Irrigation water management method and system based on digital twin

Abstract

The present invention discloses a method and system for irrigation water management in an irrigation area based on digital twins, and relates to the field of irrigation water management. The irrigation water management method for an irrigation area based on digital twins obtains irrigation area data, and the irrigation area data includes irrigation area crop growth cycle information, irrigation area historical climate information, irrigation area soil information, and several irrigation methods in the irrigation area; the irrigation area historical climate information and irrigation area soil information are respectively predicted and analyzed to obtain irrigation area crop growth cycle climate prediction information and irrigation area soil prediction information. The present invention can optimize irrigation strategies according to predicted climate change and soil moisture conditions, so that water resources can be more accurately allocated to where they are needed, reducing excessive irrigation and water resource waste. By simulating different irrigation schemes, the irrigation method that best suits current and future climate conditions can be selected, thereby ensuring that every drop of water resources is effectively utilized.

Description

Technical Field

[0001] The present invention relates to the field of irrigation water management, and specifically to an irrigation area irrigation water management method and system based on digital twins. Background Art

[0002] Irrigation water management is a vital part of agricultural production, especially in arid and semi-arid areas. Reasonable irrigation management is of great significance to ensure the stability of agricultural production and improve the efficiency of water resource utilization. Traditional irrigation management methods mainly rely on historical experience and manual operation, and there are problems such as decision-making lag and water waste. With the development of information technology and Internet of Things technology, the application of digital twin technology in agriculture has gradually received attention.

[0003] Digital twin technology is an integrated technology based on physical entities, data, models and algorithms. By establishing a digital mirror of the physical entity, it realizes real-time interaction and information sharing between the physical entity and the digital model. The application of digital twin technology in irrigation water management can monitor field parameters such as soil moisture, meteorological conditions, and water flow in real time through IoT devices, and reflect the actual conditions of the irrigation area in real time in the virtual space. Through data analysis and simulation, different irrigation strategies can be predicted and optimized, guiding actual irrigation operations, and improving irrigation efficiency and water resource utilization.

[0004] In the existing technology, traditional irrigation methods often find it difficult to accurately adjust the amount of water to meet the specific needs of crops at different growth stages, which can easily lead to water resource waste or insufficient water supply. In addition, traditional irrigation systems often find it difficult to effectively predict and adapt to climate change, especially in the context of frequent extreme weather events. Irrigation strategies lack flexibility and foresight. At the same time, traditional irrigation management usually tends to ignore various information such as climate data, soil data and crop needs, which leads to incomplete information in the decision support system and inefficient irrigation decision-making. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an irrigation water management method and system for irrigation areas based on digital twins, which solves the problem that traditional irrigation methods are often difficult to accurately adjust the amount of water to meet the specific needs of crops at different growth stages, which easily leads to waste of water resources or insufficient water supply, and traditional irrigation systems are often difficult to effectively predict and adapt to climate change, especially in the context of frequent extreme weather events. Irrigation strategies lack flexibility and foresight. At the same time, traditional irrigation management usually tends to ignore various information such as climate data, soil data and crop needs, which leads to incomplete information in the decision support system and inefficient irrigation decision-making.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for irrigation water management in irrigation areas based on digital twins, comprising the following steps: obtaining irrigation area data, the irrigation area data including irrigation area crop growth cycle information, irrigation area historical climate information, irrigation area soil information, and several irrigation methods in the irrigation area; predicting and analyzing the irrigation area historical climate information and irrigation area soil information respectively to obtain irrigation area crop growth cycle climate prediction information and irrigation area soil prediction information; inputting the irrigation area crop growth cycle climate prediction information and irrigation area data into a pre-established digital twin model for simulation analysis to obtain water use prediction benefit indicators for each irrigation method every day in the future growth cycle of the crop, and performing comparative screening, while generating a water use plan for the future growth cycle of the crop; real-time monitoring of the generated water use plan for the future growth cycle of the crop, and real-time adjustment based on the monitoring results.

[0007] Furthermore, the crop growth cycle information of the irrigation area includes crop growth interval information and crop growth stage water consumption information, the crop growth interval information is specifically the crop growth time interval, the crop growth stage water consumption information is specifically the daily water demand value of each stage of crop growth, the historical climate information of the irrigation area is specifically the historical temperature data and historical precipitation of each day in the crop growth cycle of the irrigation area in each historical year, the historical temperature data is specifically the historical temperature value of each time period, the soil information of the irrigation area is specifically the historical soil moisture value of each day in the crop growth cycle of the irrigation area in each historical year, the crop growth cycle climate prediction information of the irrigation area is specifically the future temperature data and future precipitation of each day in the future growth cycle of the irrigation area crops, the future temperature data is specifically the future temperature value of each time period, the soil prediction information of the irrigation area is specifically the future soil moisture value of each day in the future growth cycle of the irrigation area crops, and the water use plan is specifically the predicted irrigation method for each growth stage in the future growth cycle of the crops.

[0008] Furthermore, the historical climate information and soil information of the irrigation area are predicted and analyzed respectively, and the specific process of obtaining the climate prediction information and soil prediction information of the irrigation area crop growth cycle is as follows: the historical temperature values ​​and historical precipitation values ​​of each day in the historical crop growth cycle of the irrigation area and the historical soil moisture values ​​of each day in the historical crop growth cycle of the irrigation area are read respectively, and the mean, weighted mean method and moving exponential mean method are used to perform mean prediction analysis respectively, to obtain the future temperature values ​​and future precipitation values ​​of each day in the future growth cycle of the irrigation area crops and the future soil moisture values ​​of each day in the future growth cycle of the irrigation area crops.

