Variable irrigation decision-making system

Abstract

The invention relates to the technical field of agricultural automation and intelligent irrigation, in particular to a variable irrigation decision-making system, which comprises a meteorological data acquisition module used for acquiring daily meteorological data of a specified farmland in a first time period from the current time period to the future time period; the soil monitoring module is used for monitoring soil data of a specified farmland in real time; the data processing module is used for obtaining the current soil water content of the specified farmland, the evapotranspiration amount of the specified farmland in each day in the first time period in the future and the soil displacement amount of the specified farmland in each day in the first time period in the future based on the meteorological data of the specified farmland in each day from the current time period in the future and the current soil data of the specified farmland; further acquiring the soil water content of the specified farmland after the first preset time period; the control module is used for determining the current irrigation demand quantity of the specified farmland and sending a first irrigation instruction to the irrigation device corresponding to the specified farmland; the first irrigation instruction is an instruction for irrigating the specified farmland according to the irrigation demand quantity of the specified farmland at the current moment.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural automation and intelligent irrigation, and particularly to a variable irrigation decision-making system. Background Art

[0002] With the development of agricultural production, the accuracy and intelligence level of irrigation technology have gradually become one of the key factors for improving water resource utilization efficiency and ensuring the normal growth of crops. Traditional irrigation methods often rely on human experience or fixed time intervals for irrigation, and cannot be dynamically adjusted according to the actual water requirements of farmland, resulting in problems such as water resource waste and uneven crop growth. Therefore, how to accurately calculate and adjust the irrigation amount based on meteorological data, soil data, and crop requirements has become the research focus of modern agricultural irrigation management.

[0003] Currently, many irrigation decision-making systems still have the following problems: lack of accurate identification and adjustment of different crop growth stages; ignoring the comprehensive influence of multi-variables such as meteorological factors and soil humidity; lacking intelligent prediction based on real-time data, resulting in low irrigation efficiency; the system calculation and control mechanism is relatively simple, and it fails to effectively achieve automated and precise irrigation. Summary of the Invention

[0004] In view of the above-mentioned disadvantages and deficiencies of the prior art, the present invention provides a variable irrigation decision-making system.

[0005] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0006] An embodiment of the present invention provides a variable irrigation decision-making system, including:

[0007] A meteorological data acquisition module, configured to acquire the meteorological data of a specified farmland every day within a first time period starting from the current time in the future;

[0008] A soil monitoring module, configured to monitor the soil data of the specified farmland in real time;

[0009] A data processing module, configured to obtain the current soil water content of the specified farmland, the daily evapotranspiration amount within the first time period in the future, and the daily soil drainage amount within the first time period in the future based on the meteorological data of the specified farmland every day within the first time period starting from the current time in the future and the current soil data of the specified farmland, and obtain the soil water content after a first preset time period of the specified farmland according to the current soil water content of the specified farmland, the daily evapotranspiration amount within the first time period in the future, and the daily soil drainage amount within the first time period in the future;

[0010] A control module, configured to determine the irrigation requirement of a specified farmland at the current moment based on the soil water content after a first preset time period of the specified farmland, a preset soil water content threshold, and the predicted rainfall within a second preset time period after the current moment, and send a first irrigation instruction to an irrigation device corresponding to the specified farmland;

[0011] The first irrigation instruction is an instruction to irrigate the specified farmland according to the irrigation requirement of the specified farmland at the current moment.

[0012] Preferably,

[0013] Based on the meteorological data of each day within a first time period starting from the current time of the specified farmland and the current soil data of the specified farmland, a data processing module obtains the current soil water content of the specified farmland, the evapotranspiration amount of each day within the first time period in the future, and the soil drainage amount of each day within the first time period in the future, specifically including:

[0014] The data processing module obtains the evapotranspiration amount of each day within the first time period in the future of the specified farmland based on the meteorological data of each day within the first time period starting from the current time of the specified farmland;

[0015] The data processing module obtains the soil drainage amount of each day within the first time period in the future of the specified farmland based on the meteorological data of each day within the first time period starting from the current time of the specified farmland and the current soil data of the specified farmland.

[0016] Preferably,

[0017] The meteorological data includes: air temperature, humidity, wind speed, solar radiation, precipitation;

[0018] The soil data includes soil water content.

[0019] Preferably,

[0020] The data processing module obtains the evapotranspiration amount of each day within the first time period in the future of the specified farmland based on the meteorological data of each day within the first time period starting from the current time of the specified farmland, specifically including:

[0021] The data processing module obtains the evapotranspiration amount of the specified farmland on the future t-th day based on the meteorological data of the specified farmland on the future t-th day starting from the current time, using formula (1);

[0022] The formula (1) is:

[0023] ET(t) = α T ·f T (T(t)) + α H ·f H (H(t)) + α V ·f V (V(t)) + α θ·f θ (θ(t)) + α C ·f C (C);

[0024] α T is the air temperature weight coefficient; f T (T(t)) is the air temperature function; α H is the humidity weight coefficient; f H (H(t)) is the humidity function; α V is the wind speed weight coefficient; f V (V(t)) is the wind speed function; α θ is the soil water content weight coefficient; f θ (θ(t)) is the soil water content function; α C is the crop weight coefficient in the specified farmland; f C (C) is the crop coefficient function; ET(t) is the evapotranspiration of the specified farmland on the t-th day in the future.

