Water-saving control method for agricultural irrigation, cloud platform and equipment

Abstract

The invention discloses a water-saving control method for agricultural irrigation, a cloud platform and equipment, and relates to the technical field of irrigation water-saving control, and the method comprises the steps: determining the historical rainfall of the geographic position of a target farmland and the historical soil moisture content parameter of the target farmland; determining farmland water demand rule characteristics of the target farmland based on the historical rainfall and the historical soil moisture content parameters; constructing a farmland water-saving control model according to the farmland water demand rule characteristics; dividing a water demand area of the target farmland based on the crop information; based on the farmland water-saving control model and the soil moisture content parameter of each water demand area, obtaining a predicted irrigation value of each water demand area; acquiring a real-time irrigation value of each water demand area; and based on the predicted irrigation value and the real-time irrigation value, performing real-time regulation and control on a preset intelligent irrigation water valve in each water demand area. The problem that the utilization rate of water resources is low can be effectively solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of irrigation water-saving control, and in particular, to a water-saving control method, a cloud platform and a device for agricultural irrigation. Background Art

[0002] With the global climate change and population growth, the problem of water resource shortage has become increasingly prominent. As the largest water-consuming field, agriculture usually accounts for a large proportion of the total water consumption. Therefore, improving the efficiency of agricultural water use and reducing water resource waste are of great significance for alleviating water resource shortage.

[0003] Traditional agricultural irrigation usually judges the drought situation of farmland soil according to a fixed irrigation plan or manual experience, and uses traditional irrigation technologies to irrigate the farmland, such as flood irrigation, furrow irrigation and submergence irrigation. However, traditional irrigation methods often have problems such as large water consumption and low water resource utilization rate. For example, the flood irrigation method mainly introduces a large amount of water into the farmland, allows the water to flow freely and penetrate into the soil to supply the water required for crop growth. Since a large amount of evaporation and loss will occur during the free diffusion of water on the ground, the water resource utilization rate of farmland irrigation is insufficient. In addition, the flood irrigation method also has the situation of uneven irrigation. Some places may get too much water, while some places may still be dry, resulting in low water resource utilization rate.

[0004] For the above problems, there is currently no effective solution to solve the problem of low water resource utilization rate in traditional irrigation methods. Summary of the Invention

[0005] The embodiments of the present application provide a water-saving control method, a cloud platform and a device for agricultural irrigation, which are used to solve the problem of low water resource utilization rate.

[0006] To achieve the above object, the embodiments of the present application adopt the following technical solutions:

[0007] In a first aspect, a water-saving control method for agricultural irrigation, which is applied to a cloud platform, the method includes:

[0008] Obtain the geographical location of the target farmland;

[0009] Determine the historical rainfall corresponding to the geographical location of the target farmland and the historical soil moisture content parameters of the target farmland in a first preset database;

[0010] Based on the historical rainfall and the historical soil moisture content parameters, determine the farmland water demand law characteristics of the target farmland;

[0011] According to the farmland water demand law characteristics, construct a farmland water-saving control model;

[0012] Obtain the crop information of the target farmland;

[0013] Based on the crop information, divide the water demand areas of the target farmland;

[0014] Real-time collect the soil moisture parameters of each water demand area;

[0015] Based on the farmland water-saving control model and the soil moisture parameters of each water demand area, obtain the predicted irrigation values of each water demand area;

[0016] Obtain the real-time irrigation values of each water demand area;

[0017] Based on the predicted irrigation values and the real-time irrigation values, generate a water valve control instruction to perform real-time control on the preset intelligent irrigation water valves in each water demand area.

[0018] In a possible implementation manner of the first aspect, the historical soil moisture parameters include historical soil humidity data, historical soil density data, and historical soil pH value data.

[0019] In a possible implementation manner of the first aspect, the determining the farmland water demand law characteristics of the target farmland based on the historical rainfall and the historical soil moisture parameters includes:

[0020] Through the historical soil humidity data, use a preset soil water content formula to calculate the original soil water content rate of the target farmland;

[0021] Obtain the historical farmland soil water content and the historical artificial irrigation water injection volume in the second preset database;

[0022] Use a preset Pearson correlation coefficient formula to calculate the correlation coefficient between the historical farmland soil water content and the historical soil pH value data to obtain a soil pH value influence factor;

[0023] Perform parameter fitting on the soil pH value influence factor, the original soil water content rate of the farmland, and the historical soil density data to obtain a soil saturation water content pressure time-varying coefficient, and the soil saturation water content pressure time-varying coefficient is used to characterize the time-varying non-linear characteristic parameters of the farmland soil under vertical pressure;

[0024] Determine the cumulative irrigation water flow through the historical rainfall and the historical artificial irrigation water injection volume;

[0025] Perform parameter fitting on the cumulative irrigation water flow, the historical artificial irrigation water injection volume, and the soil saturation water content pressure time-varying coefficient to obtain a farmland water demand law characteristic coefficient;

[0026] In a preset database of the characteristics of the water requirement law of farmland, determine the time-varying non-linear characteristics of farmland irrigation water corresponding to the characteristic coefficient of the water requirement law of the target farmland, and the time-varying non-linear characteristics of farmland irrigation water are used to characterize the characteristics of the water requirement law of farmland.

[0027] In a possible implementation manner of the first aspect, constructing a farmland water-saving control model according to the characteristics of the water requirement law of farmland includes:

[0028] Determine the upper limit and lower limit of irrigation water injection of the target farmland through the characteristics of the water requirement law of farmland;

[0029] Construct a multi-objective function, which includes a target function for minimizing the irrigation water injection volume, a target function for minimizing the irrigation cost, and a target function for maximizing the crop yield. Among them, the target function for minimizing the irrigation water injection volume is the main target function, and the target function for minimizing the irrigation cost and the target function for maximizing the crop yield are secondary target functions;

[0030] Respectively use the upper limit and lower limit of irrigation water injection as the first target constraint condition and the second target constraint condition of the target function for minimizing the irrigation water injection volume;

[0031] Use the secondary target function as the third target constraint condition of the main target function;

[0032] Combine the first target constraint condition, the second target constraint condition, the third target constraint condition and the multi-objective function to construct a farmland water-saving control model.

[0033] In a possible implementation manner of the first aspect, determining the upper limit and lower limit of irrigation water injection of the target farmland includes:

[0034] Obtain the number of days of crop drought and the average rainfall of farm crops within a preset time period;

[0035] Obtain the types of crops planted in the target farmland, and determine the growth water requirement coefficient, crop water requirement and soil water retention of the current crop type in the first preset database;

[0036] Obtain the drought impact coefficient by the ratio of the average rainfall to the preset daily standard rainfall;

[0037] Multiply the drought impact coefficient by the number of days of crop drought to obtain the drought impact index;

[0038] Calculate the relative humidity index through the crop water requirement, the soil water retention and the average rainfall by using a preset relative humidity index formula;

[0039] Add the relative humidity index and the drought impact index to obtain a comprehensive crop water deficit index;

[0040] Multiply the comprehensive crop water deficit index by the growth water demand coefficient to obtain the lower limit of irrigation water injection;

[0041] Obtain the total area of farmland irrigation and the total available irrigation water volume of the target farmland;

[0042] Calculate the available water volume per unit area through the total available irrigation water volume and the total area of farmland irrigation;

[0043] Take the highest value between the available water volume per unit area and the crop water demand as the upper limit of irrigation water injection for the target farmland.

[0044] In a possible implementation manner of the first aspect, the crop information includes the crop type and the crop growth stage. Based on the crop information, dividing the water demand areas of the target farmland includes:

[0045] Determine the crop coefficient corresponding to the crop type and the crop growth stage in the first preset database. The crop coefficient is used to characterize the proportional coefficient between the actual water demand of the crop at different growth stages and the potential evapotranspiration;

[0046] Obtain the current environmental climate parameters;

[0047] Through the environmental climate parameters, use the Penman-Monteith formula to obtain the reference crop transpiration;

[0048] Multiply the reference crop transpiration by the crop coefficient to obtain the actual crop water demand;

[0049] Divide the target farmland into high water demand areas, medium water demand areas and low water demand areas according to the actual crop water demand.

[0050] In a possible implementation manner of the first aspect, obtaining the predicted irrigation value of each water demand area based on the farmland water-saving control model and the soil moisture parameters of each water demand area includes:

[0051] Determine the real-time soil water content, farmland water holding capacity and crop wilting critical data of each water demand area according to the soil moisture parameters of each water demand area;

[0052] Input the real-time soil water content, farmland water holding capacity and crop wilting critical data of each water demand area into the farmland water-saving control model to obtain the predicted irrigation value of each water demand area.