[0009] Furthermore, the specific formulas for calculating the future temperature value and future precipitation value of each day in the future growth cycle of the irrigated crops and the soil moisture value of each day in the future growth cycle of the irrigated crops are as follows:

[0010] ;in, The first The future temperature value of the day, For the history The historical first Historical temperature values ​​for the day, The first The future precipitation of the day, For the history The historical first Historical precipitation for the day, For the history The historical first The weighting coefficient of the historical precipitation of the day, The first Future soil moisture values ​​for the day, For the history The historical first Historical soil moisture values ​​for the day, For the history The historical first The weighting coefficient of the historical soil moisture value of the day, For the history The historical first Historical soil moisture values ​​for the day, For the history The historical first The weighting coefficient of the historical soil moisture value of the day, , , is the total number of days in the crop growth cycle in the irrigation area, , The total number of historical years to obtain.

[0011] Furthermore, the specific steps for obtaining the predicted water use benefit index of each irrigation method every day in the future growth cycle of the crop are as follows: the future temperature value and future precipitation value of each day in the future growth cycle of the crop in the irrigation area and the future soil moisture value of each day in the future growth cycle of the crop in the irrigation area are input into the pre-established digital twin model to simulate and analyze the daily irrigation water use index and crop water demand satisfaction index of each irrigation method in the future growth cycle of the crop; the daily irrigation water use index and crop water demand satisfaction index of each irrigation method in the future growth cycle of the crop are normalized respectively, and weighted calculation is performed based on the normalization results respectively to obtain the predicted water use benefit index of each irrigation method every day in the future growth cycle of the crop, and the calculation formula is as follows: ;in, For the The effect of the irrigation method on the future growth cycle of the crop The daily water use forecast benefit index, After normalization The effect of the irrigation method on the future growth cycle of the crop Irrigation water index for the day, For the preset The weighted coefficient of irrigation water index for each irrigation method is After normalization The effect of the irrigation method on the future growth cycle of the crop The crop water demand of the day meets the index, For the preset The weighted coefficient of the crop water demand satisfaction index of each irrigation method, , , The type of irrigation method.

[0012] Furthermore, the specific formula for calculating the daily irrigation water index of each irrigation method in the future growth cycle of the crop is as follows: ;in, For the The effect of the irrigation method on the future growth cycle of the crop Irrigation water index for the day, for The effect of the irrigation method on the future growth cycle of the crop The total irrigation water volume per day, for The effect of the irrigation method on the future growth cycle of the crop The future precipitation of the day, for The effect of the irrigation method on the future growth cycle of the crop The effective water use efficiency of the day, for The effect of the irrigation method on the future growth cycle of the crop Future soil moisture values ​​for the day, for The effect of the irrigation method on the future growth cycle of the crop Field water capacity per day, for The effect of the irrigation method on the future growth cycle of the crop The amount of water evaporation per day.

[0013] Furthermore, the specific process of comparing and screening and generating a water use plan for the future growth cycle of crops is as follows: under the constraint of meeting the daily water demand value at each stage of crop growth, the predicted water use benefit indicators of each irrigation method for each day in the future growth cycle of crops are compared and judged to obtain the maximum predicted benefit indicator of water use for each day in the future growth cycle of crops, and the corresponding irrigation method is marked as the predicted irrigation method.

[0014] Furthermore, the water use plan for the future growth cycle of the crops is monitored in real time, and real-time adjustments are made according to the monitoring results. Specifically, the real-time temperature value, real-time air humidity value, and real-time precipitation of the irrigation area are obtained in real time, and input into the digital twin model for real-time analysis of the actual water use benefit indicators of each irrigation method; it is determined whether the predicted water use benefit indicators of the current irrigation method are consistent with the actual water use benefit indicators; if the predicted water use benefit indicators of the current irrigation method do not meet the actual water use benefit indicators, it is determined whether there is an irrigation method with an actual water use benefit indicator higher than the predicted water use benefit indicator of the current irrigation method; if there is an irrigation method with an actual water use benefit indicator higher than the predicted water use benefit indicator of the current irrigation method, the corresponding irrigation method will replace the current irrigation method.

[0015] An irrigation water management system for irrigation areas based on digital twins, comprising: a data acquisition subsystem, a prediction subsystem, a simulation analysis and generation subsystem, and a monitoring and adjustment subsystem; the data acquisition subsystem is used to acquire irrigation area data, and the irrigation area data includes information on crop growth cycles, historical climate information, soil information, and several irrigation methods in the irrigation area;

[0016] The prediction subsystem is used to predict and analyze the historical climate information and soil information of the irrigation area respectively, so as to obtain the climate prediction information and soil prediction information of the irrigation area crop growth period; the simulation analysis and generation subsystem is used to input the climate prediction information of the irrigation area crop growth period into a pre-established digital twin model for simulation analysis, so as to obtain the water use prediction benefit index of each irrigation method every day in the future growth cycle of the crop, and perform comparative screening, and generate a water use plan for the future growth cycle of the crop; the monitoring and adjustment subsystem is used to monitor the water use plan for the future growth cycle of the crop in real time, and make real-time adjustments according to the monitoring results.

[0017] Furthermore, the simulation analysis and generation subsystem includes: an index calculation module, a water use prediction benefit index calculation module, and a water use plan generation module; the index calculation module is used to analyze and calculate based on the future temperature value and future precipitation of each day in the future growth cycle of the crops in the irrigation area and the future soil moisture value of each day in the future growth cycle of the crops in the irrigation area, and obtain the irrigation water use index and the crop water demand satisfaction index respectively; the water use prediction benefit index calculation module is used to normalize the irrigation water use index and the crop water demand satisfaction index, and perform weighted calculation based on the normalization result to obtain the water use prediction benefit index; the water use plan generation module is used to compare and judge the water use prediction benefit index of each irrigation method every day in the future growth cycle of the crops, and generate the maximum water use prediction benefit index for each day in the future growth cycle of the crops based on the judgment result, and generate the predicted irrigation method for each growth stage of the future growth cycle of the crops, that is, the water use plan for the future growth cycle of the crops.