[0025] Preferably,

[0026] The air temperature function f T (T(t)) is:

[0027] f T (T(t)) = T(t), or, f T (T(t)) = T(t) 2 ;

[0028] where T(t) is the average value of the air temperature of the specified farmland on the t-th day in the future;

[0029] The humidity function f H (H(t)) is:

[0030] f H (H(t)) = (100 - H(t)), or, f H (H(t)) = ln(100 - H(t));

[0031] where H(t) is the average value of the humidity of the specified farmland on the t-th day in the future;

[0032] The wind speed function f V (V(t)) is:

[0033] f V (V(t)) = V(t), or, f V (V(t)) = V(t) 2 ;

[0034] where V(t) is the average value of the wind speed of the specified farmland on the t-th day in the future;

[0035] The soil water content function f θ (θ(t)) is as follows:

[0036] f θ (θ(t)) = θ(t), or f θ (θ(t) = lnθ(t);

[0037] where θ(t) is the average value of the soil water content of the specified farmland on the t-th day in the future of the specified farmland;

[0038] The crop coefficient function f C (C) is as follows:

[0039] f C (C) = C, or f C (C) = C 2 ;

[0040] where C is the influence factor of the current growth stage of the crops in the specified farmland on evapotranspiration.

[0041] Preferably,

[0042] where the air temperature weight coefficient α T is calculated by using the meteorological data of each day in the historical time period starting from the current time of the specified farmland and the corresponding first evapotranspiration amount, and adopting formula (2);

[0043] where the formula (2) is:

[0044]

[0045] where, is the average value of the air temperature in the historical time period starting from the current time of the specified farmland;

[0046] a 1x is the average value of the air temperature on the x-th day in the historical time period starting from the current time of the specified farmland;

[0047] is the average value of all the first evapotranspiration amounts in the historical time period starting from the current time of the specified farmland;

[0048] Z x is the value of the first evapotranspiration amount on the x-th day in the historical time period starting from the current time of the specified farmland;

[0049] X is the total number of days in the historical time period;

[0050] where the first evapotranspiration amount on the x-th day in the historical time period starting from the current time of the specified farmland is calculated by using the Penman-Monteith formula for the meteorological data on the x-th day in the historical time period starting from the current time of the specified farmland;

[0051] Humidity weight coefficient α H It is calculated by using formula (3) with the meteorological data of each day within the historical time period starting from the current time of the specified farmland and the corresponding first evapotranspiration amount;

[0052] Among them, the formula (3) is:

[0053]

[0054] Among them, is the average value of humidity within the historical time period starting from the current time of the specified farmland;

[0055] a 2x is the average value of humidity on the x-th day within the historical time period starting from the current time of the specified farmland;

[0056] Wind speed weight coefficient α V It is calculated by using formula (4) with the meteorological data of each day within the historical time period starting from the current time of the specified farmland and the corresponding first evapotranspiration amount;

[0057] Among them, the formula (4) is:

[0058]

[0059] Among them, is the average value of wind speed within the historical time period starting from the current time of the specified farmland;

[0060] a 3x is the average value of wind speed on the x-th day within the historical time period starting from the current time of the specified farmland;

[0061] Soil water content weight coefficient α θ It is calculated by using formula (5) with the soil data of each day within the historical time period starting from the current time of the specified farmland and the corresponding first evapotranspiration amount;

[0062] Among them, the formula (5) is:

[0063]

[0064] Among them, is the average value of soil water content within the historical time period starting from the current time of the specified farmland;

[0065] a 4x is the average value of soil water content on the x-th day within the historical time period starting from the current time of the specified farmland;

[0066] When the growth stage currently corresponding to the crop in the specified farmland is the germination stage, then α C ranges from 0.4 to 0.6; when the growth stage currently corresponding to the crop in the specified farmland is the vegetative growth stage, then αC ranges from 0.7 to 0.85; when the growth stage corresponding to the crop in the designated farmland currently is the flowering stage, then α C ranges from 0.85 to 0.9; when the growth stage corresponding to the crop in the designated farmland currently is the filling stage, then α C ranges from 0.8 to 0.95; when the growth stage corresponding to the crop in the designated farmland currently is the maturity stage, then α C ranges from 0.5 to 0.7.

[0067] Preferably,

[0068] if the growth stage corresponding to the crop in the designated farmland currently is the germination stage, then C = 0.4;

[0069] if the growth stage corresponding to the crop in the designated farmland currently is the vegetative growth stage, then C = 0.8;

[0070] if the growth stage corresponding to the crop in the designated farmland currently is the flowering stage, then C = 1.1;

[0071] if the growth stage corresponding to the crop in the designated farmland currently is the filling stage, then C = 0.9;

[0072] if the growth stage corresponding to the crop in the designated farmland currently is the maturity stage, then C = 0.6.

[0073] Preferably,

[0074] Based on the meteorological data of each day within the first time period starting from the current time of the designated farmland and the current soil data of the designated farmland, the data processing module obtains the daily soil drainage volume of the designated farmland within the first time period in the future, specifically including:

[0075] Using formula (6) for iterative calculation to obtain the average value of the soil water content of the designated farmland each day within the first time period in the future;

[0076] The formula (6) is:

[0077] θ(t + 1) = θ(t) - ET(t) - k(θ(t) - θ max );

[0078] where, θ max represents the field water holding capacity corresponding to the designated farmland;

[0079] θ(t + 1) is the average value of the soil water content of the designated farmland on the (t + 1)-th day in the future of the designated farmland;

[0080] k is an empirically obtained constant;

[0081] Based on the average soil water content of the specified farmland every day within the first future time period and the field water holding capacity corresponding to the specified farmland, the soil drainage volume of the specified farmland on the t-th day within the first future time period is obtained by using formula (7);

[0082] The formula (7) is as follows:

[0083] D t = k(θ(t) - θ max );

[0084] D t is the soil drainage volume of the specified farmland on the t-th day from the current time in the future.

