Irrigation district water resource scheduling method and system, electronic equipment and storage medium

Abstract

The invention discloses an irrigation district water resource scheduling method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining current crop state data, analyzing the current crop state data through a crop water demand model, and obtaining an expected soil moisture content; wherein the crop water demand model is constructed through irrigation area crop data; obtaining the current soil moisture content, and determining expected water distribution data according to the current soil moisture content and the expected soil moisture content; performing irrigation analysis through a soil moisture movement model according to the expected water distribution data, and determining target water distribution data; wherein the soil moisture movement model is constructed through irrigation area soil data; and performing irrigation area water distribution according to the target water distribution data. According to the embodiment of the invention, the utilization rate of water resources can be effectively improved, the adaptability of the crop growth environment is improved, and accurate management of irrigation water resources is realized. The method can be widely applied to the technical field of water resource distribution.

Description

Technical Field

[0001] The present application relates to the technical field of water resource allocation, and in particular to an irrigation area water resource scheduling method, system, electronic equipment and storage medium. Background Art

[0002] "Water conservation" in traditional water resource management is centered on "balance between supply and demand", focusing on reducing water intake, while research on actual water resource consumption in various water use processes is relatively weak. In related technologies, irrigation water allocation decisions are mainly based on water allocation models established based on experience or assumed conditions. The water allocation results are difficult to match the actual water demand of crops, which is prone to problems such as over-irrigation and unnecessary water resource consumption, making it difficult to provide a suitable growth environment for crops and manage irrigation water resources.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose an irrigation area water resource scheduling method, system, electronic device and storage medium, which can effectively improve the utilization rate of water resources, improve the adaptability of crop growth environment, and realize accurate management of irrigation water resources.

[0005] To achieve the above purpose, one aspect of an embodiment of the present application provides a method for scheduling water resources in an irrigation area, the method comprising the following steps:

[0006] Acquire current crop status data, and analyze the current crop status data through a crop water requirement model to obtain an expected soil moisture content; wherein the crop water requirement model is constructed through irrigation area crop data;

[0007] Acquire current soil moisture content to determine expected water distribution data according to the current soil moisture content and the expected soil moisture content;

[0008] Performing irrigation analysis based on the expected water distribution data through a soil moisture movement model to determine target water distribution data; wherein the soil moisture movement model is constructed using soil data in the irrigation area;

[0009] Water is distributed to the irrigation area according to the target water distribution data.

[0010] In some embodiments, before performing the step of acquiring the current crop status data to analyze the current crop status data through the crop water requirement model to obtain the expected soil moisture content, the method further includes:

[0011] Acquire the irrigation area crop data to construct a crop data set based on the irrigation area crop data; wherein the irrigation area crop data includes crop type information, crop growth period information and crop water requirement information;

[0012] An initial neural network model is constructed to train the initial neural network model using the crop data set to construct the crop water requirement model.

[0013] In some embodiments, before performing the irrigation analysis based on the expected water distribution data through the soil water movement model to determine the target water distribution data, the method further includes:

[0014] Acquire irrigation area soil data; wherein the irrigation area soil data includes soil physical property parameters and soil moisture parameters;

[0015] The soil moisture movement model is constructed according to the soil physical property parameters and the soil moisture parameters.

[0016] In some embodiments, constructing the soil moisture movement model according to the soil physical property parameters and the soil moisture parameters includes:

[0017] A soil evaporation model is constructed by using the soil physical property parameters;

[0018] A soil infiltration model is constructed by using the soil moisture parameters.

[0019] In some embodiments, the step of performing irrigation analysis based on the expected water distribution data through a soil moisture movement model to determine target water distribution data includes:

[0020] determining soil infiltration data by using the soil infiltration model;

[0021] Determine water evaporation loss data through the soil evaporation model;

[0022] The soil water requirement is determined according to the expected water distribution data and the water evaporation loss data, so as to determine the target water distribution data by combining the soil water requirement, the soil infiltration data and the crop root and stem water absorption data.

[0023] In some embodiments, after performing irrigation analysis based on the expected water distribution data through a soil moisture movement model to determine target water distribution data, the method further includes:

[0024] Acquire preset water resource data; wherein the preset water resource data includes hydrological data, groundwater resource data and surface water resource data;

[0025] A water resource assessment model is constructed according to the preset water resource data to determine a target irrigation mode through the water resource assessment model.

[0026] In some embodiments, after performing irrigation analysis based on the expected water distribution data through a soil moisture movement model to determine target water distribution data, the method further includes:

[0027] Get feedback on irrigation effects;

[0028] The crop water requirement model and the soil moisture movement model are optimized according to the irrigation effect feedback information.

[0029] To achieve the above purpose, another aspect of the embodiment of the present application provides an irrigation area water resources scheduling system, the system comprising:

[0030] The first module is used to obtain current crop status data, and to analyze the current crop status data through a crop water requirement model to obtain an expected soil moisture content; wherein the crop water requirement model is constructed through irrigation area crop data;

[0031] The second module is used to obtain the current soil moisture content to determine the expected water distribution data according to the current soil moisture content and the expected soil moisture content;

[0032] The third module is used to perform irrigation analysis based on the expected water distribution data through a soil moisture movement model to determine target water distribution data; wherein the soil moisture movement model is constructed based on the irrigation area soil data;

[0033] The fourth module is used to distribute water to the irrigation area according to the target water distribution data.