[0018] The present invention has the following beneficial effects:

[0019] (1) This irrigation water management method based on digital twins can optimize irrigation strategies according to predicted climate change and soil moisture conditions, so that water resources can be allocated more accurately to where they are needed, reducing excessive irrigation and water waste. By simulating different irrigation schemes, it can select the irrigation method that best suits current and future climate conditions, thereby ensuring that every drop of water resources is effectively used.

[0020] (2) This irrigation water management method based on digital twins can effectively respond to sudden extreme climate events by real-time monitoring of climate conditions and timely adjustment of irrigation plans. It can adjust irrigation strategies through continuously updated climate data to ensure that crops can still obtain sufficient water under extreme climate conditions such as drought or rainy seasons, thereby reducing the negative impact of extreme weather on crop yields.

[0021] (3) This irrigation water management method based on digital twins ensures that crops obtain the most suitable water conditions during their growth cycle by precisely controlling the water supply at each growth stage, which helps to improve the health and growth quality of crops and thus increase the overall yield of crops. At the same time, by predicting and adjusting irrigation plans, the water needs of crops at different growth stages are better met, which helps to maximize the growth potential and market value of crops.

[0022] (4) The irrigation water management system based on digital twins has greatly enhanced the responsiveness and flexibility of irrigation management through highly integrated data acquisition, prediction, simulation analysis, monitoring and adjustment subsystems. Through the integration of real-time data and model analysis, the system can predict impending changes in conditions and adjust irrigation plans in advance, thereby ensuring that crops can obtain the best water supply under any climatic conditions.

[0023] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of an irrigation water management method for an irrigation area based on digital twins according to the present invention.

[0025] Figure 2 The present invention is a flowchart of the specific steps for obtaining the water use prediction benefit index of each irrigation method every day in the future growth cycle of crops in an irrigation area irrigation water management method based on digital twin.

[0026] Figure 3 This is a block diagram of an irrigation water management system for an irrigation area based on digital twins according to the present invention. DETAILED DESCRIPTION

[0027] The embodiment of the present application uses an irrigation water management method and system for irrigation areas based on digital twins to solve the problem that traditional irrigation methods often find it difficult to accurately adjust the amount of water to meet the specific needs of crops at different growth stages, which can easily lead to waste of water resources or insufficient water supply, and traditional irrigation systems often find it difficult to effectively predict and adapt to climate change, especially in the context of frequent extreme weather events. Irrigation strategies lack flexibility and foresight. At the same time, traditional irrigation management usually tends to ignore various information such as climate data, soil data and crop needs, which leads to incomplete information in the decision support system and inefficient irrigation decision-making.

[0028] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0029] First, historical and real-time data of the irrigation area are collected, including crop growth cycle information, historical climate information, soil information, and irrigation methods. Then, these historical data are analyzed, and statistical or machine learning methods are used to predict future climate and soil conditions. The predicted climate and soil information is then input into the digital twin model. The model simulates the daily irrigation water efficiency indicators under different irrigation methods, and compares and screens the optimal irrigation strategy based on the simulation results. Based on this, a water use plan that meets the crop growth needs and climatic conditions is generated. Finally, the implemented irrigation plan is monitored in real time, including real-time climate change, soil moisture, and crop growth conditions. Based on the comparison results of real-time data and predicted data, the irrigation strategy is dynamically adjusted to ensure the optimization of irrigation efficiency and crop growth.

[0030] See also Figure 1 The embodiment of the present invention provides a technical solution: a method for irrigation water management in an irrigation area based on digital twins, comprising the following steps: obtaining irrigation area data, the irrigation area data including irrigation area crop growth cycle information, irrigation area historical climate information, irrigation area soil information, and several irrigation methods in the irrigation area; predicting and analyzing the irrigation area historical climate information and irrigation area soil information respectively to obtain irrigation area crop growth cycle climate prediction information and irrigation area soil prediction information; inputting the irrigation area crop growth cycle climate prediction information and irrigation area data into a pre-established digital twin model for simulation analysis to obtain water use prediction benefit indicators for each irrigation method every day in the future crop growth cycle, and performing comparative screening, while generating a water use plan for the future crop growth cycle; real-time monitoring of the generated crop water use plan for the future crop growth cycle, and real-time adjustment based on the monitoring results.

[0031] The crop growth cycle information of the irrigation area includes crop growth interval information and crop growth stage water consumption information. The crop growth interval information is specifically the crop growth time interval, such as February to June. The crop growth stage water consumption information is specifically the daily water demand value of each stage of crop growth. The historical climate information of the irrigation area is specifically the historical temperature data and historical precipitation of each day in the historical crop growth cycle of the irrigation area every year. The historical temperature data is specifically the historical temperature value of each time period. The irrigation method information is specifically drip irrigation, sprinkler irrigation, micro-irrigation, etc. The soil information of the irrigation area is specifically the historical soil moisture value of each day in the historical crop growth cycle of the irrigation area every year. The crop growth cycle climate prediction information of the irrigation area is specifically the future temperature data and future precipitation of each day in the future growth cycle of the irrigation area crops. The future temperature data is specifically the future temperature value of each time period. The soil prediction information of the irrigation area is specifically the future soil moisture value of each day in the future growth cycle of the irrigation area crops. The water use plan is specifically the predicted irrigation method for each growth stage of the future growth cycle of the crops.