[0053] In a possible implementation of the first aspect, the method further includes:

[0054] For any one of the water demand areas, if the real-time soil water content is less than the crop wilting critical data, it is in an emergency irrigation state, and all preset intelligent irrigation valves in the water demand area are opened;

[0055] If the real-time soil water content is greater than or equal to the crop wilting critical data and less than or equal to the farm water holding capacity, it is in a demand-based irrigation state, and the intelligent irrigation valves in the water demand area are opened according to the predicted irrigation values of each water demand area;

[0056] If the real-time soil water content is greater than or equal to the farm water holding capacity, it is in a closed irrigation state, and all preset intelligent irrigation valves in the water demand area are closed.

[0057] In a second aspect, the present application provides a cloud platform applied to the water-saving control method for agricultural irrigation, including:

[0058] A geographic data management module for determining the geographical location of the target farmland through the geographic data management module;

[0059] A data acquisition module for determining the historical rainfall corresponding to the geographical location of the target farmland and the historical soil moisture parameters of the target farmland in a first preset database;

[0060] A regular feature determination module for determining the farmland water demand regular features of the target farmland based on the historical rainfall and the historical soil moisture parameters;

[0061] A model construction module for constructing a farmland water-saving control model according to the farmland water demand regular features;

[0062] An information acquisition module for acquiring the crop information of the target farmland;

[0063] A zoning module for dividing the water demand areas of the target farmland based on the crop information;

[0064] A data collection module for collecting the soil moisture parameters of each water demand area by using the data collection module;

[0065] An irrigation value determination module for obtaining the predicted irrigation values of each water demand area based on the farmland water-saving control model and the soil moisture parameters of each water demand area;

[0066] A real-time irrigation value acquisition module for acquiring the real-time irrigation values of each water demand area;

[0067] A real-time regulation module, configured to perform real-time regulation on preset intelligent irrigation valves in each water demand area according to the predicted irrigation value and the real-time irrigation value.

[0068] In a third aspect, the present application provides an electronic device, including:

[0069] A memory, configured to store instructions; and

[0070] A processor, configured to call the instructions from the memory and, when executing the instructions, be capable of implementing the above-mentioned water-saving control method for agricultural irrigation.

[0071] Through the above technical solutions, by comprehensively considering the geographical location of the target farmland, historical rainfall, and historical soil moisture content parameters, the water demand law characteristics of the farmland can be accurately determined, thereby constructing a water-saving control model for the farmland, making irrigation more precise and improving the utilization rate of water resources. Dividing the water demand areas of the farmland based on crop information can perform differential irrigation management according to the water requirements of different crops, improving the efficiency of agricultural irrigation. By real-time collecting the soil moisture content parameters of each water demand area and obtaining the predicted irrigation value based on the water-saving control model and parameters of the farmland, the real-time monitoring of the water condition of the farmland can be realized. At the same time, by comparing the predicted irrigation value and the real-time irrigation value, a water valve regulation instruction can be generated to real-time regulate the intelligent irrigation valve to ensure that the irrigation amount meets the crop requirements, which can greatly reduce the waste of water resources during the irrigation process, improve the utilization efficiency of irrigation water, not only help to save water resources, but also reduce the irrigation cost and improve the economic benefits of agricultural production.

[0072] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic flowchart of a water-saving control method for agricultural irrigation provided by an embodiment of the present application;

[0074] Figure 2 It is a schematic structural diagram of a cloud platform provided by an embodiment of the present application;

[0075] Figure 3 It is a schematic page diagram of real-time monitoring of soil moisture content parameters provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0076] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will, with reference to the accompanying drawings in the embodiments of this application, clearly and completely describe the technical solutions in the embodiments of this application. It should be understood that the specific embodiments described herein are only for the purpose of illustration and explanation of the embodiments of this application, and are not used to limit the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the scope of protection of this application.

[0077] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of this application, then such directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If this specific posture changes, then such directional indications will also change accordingly.

[0078] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of this application, then such descriptions of "first", "second", etc. are only for descriptive purposes, and should not be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments may be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0079] Figure 1 Schematically shows a flowchart of a water-saving control method for agricultural irrigation according to an embodiment of this application. As Figure 1 shown, the embodiments of this application provide a water-saving control method for agricultural irrigation, which is applied to a cloud platform. The method may include the following steps.

[0080] S101. Obtain the geographical location of the target farmland;

[0081] S102. Determine the historical rainfall corresponding to the geographical location of the target farmland and the historical soil moisture content parameters of the target farmland in the first preset database;

[0082] S103. Based on the historical rainfall and historical soil moisture content parameters, determine the characteristics of the water demand law of the target farmland;

[0083] S104. According to the characteristics of the water demand law of the farmland, construct a water-saving control model for the farmland;

[0084] S105. Obtain the crop information of the target farmland;

[0085] S106. Divide the water demand areas of the target farmland based on crop information;

[0086] S107. Collect the soil moisture parameters of each water demand area in real time;

[0087] S108. Based on the farmland water-saving control model and the soil moisture parameters of each water demand area, obtain the predicted irrigation values of each water demand area;

[0088] S109. Obtain the real-time irrigation values of each water demand area;

[0089] S110. Based on the predicted irrigation values and the real-time irrigation values, generate a water valve control instruction to perform real-time control on the preset intelligent irrigation water valves in each water demand area.

[0090] First, obtain the geographical location of the target farmland. In this embodiment, the target farmland can be determined according to the actual situation and can be achieved through satellite remote sensing images. Satellite remote sensing technology can capture high-resolution images of the earth's surface. Through high-resolution images, the boundaries, shapes, and distribution characteristics of the farmland can be identified. That is, use satellite remote sensing technology to obtain high-resolution images of the farmland. Through image analysis, identify the boundaries, shapes, and distribution characteristics of the farmland. And combine Geographic Information System (GIS) technology to process geospatial information, match the remote sensing image with the geographical location information, and determine the exact location of the farmland.

[0091] Next, determine the historical rainfall corresponding to the geographical location of the target farmland in the first preset database, as well as the historical soil moisture parameters of the target farmland. The soil moisture parameters refer to a series of indicators used to describe and evaluate the soil moisture status and its impact on crop growth. Specifically, it is necessary to search for the geographical location information of the target farmland in the first preset database, such as the area where the farmland is located, longitude and latitude coordinates, etc. After determining the geographical location of the target farmland, the historical rainfall data of this area can be searched in the database. Similarly, search for the historical soil moisture parameters of the target farmland in the first preset database. The historical soil moisture parameters can be data such as soil moisture content, soil temperature, and soil salinity.

[0092] After determining the historical rainfall and the historical soil moisture parameters of the target farmland, based on the historical rainfall and the historical soil moisture parameters, the characteristics of the water requirement law of the target farmland are determined. In this embodiment, the water requirement law of the farmland refers to the regularity shown by the water demand and demand characteristics of crops at each growth stage during the growth period. That is to say, by analyzing the relationship between the historical rainfall and the soil moisture parameters, the impact of rainfall on soil moisture can be understood. For example, the change in soil moisture content after rainfall and the correlation between the rainfall season and the soil moisture can be understood. In addition, by analyzing the relationship between the soil moisture parameters and the water requirement of crops, the impact of soil moisture content, soil temperature, etc. on the water requirement of crops can be grasped. For example, the impact of excessive or insufficient soil moisture on crop growth and the irrigation requirements of crops under different soil moisture conditions. According to the change trends of the historical rainfall and the soil moisture parameters, the water requirement law of the target farmland in different seasons is determined. For example, in the season with abundant rainfall, the water requirement of the farmland may be less, while in the dry season, the water requirement of the farmland may increase significantly. According to the change of the soil moisture parameters, the characteristics of the water requirement law of the target farmland are determined. For example, when the soil moisture content is too low, irrigation needs to be carried out in a timely manner; when the soil moisture content is too high, drainage measures need to be taken to avoid crop damage.

[0093] Subsequently, according to the characteristics of the water requirement law of the farmland, a farmland water-saving control model is constructed. In this embodiment, the characteristics of the water requirement law of the farmland refer to the regular water demand shown by crops at each growth stage during the growth period. Specifically, first, data can be collected. The data can be meteorological data, soil data, and crop growth data. After collecting the data, based on the collected data, the MATLAB software is used to implement the fitting function to analyze the water requirement law of crops at different growth stages and determine the critical period of water requirement of crops. Subsequently, according to the water requirement law of crops and the local water resource conditions, a suitable water-saving irrigation technology, such as drip irrigation, subsurface irrigation, sprinkler irrigation, etc., can be selected. In this embodiment, the irrigation technology is drip irrigation. Next, using mathematical methods, such as multiplication models, addition models, etc. or computer simulation technologies, a farmland water-saving control model is established. Through the farmland water-saving control model, the relationship between crop growth and water supply, and the impact of different irrigation strategies on crop growth and yield can be reflected.