[0034] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising:

[0035] at least one processor;

[0036] at least one memory for storing at least one program;

[0037] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0038] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0039] The embodiments of the present application include at least the following beneficial effects: The present application provides an irrigation area water resource scheduling method, system, electronic device and storage medium, which obtains the current crop status data and analyzes the current crop status data through the crop water requirement model constructed from the irrigation area crop data to obtain the expected soil moisture content. Then, the embodiment of the present invention obtains the current soil moisture content to determine the expected water distribution data according to the current soil moisture content and the expected soil moisture content, and then performs irrigation analysis according to the expected water distribution data through the soil moisture movement model constructed from the irrigation area soil data, determines the target water distribution data, and distributes water to the irrigation area according to the target water distribution data, thereby realizing accurate management of irrigation water resources. It is easy to understand that the embodiment of the present invention constructs a crop water requirement model through irrigation area crop data, which can more accurately determine the irrigation water required for crops according to the current state of the crops, and then predict the expected soil moisture content. At the same time, irrigation analysis is performed through a soil moisture movement model to combine soil conditions and expected water distribution data for analysis, thereby reducing the waste of water resources, effectively improving the utilization rate of water resources, and making the irrigation water volume more in line with crop needs, thereby improving the adaptability of the crop growth environment and realizing accurate management of irrigation water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flowchart of the steps of the irrigation area water resources scheduling method provided by an embodiment of the present invention;

[0041] Figure 2 is a flowchart of steps for constructing a crop water requirement model provided by an embodiment of the present invention;

[0042] Figure 3 is a flowchart of the steps of constructing a soil moisture movement model provided by an embodiment of the present invention;

[0043] Figure 4 It is a flowchart of the steps of constructing a soil moisture movement model according to soil physical property parameters and soil moisture parameters provided by an embodiment of the present invention;

[0044] Figure 5 It is a flowchart of the steps of performing irrigation analysis based on expected water distribution data through a soil moisture movement model to determine target water distribution data provided by an embodiment of the present invention;

[0045] Figure 6 is a flowchart of steps for determining a target irrigation mode through a water resource assessment model provided by an embodiment of the present invention;

[0046] Figure 7 is a flowchart of steps for optimizing a model according to irrigation effect feedback information provided by an embodiment of the present invention;

[0047] Figure 8It is a schematic diagram of the overall process of the irrigation area water resources scheduling method provided by an embodiment of the present invention;

[0048] Fig. 9 It is a structural schematic diagram of an irrigation area water resources scheduling system provided by an embodiment of the present invention;

[0049] Fig.10 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0051] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0052] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0054] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0055] SWAT model: It is a physically based model that can simulate continuous time series. The basin hydrological process simulated by the SWAT model is divided into the land phase of the hydrological cycle (i.e., the runoff and slope confluence part) and the confluence phase of the hydrological cycle (i.e., the river confluence part). The former controls the input of water, sand, nutrients and chemicals in the main river channel of each sub-basin, while the latter determines the transportation and movement of water, sand and other substances from the river network to the basin outlet.

[0056] MODFLOW model: It is a groundwater simulation software model used to simulate the flow of groundwater in porous media, and can also solve problems related to the flow of groundwater in fractured media.

[0057] "Water conservation" in traditional water resource management is centered on "balance between supply and demand", focusing on reducing water intake, while research on actual water resource consumption in various water use processes is relatively weak. In related technologies, irrigation water allocation decisions are mainly based on water allocation models established based on experience or assumed conditions. The water allocation results are difficult to match the actual water demand of crops, which is prone to problems such as over-irrigation and unnecessary water resource consumption, making it difficult to provide a suitable growth environment for crops and manage irrigation water resources.

[0058] In view of this, an irrigation area water resource scheduling method, device, equipment and medium are provided in the embodiment of the present application. The scheme obtains the current crop status data, and analyzes the current crop status data through the crop water requirement model constructed by the irrigation area crop data to obtain the expected soil moisture content. Then, the embodiment of the present invention obtains the current soil moisture content to determine the expected water distribution data according to the current soil moisture content and the expected soil moisture content, and then performs irrigation analysis according to the expected water distribution data through the soil moisture movement model constructed by the irrigation area soil data, determines the target water distribution data, and performs irrigation area water distribution according to the target water distribution data, thereby realizing the precise management of irrigation water resources, effectively improving the utilization rate of water resources, and improving the adaptability of the crop growth environment.

[0059] The irrigation district water resource scheduling method provided in the embodiment of the present application relates to the technical field of water resource allocation. The irrigation district water resource scheduling method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the irrigation district water resource scheduling method, etc., but is not limited to the above forms.

[0060] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0061] Figure 1 is an optional flow chart of the irrigation district water resources scheduling method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S140.

[0062] Step S110: obtaining current crop status data, and analyzing the current crop status data through a crop water requirement model to obtain an expected soil moisture content. The crop water requirement model is constructed through irrigation area crop data.

[0063] Step S120: obtaining the current soil moisture content to determine the expected water distribution data according to the current soil moisture content and the expected soil moisture content.

[0064] Step S130: Perform irrigation analysis based on the expected water distribution data through a soil water movement model to determine target water distribution data, wherein the soil water movement model is constructed based on the irrigation area soil data.