[0032] The pre-establishment process of the digital twin model is as follows: First, take the water use prediction benefit index as the specific goal that the digital twin model needs to achieve, collect necessary historical data and real-time data, including crop growth information, climate data, soil conditions and irrigation methods, etc., clean, process and integrate the collected data to ensure the accuracy and consistency of the data, use the hybrid model as the model framework, and build the model structure according to the collected data and preset rules. This may include setting different parameters, variables and logical relationships of the simulation, calibrating the model with historical data to ensure that the output of the model is consistent with the actual observations, and verifying the accuracy and reliability of the model by comparing the model prediction results with the known data. Based on the preliminary operation results of the model, make necessary adjustments and optimizations to improve the prediction accuracy and response speed of the model, integrate the digital twin model into the irrigation management system, and ensure that the model can work seamlessly with other system components.

[0033] Specifically, the historical climate information and soil information of the irrigation area are predicted and analyzed respectively, and the specific process of obtaining the climate prediction information and soil prediction information of the irrigation area crop growth cycle is as follows: the historical temperature value and historical precipitation of each day in the historical crop growth cycle of the irrigation area and the historical soil moisture value of each day in the historical crop growth cycle of the irrigation area are read respectively, and the mean, weighted mean method and moving exponential mean method are used to perform mean prediction analysis respectively, to obtain the future temperature value and future precipitation of each day in the future growth cycle of the irrigation area crops and the future soil moisture value of each day in the future growth cycle of the irrigation area crops, and the specific formulas for calculating the future temperature value and future precipitation of each day in the future growth cycle of the irrigation area crops and the future soil moisture value of each day in the future growth cycle of the irrigation area crops are as follows: ;in, The first The future temperature value of the day is calculated by averaging the future temperature values ​​of each period of the day. For the history The historical first The historical temperature value of the day is calculated by averaging the historical temperature values ​​of each period of the day. The first The future precipitation of the day, For the history The historical first Historical precipitation for the day, For the history The historical first The weighted coefficient of the historical precipitation of the day is calculated by the proportion of the precipitation in the year to the total precipitation in the historical years. The first Future soil moisture values ​​for the day, For the history The historical first Historical soil moisture values ​​for the day, For the history The historical first The weighting coefficient of the historical soil moisture value of the day, For the history The historical first Historical soil moisture values ​​for the day, For the history The historical first The weighting coefficient of the historical soil moisture value of the day, , and the calculation methods of the two are similar to the calculation In a similar way, , is the total number of days in the crop growth cycle in the irrigation area, , The total number of historical years to obtain.

[0034] In this implementation scheme, by using historical daily temperature, precipitation and soil moisture data and applying a variety of statistical methods such as mean, weighted mean method and moving exponential mean method, future climate and soil conditions can be predicted more accurately. This method that integrates multiple prediction techniques can provide more accurate trend analysis and change patterns, making the prediction results more reliable. Accurate future climate and soil moisture predictions allow irrigation district managers to formulate or adjust irrigation plans in advance and use water resources in the most efficient way. This not only helps ensure that crops get enough water at critical growth stages, but also helps avoid excessive irrigation and waste of water resources. By analyzing historical data to predict future climate conditions, irrigation district management systems can better adapt to climate change. This ability is particularly important for agricultural production because climate change may lead to significant changes in precipitation patterns and temperature conditions, which directly affect irrigation demand and crop production. Accurate forecast information provides strong decision-making support for irrigation water management in irrigation areas. Managers can formulate irrigation strategies based on forecast results to optimize the growing environment of crops and the allocation of water resources. This data-based decision-making process greatly enhances the scientific nature and effectiveness of management. Through effective water resource management and adaptation to future conditions, this management method helps to improve the sustainability of the entire agricultural ecosystem. It not only ensures the current water resource efficiency, but also lays a solid foundation for long-term water resource management, ensuring water security for future generations.

[0035] Specifically, Figure 2 As shown in the figure, the specific steps for obtaining the predicted water use benefit index of each irrigation method every day in the future growth cycle of crops are as follows: the future temperature value and future precipitation value of each day in the future growth cycle of crops in the irrigation area and the future soil moisture value of each day in the future growth cycle of crops in the irrigation area are input into the pre-established digital twin model to simulate and analyze the daily irrigation water use index and crop water demand satisfaction index of each irrigation method in the future growth cycle of crops; the daily irrigation water use index and crop water demand satisfaction index of each irrigation method in the future growth cycle of crops are normalized respectively, and weighted calculation is performed based on the normalization results respectively to obtain the predicted water use benefit index of each irrigation method every day in the future growth cycle of crops, and the calculation formula is as follows: ;in, For the The effect of the irrigation method on the future growth cycle of the crop The daily water use forecast benefit index, After normalization The effect of the irrigation method on the future growth cycle of the crop Irrigation water index for the day, For the preset The weighted coefficient of irrigation water index for each irrigation method is After normalization The effect of the irrigation method on the future growth cycle of the crop The crop water demand of the day meets the index, For the preset The weighted coefficient of the crop water demand satisfaction index of each irrigation method, , , The type of irrigation method.