[0085] Preferably,

[0086] According to the current soil water content of the specified farmland, the evapotranspiration amount every day within the first future time period, and the soil drainage volume every day within the first future time period, the soil water content of the specified farmland after the first preset time period is obtained, specifically including:

[0087] According to the current soil water content of the specified farmland, the evapotranspiration amount every day within the first future time period, and the soil drainage volume every day within the first future time period, the soil water content of the specified farmland after the first preset time period is obtained by using formula (8);

[0088] The formula (8) is as follows:

[0089] θ N = θ(0) - (ET(1) + D1) - … - (ET(t) + D t )… - (ET(N) + D N );

[0090] θ(0) is the current soil water content of the specified farmland;

[0091] θ N is the soil water content of the specified farmland on the N-th day from the current time in the future;

[0092] N is the total number of days in the first time period.

[0093] Preferably,

[0094] The control module determines the irrigation demand of the specified farmland at the current moment based on the soil water content of the specified farmland after the first preset time period, the preset soil water content threshold, and the predicted rainfall within the second preset time period after the current moment, specifically including:

[0095] According to the soil water content of the specified farmland after the first preset time period and the predicted rainfall within the second preset time period after the current moment, the first water content value is calculated by using formula (9);

[0096] Among them, the formula (9) is:

[0097] W = θ N + β·P;

[0098] W is the first water content value;

[0099] P is the predicted rainfall within the second preset time period after the current moment;

[0100] β is the influence coefficient of the preset rainfall;

[0101] Judge whether the preset soil water content threshold is greater than the first water content value. If it is greater, based on the preset soil water content threshold and the first water content value, use formula (10) to calculate the irrigation demand of the specified farmland at the current moment;

[0102] Among them, the formula (10) is:

[0103] M = γ·(θ a - W);

[0104] M is the irrigation demand of the specified farmland at the current moment;

[0105] γ is the preset irrigation adjustment coefficient;

[0106] θ a is the preset soil water content threshold.

[0107] The beneficial effects of the present invention are as follows: A variable irrigation decision-making system of the present invention can automatically adjust the irrigation amount according to the real-time state of the farmland by integrating meteorological data, soil data, and crop growth data, ensuring that the crops obtain the best water conditions. Specifically, there are the following beneficial effects:

[0108] A variable irrigation decision-making system of the present invention can accurately predict the future change of soil water content according to various factors such as the soil water content, evapotranspiration, and soil drainage of the specified farmland, and thus formulate a reasonable irrigation demand for the farmland through mathematical models and formulas.

[0109] A variable irrigation decision-making system of the present invention dynamically adjusts the crop coefficient according to the growth stage of the crop (such as the germination stage, flowering stage, maturity stage, etc.), ensuring accurate adjustment of the irrigation amount at different growth stages to meet the specific growth needs of the crop. Through the automatic control of the irrigation device, combined with real-time meteorological prediction and soil data, the system can automatically judge the irrigation demand and issue an irrigation command, reducing human intervention and improving work efficiency and accuracy.

[0110] A variable irrigation decision-making system of the present invention conducts short-term prediction based on future meteorological data, can make irrigation plans in advance, and reasonably arrange the irrigation time and amount, thereby further reducing unnecessary water loss.

[0111] In summary, the variable irrigation decision-making system provided by the present invention effectively improves the accuracy and intelligence level of agricultural irrigation by integrating multi-dimensional data sources and advanced algorithms, solves many problems existing in traditional irrigation methods, has remarkable water-saving and crop yield-increasing effects, and is of great significance to modern agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 It is a schematic diagram of a variable irrigation decision-making system of the present invention;

[0113] Figure 2 It is a schematic diagram of the connection between a variable irrigation decision-making system of the present invention and an irrigation device. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0114] In order to better explain the present invention for easy understanding, the present invention will be described in detail below with reference to the drawings through specific embodiments.

[0115] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more clear and thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0116] Embodiment 1

[0117] Refer to Figure 1 , this embodiment provides a variable irrigation decision-making system, including:

[0118] A meteorological data acquisition module, configured to acquire the meteorological data of each day within the first time period starting from the current time of the specified farmland in the future;

[0119] A soil monitoring module, configured to monitor the soil data of the specified farmland in real time;

[0120] A data processing module, configured to obtain the current soil moisture content of the specified farmland, the evapotranspiration amount of each day within the first time period in the future, and the soil drainage amount of each day within the first time period in the future based on the meteorological data of each day within the first time period starting from the current time of the specified farmland and the current soil data of the specified farmland, and obtain the soil moisture content after the first preset time period of the specified farmland according to the current soil moisture content of the specified farmland, the evapotranspiration amount of each day within the first time period in the future, and the soil drainage amount of each day within the first time period in the future;

[0121] Among them, in this embodiment, the data processing module obtains the current soil moisture content of the designated farmland, the daily evapotranspiration amount within the first time period starting from the current time in the future, and the daily soil drainage amount within the first time period starting from the current time in the future based on the meteorological data of each day within the first time period starting from the current time of the designated farmland and the current soil data of the designated farmland, specifically including:

[0122] The data processing module obtains the daily evapotranspiration amount within the first time period starting from the current time of the designated farmland based on the meteorological data of each day within the first time period starting from the current time of the designated farmland;

[0123] The data processing module obtains the daily soil drainage amount within the first time period starting from the current time of the designated farmland based on the meteorological data of each day within the first time period starting from the current time of the designated farmland and the current soil data of the designated farmland.

[0124] The control module is used to determine the irrigation demand of the designated farmland at the current moment based on the soil moisture content after the first preset time period of the designated farmland, the preset soil moisture content threshold, and the predicted rainfall amount within the second preset time period after the current moment, and send a first irrigation instruction to the irrigation device corresponding to the designated farmland, see Figure 2 ;

[0125] The first irrigation instruction is an instruction to irrigate the designated farmland according to the irrigation demand of the designated farmland at the current moment.

[0126] In this embodiment, the variable irrigation decision-making system in this embodiment makes intelligent decisions based on real-time meteorological data, soil data, and crop requirements, and can accurately calculate the irrigation demand. Through this precise control of the irrigation amount, the common phenomena of over-irrigation or under-irrigation in traditional irrigation methods are avoided, thereby saving water resources and improving the water utilization efficiency.