[0065] Step S140: distribute water to the irrigation area according to the target water distribution data.

[0066] During the working process of this specific embodiment, the embodiment of the present invention first obtains the current crop state data, so as to analyze the current crop state data through the crop water requirement model to obtain the expected soil moisture content. Specifically, the current crop state data in the embodiment of the present invention refers to the growth state of the crop, such as crop type, growth cycle, growth state, etc. Correspondingly, the irrigation area crop data in the embodiment of the present invention refers to the relevant data of the crops in the irrigation area, such as crop type information, growth period and water requirement information, etc. Among them, the embodiment of the present invention constructs a crop water requirement model through the irrigation area crop data, so as to predict the optimal water content of different crops in each growth period through the crop water requirement model. Accordingly, the embodiment of the present invention inputs the collected current crop state data into the crop water requirement model to predict the optimal water content of the current crop growth, and then obtains the expected soil moisture content. Next, the embodiment of the present invention obtains the current soil moisture content to determine the expected water distribution data according to the current soil moisture content and the expected soil moisture content. Specifically, the current soil moisture content in the embodiment of the present invention refers to the moisture content of the current soil in the area to be irrigated, such as soil moisture. Accordingly, the embodiment of the present invention calculates the expected water distribution data by comparing the current soil moisture content with the expected soil moisture content required for crop growth. Furthermore, the embodiment of the present invention performs irrigation analysis through a soil moisture movement model based on the expected water distribution data to determine the target water distribution data. Specifically, the soil moisture movement model in the embodiment of the present invention is constructed by the irrigation area soil data. Among them, the irrigation area soil data in the embodiment of the present invention refers to the characteristic parameters of the irrigation area soil, such as permeability, evaporation and other parameters. Accordingly, the embodiment of the present invention constructs a soil moisture movement model through the collected irrigation area soil data to predict the moisture changes of the irrigation area soil. The embodiment of the present invention predicts the soil moisture changes in the corresponding area through the soil moisture movement model, and then performs irrigation analysis in combination with the expected water distribution data, so as to determine the water distribution amount required for the area, that is, the target water distribution data. Finally, the embodiment of the present invention performs water distribution in the irrigation area according to the determined target water distribution data, so as to achieve accurate scheduling of water resources in the irrigation area.

[0067] Reference Figure 2 In some embodiments of the present invention, before obtaining the current crop status data to analyze the current crop status data through the crop water requirement model to obtain the expected soil moisture content, the irrigation area water resource scheduling method provided by the embodiment of the present invention further includes but is not limited to the following steps:

[0068] Step S210: Acquire the irrigation area crop data to construct a crop data set based on the irrigation area crop data, wherein the irrigation area crop data includes crop type information, crop growth period information, and crop water requirement information.

[0069] Step S220: construct an initial neural network model, train the initial neural network model using the crop data set, and construct a crop water requirement model.

[0070] In this specific embodiment, the embodiment of the present invention first obtains the irrigation area crop data to construct a crop data set based on the irrigation area crop data. Specifically, the irrigation area crop data in the embodiment of the present invention includes crop type information, crop growth period information and crop water requirement information. Among them, the crop type information in the embodiment of the present invention refers to the crop types in the irrigation area, such as wheat, corn, etc., and different types of crops have different water requirements. Correspondingly, the crop growth period information refers to the growth stage of the crop, such as the sowing period, the growth period, the fruiting period, etc., and the water demand and water demand frequency of the crop at different growth stages will also be different. In addition, the crop water requirement information in the embodiment of the present invention refers to the total water demand per unit area of ​​the crop at different growth stages. Accordingly, the embodiment of the present invention constructs a crop data set by obtaining the irrigation area crop data. For example, the embodiment of the present invention performs data preprocessing on the obtained irrigation area crop data, such as data cleaning and correction. Then, the embodiment of the present invention divides the preprocessed data into a training data set and a test data set according to a ratio of 8:2, so as to construct a crop data set. Further, the embodiment of the present invention constructs an initial neural network model to train the initial neural network model through the crop data set to obtain a crop water requirement model. Specifically, the initial neural network model in the embodiment of the present invention refers to a deep learning neural network model, such as a radial basis function neural network model, a BP neural network model, and a convolutional neural network model. Accordingly, the embodiment of the present invention trains the initial neural network model by constructing a training data set to find the soil moisture content that maximizes crop yield, and then tests the prediction effect of the trained model through a test data set, and adjusts the model parameters to improve the prediction accuracy, thereby constructing a crop water requirement model. Accordingly, according to the crop type and the corresponding crop growth period, the crop water requirement model obtained through training in the embodiment of the present invention can predict the optimal soil moisture content for crop growth, that is, the expected soil moisture content.

[0071] Reference Figure 3 In some embodiments of the present invention, before performing irrigation analysis based on the expected water distribution data through the soil moisture movement model to determine the target water distribution data, the irrigation area water resource scheduling method provided by the embodiment of the present invention also includes but is not limited to the following steps:

[0072] Step S310: Acquire irrigation area soil data, wherein the irrigation area soil data includes soil physical property parameters and soil moisture parameters.

[0073] Step S320: constructing a soil moisture movement model based on soil physical property parameters and soil moisture parameters.