[0036] In this implementation scheme, by inputting daily climate data, such as temperature and precipitation, and soil moisture values ​​into the digital twin model, and analyzing the degree to which each irrigation method meets the crop water demand, the system is able to develop the most suitable irrigation strategy for each crop. This personalized irrigation plan takes into account the specific needs of the crop and environmental conditions, thereby optimizing the use of water resources, ensuring that each crop can obtain the most suitable water during its growth cycle, and improving the growth quality and yield of the crop. Through simulation analysis and normalization processing, the benefits of each irrigation method can be quantified, and the optimal irrigation method can be determined through weighted calculation, which not only reduces the waste of water resources, but also ensures the effective allocation of water resources. By accurately calculating the water use forecast for each irrigation method every day, By measuring benefit indicators, irrigation systems can be dynamically adjusted to adapt to changing climate conditions and soil moisture, thereby maximizing the use of water resources. Reasonable irrigation plans and optimized water resource management can help reduce the pressure of agricultural activities on natural resources, especially in areas with water shortages. By precisely controlling the amount of irrigation, the risk of groundwater decline and soil degradation can be significantly reduced, while reducing the loss of fertilizers and pesticides, which are usually associated with excessive irrigation. With the increase in extreme climate events caused by climate change, such as droughts and heavy rains, traditional irrigation strategies may no longer be effective. This irrigation management system based on real-time data and advanced simulation can respond quickly to climate change and adjust irrigation plans in real time, thereby protecting crops from adverse climate conditions.

[0037] Specifically, the specific formula for calculating the daily irrigation water index of each irrigation method in the future growth cycle of the crop is as follows: ;in, For the The effect of the irrigation method on the future growth cycle of the crop Irrigation water index for the day, for The effect of the irrigation method on the future growth cycle of the crop The total irrigation water volume per day, for The effect of the irrigation method on the future growth cycle of the crop The future precipitation of the day, for The effect of the irrigation method on the future growth cycle of the crop The effective water use rate of the day refers to the proportion of water from irrigation and precipitation that actually reaches the roots of crops and is available for use by crops. It can be estimated based on historical data, experimental data or expert experience. for The effect of the irrigation method on the future growth cycle of the crop The future soil moisture value of the day, that is, the soil moisture before irrigation, for The effect of the irrigation method on the future growth cycle of the crop The field water holding capacity is the maximum amount of water that the soil can hold without causing water seepage, and is closely related to the soil type. It is predetermined by testing the soil physical properties. for The effect of the irrigation method on the future growth cycle of the crop The daily water evaporation is obtained by analyzing the maximum temperature, minimum temperature and temperature coefficient adjusted according to local climatic conditions, and the temperature coefficient is between 0.5 and 0.8.

[0038] The specific formula for calculating the daily crop water demand satisfaction index for each irrigation method during the future crop growth cycle is as follows: ;in, For the The effect of the irrigation method on the future growth cycle of the crop The crop water demand of the day meets the index, for The effect of the irrigation method on the future growth cycle of the crop The potential evapotranspiration per day is the evapotranspiration of a hypothetical grassland surface with sufficient water supply under specific climatic conditions, which can be calculated by the Penman-Monteith equation. The crop coefficient varies according to the type and growth stage of the crop and reflects the evapotranspiration requirement of the actual crop relative to a reference crop, such as grassland. The value of the crop coefficient is usually based on extensive agricultural research and field testing and can be found in guidance documents from the agricultural sector or publications from international organizations such as FAO.

[0039] In this embodiment, by calculating the irrigation water use index and crop water demand satisfaction index corresponding to each irrigation method, it is possible to ensure that each crop obtains the right amount of water during its growth cycle. This precise water management helps to optimize the growth conditions of crops, thereby directly improving the health and yield of crops. The optimization of irrigation water not only reduces the waste of water resources, but also ensures that crops obtain sufficient water during key growth stages, thereby improving the quality and market value of crops. By incorporating future climate variables, such as temperature and precipitation, and soil moisture values ​​into the formulation of irrigation strategies, it is possible to more effectively cope with uncertainties caused by climate change. This method enables agricultural producers to adjust irrigation plans in advance to cope with expected droughts or excessive rainfall, thereby reducing the negative impact of extreme weather conditions on crop production. Calculating and implementing irrigation strategies based on actual water demand and environmental conditions helps to reduce excessive irrigation and related environmental problems, such as soil salinization, water pollution, and groundwater level decline. By optimizing irrigation methods, water waste can be significantly reduced while protecting natural resources and ecosystem health. By introducing complex calculation and simulation methods, the system transforms traditional irrigation management into an intelligent irrigation system that can automatically adjust irrigation plans, respond to environmental changes in real time, reduce human intervention, and improve operational efficiency and accuracy.

[0040] Specifically, the specific process of comparing and screening and generating a water use plan for the future growth cycle of crops is as follows: under the constraint of meeting the daily water demand value at each stage of crop growth, the predicted water use benefit indicators of each irrigation method for each day in the future growth cycle of crops are compared and judged to obtain the maximum predicted benefit indicator of water use for each day in the future growth cycle of crops, and the corresponding irrigation method is marked as the predicted irrigation method.

[0041] In this implementation scheme, the optimal irrigation method for each day is selected based on a systematic comparison of water use prediction benefit indicators to ensure that the use of water resources achieves maximum benefits. This means that each irrigation is optimized according to the environmental conditions and crop needs of the day, thereby improving the efficiency of water resource use and the growth effect of crops. By selecting the most appropriate irrigation method for each day of each growth stage, it can be ensured that crops receive a uniform and most appropriate water supply during their growth cycle. This method of precisely controlling water helps to improve the stability of crop growth and reduce stress during the growth process, thereby improving the overall quality and yield of crops. Systematically evaluating the benefits of each irrigation method and selecting the best option can effectively reduce the waste of water resources caused by non-optimal irrigation strategies, which not only saves water resources, but also reduces energy consumption and other costs associated with irrigation operations.