[0094] After constructing the farmland water-saving control model, crop information of the target farmland can be obtained through agricultural remote sensing. Agricultural remote sensing is an efficient and accurate means of obtaining farmland information. It uses remote sensing platforms such as satellites or drones to acquire remote sensing image data of the farmland, and uses image processing technology and machine learning algorithms to automatically classify and interpret the images, so as to identify key information such as the vegetation type and crop growth trend of the farmland. Through remote sensing platforms such as satellites or drones, remote sensing image data of the farmland is acquired. Using image processing technology and machine learning algorithms, the remote sensing images are automatically classified and interpreted to identify key information such as the vegetation type and crop growth trend of the farmland. In this embodiment, the machine learning algorithms can be methods such as support vector machines, random forests, and K-nearest neighbors.

[0095] Based on the crop information, the water demand areas of the target farmland are divided. In this embodiment, the crop information is the crop type and the crop growth stage. Since different types of crops have different water requirements. For example, some crops may be more drought-tolerant, while others are more sensitive to water. Different growth stages of the same crop also have different water requirements. For example, during the growth period and the fruiting period, the crop may need more water. Therefore, by analyzing the crop type and the crop growth stage, the water demand areas of the target farmland are divided. That is to say, according to the crop type and the crop growth stage of the crop, the irrigation demand is determined, and according to the irrigation demand, the farmland is divided into different irrigation demand levels. The irrigation demand level can be comprehensively determined based on factors such as the transpiration and evaporation of the crop, the soil moisture condition, and the crop water use efficiency.

[0096] After determining the water demand areas of the target farmland, the soil moisture parameters of each water demand area are collected in real time, which can be achieved through soil moisture sensors. The soil moisture sensors are soil moisture sensors (such as capacitive, TDR sensors, etc.), temperature sensors, conductivity sensors, and pH electrodes, etc., which are used to measure key parameters such as the water content, temperature, salinity (reflected by conductivity), and pH value in the soil. That is to say, through soil moisture sensors (such as capacitive, TDR sensors, etc.), temperature sensors, conductivity sensors, and pH electrodes, etc., the soil moisture data, soil temperature data, soil pH value data, etc. of each water demand area are collected in real time.

[0097] Subsequently, based on the farmland water-saving control model and the soil moisture parameters of each water demand area, the predicted irrigation value of each water demand area is obtained. Specifically, first, soil moisture sensors are used to collect in real time parameters such as the soil moisture, temperature, heat flux, water potential, and conductivity of each water demand area. Secondly, the real-time collected soil moisture parameters are input into the previously constructed farmland water-saving control model. Using the farmland water-saving control model, according to the input soil moisture parameters and crop growth data, the predicted irrigation value of each water demand area is obtained. The predicted irrigation value can be information such as the irrigation amount, irrigation time, and irrigation frequency.

[0098] After obtaining the predicted irrigation values for each water - required area, the real - time irrigation values for each water - required area are acquired. The irrigation data can be collected in real - time by using intelligent irrigation devices (such as intelligent irrigation controllers, flow meters, etc.) installed in the farmland. By obtaining the real - time irrigation values for each water - required area, information such as the irrigation volume, irrigation time, and irrigation speed of each water - required area can be accurately recorded.

[0099] Based on the predicted irrigation values and the real - time irrigation values, a water valve control instruction is generated to perform real - time control on the preset intelligent irrigation water valves in each water - required area. That is to say, the real - time irrigation values are compared with the predicted irrigation values to analyze the differences between them. If the real - time irrigation value is lower than the predicted irrigation value, it indicates that the irrigation volume needs to be increased. If the real - time irrigation value is higher than the predicted irrigation value, it may be necessary to reduce the irrigation volume or stop irrigation. Next, according to the comparison result, the corresponding water valve control instruction is generated, and the generated control instruction is sent to the intelligent irrigation water valves in each water - required area through wireless communication technology. The intelligent irrigation water valves will automatically adjust the irrigation status according to the received instructions to achieve precise irrigation control. After executing the control instruction, the irrigation situation of each water - required area can be continuously monitored and necessary adjustments can be made according to the actual situation.

[0100] By comprehensively considering the geographical location of the target farmland, historical rainfall, and historical soil moisture parameters, the water - requirement law characteristics of the farmland can be accurately determined, thereby constructing a farmland water - saving control model, making irrigation more precise, and improving the utilization rate of water resources. Dividing the water - required areas of the farmland based on crop information can perform differentiated irrigation management according to the water requirements of different crops, improving the efficiency of agricultural irrigation. By real - time collecting the soil moisture parameters of each water - required area and obtaining the predicted irrigation values based on the farmland water - saving control model and parameters, the real - time monitoring of the farmland water condition can be realized. At the same time, by comparing the predicted irrigation values and the real - time irrigation values, a water valve control instruction can be generated to real - time control the intelligent irrigation water valves, ensuring that the irrigation volume meets the crop requirements, which can greatly reduce the waste of water resources during irrigation, improve the utilization efficiency of irrigation water, not only help to save water resources, but also reduce the irrigation cost and improve the economic benefits of agricultural production.

[0101] In one implementation manner of this embodiment, based on historical rainfall and historical soil moisture parameters, determining the water - requirement law characteristics of the target farmland includes:

[0102] S210. Using the preset soil water content formula through historical soil humidity data to calculate the original moisture content of the farmland soil of the target farmland;

[0103] S220. Obtaining the historical farmland soil water content and historical artificial irrigation water injection volume in the second preset database;

[0104] S230. Use the preset Pearson correlation coefficient formula to calculate the correlation coefficient between the historical farmland soil moisture content and the historical soil pH value data, and obtain the soil pH value influencing factor;

[0105] S240. Perform parameter fitting on the soil pH value influencing factor, the original moisture content of the farmland soil, and the historical soil density data to obtain the time-varying coefficient of the soil saturated moisture content under pressure, and the time-varying coefficient of the soil saturated moisture content under pressure is used to characterize the time-varying non-linear characteristic parameter of the farmland soil under vertical pressure;

[0106] S250. Determine the cumulative irrigation water volume through the historical rainfall and the historical artificial irrigation injection volume;

[0107] S260. Perform parameter fitting on the cumulative irrigation water volume, the historical artificial irrigation injection volume, and the time-varying coefficient of the soil saturated moisture content under pressure to obtain the characteristic coefficient of the water demand law of the farmland;

[0108] S270. In the preset database of the characteristic coefficients of the farmland water demand law, determine the time-varying non-linear characteristic of the farmland irrigation water use corresponding to the characteristic coefficient of the farmland water demand law of the target farmland, and the time-varying non-linear characteristic of the farmland irrigation water use is used to characterize the characteristic of the farmland water demand law.

[0109] In this embodiment, the historical soil moisture condition parameters include historical soil moisture data, historical soil density data, and historical soil pH value data. Figure 3 The page schematic diagram for real-time monitoring of soil moisture condition parameters provided by the embodiment of the present application is as Figure 3 shown. Through the electronic device, the soil moisture condition parameters can be monitored in real time, and parameters such as soil temperature, soil moisture, and light intensity can be obtained.

[0110] In this embodiment, the electronic device can be a device with a processor such as a tablet computer, a desktop type, a laptop, a handheld computer, a wearable device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, etc. Of course, the electronic device can also be a server. The specific form of the electronic device in the embodiment of the present application is not particularly limited.

[0111] First, through the historical soil moisture data, use the preset soil moisture content formula to calculate the original moisture content of the farmland soil of the target farmland, that is, input the preprocessed historical soil moisture data into the preset soil moisture content formula to calculate the soil moisture content at each time point. The preset soil moisture content formula in this embodiment is as follows:

[0112] Soil moisture content (weight %) = weight of water / weight of dried soil × 100%

[0113] Specifically, in the preset soil moisture content formula, the water weight can be obtained by subtracting the dry soil weight from the original soil weight. Here, the original soil weight refers to the weight of the soil sample before drying treatment, and the dry soil weight refers to the weight of the soil sample after drying treatment when the moisture in the soil has been removed. For example, assume a soil sample with an original soil weight of 150 grams. After drying treatment, the dry soil weight is 120 grams. Then, calculate the moisture content of this soil sample:

[0114] Water weight = 150 grams - 120 grams = 30 grams; Soil moisture content (weight %) = 30 grams / 120 grams × 100% = 25%;

[0115] Therefore, the moisture content of the soil sample is 25%.