[0074] In this specific embodiment, the embodiment of the present invention first obtains the soil data of the irrigation area. Specifically, the soil data of the irrigation area in the embodiment of the present invention includes soil physical property parameters and soil moisture parameters. Among them, the soil physical property parameters in the embodiment of the present invention refer to the physical and chemical property parameters of the soil, such as the fixed ratio of the soil, the air exchange rate on the soil surface, the characteristic radius of the soil pores, etc. In addition, the soil moisture parameters in the embodiment of the present invention refer to the soil moisture content or related parameters of the infiltration performance, such as the infiltration amount, the penetration depth, the critical moisture content, etc. Further, the embodiment of the present invention constructs a soil moisture movement model based on the soil physical property parameters and the soil moisture parameters. Specifically, the embodiment of the present invention constructs a soil moisture movement model through the soil data of the irrigation area, that is, the soil physical property parameters and the soil moisture parameters, so as to analyze the evaporation and infiltration of the water in the irrigation area soil. Among them, since in the process of irrigating the irrigation area, not only the water demand of the crop needs to be considered, but also the movement and change state of the water in the soil of the irrigation area will affect the irrigation effect. Therefore, the embodiment of the present invention constructs a soil moisture movement model by combining the soil physical property parameters and the soil moisture parameters, so as to determine the target water distribution data through the soil moisture movement model.

[0075] Reference Figure 4 In some embodiments of the present invention, a soil moisture movement model is constructed based on soil physical property parameters and soil moisture parameters, including:

[0076] Step S410: constructing a soil evaporation model through soil physical property parameters.

[0077] Step S420: constructing a soil infiltration model through soil moisture parameters.

[0078] In this specific embodiment, the soil moisture movement model includes a soil evaporation model and a soil infiltration model. Among them, the embodiment of the present invention first constructs a soil evaporation model through soil physical property parameters. Specifically, the soil evaporation model in the embodiment of the present invention refers to a model used to simulate the evaporation process of soil surface moisture. Accordingly, the evaporation of soil surface moisture will directly affect the loss rate of irrigation water. Among them, the evaporation of soil surface moisture will reduce the amount of water in the soil available for plant roots to absorb, that is, when calculating the irrigation needs of plants, it is necessary to take into account the water loss caused by soil evaporation. In addition, the amount of soil evaporation depends on multiple factors. The soil evaporation model in the embodiment of the present invention is modeled by measuring part of the data, and then by measuring these environmental parameters, the evaporation model is applied to predict the evaporation under given conditions, so as to more accurately formulate a plant irrigation plan. Among them, since soil is a porous medium, the embodiment of the present invention is based on the gas theory in the porous medium and combines the soil physical property parameters to construct a soil evaporation model, as shown in the following formula (1):

[0079]

[0080] Where E represents soil evaporation, a 1 is the fixed ratio of the soil, u is the air exchange rate on the soil surface, ψ(u) is a function of the air exchange rate, δ p is the characteristic radius of soil pores, is the soil water vapor saturation (soil saturated water content), is the water vapor saturation of the soil air layer, D p is the water vapor diffusion coefficient, ρ pn is the water vapor density, ρ pv is the density of air at the reference plane and ρ is the density of water.

[0081] At the same time, the embodiment of the present invention constructs a soil permeability model through soil moisture parameters. Specifically, the soil permeability model in the embodiment of the present invention is used to simulate the process of water entering the soil and transferring in the soil, including how the water generated by rainfall or irrigation penetrates the soil nominal and reaches the root area downward. Among them, the permeability of the soil in the embodiment of the present invention affects the movement speed and arrival depth of the irrigation water in the soil. For example, when the permeability of the soil is very low, the water may accumulate in the shallower soil layer and cannot effectively replenish the water in the deep root layer, which will lead to water waste and may cause soil erosion. When the permeability is too strong, the irrigation water may quickly infiltrate below the plant roots and exceed the plant root water absorption layer, which also causes a waste of water resources. Therefore, the embodiment of the present invention constructs a soil permeability model through soil moisture parameters, as shown in the following formula (2):

[0082]

[0083] Where I is the infiltration rate, is the initial moisture content, is the critical moisture content, is the saturated water content, h is the penetration depth, and the penetration depth is positively correlated with the size of the pores without considering the soil expansion factor. The larger the pores, the deeper the penetration depth. Accordingly, the permeability in the embodiment of the present invention is shown in the following formula (3):

[0084]

[0085] Accordingly, substituting the above formula (2) into formula (3), we get the following formula (4):

[0086]

[0087] It is easy to understand that evaporation and infiltration are closely linked in the entire water cycle. For example, in an irrigation cycle, the soil evaporation model may predict that a considerable proportion of the irrigation water will be lost through evaporation, while the soil infiltration model can predict the amount of water remaining after the irrigation water passes through the soil layer. Among them, after the water penetrates into the root zone, it will also participate in the soil evaporation process to a certain extent. Therefore, when formulating an irrigation plan, the embodiment of the present invention takes into account both the evaporation amount and the water infiltration behavior of the soil to ensure that the appropriate amount of water can be supplied to the plants while reducing evaporation losses and excess infiltration.