[0127] For example, in a dry season, the farmland may require a large amount of irrigation water. However, if the weather forecast shows a large amount of precipitation predicted in the next few days, the system will automatically adjust the irrigation demand according to the predicted precipitation amount, avoiding over-irrigation and saving a large amount of water resources.

[0128] The system obtains real-time monitoring of soil humidity and meteorological conditions through the soil monitoring module and the meteorological data acquisition module, ensuring that the monitoring data of the farmland environment by the system is always up-to-date. In addition, based on these real-time data, the system can dynamically adjust the irrigation decision-making to avoid irrigation errors caused by delayed data. If the temperature suddenly rises within a certain period, the system can quickly calculate the change in evapotranspiration amount based on data such as temperature, humidity, and wind speed, so as to increase the irrigation amount in a timely manner and avoid the crops being affected due to insufficient water.

[0129] In this embodiment, the variable irrigation decision-making system can predict the irrigation demand in the future for a period of time in advance and formulate an irrigation plan in advance by combining historical meteorological data, soil moisture content, and predicted meteorological data (such as precipitation, etc.) within a preset future time period. Through this intelligent prediction, it can effectively avoid the high water consumption and high cost caused by temporary irrigation. For example, in the early stage before the upcoming rainy season, the system reduces the irrigation amount in advance according to the precipitation prediction data to avoid unnecessary waste of water resources. This advance planning enables more scientific water management for farmland.

[0130] The precise irrigation system can ensure that crops obtain the optimal water supply during the growth process, avoiding the negative impacts of over-irrigation or insufficient water on crop growth. This not only helps to increase the crop yield but also improves the crop quality. The irrigation demand is different at different growth stages of crops. For example, during the flowering stage, the water demand of crops is relatively high, while the irrigation amount can be appropriately reduced during the maturity stage. In this embodiment, the variable irrigation decision-making system automatically adjusts the irrigation plan according to the crop growth stage to ensure that the crops obtain the required water at each stage, thereby increasing the crop yield and quality.

[0131] In this embodiment, the meteorological data includes: temperature, humidity, wind speed, solar radiation, precipitation; the soil data includes soil moisture content.

[0132] The data processing module obtains the evapotranspiration of the designated farmland every day within the first time period from the current day to the future based on the meteorological data of the designated farmland every day within the first time period from the current day to the future, specifically including:

[0133] The data processing module obtains the evapotranspiration of the designated farmland on the future day t based on the meteorological data of the designated farmland on the future day t from the current day using formula (1);

[0134] The formula (1) is:

[0135] ET(t) = α T ·f T (T(t)) + α H ·f H (H(t)) + α V , ·f V (V(t)) + α θ ·f θ (θ(t)) + α C ·f C (C);

[0136] α T is the air temperature weight coefficient; f T (T(t)) is the air temperature function; α H is the humidity weight coefficient; f H (H(t)) is the humidity function; αV is the wind speed weight coefficient; f V (V(t)) is the wind speed function; α θ is the soil water content weight coefficient; f θ (θ(t)) is the soil water content function; α C is the crop weight coefficient in the specified farmland; f C (C) is the crop coefficient function; ET(t) is the evapotranspiration of the specified farmland on the t-th day in the future.

[0137] In this embodiment, the evapotranspiration in formula (1) is a function of multiple factors, including the air temperature weight coefficient α T , the air temperature function f T (T(t)), the humidity weight coefficient α H , the humidity function f H (H(t)), the wind speed weight coefficient α V , the wind speed function f V (V(t)), the soil water content weight coefficient α θ , the soil water content function f θ (θ(t)), the crop weight coefficient α in the specified farmland C , the crop coefficient function f C (C). These variables cover the main factors affecting evapotranspiration, such as: air temperature affecting the evaporation rate, humidity reflecting the saturation of water vapor in the air, wind speed affecting evaporation and transpiration, soil water content restricting the evaporation source, and crop coefficient combining the growth requirements of specific crops. Therefore, the variable irrigation decision system in this embodiment comprehensively considers meteorological, soil and crop characteristics, avoiding prediction deviation caused by a single factor. Dynamically adapting to different climate conditions and crop demands, improving the accuracy and universality of prediction results.

[0138] The air temperature function f T (T(t)) is:

[0139] f T (T(t)) = T(t), or, f T (T(t)) = T(t) 2 ;

[0140] wherein, T(t) is the average value of the air temperature of the specified farmland on the t-th day in the future;

[0141] The humidity function f H (H(t)) is:

[0142] f H (H(t)) = (100 - H(t)), or, f H (H(t)) = ln(100 - H(t));

[0143] Among them, H(t) is the average humidity of the specified farmland on the t-th day in the future;

[0144] The wind speed function f V (V(t)) is:

[0145] f V (V(t)) = V(t), or, f V (V(t)) = V(t) 2 ;

[0146] Among them, V(t) is the average wind speed of the specified farmland on the t-th day in the future;

[0147] The soil water content function f θ (θ(t)) is:

[0148] f θ (θ(t)) = θ(t), or, f θ (θ(t)) = lnθ(t);

[0149] Among them, θ(t) is the average soil water content of the specified farmland on the t-th day in the future;

[0150] The crop coefficient function fC(C) is:

[0151] f C (C) = C, or, f C (C) = C 2 ;

[0152] Among them, C is the influence factor of the current growth stage of the crops in the specified farmland on evapotranspiration.