[0116] After obtaining the original moisture content of the farmland soil in the target farmland, obtain the historical farmland soil moisture content and historical artificial irrigation water injection volume in the second preset database. In this embodiment, the historical farmland soil moisture content refers to the moisture content in the farmland soil over a past period of time, and the historical artificial irrigation water injection volume refers to the amount of water artificially injected into the farmland through the irrigation system over a past period of time. In specific implementation, a query statement can be input into the second preset database to extract the required historical farmland soil moisture content and historical artificial irrigation water injection volume data.

[0117] Subsequently, using the preset Pearson correlation coefficient formula, calculate the correlation coefficient between the historical farmland soil moisture content and the historical soil pH value data to obtain the soil pH value influence factor. That is, substitute the collected data into the Pearson correlation coefficient formula for calculation to determine the correlation coefficient between the historical farmland soil moisture content and the historical soil pH value data, and use the correlation coefficient as the soil pH value influence factor. In this embodiment, the soil pH value influence factor is the influence factor of the soil pH value on the historical farmland soil moisture content. The preset Pearson correlation coefficient formula is as follows:

[0118]

[0119] Where r represents the Pearson correlation coefficient; n represents the number of data points; x i and y i represent the two variable values of the i-th data point. In this embodiment, x i represents the historical farmland soil moisture content, and y i represents the historical soil pH value; and represent the means of the two variables.

[0120] The influencing factors of soil pH value, the original moisture content of farmland soil, and the historical soil density data are subjected to parameter fitting to obtain the time-varying coefficient of soil saturated moisture content pressure. In this embodiment, the time-varying coefficient of soil saturated moisture content pressure is used to characterize the time-varying non-linear characteristic parameters of farmland soil under vertical pressure. That is to say, the influencing factors of soil pH value, the original moisture content of farmland soil, and the historical soil density data of the farmland are subjected to time-varying relationship fitting by using the parameter fitting method. The parameter fitting method can be the linear regression fitting method. By means of linear regression, the fitting formulas of the influencing factors of soil pH value, the original moisture content of farmland soil, and the historical soil density data of the farmland are obtained as follows:

[0121]

[0122] Among them, w represents the time-varying coefficient of soil saturated moisture content pressure; a represents the influencing factor of soil pH value; σ represents the vertical pressure exerted on the soil; w 0 represents the original moisture content of farmland soil; ρ represents the historical soil density data.

[0123] After obtaining the time-varying coefficient of soil saturated moisture content pressure, the cumulative irrigation water volume is determined through the historical rainfall and the historical artificial irrigation injection volume. In this embodiment, the historical artificial irrigation injection volume refers to the irrigation water volume artificially introduced in the past period to meet the growth needs of crops, and the cumulative irrigation water volume refers to the total irrigation water volume including natural rainfall and artificial irrigation within a certain period. That is to say, the historical rainfall and the historical artificial irrigation injection volume in the same period are directly added to obtain the cumulative irrigation water volume in this period. The formula is as follows:

[0124] Cumulative irrigation water volume = Historical rainfall + Historical artificial irrigation injection volume

[0125] Subsequently, the cumulative irrigation water volume, the historical artificial irrigation injection volume, and the time-varying coefficient of soil saturated moisture content pressure are subjected to parameter fitting to obtain the characteristic coefficient of the water requirement law of the farmland. In this embodiment, the time-varying coefficient of soil saturated moisture content pressure refers to the coefficient of the change of soil saturated moisture content with time when the soil is under pressure; the characteristic coefficient of the water requirement law of the farmland. That is to say, the cumulative irrigation water volume, the historical artificial irrigation injection volume, and the time-varying coefficient of soil saturated moisture content pressure are subjected to time-varying relationship fitting by using the parameter fitting method. The parameter fitting method can be the linear regression fitting method. By means of linear regression, the fitting formulas of the cumulative irrigation water volume, the historical artificial irrigation injection volume, and the time-varying coefficient of soil saturated moisture content pressure are obtained as follows:

[0126]

[0127] Among them, μ 0represents the characteristic coefficient of the water requirement law of farmland, w represents the time-varying coefficient of the soil saturation moisture content pressure; μ represents the cumulative irrigation water flow; d represents the historical artificial irrigation water injection volume; m 0 represents the upper limit of the water requirement of crops during the entire growth period;

[0128] After obtaining the characteristic coefficient of the water requirement law of farmland, in the preset database of the characteristic coefficients of the water requirement law of farmland, determine the time-varying non-linear characteristic of the farmland irrigation water corresponding to the characteristic coefficient of the water requirement law of the target farmland. In this embodiment, the time-varying non-linear characteristic of the farmland irrigation water refers to the non-linear change law shown by the farmland irrigation water volume changing with time. That is to say, in the preset database of the characteristic coefficients of the water requirement law of farmland, according to the characteristic coefficient of the water requirement law of farmland, screen out the time-varying non-linear characteristic of the farmland irrigation water corresponding to the characteristic coefficient of the water requirement law of farmland, and determine the difference in water requirements at different growth stages of crops. For example, when the characteristic coefficient of the water requirement law of farmland is within a specific threshold, by determining the characteristic of the current farmland irrigation water of the crops changing with time within the current specific threshold, the time-varying non-linear characteristic of the farmland irrigation water can be obtained.

[0129] By determining the characteristic coefficient of the water requirement law of the target farmland, the overall trend and distribution law of the farmland irrigation water can be better grasped, providing a scientific basis for the management and scheduling of water resources, ensuring the reasonable allocation and effective utilization of water resources, avoiding over-irrigation or under-irrigation, and improving the utilization efficiency of irrigation water.

[0130] In one implementation manner of this embodiment, according to the characteristic coefficient of the water requirement law of farmland, construct a farmland water-saving control model, including:

[0131] S310. Determine the upper limit and lower limit of the irrigation water injection of the target farmland through the characteristic coefficient of the water requirement law of farmland;

[0132] S320. Construct a multi-objective function, which includes a minimum irrigation water injection volume objective function, a minimum irrigation cost objective function, and a maximum crop yield objective function. Among them, the minimum irrigation water injection volume objective function is the main objective function, and the minimum irrigation cost objective function and the maximum crop yield objective function are secondary objective functions;

[0133] S330. Respectively use the upper limit and lower limit of the irrigation water injection as the first objective constraint condition and the second objective constraint condition of the minimum irrigation water injection volume objective function;

[0134] S340. Use the secondary objective function as the third objective constraint condition of the main objective function;

[0135] S350. Combine the first objective constraint condition, the second objective constraint condition, the third objective constraint condition and the multi-objective function to construct a farmland water-saving control model.

[0136] Based on the characteristics of the water requirement law of farmland, determine the upper limit and lower limit of irrigation water injection for the target farmland. In this embodiment, the upper limit of irrigation water injection refers to the level at which the soil moisture content reaches or exceeds this value during the farmland irrigation process, and the crop will no longer require additional irrigation water. The lower limit of irrigation water injection refers to the level at which the soil moisture content drops to this value during the farmland irrigation process, and the crop will begin to suffer from drought stress and immediate irrigation is required to supplement the water. Specifically, first, it is necessary to understand the water requirement law of the crops planted in the target farmland, including the water requirements and water-sensitive periods in different growth stages, which can be obtained by referring to relevant literature. Subsequently, it is necessary to consider the soil moisture retention capacity of the target farmland, including the texture, structure, and water retention capacity of the soil. Since the soil moisture retention capacity affects the soil's ability to absorb, store, and release water, it thus affects the water supply to the crops. Climate conditions are also important factors in determining the upper and lower limits of irrigation water injection. Climate factors such as precipitation, temperature, and wind speed will affect the transpiration of the crops, the evaporation of soil moisture, and the water requirements of the crops. Finally, based on determining the crop water requirement law, soil moisture retention capacity, and considering climate conditions, the upper and lower limits of irrigation water injection for the target farmland can be comprehensively determined. For example, by obtaining the historical information of the target farmland within a preset time period and determining the characteristics of the water requirement law of the farmland each month, such as the irrigation water injection value each month, the maximum and minimum values of the irrigation water injection value are obtained within the preset time period. The maximum value is used as the upper limit of irrigation water injection, and the minimum value is used as the lower limit of irrigation water injection.