[0088] It should be noted that the semi-empirical and semi-theoretical formula for calculating the evaporation capacity (water surface evaporation) E0 in the related art is shown in the following formula (5):

[0089]

[0090] Among them, Δ = (es-ea) / (Ts-Ta), which represents the derivative of saturated water vapor pressure with respect to temperature, that is, the slope of the saturated water vapor pressure curve at the average temperature Ta, which is only a function of temperature; R is the radiation balance, and the empirical calculation formula established based on relevant data is shown in the following formula (6):

[0091]

[0092] Among them, Ra is astronomical radiation, α is the reflectivity of the evaporation surface, n / N is the percentage of sunshine, σ is the Stefan-Boltzmann constant, Ta is the temperature expressed in absolute temperature; L is the latent heat of evaporation; γ is the psychrometric constant, which is numerically equal to CpPa / 0.622L, Cp is the specific heat at constant pressure, and Pa is the atmospheric pressure; Ea is the air drying power, which is related to the air saturation difference and the Dalton evaporation wind speed function, and the actual situation in each region may be different. In practice, relevant personnel (such as farmers or water resource managers) usually use local or regional specific data to perform these calculations with the help of weather stations or online decision support systems.

[0093] Reference Figure 5 In some embodiments of the present invention, irrigation analysis is performed based on the expected water distribution data through a soil moisture movement model to determine the target water distribution data, including but not limited to the following steps:

[0094] Step S510: Determine soil infiltration data through a soil infiltration model.

[0095] Step S520: Determine water evaporation loss data through a soil evaporation model.

[0096] Step S530: determining soil water requirement according to the expected water distribution data and the water evaporation loss data, and determining target water distribution data by combining the soil water requirement, soil infiltration data and crop root and stem water absorption data.

[0097] In this specific embodiment, the embodiment of the present invention first determines the soil infiltration data through the soil infiltration model, and determines the water evaporation loss data through the soil evaporation model, and then determines the soil water requirement according to the expected water distribution data and the water evaporation loss data, so as to determine the target water distribution data by combining the soil water requirement, the soil infiltration data and the crop root and stem water absorption data. Specifically, the soil infiltration data in the embodiment of the present invention refers to the soil infiltration capacity parameter of the relevant irrigation area, and the water evaporation loss data refers to the soil surface evaporation parameter of the relevant irrigation area, which reflects the water evaporation loss in the soil. Accordingly, the calculation of the irrigation water amount in the embodiment of the present invention should be based on the soil water requirement (depending on the plant water requirement and the evaporation loss predicted by the soil evaporation model) and the soil infiltration capacity (determined by the soil infiltration model). To this end, to determine the appropriate irrigation water amount, it is necessary to combine the plant water requirement rate, the soil surface evaporation and the soil infiltration capacity. Among them, the plant water requirement rate in the embodiment of the present invention is different in different growth stages of the plant, so the embodiment of the present invention first determines the expected water distribution data in combination with the crop water requirement model. At the same time, the embodiment of the present invention evaluates the evaporation of the soil surface through the soil evaporation model to supplement the water loss in addition to the water demand of the plant, that is, the soil water demand is determined according to the expected water distribution data and the water evaporation loss data. Accordingly, the embodiment of the present invention evaluates the soil infiltration rate through the soil permeability model to adapt to the soil type, so that the irrigated water can reach the root zone without excessive infiltration, and the final required irrigation water volume, that is, the target water distribution data, is determined by combining the evaluated soil infiltration data and the soil water demand. In addition, the irrigation water volume in the embodiment of the present invention is also affected by the growth state of the crop rhizomes and the water absorption capacity of the rhizomes. Therefore, the present invention combines the soil water demand, the soil infiltration data and the crop rhizomes water absorption data to determine the final target water distribution data. Among them, the water absorption rate of the crop in the embodiment of the present invention is related to the area occupied by the rhizomes of the crop (that is, the growth depth of the rhizomes). Accordingly, during the growth period of the crop rhizomes, the method of watering thoroughly can increase the growth depth of the rhizomes of the crop, and the rhizomes of the crop can increase their water absorption rate and affect the amount of irrigation water. Among them, the crop water absorption rate in the embodiment of the present invention is shown in the following formula (7):

[0098]

[0099] Where P is the water absorption rate of the crop, k 0 is the water absorption coefficient of the crop in the soil, K is the proportionality coefficient, d is the root length, It is the soil moisture content where the rhizome ends.

[0100] Accordingly, the water absorption of the crop (crop root and stem water absorption data) in the embodiment of the present invention is as shown in the following formula (8):

[0101] S=Pt (8)

[0102] Among them, t is the water absorption time.

[0103] It is easy to understand that the root depth of the crop in the embodiment of the present invention directly affects its ability to absorb water and nutrients; deeper roots can access deeper water, and at the same time, the permeability of the soil determines the depth to which water can penetrate, thereby affecting the position where the root system can absorb water. When the soil permeability is high, irrigation water and rainfall water can quickly penetrate through the surface soil and penetrate downward. This requires crops to have deeper roots to reach the moist soil layer to absorb the required water. Therefore, in this case, the irrigation strategy needs to ensure that water can fully penetrate to the depth that the crop root system can reach, promoting the downward growth of the root system. Correspondingly, soil with low permeability causes water to be retained in the shallower soil layer, increasing the chance of evaporation, and may be unfavorable for the deep development of the root system. Therefore, in this case, the irrigation amount needs to be adjusted appropriately to avoid poor root development or disease due to oversaturation of surface water. The embodiment of the present invention determines the corresponding irrigation strategy and irrigation amount by combining the relationship between soil permeability and root development. For example, for soil with good permeability, the embodiment of the present invention adopts a deep irrigation strategy to ensure that water can reach the deep root system, while for soil with poor permeability, the embodiment of the present invention adopts surface irrigation combined with appropriate humidification to promote the development of the root system at an appropriate depth.