[0042] Specifically, real-time monitoring is carried out for the water use plan for the future growth cycle of crops, and real-time adjustments are made according to the monitoring results. Specifically, the real-time temperature value, real-time air humidity value, and real-time precipitation of the irrigation area are obtained in real time, and input into the digital twin model for real-time analysis of the actual water use benefit indicators of each irrigation method; it is determined whether the predicted water use benefit indicators of the current irrigation method are consistent with the actual water use benefit indicators; if the predicted water use benefit indicators of the current irrigation method do not meet the actual water use benefit indicators, it is determined whether there is an irrigation method with an actual water use benefit indicator higher than the predicted water use benefit indicator of the current irrigation method; if there is an irrigation method with an actual water use benefit indicator higher than the predicted water use benefit indicator of the current irrigation method, the corresponding irrigation method will replace the current irrigation method.

[0043] In this implementation scheme, by real-time monitoring of key climate parameters such as temperature, humidity, and precipitation, and inputting this data into the digital twin model for analysis, the system can accurately assess current environmental conditions and adjust irrigation strategies to match these conditions. This approach ensures that irrigation volume and timing are always closely aligned with actual weather conditions and soil requirements, thereby improving water use efficiency and crop water absorption efficiency. Rapid changes in environmental conditions are one of the main challenges facing modern agriculture. The system dynamically adjusts irrigation plans through real-time data, enabling agricultural producers to respond quickly to sudden weather changes, such as unexpected rainfall or heat waves, thereby reducing potential losses caused by sudden changes in conditions. The system adjusts irrigation methods based on real-time analysis results. Resource waste, such as over-irrigation or inefficient use of water resources, can be avoided, which not only saves water resources, but also reduces energy consumption for pumping and distributing water resources, and other operating costs, making the entire irrigation process more economical and efficient. Accurate irrigation can significantly improve crop growth conditions and avoid stress caused by excessive or insufficient water. Timely and appropriate irrigation helps improve the overall health and productivity of crops, thereby increasing yields and improving the market quality of crops. Real-time adjustment of irrigation plans not only improves the efficiency of water resources, but also helps protect the environment and reduce pressure on natural resources, supporting sustainable development goals. By reducing excessive irrigation, it can also reduce the impact of agriculture on surrounding ecosystems, such as reducing the loss of fertilizers and pesticides and protecting groundwater quality.

[0044] See also Figure 3The embodiment of the present invention provides a technical solution: an irrigation water management system for an irrigation area based on a digital twin, comprising: a data acquisition subsystem, a prediction subsystem, a simulation analysis and generation subsystem, and a monitoring and adjustment subsystem; the data acquisition subsystem is used to acquire irrigation area data, the irrigation area data including the irrigation area crop growth cycle information, the irrigation area historical climate information, the irrigation area soil information, and several irrigation methods of the irrigation area; the prediction subsystem is used to respectively perform prediction analysis on the irrigation area historical climate information and the irrigation area soil information, and obtain the irrigation area crop growth cycle climate prediction information and the irrigation area soil prediction information; the simulation analysis and generation subsystem is used to input the irrigation area crop growth cycle climate prediction information into a pre-established digital twin model for simulation analysis, obtain the water use prediction benefit index of each irrigation method every day in the future crop growth cycle, and perform comparative screening, and generate a water use plan for the future crop growth cycle; the monitoring and adjustment subsystem is used to perform real-time monitoring on the generated crop water use plan for the future growth cycle, and make real-time adjustments based on the monitoring results.

[0045] Specifically, the simulation analysis and generation subsystem includes: an index calculation module, a water use prediction benefit index calculation module, and a water use plan generation module; the index calculation module is used to analyze and calculate based on the future temperature value and future precipitation value of each day in the future growth cycle of the irrigation area crops and the future soil moisture value of each day in the future growth cycle of the irrigation area crops, and obtain the irrigation water use index and the crop water demand satisfaction index respectively; the water use prediction benefit index calculation module is used to normalize the irrigation water use index and the crop water demand satisfaction index, and perform weighted calculation based on the normalization result to obtain the water use prediction benefit index; the water use plan generation module is used to compare and judge the water use prediction benefit index of each irrigation method every day in the future growth cycle of the crop, and generate the maximum water use prediction benefit index for each day in the future growth cycle of the crop based on the judgment result, and generate the predicted irrigation method for each growth stage of the future growth cycle of the crop, that is, the water use plan for the future growth cycle of the crop.

[0046] In this implementation scheme, by accurately analyzing future climate conditions such as temperature, precipitation, and soil moisture every day, the system can quantitatively evaluate irrigation water use indicators and crop water demand satisfaction indicators for each irrigation method. This precise analysis ensures that irrigation strategies can specifically correspond to the physiological needs and environmental conditions of crops, thereby improving the adaptability and survival rate of crops to adverse climate change. Through normalization and weighted calculation, water use prediction benefit indicators can comprehensively evaluate the comprehensive benefits of each irrigation method and help select the most water-saving and effective irrigation strategy. This not only optimizes the use of water resources and reduces water waste, but also reduces the energy and financial costs caused by unnecessary excessive irrigation. The water use plan generation module automatically generates the optimal irrigation strategy, reducing The need for human intervention has improved the operational efficiency of the irrigation system. Automated irrigation plans can ensure timely response to environmental changes, realize intelligent irrigation management, and enhance the overall reliability and efficiency of the system. The various modules within the system work together to provide comprehensive data support for irrigation management, from climate to soil moisture to every detail of irrigation needs. This data-driven decision support helps to plan irrigation scientifically and ensure that each decision is based on the most accurate information, thereby maximizing the potential and quality of crop production. By ensuring that irrigation water is highly matched to crop needs, the system helps agricultural producers achieve more sustainable irrigation practices. This finely controlled irrigation method helps protect the environment and reduce over-exploitation of natural resources while supporting long-term agricultural production efficiency and sustainability.