[0137] After determining the upper limit and lower limit of irrigation water injection for the target farmland, it is necessary to construct a multi-objective function. The multi-objective function includes a minimum irrigation water volume objective function, a minimum irrigation cost objective function, and a maximum crop yield objective function. In this embodiment, the minimum irrigation water volume objective function is the main objective function, indicating that among all irrigation schemes, priority should be given to reducing the amount of irrigation water used. The minimum irrigation cost objective function and the maximum crop yield objective function are secondary objective functions, aiming to increase the crop yield and reduce the irrigation cost. Specifically, the minimum irrigation water volume objective function can be expressed as:

[0138] f 1 (W) = W + h

[0139] where, f 1 (W) represents the minimum irrigation water volume; W represents the irrigation water volume; h represents the real-time soil water content;

[0140] The minimum irrigation cost objective function can be expressed as:

[0141] f 2 (C) = kW + b

[0142] where, f2 (C) represents minimizing irrigation cost; C represents irrigation cost; k represents the cost coefficient per unit of irrigation water injection; b represents the fixed cost (such as equipment maintenance cost).

[0143] The objective function of maximizing crop yield can be expressed as:

[0144] f 3 (Y) = -(aW 2 + bW + c)

[0145] where f 3 (Y) represents maximizing crop yield; Y represents crop yield; W represents the amount of irrigation water injection; a and b represent the correlation coefficients of the irrigation water injection amount, and a < 0 (because the yield usually increases with the increase of the water injection amount, but will decrease due to excessive moisture after increasing to a certain extent); c represents the critical data of crop wilting.

[0146] Subsequently, using the weighted sum method, a multi-objective function is constructed. The weighted sum method is to add multiple objective functions by assigning different weight values to form an objective function. The multi-objective function can be expressed as:

[0147] F(W) = λ 1 f 1 (W) + λ 2 f 2 (W) + λ 3 f 3 (Y(W))

[0148] where F(W) represents the multi-objective function; λ 1 、λ 2 and λ 3 represent non-negative weights; f 1 (W) represents minimizing the amount of irrigation water injection; f 1 (W) represents minimizing the amount of irrigation water injection; f 3 (Y) represents maximizing crop yield; in this embodiment, the non-negative weights can be determined according to the actual situation.

[0149] Next, the upper limit of irrigation water injection and the lower limit of irrigation water injection are respectively used as the first objective constraint condition and the second objective constraint condition of the objective function of minimizing the amount of irrigation water injection. The upper limit of irrigation water injection refers to the maximum allowable irrigation water volume, which is determined based on the water demand of crops, soil type, climate conditions, and experience in agricultural practices. The first objective constraint condition can be expressed as:

[0150] W ≤ W max

[0151] where W represents the actual irrigation water volume, and W max represents the upper limit of the irrigation water injection amount.

[0152] The lower limit of irrigation water injection refers to the amount of irrigation water required to ensure that crops can obtain sufficient water to maintain normal growth and development. It is determined based on factors such as the water deficit sensitivity of crops, growth stages, and the water retention capacity of the soil. If the irrigation water volume is lower than this lower limit, it may lead to crop yield reduction or even death. The second objective constraint condition can be expressed as:

[0153] W≥W min

[0154] where W represents the actual irrigation water volume, and W min represents the lower limit of the irrigation water injection volume.

[0155] After determining the first objective constraint condition and the second objective constraint condition, the sub-objective function is used as the third objective constraint condition of the main objective function. That is, the objective function of minimizing irrigation cost and the objective function of maximizing crop yield are used as the third objective constraint conditions. Specifically, converting the objective function of minimizing irrigation cost and the objective function of maximizing crop yield into the third objective constraint conditions can be expressed as:

[0156] C(x)≤C max ; Y(x)≥Y min

[0157] where C(x) represents the irrigation cost function; C max represents the maximum acceptable irrigation cost; Y(x) represents the crop yield function; Y min represents the minimum acceptable yield.

[0158] Another constraint condition in this embodiment is:

[0159] S + I≥D (to ensure that the soil water content after irrigation meets the water demand of the crops);

[0160] S + I≤H (to ensure that the soil water content after irrigation does not exceed the field water holding capacity);

[0161] where the real-time soil water content is S; the field water holding capacity is H; the water demand of the crops is D; the minimum irrigation volume is I.

[0162] Through another constraint condition, the soil water content after irrigation can not only meet the water demand of the crops but also not exceed the field water holding capacity.

[0163] Subsequently, a farmland water-saving control model is constructed by combining the first objective constraint condition, the second objective constraint condition, the third objective constraint condition, and the multi-objective function. That is to say, a multi-objective optimization model is constructed through the above-mentioned constraint conditions and multi-objective function, and the multi-objective optimization model is used to represent the farmland water-saving control model. Through the farmland water-saving control model, an operation plan that minimizes agricultural irrigation volume is found.

[0164] By constructing a farmland water-saving control model, the optimal irrigation strategy can be determined to guide agricultural producers in precise irrigation management. It can also optimize the growth environment of crops, thereby increasing crop yield and quality and increasing farmers' income.

[0165] In one implementation manner of this embodiment, determining the upper limit and lower limit of irrigation water injection for the target farmland includes:

[0166] S401. Obtain the number of dry days of farm crops and the average rainfall within a preset time period;

[0167] S402. Obtain the types of crops planted in the target farmland, and determine the growth water demand coefficient, crop water demand, and soil water retention capacity of the current crop type in the first preset database;

[0168] S403. Obtain the drought impact coefficient by the ratio of the average rainfall to the preset daily standard rainfall;

[0169] S404. Multiply the drought impact coefficient by the number of dry days of farm crops to obtain the drought impact index;

[0170] S405. Calculate the relative humidity index through the crop water demand, soil water retention capacity, and average rainfall using the preset relative humidity index formula;

[0171] S406. Add the relative humidity index and the drought impact index to obtain the comprehensive crop water deficit index;

[0172] S407. Multiply the comprehensive crop water deficit index by the growth water demand coefficient to obtain the lower limit of irrigation water injection;

[0173] S408. Obtain the total farmland irrigation area and the total available irrigation water volume of the target farmland;

[0174] S409. Calculate the available water volume per unit area through the total available irrigation water volume and the total farmland irrigation area;

[0175] S410. Take the highest value between the available water volume per unit area and the crop water demand as the upper limit of irrigation water injection for the target farmland.

[0176] First, obtain the number of drought days of farm crops and the average rainfall within a preset time period. This can be done by accessing the official website of the local meteorological department, etc. It may also be necessary to collect daily rainfall data of the target area within the preset time period. Then, calculate the average value of these data to obtain the average rainfall. Subsequently, obtain the types of crops planted in the target farmland. This can be achieved through on-site investigations of the target farmland to determine the types of crops planted in the farmland.

[0177] After determining the current crop type, determine the growth water requirement coefficient, crop water requirement, and soil water retention capacity of the current crop type in the first preset database. In this embodiment, the growth water requirement coefficient of the crop type refers to the degree of water demand of the crop at different growth stages and is one of the key factors for determining the minimum water consumption for farmland irrigation; the crop water requirement refers to the total amount of water required by the crop during the growth process; the soil water retention capacity refers to the amount of water that the soil can retain. That is to say, in the first preset database, according to the type of crop planted in the target farmland, find the corresponding growth water requirement coefficient. And in the first preset database, according to the type of crop planted in the target farmland and the growth water requirement coefficient, determine the crop water requirement and the soil water retention capacity.

[0178] Secondly, obtain the drought impact coefficient by the ratio of the average rainfall to the preset daily standard rainfall. In this embodiment, the preset daily standard rainfall is a preset value used to reflect the daily rainfall demand in this area under normal circumstances. Subsequently, use the following formula:

[0179] Drought impact coefficient = average rainfall / daily standard rainfall

[0180] Calculate the drought impact coefficient through the formula. If the obtained ratio is less than 1, it indicates that the actual rainfall is lower than the standard rainfall and there is a drought risk; the smaller the ratio, the more severe the drought degree. If the ratio is greater than or equal to 1, it indicates that the actual rainfall meets or exceeds the standard rainfall and the drought risk is lower or non-existent.

[0181] After obtaining the drought impact coefficient, multiply the drought impact coefficient by the number of drought days of the crops to obtain the drought impact index. In this embodiment, the drought impact index is a comprehensive quantification index used to evaluate the overall impact of drought on crop growth, yield, etc. By multiplying the drought impact coefficient (a quantification index reflecting the drought degree) by the number of drought days of the crops (a quantification index reflecting the drought duration), a comprehensive value is obtained, thereby more comprehensively reflecting the impact of drought on the crops. That is to say, use the formula to calculate the drought impact index as follows:

[0182] Drought impact index = drought impact coefficient × number of drought days of crops

[0183] By multiplying the drought impact coefficient by the number of drought days of the crops, the drought impact index can be obtained, which can reflect the overall impact of drought on the crops.