[0104] Reference Figure 6 In some embodiments of the present invention, after performing irrigation analysis based on the expected water distribution data through the soil moisture movement model to determine the target water distribution data, the irrigation area water resource scheduling method provided by the embodiment of the present invention also includes but is not limited to the following steps:

[0105] Step S610: Acquire preset water resource data, wherein the preset water resource data includes hydrological data, groundwater resource data and surface water resource data.

[0106] Step S620: constructing a water resource assessment model according to preset water resource data to determine a target irrigation mode through the water resource assessment model.

[0107] In this specific embodiment, the embodiment of the present invention first obtains preset water resource data, and then constructs a water resource assessment model based on the preset water resource data to determine the target irrigation mode through the water resource assessment model. Specifically, the preset water resource data in the embodiment of the present invention includes hydrological data, groundwater resource data and surface water resource data. Among them, the hydrological data in the embodiment of the present invention includes rainfall data, river flow data and evaporation and transpiration data. Correspondingly, rainfall data refers to the regional rainfall record of the irrigation area, which is obtained through ground meteorological stations or satellite data. In addition, the embodiment of the present invention sets a hydrological station in the river to measure the flow rate and flow of the river, so as to determine the corresponding water resource input, that is, river flow data. At the same time, the embodiment of the present invention determines the evaporation through meteorological data and estimates the transpiration in combination with the vegetation coverage. Accordingly, the groundwater resource data in the embodiment of the present invention includes aquifer characteristic data, water level data and pumping test data. Among them, in the process of groundwater resource assessment, the embodiment of the present invention first analyzes the physical properties of the aquifer, such as thickness, permeability and lithology. At the same time, the embodiment of the present invention installs a groundwater level meter at a preset position to regularly record water level change data, and conducts a pumping test on the aquifer to evaluate its refilling capacity and flow characteristics. In addition, in the process of evaluating surface water resources, the embodiment of the present invention first monitors the water levels of lakes and reservoirs to evaluate their total reserves, and at the same time calculates the seasonal flow of rivers and the amount of water transfer between basins through a hydrological model. Further, the embodiment of the present invention calculates the water balance at the basin scale, that is, precipitation-evaporation-transpiration-surface runoff-groundwater outflow = change in water volume in the basin. At the same time, the embodiment of the present invention performs numerical simulation of water balance through relevant hydrological models, such as SWAT (Soil and Water Assessment Tool) model and MODFLOW model. Then, according to the value of the water balance, it is judged whether there is a water shortage in the area. If it is lower than the preset threshold, it is judged that there is a water shortage in the area. Accordingly, the embodiment of the present invention determines the irrigation mode adopted according to the state of determining the water balance, such as sprinkler irrigation, drip irrigation or visible irrigation.

[0108] Exemplarily, in constructing a water resource assessment model, the embodiment of the present invention first collects data on water source quantity (such as river flow, lake and reservoir water level), water quality (such as pollution, salinity), groundwater level and hydrological cycle (such as seasonal distribution of rainfall, drought period) from upstream and downstream. For example, the embodiment of the present invention uses a certain time period as a standard (such as spring and summer are generally rainy seasons with abundant water resources, while winter water resources are scarce), analyzes the collected data through hydrological methods and geographic information system (GIS) technology, and evaluates the water resource situation of the entire river basin. Among them, the embodiment of the present invention uses hydrological models such as the SWAT model to simulate the water cycle of the river basin and predict the seasonal changes of water sources. At the same time, according to the water supply and demand of the upstream and downstream, the available amount of water resources in the current river basin is calculated, and the water resources are divided into different levels. For example, the embodiment of the present invention sets different thresholds. If the river flow exceeds a certain threshold in a specific season, it is considered that the water resources are sufficient, and if it is lower than another threshold, it is considered that the water resources are insufficient. Accordingly, the embodiment of the present invention determines the appropriate irrigation method according to the above water resource level. For example, when the assessed water source volume is X cubic meters per second, the embodiment of the present invention sets a preset rule. If the available water volume in the basin is at least three times the water demand of the irrigation area, Y cubic meters per second (i.e., X / Y ≥ 3), the water resources are irrigated by wet irrigation. In the case of insufficient water resources, drip irrigation, sprinkler irrigation and other water-saving irrigation methods are adopted.

[0109] Reference Figure 7 In some embodiments of the present invention, after performing irrigation analysis based on the expected water distribution data through the soil moisture movement model to determine the target water distribution data, the irrigation area water resource scheduling method provided by the embodiment of the present invention also includes but is not limited to the following steps:

[0110] Step S710: Obtain irrigation effect feedback information.

[0111] Step S720: Optimize the crop water requirement model and the soil moisture movement model according to the irrigation effect feedback information.