[0153] Among them, the air temperature weight coefficient α T is calculated by using the meteorological data of each day in the historical time period starting from the current time of the specified farmland and the corresponding first evapotranspiration amount, using formula (2);

[0154] Among them, the formula (2) is:

[0155]

[0156] Among them, is the average air temperature in the historical time period starting from the current time of the specified farmland;

[0157] a 1x is the average air temperature on the x-th day in the historical time period starting from the current time of the specified farmland;

[0158] is the average of all the first evapotranspiration amounts in the historical time period starting from the current time of the specified farmland;

[0159] Zx is the value of the first evapotranspiration on the x-th day within the historical time period starting from the current time for the specified farmland;

[0160] X is the total number of days in the historical time period;

[0161] Among them, the first evapotranspiration on the x-th day within the historical time period starting from the current time for the specified farmland is calculated by using the Penman-Monteith formula for the meteorological data on the x-th day within the historical time period starting from the current time for the specified farmland;

[0162] In this embodiment, by introducing the influence of air temperature on evapotranspiration, the water requirement of crops can be predicted more accurately, thereby optimizing irrigation management. Considering the temperature changes under different weather conditions makes the model more flexible and applicable to different climate regions. Air temperature is one of the important factors affecting evapotranspiration, and its change directly affects the water evaporation rate. By quantifying this influence, the irrigation strategy can be better adjusted to meet the crop needs.

[0163] Humidity weight coefficient α H is calculated by using formula (3) for the meteorological data and the corresponding first evapotranspiration on each day within the historical time period starting from the current time for the specified farmland;

[0164] Among them, the formula (3) is:

[0165]

[0166] Among them, is the average humidity within the historical time period starting from the current time for the specified farmland;

[0167] a 2x is the average humidity on the x-th day within the historical time period starting from the current time for the specified farmland;

[0168] In this embodiment, humidity also significantly affects the evapotranspiration process. Adding the humidity weight coefficient helps to further improve the accuracy of the model. It allows adjusting the irrigation plan according to different humidity conditions and reducing water resource waste. There is a direct correlation between air temperature and evapotranspiration. Evaporation slows down under high humidity conditions and speeds up under low humidity. Therefore, considering the humidity factor can make irrigation decisions more scientific and reasonable.

[0169] Wind speed weight coefficient α V is calculated by using formula (4) for the meteorological data and the corresponding first evapotranspiration on each day within the historical time period starting from the current time for the specified farmland;

[0170] Among them, the formula (4) is:

[0171]

[0172] Among them, is the average wind speed within the historical time period starting from the current time for the specified farmland;

[0173] a 3x is the average wind speed on the x-th day within the historical time period starting from the current time for the specified farmland;

[0174] In this embodiment, the wind speed affects the air flow velocity and thus changes the evaporation rate. Through the wind speed weight coefficient, the irrigation water volume can be better controlled to avoid excess or deficiency. It helps to reduce water resource loss caused by improper irrigation and promotes sustainable development. An increase in wind speed will accelerate the evaporation of surface moisture and reduce the soil moisture content. Accurately evaluating the role of wind speed can help formulate a more reasonable irrigation schedule.

[0175] Soil water content weight coefficient α θ is calculated by using formula (5) based on the soil data of each day within the historical time period starting from the current time for the specified farmland and the corresponding first evapotranspiration amount;

[0176] wherein, the said formula (5) is:

[0177]

[0178] wherein, is the average soil water content within the historical time period starting from the current time for the specified farmland;

[0179] a 4x is the average soil water content on the x-th day within the historical time period starting from the current time for the specified farmland;

[0180] directly reflects the actual water holding capacity of the soil, ensuring that each irrigation can achieve the best effect, neither wasting nor lacking water. Maintaining an appropriate soil moisture level is beneficial to root development and improves crop yield and quality. Soil water content is a key indicator determining the available water for plants. By monitoring and adjusting this parameter, optimal water supply can be achieved to support the healthy growth of crops.

[0181] When the growth stage corresponding to the crop in the specified farmland is the germination stage, then α C ranges from 0.4 to 0.6; when the growth stage corresponding to the crop in the specified farmland is the vegetative growth stage, then α C ranges from 0.7 to 0.85; when the growth stage corresponding to the crop in the specified farmland is the flowering stage, then α C ranges from 0.85 to 0.9; when the growth stage corresponding to the crop in the specified farmland is the filling stage, then α C ranges from 0.8 to 0.95; when the growth stage corresponding to the crop in the specified farmland is the maturity stage, then α C ranges from 0.5 to 0.7.

[0182] In this embodiment, the water requirements of crops at different growth stages vary significantly, and customized weight coefficients can better meet the requirements of each stage. Providing appropriate water supply during the germination period, vegetative growth period, flowering period, filling period, and maturity period helps to promote crop growth and improve the quality and yield of the final product. The water requirements and response mechanisms of each stage in the crop growth cycle are different. For example, appropriately increasing the water supply during the vegetative growth period can promote the rapid growth of plants; while appropriately reducing the water during the maturity period helps the fruits to mature. Therefore, it is very necessary to dynamically adjust the irrigation strategy according to the growth stage.

[0183] If the growth stage corresponding to the crops in the designated farmland is the germination period, then C = 0.4;

[0184] If the growth stage corresponding to the crops in the designated farmland is the vegetative growth period, then C = 0.8;

[0185] If the growth stage corresponding to the crops in the designated farmland is the flowering period, then C = 1.1;

[0186] If the growth stage corresponding to the crops in the designated farmland is the filling period, then C = 0.9;

[0187] If the growth stage corresponding to the crops in the designated farmland is the maturity period, then C = 0.6.

[0188] In this embodiment, the impact factor C of evapotranspiration by crops at different growth stages in the designated farmland indicates that the water requirements of crops at different growth stages are different. For example, less water is needed during the germination period to prevent root rot; while more water is needed during the vegetative growth period to support rapid growth. Customized irrigation strategies can better meet these needs. Appropriate water supply helps crops obtain the best growth conditions during each critical period, thus improving the quality and yield of the final product. There are significant differences in the water requirements of crops at different growth stages, and this difference has been confirmed by agricultural scientific research. Therefore, adjusting the irrigation strategy according to different growth stages has sufficient scientific basis. Many modern agricultural practices have also proven that fine irrigation management can significantly improve crop growth conditions and economic benefits. Modern sensor technology and data analysis capabilities make it possible to monitor and respond to crop needs in real time, providing technical support for implementing such precise irrigation.