[0184] Next, through the crop water requirement, soil water retention capacity, and average rainfall, using the preset relative humidity index formula, the relative humidity index is calculated. In this embodiment, the relative humidity index is an indicator reflecting the relationship between soil moisture conditions and crop water requirements, and can be obtained by considering rainfall, crop water requirements, and soil water retention capacity. Specifically, first, according to factors such as the growth stage of the crop, soil type, and climate conditions, the crop water requirement is determined, which can be obtained by referring to relevant literature, using a crop water demand model, or conducting on-site monitoring. The soil water retention capacity refers to the maximum amount of water that the soil can hold and can be obtained from the first preset database. By using the crop water requirement, soil water retention capacity, and average rainfall, the relative humidity index can be calculated using the following formula:

[0185] Relative humidity index = (average rainfall - crop water requirement + soil water retention capacity) / soil water retention capacity

[0186] Adding the relative humidity index and the drought impact index gives the comprehensive crop water deficit index. In this embodiment, the comprehensive crop water deficit index is a comprehensive quantitative indicator used to comprehensively evaluate the impact on crops due to water deficit during a specific period. The index combines the relative humidity index (reflecting the income and expenditure of soil moisture) and the drought impact index (reflecting the overall impact of drought on crops), and a comprehensive value is obtained by adding the two through a specific calculation method. That is, adding the relative humidity index and the drought impact index gives the comprehensive crop water deficit index. For example, the average rainfall R in a certain period is 50mm, the preset daily standard rainfall R 1 = 100mm, the crop water requirement = 40mm, the soil water retention capacity = 30mm, and then calculate the drought impact index:

[0187] Drought impact coefficient K = 0.5; number of drought days of crops D = 10 days;

[0188] Then calculate the drought impact index:

[0189] Drought impact index = 0.5 × 10 = 5;

[0190] Next, calculate the relative humidity index:

[0191] Relative humidity index = (50mm - 40mm + 30mm) / 30mm ≈ 1.33;

[0192] Finally, calculate the comprehensive crop water deficit index:

[0193] Comprehensive crop water deficit index = 1.33 + 5 = 6.33;

[0194] Therefore, the comprehensive crop water deficit index is 6.33, which comprehensively reflects the water deficit situation of crops during a specific period.

[0195] After obtaining the comprehensive crop water deficit index, multiply the comprehensive crop water deficit index by the growth water demand coefficient to obtain the lower limit of irrigation water injection. In this embodiment, the growth water demand coefficient refers to the coefficient of water demand of crops at different growth stages. Different crops and different growth stages have different water demands. That is to say, the process of multiplying the comprehensive crop water deficit index by the growth water demand coefficient to obtain the lower limit of irrigation water injection is actually to determine a reasonable irrigation amount on the basis of comprehensively considering the current water condition and growth demand of crops, which can more accurately reflect the actual water demand of crops and improve the accuracy and efficiency of irrigation. If the comprehensive crop water deficit index is high, it indicates that the current water deficit of the crop is relatively serious and the irrigation amount needs to be increased; if the growth water demand coefficient is high, it means that the crop has a large water demand at the current growth stage and the irrigation amount also needs to be increased. By multiplying to obtain the lower limit of irrigation water injection, it can ensure that both the water deficit and growth demand of the crop are fully considered and met.

[0196] To determine the upper limit of irrigation water injection for the target farmland, first, obtain the total irrigation area and total available irrigation water volume of the target farmland. This can be done by querying official statistical data, accessing the statistical data section of the official website, and searching for information on the irrigation area and total available irrigation water volume.

[0197] Subsequently, calculate the available water volume per unit area through the total available irrigation water volume and the total irrigation area of the farmland. That is to say, evenly distribute the total available irrigation water volume to each unit of farmland area to obtain the water volume that can be obtained per unit area. Take the highest value between the available water volume per unit area and the water demand of the crops as the upper limit of irrigation water injection for the target farmland. That is to say, by comparing the highest value between the available water volume per unit area and the water demand of the crops and setting it as the upper limit of irrigation water injection for the target farmland. If the upper limit of irrigation water injection is set as the higher value of the two, it can ensure that the crops obtain sufficient water during the growth process to meet their physiological needs and growth and development. If the available water volume per unit area is higher than the water demand of the crops, setting the upper limit as the available water volume may lead to unnecessary waste of water resources. Therefore, choosing the higher value of the two as the upper limit can avoid this situation to a certain extent.

[0198] By determining the upper limit and lower limit of irrigation water injection for the target farmland, it is possible to ensure that the farmland receives an appropriate amount of water when needed, avoid over-irrigation or under-irrigation, thereby improving the utilization efficiency of irrigation water. It can also achieve multiple effects such as precise irrigation management, promoting sustainable utilization of water resources, and improving agricultural production benefits, which helps to improve the water use efficiency of farmland, ensure crop growth and yield.

[0199] In one implementation manner of this embodiment, the crop information includes the crop variety and the crop growth stage. Based on the crop information, the water demand areas of the target farmland are divided, including:

[0200] S510. Determine the crop coefficient corresponding to the crop variety and the crop growth stage in the first preset database. The crop coefficient is used to represent the proportional coefficient between the actual water demand of the crop at different growth stages and the potential evapotranspiration;

[0201] S520. Obtain the current environmental climate parameters;

[0202] S530. Through the environmental climate parameters, use the Penman-Monteith formula to obtain the reference crop transpiration;

[0203] S540. Multiply the reference crop transpiration by the crop coefficient to obtain the actual water demand of the crop;

[0204] S550. According to the actual water demand of the crop, divide the target farmland into high water demand areas, medium water demand areas, and low water demand areas.

[0205] First, determine the crop coefficient corresponding to the crop variety and the crop growth stage in the first preset database. The crop coefficient is used to represent the proportional coefficient between the actual water demand of the crop at different growth stages and the potential evapotranspiration. In this embodiment, the crop coefficient is a parameter representing the degree of water utilization of the crop, and is used to describe the ratio of the water demand to the possible evapotranspiration of the crop during different growth periods. That is to say, by querying the crop variety in the first preset database, the crop coefficient corresponding to the crop variety and the crop growth stage is determined. Since different crops have different water demands, it is necessary to query the corresponding crop coefficient according to the crop variety. And the water demand of the crop also changes at different growth stages, so it is necessary to query the corresponding crop coefficient according to the growth stage of the crop.

[0206] Secondly, obtain the current environmental climate parameters. In this embodiment, the environmental climate parameters can be climate parameters such as net radiation, soil heat flux, psychrometric constant, average temperature, wind speed, saturation vapor pressure, actual vapor pressure, etc., which can be obtained through databases and official meteorological websites. Subsequently, through the environmental climate parameters, using the Penman-Monteith formula, the reference crop evapotranspiration is obtained. The Penman-Monteith formula is a widely used formula for calculating the reference crop evapotranspiration. The reference crop evapotranspiration refers to the evapotranspiration of a specific reference crop under ideal conditions. The Penman-Monteith formula is as follows:

[0207]

[0208] Where, ET 0 is the reference crop evapotranspiration; Δ is the slope of the saturation vapor pressure curve; R n is the net radiation; G is the soil heat flux; γ is the psychrometric constant; T is the average temperature; u 2 is the wind speed at 2 meters height; e s is the saturation vapor pressure; e a is the actual vapor pressure;

[0209] By substituting multiple environmental climate parameters into the Penman-Monteith formula, the reference crop evapotranspiration is obtained.

[0210] Next, multiply the reference crop evapotranspiration by the crop coefficient to obtain the actual water requirement of the crop. That is to say, using the Penman-Monteith formula, the reference crop evapotranspiration (ET 0 ) is calculated according to the current environmental climate parameters. According to the type of crop and the current growth stage, the crop coefficient (Kc) is found or calculated from relevant databases or literature. Multiply ET 0 by Kc to obtain the actual water requirement of the crop. For example, assume that the ET0 for a certain day has been calculated to be 5 mm / day, and for a certain crop at the current growth stage, its Kc is 0.8.

[0211] Then, the actual water requirement ETc of the crop is:

[0212] ETc = 5 mm / day × 0.8 = 4 mm / day

[0213] This means that under these environmental conditions, the actual daily water requirement of this crop is 4 mm of water depth.