[0112] In this specific embodiment, the embodiment of the present invention obtains irrigation effect feedback information to optimize the crop water requirement model and the soil moisture movement model according to the irrigation effect feedback information. Specifically, the irrigation effect feedback information in the embodiment of the present invention refers to the actual irrigation effect data fed back by relevant personnel. Among them, the embodiment of the present invention sets a user feedback channel to obtain the actual irrigation conditions, experience data and suggestion information fed back by relevant personnel (such as farmers and irrigation management personnel) to obtain irrigation effect feedback information. At the same time, the embodiment of the present invention sets a feedback loop to timely input the obtained irrigation effect feedback information into the crop water requirement model, that is, the soil moisture movement model for iterative learning, thereby realizing iterative update of the model and parameter optimization.

[0113] In addition, in the embodiment of the present invention, the model parameters are initially set based on historical data, literature or expert knowledge, and the model parameters are calibrated by the actual irrigation and crop growth data collected recently, so as to be closer to the actual situation. Accordingly, the embodiment of the present invention continuously collects real-time data on climate, soil conditions, crop growth and irrigation applications to identify the difference between the parameters in the model and the actual irrigation effect by analyzing these data, and then compares the output of the existing model (such as predicted irrigation demand) with the actual observation (such as actual crop water consumption, soil moisture changes, etc.) to evaluate the model accuracy and determine the target and direction of improvement. Then, according to the evaluation results, the embodiment of the present invention adjusts the model parameters, including water transfer coefficient, crop coefficient, evaporation data, rainfall and irrigation events. At the same time, after the model parameters are adjusted, the embodiment of the present invention performs model verification to ensure that the adjusted model can better predict the actual situation. Accordingly, in the optimization process, the embodiment of the present invention automatically adjusts the model parameters through statistical or machine learning methods, such as genetic algorithms, gradient descent methods, etc., to find the optimal parameter configuration, thereby ensuring that the learning algorithm runs on the basis of sufficient and diverse data.

[0114] It should be noted that the embodiments of the present invention use field tests or experiments to verify whether the model optimization improves irrigation efficiency and crop yield in practice. Among them, the embodiments of the present invention apply the model in the control area and compare the results with the control area where the optimization model is not used to determine the effect of the model application. At the same time, a part of the data in the embodiments of the present invention is used as a training set to optimize the model, and the other part is used as a test set to verify the accuracy and universality of the model.

[0115] It should be noted that in some embodiments of the present invention, data information from various sources, such as soil moisture, temperature, pH value, permeability, organic matter content and other data, is obtained through corresponding data acquisition modules. Among them, soil temperature directly affects the evaporation rate of water in the soil and the physiological activities of crops. On the one hand, the increase in soil temperature will accelerate the evaporation of water. On the other hand, too low or too high soil temperature may inhibit the growth of crops. Accordingly, in the irrigation system of the embodiment of the present invention, the most suitable irrigation time (for example, irrigation at low temperatures to reduce evaporation losses) and amount (irrigation may need to be reduced under cold conditions) are determined by monitoring the soil temperature. In addition, the soil pH value affects the availability of nutrients, so the soil pH value can guide the quality adjustment of irrigation water. For example, the pH of irrigation water is adjusted to balance acidic or alkaline soils to provide an environment that is more conducive to crop absorption. The pH value also affects the activity of soil microorganisms and the root health of crops. The soil pH value is combined in the irrigation decision of the embodiment of the present invention to avoid crop growth problems caused by pH inappropriateness. At the same time, the above data can be used for subsequent monitoring of crop growth and estimation of its water demand, and these data also determine the types of crops in the irrigation area. In addition, the embodiment of the present invention also collects climate information, such as temperature, precipitation, wind speed, relative humidity, solar radiation, etc., to calculate the evaporation of the soil.

[0116] Next, the scheme of the embodiment of the present invention is described in detail and explained in conjunction with a specific application example of irrigation area water resources scheduling:

[0117] For example, Figure 8 As shown, Figure 8: This is a schematic diagram of the overall process of the irrigation area water resource scheduling method provided by an embodiment of the present invention. Specifically, the embodiment of the present invention first obtains the irrigation area soil data to establish the irrigation area soil evaporation model and the soil infiltration model. At the same time, the embodiment of the present invention obtains the crop data to train the neural network model according to the crop data to obtain the water demand of the crop under different growth cycles. Finally, according to the soil moisture and crop state, combined with the predicted water demand, the soil evaporation model and the soil infiltration model, accurate water application is achieved. For example, a farmland planted with wheat, two varieties of wheat are planted respectively, and the root lengths of the two varieties are different, and other conditions are the same. In the same soil, according to the different lengths of the root stems, the amount of water that can be absorbed is also different. For wheat varieties with more developed root stems, the amount of irrigation required is lower than that of wheat varieties with less developed root stems. When two farmlands with different soil permeability coefficients plant the same variety of wheat, when the root stem growth state during the growth process is consistent (the root stem growth state of wheat in the soil with a high permeability coefficient is better than that in the soil with a low permeability coefficient), and the root system of the wheat penetrates 30 cm into the soil. Accordingly, if soil testing shows that the soil permeability in the area is high, the irrigation plan needs to ensure that water can penetrate to a depth of at least 30 centimeters to fully supply the water needs of the wheat roots. At this time, the embodiment of the present invention increases the amount of water for a single irrigation, or uses irrigation methods such as drip irrigation to reduce surface evaporation to ensure that water can penetrate deep into the ground.