[0047] In summary, this application has at least the following effects:

[0048] It can optimize irrigation strategies based on predicted climate change and soil moisture conditions, so that water resources can be allocated more accurately to where they are needed, reducing over-irrigation and water waste. By simulating different irrigation plans, it can select the irrigation method that best suits current and future climate conditions, ensuring that every drop of water resources is effectively used.

[0049] It can effectively respond to sudden extreme climate events by real-time monitoring of climate conditions and timely adjustment of irrigation plans. It can adjust irrigation strategies through continuously updated climate data to ensure that crops can still get enough water under extreme climate conditions such as drought or rainy seasons, thereby reducing the negative impact of extreme weather on crop yields.

[0050] By precisely controlling the water supply at each growth stage and ensuring that crops obtain the most suitable water conditions during their growth cycle, it helps to improve the health and growth quality of crops, thereby increasing the overall yield of crops. At the same time, by predicting and adjusting irrigation plans, the water needs of crops at different growth stages are better met, which helps to maximize the growth potential and market value of crops.

[0051] Through highly integrated data acquisition, prediction, simulation analysis, monitoring and adjustment subsystems, the responsiveness and flexibility of irrigation management are greatly enhanced. Through the integration of real-time data and model analysis, the system can predict impending changes in conditions and adjust irrigation plans in advance to ensure that crops receive the best water supply under any climatic conditions.

[0052] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for irrigation water management in irrigation areas based on digital twins, characterized in that: The following steps are involved: Acquire irrigation area data, wherein the irrigation area data includes crop growth cycle information, historical climate information, soil information, and several irrigation methods in the irrigation area; The historical climate information and soil information of the irrigation area are predicted and analyzed respectively to obtain the climate prediction information of the crop growth period and the soil prediction information of the irrigation area; Input the climate forecast information of the crop growth cycle in the irrigation area and the irrigation area data into the pre-established digital twin model for simulation analysis, obtain the water use forecast benefit index of each irrigation method every day in the future crop growth cycle, and compare and screen, and generate the water use plan in the future crop growth cycle; Monitor the water use plan for future crop growth cycles in real time and make real-time adjustments based on the monitoring results; The crop growth cycle information of the irrigation area includes crop growth interval information and crop growth stage water consumption information, the crop growth interval information is specifically the crop growth time interval, the crop growth stage water consumption information is specifically the daily water demand value of each stage of crop growth, the irrigation area historical climate information is specifically the historical temperature data and historical precipitation of each day in the irrigation area crop growth cycle of each year in history, the historical temperature data is specifically the historical temperature value of each time period, the irrigation area soil information is specifically the historical soil moisture value of each day in the irrigation area crop growth cycle of each year in history, the irrigation area crop growth cycle climate prediction information is specifically the future temperature data and future precipitation of each day in the future growth cycle of the irrigation area crops, the future temperature data is specifically the future temperature value of each time period, the irrigation area soil prediction information is specifically the future soil moisture value of each day in the future growth cycle of the irrigation area crops, and the water use plan is specifically the predicted irrigation method of each growth stage in the future growth cycle of the crops. The specific steps to obtain the predicted benefit index of water use for each irrigation method every day in the future growth cycle of the crop are as follows: The future temperature values ​​and future precipitation values ​​of each day in the future growth cycle of the crops in the irrigation area, as well as the future soil moisture values ​​of each day in the future growth cycle of the crops in the irrigation area, are input into the pre-established digital twin model to simulate and analyze the daily irrigation water use index and crop water demand satisfaction index of each irrigation method in the future growth cycle of the crops; The daily irrigation water use index and crop water demand satisfaction index of each irrigation method in the future crop growth cycle are normalized respectively, and weighted calculations are performed based on the normalized results to obtain the water use prediction benefit index of each irrigation method in the future crop growth cycle. The calculation formula is as follows: Ysy ki =b k1 *GYs′ ki +b k2 *ZWx′ ki ; Among them, Ysy ki GYs′ is the predicted benefit index of water use for the kth irrigation method on the ith day in the future crop growth cycle, ki is the normalized irrigation water index of the kth irrigation method on the i-th day in the future growth cycle of the crop, β k1 is the weighted coefficient of the irrigation water index of the preset k-th irrigation method, ZWx′ ki is the normalized crop water demand satisfaction index of the kth irrigation method on the i-th day in the future growth cycle of the crop, β k2 is the weighted coefficient of the crop water demand satisfaction index of the preset k-th irrigation method, β k1 +β k2 =1, k = 1, 2, 3, ..., U, U is the type of irrigation method; The specific formula for calculating the daily irrigation water use index for each irrigation method during the future crop growth cycle is as follows: Among them, GYs ki Gg is the irrigation water index of the kth irrigation method on the i-th day in the future growth cycle of the crop, ki is the total irrigation water volume of k irrigation methods on the i-th day in the future growth cycle of the crop, WJ ki is the future precipitation on the i-th day in the future growth cycle of the crop under k irrigation methods, SL ki Ws is the effective water utilization rate of k irrigation methods on the i-th day in the future growth cycle of the crop, k(i-1) is the future soil moisture value of the i-th day in the future crop growth cycle under k irrigation methods, CS ki is the field water holding capacity of k irrigation methods on the i-th day in the future growth cycle of the crop, ZF ki is the amount of water evaporation on the i-th day in the future growth cycle of the crop under k irrigation methods; The specific formula for calculating the daily crop water demand satisfaction index for each irrigation method during the future crop growth cycle is as follows: Among them, ZWx ki Qz is the crop water demand satisfaction index of the kth irrigation method on the i-th day in the future growth cycle of the crop, ki is the potential evapotranspiration of k irrigation methods on the i-th day in the future crop growth cycle, and ZX is the crop coefficient.