[0189] Germination period (C = 0.4): At this stage, the water requirement of the crop is low, and too much water may cause root diseases. The lower C value ensures that the water supply will not be excessive, protecting the healthy growth of seedlings.

[0190] Vegetative growth stage (C = 0.8): This is a crucial period for the rapid growth of crops. Appropriate increase in water supply can promote plant development without wasting water resources.

[0191] Flowering stage (C = 1.1): Flowering is an important stage in the reproductive growth of crops. Sufficient water is crucial for pollen dissemination and successful pollination. A higher C value ensures sufficient water supply and promotes high yields.

[0192] Filling stage (C = 0.9): During this stage, the crop begins to accumulate dry matter. Appropriate water helps to fill the fruits or seeds, but excessive water should be avoided to prevent excessive growth.

[0193] Maturity stage (C = 0.6): As the harvest approaches, the water requirement of the crop gradually decreases. Reducing the C value helps the crop to mature and reduces the later irrigation cost.

[0194] In this embodiment, the data processing module obtains the daily soil drainage volume of the specified farmland in the first time period from the current time based on the daily meteorological data of the specified farmland in the first time period from the current time and the current soil data of the specified farmland, specifically including:

[0195] Using formula (6) for iterative calculation to obtain the average value of the daily soil water content of the specified farmland in the first time period from the current time;

[0196] The formula (6) is:

[0197] θ(t + 1) = θ(t) - ET(t) - k(θ(t) - θ max );

[0198] where, θ max represents the field capacity corresponding to the specified farmland;

[0199] θ(t + 1) is the average value of the soil water content of the specified farmland on the (t + 1)-th day in the future of the specified farmland;

[0200] k is an empirically obtained constant;

[0201] In this embodiment, formula (6) predicts the future soil water content by iterative calculation, considering the current soil water content, evapotranspiration, and the difference from the field capacity.

[0202] Based on the average value of the daily soil water content of the specified farmland in the first time period from the current time and the field capacity corresponding to the specified farmland, formula (7) is used to obtain the soil drainage volume of the specified farmland on the t-th day in the first time period from the current time;

[0203] The formula (7) is:

[0204] D t= k(θ(t) - θ max );

[0205] D t is the soil drainage volume of the designated farmland on the t-th day in the future starting from the current time.

[0206] In this embodiment, formula (7) predicts the soil drainage volume by calculating the difference between the current soil water content and the field capacity and multiplying it by the empirical constant k.

[0207] In this embodiment, through iterative calculation using formula (6), the current soil water content, evapotranspiration, and the difference from the field capacity are considered to ensure more accurate prediction of the soil water content. By calculating the difference between the current soil water content and the field capacity using formula (7) and multiplying it by the empirical constant k, the soil drainage volume is predicted, thus achieving precise irrigation.

[0208] In summary, these formulas achieve more precise and efficient irrigation management by accurately predicting the soil water content and drainage volume, not only improving agricultural production efficiency but also promoting the development of resource-saving agriculture.

[0209] Obtaining the soil water content of the designated farmland after the first preset time period based on the current soil water content of the designated farmland, the daily evapotranspiration in the first future time period, and the daily soil drainage volume in the first future time period specifically includes:

[0210] Based on the current soil water content of the designated farmland, the daily evapotranspiration in the first future time period, and the daily soil drainage volume in the first future time period, use formula (8) to obtain the soil water content of the designated farmland after the first preset time period;

[0211] The said formula (8) is:

[0212] θ N = θ(0) - (ET(1) + D1) - … - (ET(t) + D t ) … - (ET(N) + D N );

[0213] θ(0) is the current soil water content of the designated farmland;

[0214] θ N is the soil water content of the designated farmland on the N-th day in the future starting from the current time;

[0215] N is the total number of days in the first time period.

[0216] In the initial condition of formula (8) in this embodiment, θ(0) represents the current soil water content. The daily evapotranspiration ET(t) and the soil drainage volume D tIt will be deducted from the current soil water content to reflect the actual water loss. Cumulative effect: By accumulating these water losses day by day, the soil water content on the Nth day in the future can be accurately predicted.

[0217] In this embodiment, by considering the evapotranspiration and soil drainage, this method can more accurately predict the future soil water content and avoid the errors caused by relying only on current data. The daily updated data (evapotranspiration and drainage) make the prediction model more flexible and can adapt to weather changes and changes in crop growth status in a timely manner. By predicting the soil water content in the next N days, farmers can make irrigation plans in advance and optimize the water management throughout the growth cycle.

[0218] The control module determines the irrigation demand of the specified farmland at the current moment based on the soil water content after the first preset time period of the specified farmland, the preset soil water content threshold, and the predicted rainfall within the second preset time period after the current moment, specifically including:

[0219] According to the soil water content after the first preset time period of the specified farmland and the predicted rainfall within the second preset time period after the current moment, the first water content value is calculated using formula (9);

[0220] Among them, the formula (9) is:

[0221] W = θ N +β·P;

[0222] W is the first water content value; P is the predicted rainfall within the second preset time period after the current moment; β is the influence coefficient of the preset rainfall;

[0223] Judge whether the preset soil water content threshold is greater than the first water content value. If it is greater, then based on the preset soil water content threshold and the first water content value, the irrigation demand of the specified farmland at the current moment is calculated using formula (10);

[0224] Among them, the formula (10) is:

[0225] M = γ·(θ a -W);

[0226] M is the irrigation demand of the specified farmland at the current moment; γ is the preset irrigation adjustment coefficient; θ a is the preset soil water content threshold.

