted 65mm of heavy rainfall on the third day. The water level in the paddy fields was lowered from 50mm to 20mm one day in advance to allow for water storage. To avoid waterlogging, drainage volume is reduced by 40%.

[0166] 6. Generate irrigation system and annual water demand for the entire growth period; 6.1 Rice; The quota for paddy field irrigation is 80 m³ / mu. Irrigation during the growing season: 222 m³ / mu; Drainage: 11.5 m³ / mu; Total quota: 302 m³ / mu.

[0167] 6.2 Winter wheat; Irrigation throughout the entire growth period: 128 m³ / mu; Precision irrigation during critical periods (jointing and heading) reduces the risk of yield reduction by 12%.

[0168] This technical solution accurately deducts lateral seepage in the geological environment, reducing the simulation error of sloping farmland from 19% to 7.8%. By adopting a mechanism that links dynamic thresholds with forecasts, water resource utilization is improved by 15%. Furthermore, the daily rolling iteration of this solution ensures accurate prediction even under extreme weather conditions, making it easy to promote and apply in multiple regions.

[0169] Example 2 Figure 2 This is a schematic diagram of the farmland water demand prediction and irrigation control device provided in Embodiment 2 of this application. Figure 2 As shown, the device includes: The data acquisition module 201 is used to acquire historical daily meteorological data of the target farmland, daily meteorological data that has occurred in the current year, meteorological forecast data for the future preset period, evaporation, soil moisture, crop type and geological structure characteristics; The correction parameter determination module 202 is used to determine the geological water storage correction parameters based on the geological structure characteristics. The model configuration module 203 is used to construct a daily water balance model suitable for the current crop type based on historical daily meteorological data, soil moisture and geological water storage correction parameters, and to complete the model parameter calibration. The daily forecasting module 204 is used to make daily forecasts of the field water conditions for a future preset period, taking the current year's field water conditions as initial conditions and combining the dynamic changes of crops throughout their growth period with the water balance model. The control scheme generation module 205 is used to generate irrigation or drainage control schemes when the prediction results deviate from the preset suitable range. The total water demand prediction module 206 is used to output the prediction results of the irrigation system and the total annual water demand for the entire growth period based on the irrigation or drainage control scheme.

[0170] The farmland water demand prediction and irrigation control device in this application embodiment can be a system, or a component, integrated circuit, or chip in a terminal. The system can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0171] The farmland water demand prediction and irrigation control device in this application embodiment can be a system with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0172] The farmland water demand prediction and irrigation control device provided in this application embodiment can realize the various processes of the above embodiments, and will not be described again here to avoid repetition.

[0173] Example 3 like Figure 3 As shown, this application embodiment also provides an electronic device 300, including a processor 301, a memory 302, and a program or instructions stored in the memory 302 and executable on the processor 301. When the program or instructions are executed by the processor 301, they implement the various processes of the above-described farmland water demand prediction and irrigation control method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0174] It should be noted that the electronic devices in the embodiments of this application include mobile electronic devices and non-mobile electronic devices as described above.

[0175] Example 4 This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described embodiments of the farmland water demand prediction and irrigation control method, and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0176] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0177] Example 5 This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiments of the farmland water demand prediction and irrigation control method, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0178] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0179] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element. Furthermore, it should be noted that the scope of the methods and systems in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0180] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0181] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above, which are merely illustrative and not restrictive. Those skilled in the art, under the guidance of this application, can make many modifications without departing from the spirit and scope of the claims, all of which fall within the protection scope of this application.

[0182] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A method for predicting farmland water demand and controlling irrigation, characterized in that, The method includes: Acquire historical daily meteorological data of the target farmland, daily meteorological data that has occurred in the current year, meteorological forecast data for the future preset period, evaporation, soil moisture, crop type and geological structure characteristics; Based on the geological structure characteristics, determine the crop water requirement, drainage capacity, and geological water storage correction parameters; Based on historical daily meteorological data, soil moisture, crop water requirements, drainage, and geological water storage correction parameters, a daily water balance model suitable for the current crop type was constructed, and the model parameters were calibrated. Using the current year's field water conditions as initial conditions, and combining the dynamic changes of crops throughout their growth period, the water balance model is used to predict the field water conditions for a preset future period on a daily basis. When the predicted field moisture conditions deviate from the preset suitable range, an irrigation or drainage control plan is generated. Based on the irrigation or drainage control scheme, output the irrigation system and annual total water demand prediction results for the entire growth period.