[0214] After obtaining the actual water requirement of the crops, the target farmland is divided into high water requirement areas, medium water requirement areas, and low water requirement areas according to the actual water requirement of the crops. That is to say, by statistically analyzing the actual water requirement data of the crops, the overall distribution and change trend are understood. According to the water requirement characteristics of the crops and the actual situation of the farmland, a reasonable water requirement division area is formulated, and the farmland is divided into high water requirement areas, medium water requirement areas, and low water requirement areas. Among them, the high water requirement area means that the supply of irrigation water sources should be guaranteed first; the medium water requirement area means that the irrigation time and irrigation amount should be reasonably arranged according to the actual water requirement of the crops and the soil moisture; the low water requirement area means that the irrigation frequency and irrigation amount can be appropriately reduced to avoid water resource waste caused by over-irrigation.

[0215] By dividing the water requirement areas of the target farmland, precise irrigation can be carried out according to the actual needs of the crops in different areas, avoiding the situations of over-irrigation or under-irrigation, thereby improving the irrigation efficiency. It can also optimize the irrigation strategy, reduce water resource consumption, reduce the negative impact of agriculture on the environment, and contribute to the sustainable development of agriculture.

[0216] In one implementation manner of this embodiment, based on the farmland water-saving control model and the soil moisture parameters of each water requirement area, the predicted irrigation values of each water requirement area are obtained, including:

[0217] S610. According to the soil moisture parameters of each water requirement area, determine the real-time soil water content, farmland water holding capacity, and crop wilting critical data of each water requirement area;

[0218] S620. Input the real-time soil water content, farmland water holding capacity, and crop wilting critical data of each water requirement area into the farmland water-saving control model to obtain the predicted irrigation values of each water requirement area.

[0219] First, according to the soil moisture parameters of each water requirement area, determine the real-time soil water content, farmland water holding capacity, and crop wilting critical data of each water requirement area. In this embodiment, the crop wilting critical data refers to the critical data of the water content when the crops begin to show wilting symptoms when the soil moisture is insufficient to a certain extent. That is to say, by regularly monitoring and comprehensively analyzing the soil moisture parameters of each water requirement area, the real-time soil water content, farmland water holding capacity, and crop wilting critical data can be determined. Another implementation manner of determining the real-time soil water content, farmland water holding capacity, and crop wilting critical data of each water requirement area in this embodiment is that the real-time soil water content of each water requirement area can be obtained through a soil moisture sensor; the farmland water holding capacity can be measured by simulating different soil moisture conditions and measuring the soil moisture holding capacity. The crop wilting critical data can be determined by observing the changes in the physiological indexes of the crops (such as leaf water potential, stomatal conductance, etc.) and combining the soil moisture content.

[0220] Subsequently, the real-time soil water content, field water holding capacity, and crop wilting critical data of each water demand area are input into the farmland water-saving control model to obtain the predicted irrigation value of each water demand area. In this embodiment, the predicted irrigation value refers to the amount of water that needs to be irrigated in each water demand area within a specific time period calculated according to the farmland water-saving control model. That is to say, by inputting key parameters such as real-time soil water content, field water holding capacity, and crop wilting critical data, and combining information such as the growth stage of the crop, soil type, and climate conditions, the predicted irrigation value of each water demand area can be obtained through the farmland water-saving control model.

[0221] By obtaining the predicted irrigation value of each water demand area, the irrigation plan can be dynamically adjusted according to real-time data to achieve precise irrigation, which can not only improve irrigation efficiency, reduce water resource waste, but also promote the healthy growth of crops, increase yield and quality.

[0222] In one implementation manner of this embodiment, the method further includes:

[0223] S710. For any water demand area, if the real-time soil water content is less than the crop wilting critical data, it is in an emergency irrigation state, and all preset intelligent irrigation valves in the water demand area are opened;

[0224] S720. If the real-time soil water content is greater than or equal to the crop wilting critical data and less than or equal to the field water holding capacity, it is in a demand-based irrigation state, and the intelligent irrigation valves in the water demand area are opened according to the predicted irrigation value of each water demand area;

[0225] S730. If the real-time soil water content is greater than or equal to the field water holding capacity, it is in a closed irrigation state, and all preset intelligent irrigation valves in the water demand area are closed.

[0226] For any water demand area, if the real-time soil water content is less than the crop wilting critical data, it is in an emergency irrigation state, and all preset intelligent irrigation valves in the water demand area are opened. That is to say, when the real-time soil water content is lower than the crop wilting critical data, it means that the crop is already in a state of severe water shortage. If irrigation is not carried out in time, it may cause the growth of the crop to be hindered or even die. Then this area is considered to be in an emergency irrigation state. When it is determined that a certain water demand area is in an emergency irrigation state, immediate measures need to be taken for irrigation. In this case, all preset intelligent irrigation valves in this area can be opened. The intelligent irrigation valve is an irrigation device that can be automatically opened or closed according to preset conditions, and it can achieve remote control and precise irrigation through wireless communication with the cloud platform.

[0227] Subsequently, if the real-time soil water content is greater than or equal to the crop wilting critical data and less than or equal to the field water holding capacity, it is in the state of on-demand irrigation. According to the predicted irrigation value of each water demand area, the intelligent irrigation water valves in the water demand area are opened. That is to say, when the real-time soil water content is greater than or equal to the crop wilting critical data, it indicates that the moisture in the soil can still meet the basic needs of the crops, and the crops will not show wilting symptoms due to water shortage. When the real-time soil water content is less than or equal to the field water holding capacity, it means that the soil has not reached its maximum water retention capacity and there is still room to absorb and store more water. When the real-time soil water content is between the crop wilting critical data and the field water holding capacity, the farmland is in the state of on-demand irrigation. This means that although the crops will not be immediately damaged due to water shortage, according to the growth stage of the crops, climatic conditions and the predicted evapotranspiration, the farmland may still require appropriate irrigation to maintain the optimal soil moisture condition. According to the predicted irrigation value, the intelligent irrigation water valves in the water demand area can be remotely controlled to carry out irrigation according to the predetermined irrigation plan and irrigation volume.

[0228] If the real-time soil water content is greater than or equal to the field water holding capacity, it is in the closed irrigation state, and all preset intelligent irrigation water valves in the water demand area are closed. That is to say, when the real-time soil water content reaches or exceeds the field water holding capacity, it means that the soil has absorbed the water within its maximum water holding capacity range, and continued irrigation at this time will cause water overflow and waste. When the real-time soil water content is greater than or equal to the field water holding capacity, the farmland is in the closed irrigation state. This means that the soil already contains enough water to meet the needs of the crops, and the water retention capacity of the soil has reached its limit and cannot absorb more water. At this time, if irrigation continues, the water will not be effectively absorbed by the soil but will flow out of the farmland, resulting in waste of water resources. In the closed irrigation state, all preset intelligent irrigation water valves in the water demand area should be closed. The intelligent irrigation system is usually equipped with sensors and controllers that can monitor the soil moisture condition in real time and automatically adjust the irrigation plan according to the preset conditions. When the real-time soil water content reaches or exceeds the field water holding capacity, the intelligent irrigation system should automatically close the irrigation water valve and stop the irrigation operation.

[0229] By determining the irrigation state of the water demand area, the irrigation volume and irrigation time can be controlled more precisely, thereby optimizing the utilization of water resources, helping to reduce water resource waste in the irrigation process, and improving the utilization efficiency of irrigation water. It can also meet the water requirements of crops at different growth stages, helping to increase the yield and quality of crops.

[0230] The embodiment of the present application provides a cloud platform, Figure 2 showing a schematic structural diagram of a cloud platform provided by the embodiment of the present application, as Figure 2 shown, and an agricultural irrigation water-saving control method, including:

[0231] 01 Geographic data management module, which is used to determine the geographical location of the target farmland through the geographic data management module;

[0232] 02 Data acquisition module, which is used to determine the historical rainfall corresponding to the geographical location of the target farmland and the historical soil moisture parameters of the target farmland in the first preset database;

[0233] 03 Regular feature determination module, which is used to determine the farmland water requirement regular features of the target farmland based on the historical rainfall and historical soil moisture parameters;

[0234] 04 Model construction module, which is used to construct a farmland water-saving control model according to the farmland water requirement regular features;

[0235] 05 Information acquisition module, which is used to acquire the crop information of the target farmland;

[0236] 06 Area division module, which is used to divide the water requirement areas of the target farmland based on the crop information;

[0237] 07 Data collection module, which is used to collect the soil moisture parameters of each water requirement area by using the data collection module;

[0238] 08 Irrigation value determination module, which is used to obtain the predicted irrigation value of each water requirement area based on the farmland water-saving control model and the soil moisture parameters of each water requirement area;

[0239] 09 Real-time irrigation value acquisition module, which is used to acquire the real-time irrigation value of each water requirement area;

[0240] 10 Real-time regulation module, which is used to perform real-time regulation on the preset intelligent irrigation valves in each water requirement area according to the predicted irrigation value and the real-time irrigation value.