[0227] In this embodiment, since the soil water content in different time periods has a significant impact on crop growth, these relationships have been confirmed by agricultural scientific research. Through formulas (9) and (10), the future soil water content can be predicted more accurately, and the irrigation amount can be adjusted according to actual needs.

[0228] A variable irrigation decision-making system in this embodiment can accurately predict the future change of soil water content based on various factors such as the soil water content, evapotranspiration, and soil drainage of the designated farmland through mathematical models and formulas, so as to formulate a reasonable irrigation demand for the farmland.

[0229] A variable irrigation decision-making system in this embodiment dynamically adjusts the crop coefficient according to the growth stages of the crops (such as the germination stage, flowering stage, maturity stage, etc.) to ensure accurate adjustment of the irrigation amount in different growth stages and meet the specific growth needs of the crops. Through the automatic control of the irrigation device, combined with real-time weather prediction and soil data, the system can automatically judge the irrigation demand and issue irrigation instructions, reducing human intervention and improving work efficiency and accuracy.

[0230] Embodiment 2

[0231] This embodiment also provides a variable irrigation decision-making method, which is executed by the variable irrigation decision in Embodiment 1.

[0232] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0233] In the present invention, unless otherwise clearly defined and limited, the terms "installation", "connection", "connection", "fixation" and other terms should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium; it can be the internal communication of two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0234] In the present invention, unless otherwise clearly defined and limited, a first feature being "on" or "under" a second feature may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact via an intermediate medium. Moreover, a first feature being "above", "over" and "on top of" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the horizontal height of the first feature is higher than that of the second feature. A first feature being "under", "below" and "beneath" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the horizontal height of the first feature is lower than that of the second feature.

[0235] In the description of this specification, the descriptions of terms such as "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples", etc., mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0236] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A variable irrigation decision system, characterized in that: include: A meteorological data acquisition module is used to obtain the meteorological data of the specified farmland every day within the first time period in the future; Soil monitoring module, used to monitor soil data of designated farmland in real time; A data processing module is used to obtain the current soil moisture content of the designated farmland, the daily evapotranspiration in the first time period in the future, and the daily soil drainage in the first time period in the future based on the daily meteorological data of the designated farmland in the first time period in the future and the current soil data of the designated farmland, and to obtain the soil moisture content of the designated farmland after the first preset time period according to the current soil moisture content of the designated farmland, the daily evapotranspiration in the first time period in the future, and the daily soil drainage in the first time period in the future; A control module, configured to determine the irrigation demand of the designated farmland at the current moment based on the soil moisture content of the designated farmland after a first preset time period and a preset soil moisture threshold and the predicted rainfall within a second preset time period after the current moment, and to issue a first irrigation instruction to the irrigation device corresponding to the designated farmland; The first irrigation instruction is an instruction to irrigate the designated farmland according to the irrigation demand of the designated farmland at the current moment.

2. The variable irrigation decision system according to claim 1, characterized in that: The data processing module obtains the current soil moisture content of the designated farmland, the daily evapotranspiration in the first time period in the future, and the daily soil drainage in the first time period in the future based on the daily meteorological data of the designated farmland and the current soil data of the designated farmland, specifically including: The data processing module obtains the daily evapotranspiration of the designated farmland within the first time period in the future based on the daily meteorological data of the designated farmland within the first time period in the future from the current time; The data processing module obtains the daily soil drainage volume of the designated farmland within the first time period from now to the future based on the daily meteorological data of the designated farmland and the current soil data of the designated farmland.

3. The variable irrigation decision system according to claim 2, characterized in that: The meteorological data include: temperature, humidity, wind speed, solar radiation, and precipitation; The soil data includes soil moisture content.

4. The variable irrigation decision system according to claim 3, characterized in that: The data processing module obtains the daily evapotranspiration of the specified farmland in the first time period in the future based on the daily meteorological data of the specified farmland from the current time period in the future, specifically including: The data processing module uses formula (1) to obtain the evapotranspiration of the designated farmland on the tth day in the future based on the meteorological data of the designated farmland from the current day to the tth day in the future; The formula (1) is: ET(t)=α T ·f T (T(t))+α H ·f H (H(t))+α V ·f V (V(t))+α θ ·f θ (θ(t))+α C ·f C (C); α T is the temperature weight coefficient; f T (T(t)) is the temperature function; α H is the humidity weight coefficient; f H (H(t)) is the humidity function; α V is the wind speed weight coefficient; f V (V(t)) is the wind speed function; α θ is the weight coefficient of soil moisture content; f θ (θ(t)) is the soil moisture function; α C is the crop weight coefficient in the specified farmland; f C (C) is the crop coefficient function; ET(t) is the evapotranspiration of the specified farmland on the tth day in the future.

5. The variable irrigation decision system according to claim 4, characterized in that: Temperature function f T (T(t)) is: f T (T(t)) = T(t), or, fT(T(t)) = T(t) 2 ; Where T(t) is the average temperature of the specified farmland on the tth day in the future; Humidity function f H (H(t)) is: f H (H(t)) = (100 - H(t)), or, f H (H(t)) = ln(100 - H(t)); Among them, H(t) is the average humidity of the specified farmland on the tth day in the future; Wind speed function f V (V(t)) is: f V (V(t)) = V(t), or, f V (V(t)) = V(t) 2 ; Among them, V(t) is the average wind speed of the specified farmland on the tth day in the future; Soil moisture function f θ (θ(t)) is: f θ (θ(t))=θ(t),or,f θ (θ(t))=lnθ(t); Among them, θ(t) is the average soil moisture content of the specified farmland on the tth day in the future; The crop coefficient function fC(C) is: f C (C) = C, or f C (C)=C 2 ; Among them, C is the influencing factor of the current growth stage of the crops in the specified farmland on evapotranspiration.