2. The method according to claim 1, characterized in that, The process of determining geological water storage correction parameters based on the geological structural characteristics includes: Based on the geological structure characteristics of the farmland area, obtain the stratum dip angle, bedrock burial depth and soil permeability characteristics; Based on the dip angle of the strata, determine whether there is a significant lateral runoff trend; If there is a significant lateral runoff trend, increase the weight of the geological water storage correction parameters; among them, for farmland on the slopes beside mountains, reduce the effective water storage depth in combination with the bedrock burial depth.

3. The method according to claim 1, characterized in that, The daily water balance model includes: For paddy fields, a water layer balance relationship is constructed with the change of surface water layer as the core, and a geological water storage correction parameter is introduced into the relationship to deduct lateral seepage loss; For drought-resistant crops, a soil moisture balance relationship is constructed with the change in the planned soil wetting layer water storage as the core, and a geological water storage correction parameter is introduced into this relationship to correct the groundwater recharge.

4. The method according to claim 1, characterized in that, The completion of model parameter calibration includes: Based on historical daily meteorological data, the water requirement coefficients for each growth stage of crops were determined. Based on soil moisture conditions, the upper and lower limits of soil moisture content and the planned depth of the wetting layer were determined. Based on geological water storage correction parameters, the lateral runoff loss ratio is calibrated to ensure that the error between the historical simulation values ​​and the field measured values ​​meets the preset accuracy.

5. The method according to claim 1, characterized in that, The method for determining the dynamic changes of the crop throughout its entire growth period includes: Identify the current growth stage of crops by conducting field leaf age surveys or remote sensing vegetation index inversion; Establish a correlation between growth stages and water requirement coefficients, tighten the irrigation trigger threshold during the booting to heading stage, and relax the drainage trigger threshold during the seedling stage.

6. The method according to claim 1, characterized in that, The method of using the water balance model to predict field moisture conditions daily for a predetermined period includes: Using the current field moisture conditions as the initial condition, input the meteorological forecast data for the future preset time period, and calculate the predicted field moisture value on a daily basis; The forecast results for the current day are used as the initial conditions for the forecast of the next period, and the process is repeated in a rolling iteration. When future weather forecasts are updated, the forecasting steps are re-executed based on the new forecast data to achieve dynamic updates of the forecast results.

7. The method according to claim 1, characterized in that, When the predicted field moisture conditions deviate from the preset suitable range, an irrigation or drainage control plan is generated, including: When field moisture is predicted to be below the suitable range, determine the irrigation time and amount; When field moisture is predicted to be above the suitable range, determine the drainage time and drainage volume; The regulation plan is coupled and optimized with future weather forecasts. If heavy rainfall is forecast in the future, the water level in the fields will be lowered in advance.

8. The method according to claim 1, characterized in that, The water balance model also includes: Obtain the crop leaf area index and divide the crop field water requirement into soil evaporation component and vegetation transpiration component; The vegetation cover influence factor is calculated based on the leaf area index. The weight of the transpiration component is reduced when the vegetation cover is low, and the weight of the transpiration component is increased when the vegetation cover is high.

9. A device for predicting farmland water demand and controlling irrigation, characterized in that, The device includes: The data acquisition module is used to acquire historical daily meteorological data of the target farmland, daily meteorological data that has occurred in the current year, meteorological forecast data for the future preset period, evaporation, soil moisture, crop type and geological structure characteristics; The correction parameter determination module is used to determine the crop water requirement, drainage volume, and geological water storage correction parameters based on the geological structure characteristics. The model configuration module is used to construct a daily water balance model suitable for the current crop type based on historical daily meteorological data, soil moisture, crop water requirements, drainage, and geological water storage correction parameters, and to complete the model parameter calibration. The daily forecasting module is used to forecast the field water conditions for a future preset period using the current year's existing field water conditions as initial conditions, combined with the dynamic changes of crops throughout their growth period, and the water balance model. The control scheme generation module is used to generate irrigation or drainage control schemes when the prediction results deviate from the preset suitable range. The total water demand prediction module is used to output the prediction results of the irrigation system and annual total water demand for the entire growth period based on the irrigation or drainage control scheme.

10. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the farmland water demand prediction and irrigation control method as described in any one of claims 1-8.

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