[0118] It is easy to understand that the embodiments of the present invention analyze data using prediction models and machine learning algorithms to achieve precise control of irrigation water volume and timing, reduce water resource waste, and ensure that water is effectively used when and where it is needed. At the same time, the embodiments of the present invention predict the growth status of plant roots and rhizomes and their root water absorption capacity by real-time monitoring of factors such as soil moisture and crop growth status, and calculate the water required for irrigation by integrating soil infiltration models and soil evaporation models, so as to further enable the irrigation system to better meet the growth needs of crops and improve crop productivity and quality. In addition, the embodiments of the present invention implement irrigation strategies based on the water required by plants, which can reduce unnecessary irrigation and significantly reduce water consumption, helping to protect and maintain the health of the natural water cycle and water ecosystem.

[0119] See also Fig. 9 The embodiment of the present application also provides an irrigation area water resource scheduling system, which can implement the above irrigation area water resource scheduling method, and the system includes:

[0120] The first module 810 is used to obtain current crop status data, and to analyze the current crop status data through a crop water requirement model to obtain an expected soil moisture content. The crop water requirement model is constructed through irrigation area crop data.

[0121] The second module 820 is used to obtain the current soil moisture content to determine the expected water distribution data according to the current soil moisture content and the expected soil moisture content.

[0122] The third module 830 is used to perform irrigation analysis based on the expected water distribution data through the soil water movement model to determine the target water distribution data. The soil water movement model is constructed based on the soil data of the irrigation area.

[0123] The fourth module 840 is used to distribute water to the irrigation area according to the target water distribution data.

[0124] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0125] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above irrigation area water resource scheduling method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0126] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0127] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0128] The processor 910 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0129] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 920, and the processor 910 calls and executes the irrigation area water resources scheduling method of the embodiment of this application;

[0130] Input / output interface 930, used to implement information input and output;

[0131] Communication interface 940, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0132] bus 950 , which transmits information between the various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0133] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0134] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned irrigation area water resource scheduling method is implemented.

[0135] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0136] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0137] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0138] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0139] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0141] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0142] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0143] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0146] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0147] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for dispatching water resources in an irrigation area, characterized in that: The method comprises the following steps: Acquire current crop status data, and analyze the current crop status data through a crop water requirement model to obtain an expected soil moisture content; wherein the crop water requirement model is constructed through irrigation area crop data; Acquire current soil moisture content to determine expected water distribution data according to the current soil moisture content and the expected soil moisture content; Performing irrigation analysis based on the expected water distribution data through a soil moisture movement model to determine target water distribution data; wherein the soil moisture movement model is constructed using soil data in the irrigation area; Water is distributed to the irrigation area according to the target water distribution data.

2. The method according to claim 1, characterized in that Before executing the step of acquiring the current crop status data and analyzing the current crop status data through the crop water requirement model to obtain the expected soil moisture content, the method further includes: Acquire the irrigation area crop data to construct a crop data set based on the irrigation area crop data; wherein the irrigation area crop data includes crop type information, crop growth period information and crop water requirement information; An initial neural network model is constructed to train the initial neural network model using the crop data set to construct the crop water requirement model.

3. The method according to claim 1, characterized in that Before performing the irrigation analysis based on the expected water distribution data through the soil moisture movement model to determine the target water distribution data, the method further includes: Acquire irrigation area soil data; wherein the irrigation area soil data includes soil physical property parameters and soil moisture parameters; The soil moisture movement model is constructed according to the soil physical property parameters and the soil moisture parameters.

4. The method according to claim 3, characterized in that The soil moisture movement model is constructed according to the soil physical property parameters and the soil moisture parameters, including: A soil evaporation model is constructed by using the soil physical property parameters; A soil infiltration model is constructed by using the soil moisture parameters.

5. The method according to claim 4, characterized in that The step of performing irrigation analysis based on the expected water distribution data through a soil moisture movement model to determine target water distribution data includes: determining soil infiltration data by using the soil infiltration model; Determine water evaporation loss data through the soil evaporation model; The soil water requirement is determined according to the expected water distribution data and the water evaporation loss data, so as to determine the target water distribution data by combining the soil water requirement, the soil infiltration data and the crop root and stem water absorption data.

6. The method according to claim 1, characterized in that After performing irrigation analysis according to the expected water distribution data through a soil moisture movement model to determine target water distribution data, the method further includes: Acquire preset water resource data; wherein the preset water resource data includes hydrological data, groundwater resource data and surface water resource data; A water resource assessment model is constructed according to the preset water resource data to determine a target irrigation mode through the water resource assessment model.

7. The method according to claim 1, characterized in that After performing irrigation analysis according to the expected water distribution data through a soil moisture movement model to determine target water distribution data, the method further includes: Get feedback on irrigation effects; The crop water requirement model and the soil moisture movement model are optimized according to the irrigation effect feedback information.

8. An irrigation area water resources scheduling system, characterized in that: The system comprises: The first module is used to obtain current crop status data, and to analyze the current crop status data through a crop water requirement model to obtain an expected soil moisture content; wherein the crop water requirement model is constructed through irrigation area crop data; The second module is used to obtain the current soil moisture content to determine the expected water distribution data according to the current soil moisture content and the expected soil moisture content; The third module is used to perform irrigation analysis based on the expected water distribution data through a soil moisture movement model to determine target water distribution data; wherein the soil moisture movement model is constructed based on the irrigation area soil data; The fourth module is used to distribute water to the irrigation area according to the target water distribution data.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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