6. The variable irrigation decision system according to claim 5, characterized in that: Among them, the temperature weight coefficient α T It is calculated by specifying the meteorological data of each day in the historical period from the current time of the farmland and the corresponding first evapotranspiration using formula (2); Wherein, the formula (2) is: in, It is the average temperature of the specified farmland in the historical period from the current time; a 1x The average temperature of the xth day in the historical period starting from the current time of the specified farmland; It is the average value of all first evapotranspiration of the specified farmland in the historical period from the current time; Z x It is the value of the first evapotranspiration of the specified farmland on the xth day in the historical time period from the current time. X is the total number of days in the historical period; Among them, the first evapotranspiration of the designated farmland on the xth day in the historical time period from the current time is calculated by using the Penman-Monteith formula for the meteorological data of the xth day in the historical time period from the current time of the designated farmland; Humidity weight coefficient α H It is calculated by specifying the meteorological data of each day in the historical period from the current time of the farmland and the corresponding first evapotranspiration using formula (3); Wherein, the formula (3) is: in, It is the average humidity of the specified farmland in the historical period from the current time; a 2x The average humidity of the specified farmland on the xth day in the historical period from the current time; Wind speed weight coefficient α V It is calculated by specifying the meteorological data of each day in the historical period from the current time of the farmland and the corresponding first evapotranspiration using formula (4); Wherein, the formula (4) is: in, It is the average wind speed of the specified farmland in the historical period from the current time; a 3x The average wind speed of the specified farmland on the xth day in the historical period from the current time; Soil moisture weight coefficient α θ It is calculated by specifying the soil data of the farmland for each day in the historical period from the current time and the corresponding first evapotranspiration using formula (5); Wherein, the formula (5) is: in, It is the average value of soil moisture content of the specified farmland in the historical period from the current time; a 4x It is the average soil moisture content of the specified farmland on the xth day in the historical period from the current time; When the current growth stage of the crop in the specified farmland is the germination stage, α C The range is 0.4-0.6; when the current growth stage of the crop in the specified farmland is the vegetative growth stage, then α C The range is 0.7-0.85; when the current growth stage of the crop in the specified farmland is the flowering stage, then α C The range is 0.85-0.9; when the current growth stage of the crop in the specified farmland is the filling stage, then α C The range is 0.8-0.95; when the current growth stage of the crop in the specified farmland is the maturity stage, then α C The range is 0.5-0.

7.

7. The variable irrigation decision system according to claim 6, characterized in that: The current growth stage of the crops in the designated farmland is the budding stage, then C = 0.4; The current growth stage of the crops in the designated farmland is the vegetative growth stage, then C = 0.8; The current growth stage of the crops in the designated farmland is the flowering stage, then C = 1.1; The current growth stage of the crops in the designated farmland is the grain filling stage, then C = 0.9; The current growth stage of the crops in the designated farmland is the maturity stage, then C = 0.

6.

8. The variable irrigation decision system according to claim 7, characterized in that: The data processing module obtains the daily soil drainage of the specified farmland within the first time period from now to the future based on the daily meteorological data of the specified farmland and the current soil data of the specified farmland, specifically including: Formula (6) is used for iterative calculation to obtain the average value of soil moisture content of the specified farmland every day in the first time period in the future; The formula (6) is: θ(t+1)=θ(t)-ET(t)-k(θ(t)-θ max ); Among them, θ max Indicates the field water capacity corresponding to the specified farmland; θ(t+1) is the average soil moisture content of the specified farmland on the future day t+1; k is a pre-acquired empirical constant; Based on the average value of the soil moisture content of the specified farmland in the first time period in the future and the field water holding capacity corresponding to the specified farmland, the soil drainage volume of the specified farmland on the tth day in the first time period in the future is obtained using formula (7); The formula (7) is: D t =k(θ(t)-θ max ); D t It is the soil drainage of the specified farmland on the tth day in the future from the current time.

9. The variable irrigation decision system according to claim 8, characterized in that: The soil moisture content of the designated farmland after the first preset time period is obtained according to the current soil moisture content of the designated farmland, the daily evapotranspiration in the first time period in the future, and the daily soil drainage in the first time period in the future, specifically including: According to the current soil moisture content of the designated farmland, the daily evapotranspiration in the first time period in the future, and the daily soil drainage in the first time period in the future, the soil moisture content of the designated farmland after the first preset time period is obtained using formula (8); The formula (8) is: θ N =θ(0)(ET(1)+D1)-…-(ET(t)+D t )…-(AND(N)+D N ); θ(0) is the current soil moisture content of the specified farmland; θ N The soil moisture content of the specified farmland N days from the current time; N is the total number of days in the first time period.

10. The variable irrigation decision system according to claim 9, characterized in that: The control module determines the irrigation demand of the designated farmland at the current moment based on the soil moisture content of the designated farmland after the first preset time period and the preset soil moisture threshold and the predicted rainfall within the second preset time period after the current moment, specifically including: According to the soil moisture content of the designated farmland after the first preset time period and the predicted rainfall in the second preset time period after the current moment, the first moisture content value is calculated using formula (9); Wherein, the formula (9) is: W=θ N +β·P; W is the first water content value; P is the predicted rainfall in the second preset time period after the current moment; β is the influence coefficient of the pre-set rainfall; Determine whether the preset soil moisture threshold is greater than the first moisture content value; if so, calculate the irrigation demand of the specified farmland at the current moment based on the preset soil moisture threshold and the first moisture content value using formula (10); Wherein, the formula (10) is: M=γ·(θ a -W); M is the irrigation demand of the specified farmland at the current moment; γ is the preset irrigation adjustment coefficient; θ a is a preset soil moisture threshold.

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