2. The irrigation water management method for irrigation areas based on digital twin according to claim 1, characterized in that: The specific process of forecasting and analyzing the historical climate information and soil information of the irrigation area to obtain the climate forecast information of the crop growth period and the soil forecast information of the irrigation area is as follows: The historical temperature values ​​and historical precipitation values ​​for each day in the crop growth cycle of the irrigation area in each year in history, as well as the historical soil moisture values ​​for each day in the crop growth cycle of the irrigation area in each year in history, are read respectively, and mean prediction analysis is performed using the mean, weighted mean method, and moving exponential mean method respectively to obtain the future temperature values ​​and future precipitation values ​​for each day in the future growth cycle of the irrigation area crops, as well as the future soil moisture values ​​for each day in the future growth cycle of the irrigation area crops.

3. The method for irrigation water management in an irrigation area based on digital twin according to claim 2, characterized in that: The specific formulas for calculating the future temperature value and future precipitation value of each day in the future growth cycle of the irrigated crops and the soil moisture value of each day in the future growth cycle of the irrigated crops are as follows: Among them, Ww i is the future temperature value of the ith day in the future growth cycle of the irrigated crop, Lw ji WJ is the historical temperature value of the i-th day in the crop growth cycle of the irrigation area in the j-th year. i is the future precipitation on the ith day in the future growth cycle of the crops in the irrigation area, LJ ji is the historical precipitation on the i-th day of the crop growth cycle in the irrigated area in the j-th year, Q ji Ws is the weighted coefficient of historical precipitation on the i-th day in the crop growth cycle of the irrigation area in the j-th year. i Ls is the future soil moisture value of the ith day in the future growth cycle of the irrigated crop. ji is the historical soil moisture value of the i-th day in the crop growth cycle of the irrigation area in the j-th year, α i1 is the weighted coefficient of the historical soil moisture value on the i-th day of the crop growth cycle in the irrigation area in the j-th year, Ls( j+1 ) i is the historical soil moisture value of the i-th day in the crop growth cycle of the irrigation area in the j+1th year, α i2 is the weighted coefficient of the historical soil moisture value on the i-th day in the crop growth cycle of the irrigation area in the j+1th year, α i1 +α i2 =1, i = 1, 2, 3, ..., N, N is the total number of days in the crop growth cycle of the irrigation area, j = 1, 2, 3, ..., M, M is the total number of historical years obtained.

4. The method for irrigation water management in an irrigation area based on digital twin according to claim 1, characterized in that: The specific process of comparing and screening and generating the water use plan for the future growth cycle of crops is as follows: Under the constraint of meeting the daily water demand value of each stage of crop growth, the predicted water use benefit index of each irrigation method every day in the future growth cycle of crops is compared and judged respectively, and the maximum predicted benefit index of water use for each day in the future growth cycle of crops is obtained, and the corresponding irrigation method is marked as the predicted irrigation method.

5. The method for irrigation water management in an irrigation area based on digital twin according to claim 1, characterized in that: Real-time monitoring of water use plans for future crop growth cycles and real-time adjustments based on monitoring results are carried out as follows: Obtain the real-time temperature, air humidity, and precipitation of the irrigation area in real time, and input them into the digital twin model to analyze the actual water use benefit indicators of each irrigation method in real time; Determine whether the water use prediction benefit index of the current irrigation method is consistent with the actual water use benefit index; If the predicted water use benefit index of the current irrigation method does not meet the actual water use benefit index, determine whether there is an irrigation method whose actual water use benefit index is higher than the predicted water use benefit index of the current irrigation method; if there is an irrigation method whose actual water use benefit index is higher than the predicted water use benefit index of the current irrigation method, replace the current irrigation method with the corresponding irrigation method.

6. An irrigation water management system for an irrigation area based on digital twins, applying the irrigation water management method for an irrigation area based on digital twins according to any one of claims 1 to 5, characterized in that: include: Data acquisition subsystem, prediction subsystem, simulation analysis and generation subsystem, monitoring and adjustment subsystem; The data acquisition subsystem is used to acquire irrigation area data, wherein the irrigation area data includes crop growth cycle information, historical climate information, soil information, and several irrigation methods in the irrigation area. The prediction subsystem is used to predict and analyze the historical climate information and soil information of the irrigation area, respectively, to obtain the climate prediction information of the crop growth period of the irrigation area and the soil prediction information of the irrigation area; The simulation analysis and generation subsystem is used to input the climate prediction information of the crop growth cycle in the irrigation area into the pre-established digital twin model for simulation analysis, obtain the water use prediction benefit index of each irrigation method every day in the future growth cycle of the crop, and conduct comparative screening, and generate the water use plan in the future growth cycle of the crop; The monitoring and adjustment subsystem is used to monitor the water use plan for the future growth cycle of the crop in real time and make real-time adjustments based on the monitoring results.

7. The irrigation water management system for irrigation areas based on digital twin according to claim 6, characterized in that: The simulation analysis and generation subsystem includes: an index calculation module, a water use prediction benefit index calculation module, and a water use plan generation module; The index calculation module is used to analyze and calculate the future temperature value and future precipitation value of each day in the future growth cycle of the crops in the irrigation area and the future soil moisture value of each day in the future growth cycle of the crops in the irrigation area, and obtain the irrigation water index and the crop water demand satisfaction index respectively; The water use prediction benefit index calculation module is used to normalize the irrigation water use index and the crop water demand satisfaction index, and perform weighted calculation based on the normalization result to obtain the water use prediction benefit index; The water use plan generation module is used to compare and judge the water use prediction benefit index of each irrigation method every day in the future growth cycle of the crop, and generate the maximum water use prediction benefit index for each day in the future growth cycle of the crop based on the judgment result, and generate the predicted irrigation method for each growth stage of the future growth cycle of the crop.

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