[0241] An embodiment of the present application also provides an electronic device, including:

[0242] A memory configured to store instructions; and

[0243] A processor configured to call instructions from the memory and be able to implement the above-mentioned water-saving control method for agricultural irrigation when executing the instructions.

[0244] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0245] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0246] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0247] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0248] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0249] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory. The memory is an example of computer-readable media.

[0250] A computer-readable medium includes permanent and non-permanent, removable and non-removable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0251] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0252] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A water-saving control method for agricultural irrigation, characterized in that: Applied to a cloud platform, the method includes: Obtain the geographical location of the target farmland; Determining in a first preset database the historical rainfall corresponding to the geographical location of the target farmland and the historical soil moisture parameters of the target farmland; Determining the farmland water demand characteristics of the target farmland based on the historical rainfall and the historical soil moisture parameters; According to the characteristics of farmland water demand law, a farmland water-saving control model is constructed; Acquiring crop information of the target farmland; Based on the crop information, dividing the water demand area of ​​the target farmland; Collecting soil moisture parameters of each water-demanding area in real time; Based on the farmland water-saving control model and the soil moisture parameters of each water-demanding area, an estimated irrigation value for each water-demanding area is obtained; Get real-time irrigation values ​​for each water-demanding area; Based on the predicted irrigation value and the real-time irrigation value, a water valve control instruction is generated to perform real-time control on the preset intelligent irrigation water valve in each of the water-demanding areas.

2. The method according to claim 1, characterized in that The historical soil moisture parameters include historical soil moisture data, historical soil density data and historical soil pH value data.

3. The method according to claim 2, characterized in that The determining of the farmland water demand characteristics of the target farmland based on the historical rainfall and the historical soil moisture parameters includes: Calculate the original soil moisture content of the target farmland using the historical soil moisture data and a preset soil moisture content formula; Obtain historical farmland soil moisture content and historical artificial irrigation water injection volume in a second preset database; Using a preset Pearson correlation coefficient formula, the correlation coefficient between the historical farmland soil moisture content and the historical soil pH value data is calculated to obtain a soil pH value influencing factor; The soil pH influencing factor, the original moisture content of the farmland soil and the historical soil density data are subjected to parameter fitting to obtain a soil saturated moisture content pressure time-varying coefficient, wherein the soil saturated moisture content pressure time-varying coefficient is used to characterize a time-varying nonlinear characteristic parameter of the farmland soil under vertical pressure; Determine the cumulative irrigation flow volume by using the historical rainfall and the historical artificial irrigation injection volume; Perform parameter fitting on the cumulative irrigation flow, the historical artificial irrigation water injection, and the soil saturated moisture content pressure time-varying coefficient to obtain the characteristic coefficient of farmland water demand law; In a preset farmland water demand law characteristic database, the time-varying nonlinear characteristics of farmland irrigation water corresponding to the farmland water demand law characteristic coefficient of the target farmland are determined, and the time-varying nonlinear characteristics of farmland irrigation water are used to characterize the farmland water demand law characteristics.

4. The method according to claim 1, characterized in that: The method of constructing a farmland water-saving control model according to the farmland water demand law characteristics comprises: Determine the upper limit and lower limit of irrigation water injection of the target farmland according to the water demand law characteristics of the farmland; Constructing a multi-objective function, wherein the multi-objective function includes an objective function of minimizing irrigation water injection amount, an objective function of minimizing irrigation cost, and an objective function of maximizing crop yield, wherein the objective function of minimizing irrigation water injection amount is a primary objective function, and the objective function of minimizing irrigation cost and the objective function of maximizing crop yield are secondary objective functions; The irrigation water injection upper limit and the irrigation water injection lower limit are respectively used as the first objective constraint condition and the second objective constraint condition of the objective function of minimizing the irrigation water injection amount; Using the secondary objective function as the third objective constraint of the primary objective function; A farmland water-saving control model is constructed by combining the first objective constraint condition, the second objective constraint condition, the third objective constraint condition and the multi-objective function.

5. The method according to claim 4, characterized in that The step of determining the upper limit and the lower limit of the irrigation water injection of the target farmland comprises: Obtain the number of dry days and average rainfall of crops in a preset time period; Acquire the types of crops planted in the target farmland, and determine the growth water requirement coefficient, crop water requirement and soil water retention capacity of the current crop types in a first preset database; Obtaining a drought impact coefficient by the ratio of the average rainfall to a preset daily standard rainfall; Multiplying the drought impact coefficient by the number of drought days for the crop to obtain a drought impact index; Calculate the relative humidity index by using the preset relative humidity index formula through the water requirement of the crop, the water retention capacity of the soil and the average rainfall; Adding the relative humidity index and the drought impact index to obtain a comprehensive index of crop water deficit; Multiplying the crop water deficit comprehensive index by the growth water requirement coefficient to obtain the irrigation water lower limit; Obtaining the total irrigated area and total available irrigation water volume of the target farmland; The available water volume per unit area is calculated by using the total available irrigation water volume and the total irrigated farmland area; The highest value between the available water per unit area and the water requirement of the crops is used as the upper limit of irrigation water injection for the target farmland.

6. The method according to claim 1, characterized in that The crop information includes crop types and crop growth stages, and the dividing of the water demand area of ​​the target farmland based on the crop information includes: Determine in a first preset database the crop coefficient corresponding to the crop type and the crop growth stage, the crop coefficient being used to characterize the proportionality coefficient between the actual water demand of the crop at different growth stages and the potential evapotranspiration; Get current environmental climate parameters; The reference crop transpiration is obtained by using the Penman-Montes formula through the environmental climate parameters; Multiplying the reference crop transpiration by the crop coefficient to obtain the actual water requirement of the crop; According to the actual water demand of the crops, the target farmland is divided into a high water demand area, a medium water demand area and a low water demand area.

7. The method according to claim 1, characterized in that The method of obtaining the estimated irrigation value of each water-demanding area based on the farmland water-saving control model and the soil moisture parameters of each water-demanding area includes: Determine the real-time soil moisture content, farmland water holding capacity and crop wilting critical data of each water-demanding area according to the soil moisture parameters of each water-demanding area; The real-time soil moisture content, farmland water holding capacity and crop wilting critical data of each water-demanding area are input into the farmland water-saving control model to obtain the estimated irrigation value of each water-demanding area.

8. The method according to claim 7, characterized in that The method further comprises: For any of the water-demanding areas, if the real-time soil moisture content is less than the crop wilting critical data, it is an emergency irrigation state, and all preset intelligent irrigation valves in the water-demanding area are opened; If the real-time soil moisture content is greater than or equal to the crop wilting critical data and less than or equal to the farmland water holding capacity, it is in the on-demand irrigation state, and the intelligent irrigation water valve in the water demanding area is opened according to the estimated irrigation value of each water demanding area; If the real-time soil moisture content is greater than or equal to the farmland water holding capacity, the irrigation is closed and all preset intelligent irrigation valves in the water-demanding area are closed.

9. A cloud platform, characterized in that: The water-saving control method for agricultural irrigation as claimed in any one of claims 1 to 8 comprises: A geographic data management module, used to determine the geographic location of the target farmland through the geographic data management module; A data acquisition module, used to determine the historical rainfall corresponding to the geographical location of the target farmland and the historical soil moisture parameters of the target farmland in a first preset database; A regularity characteristic determination module, used for determining the regularity characteristic of farmland water demand of the target farmland based on the historical rainfall and the historical soil moisture parameter; A model building module, used to build a farmland water-saving control model according to the farmland water demand law characteristics; An information acquisition module, used to acquire crop information of the target farmland; A region division module, used for dividing the water demand area of ​​the target farmland based on the crop information; A data acquisition module, used to collect soil moisture parameters of each water-demanding area using the data acquisition module; An irrigation value determination module, used for obtaining an estimated irrigation value for each water-demanding area based on the farmland water-saving control model and the soil moisture parameters of each water-demanding area; A real-time irrigation value acquisition module is used to obtain the real-time irrigation value of each water-demanding area; The real-time control module is used to perform real-time control on the preset intelligent irrigation water valve in each water-demanding area according to the predicted irrigation value and the real-time irrigation value.

10. An electronic device, characterized in that: The cloud platform according to claim 9 is deployed, comprising: a memory configured to store instructions; and A processor is configured to call the instructions from the memory and implement the water-saving control method for agricultural irrigation according to any one of claims 1 to 8 when executing the instructions.

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