Irrigation calculation method, equipment and medium for low-lying crops

Abstract

The present application discloses an irrigation calculation method, device, and medium for low-lying crops, and relates to the field of agricultural irrigation technology. The method includes: obtaining image data of the irrigation area and meteorological data within a first preset time period, the image data including remote sensing data and LIDAR data, and the intelligent irrigation model including a crop sub-model, a soil sub-model, and a terrain sub-model; inputting the remote sensing data into the crop sub-model to obtain crop data, inputting the remote sensing data into the soil sub-model to obtain soil data, and inputting the LIDAR data into the terrain sub-model to obtain terrain data; using MapReduce to divide and calculate the crop data, soil data, and meteorological data according to preset dimensions to obtain the total water supply of the irrigation area. The present application implements the terrain slope calculated by LIDAR data through the above method, formulates a more accurate irrigation strategy after combining the terrain slope, and performs feature extraction, classification, and other operations in parallel on multiple nodes, thereby significantly reducing processing time.

Description

Technical Field

[0001] The present application relates to the field of agricultural irrigation technology, and in particular to an irrigation calculation method, equipment, and medium for low-growing crops. Background Art

[0002] The formulation of irrigation plans requires the use of modern scientific and technological means. Scientific and reasonable irrigation plans can not only improve the efficiency of water resource utilization, but also promote the growth and development of crops, enhance soil fertility, and reduce agricultural production costs.

[0003] However, existing irrigation plans often ignore terrain slope, which can lead to poor irrigation system design and uneven distribution of irrigation water across farmland, resulting in over-irrigation in low-lying areas and under-irrigation in high-slope areas. Furthermore, the high computational complexity of using remote sensing data as a data source can make the development of irrigation plans time-consuming and prevent timely adjustments to crop growth and climatic conditions. This can also lead to water shortages or overwatering of crops during critical growth stages, impacting their normal growth and yield. Furthermore, the inability to simulate the next phase means it's impossible to predict future risks and issues.

[0004] Through the above analysis, the problems and defects of the existing technology are as follows:

[0005] Existing irrigation schemes ignore the issue of terrain slope, and use remote sensing data as a data source, which requires a large amount of calculation, takes a long time to calculate, and has low accuracy, resulting in water shortages or excessive water for crops during critical growth stages. Summary of the Invention

[0006] The embodiments of the present application provide an irrigation calculation method, equipment, and medium for low-lying crops, which can solve the problem that irrigation schemes in the prior art ignore the terrain slope, and use remote sensing data as a data source, which has a large amount of calculation, long calculation time, and low accuracy, resulting in water shortage or excessive water for crops during critical growth stages.

[0007] In a first aspect, an embodiment of the present application provides an irrigation calculation method for low crops, the method comprising: obtaining image data of the irrigation area and meteorological data within a first preset time period, the image data comprising remote sensing data and LIDAR data, the intelligent irrigation model comprising a crop sub-model, a soil sub-model and a terrain sub-model; inputting the remote sensing data into the crop sub-model to obtain crop data, inputting the remote sensing data into the soil sub-model to obtain soil data, and inputting the LIDAR data into the terrain sub-model to obtain terrain data; dividing and calculating the crop data, soil data and meteorological data according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

[0008] In one implementation of the present application, based on LIDAR data as three-dimensional point cloud data of the irrigation area, the LIDAR data is input into a terrain sub-model to obtain terrain data, specifically including: preprocessing the three-dimensional point cloud data and converting it into grid data; calculating the slope value of the grid unit based on the elevation information of the grid data; and statistically analyzing the slope values ​​of the grid units to obtain the slope value of the irrigation area.

[0009] In one implementation of the present application, after obtaining the slope value of the irrigation area, the method further includes: when the slope value is lower than a first preset threshold, adopting a directional irrigation method; when the slope value is higher than the first preset threshold, adopting a spraying method.

[0010] In one implementation of the present application, remote sensing data is input into a crop sub-model to obtain crop data, specifically including: converting the remote sensing data into surface temperature values; extracting and calculating the surface temperature values ​​during the day and at night to obtain the surface temperature difference value; obtaining the soil thermal inertia based on the full-band albedo and the surface temperature difference value; and obtaining the soil moisture content based on the inversion of the soil thermal inertia.

[0011] In one implementation of the present application, remote sensing data is input into a soil sub-model to obtain soil data, specifically including: extracting the color characteristics, texture characteristics, and shape characteristics of crops in the remote sensing data to obtain the type and growth stage of the crops; counting the number of pixels of the same crop type and growth stage to obtain the actual area of ​​the crops.

[0012] In one implementation of the present application, crop data, soil data, and meteorological data are divided and calculated according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area, specifically including: dividing the actual area according to a preset ratio to obtain irrigation units; obtaining the type and growth stage of the crop, soil moisture content, and meteorological data of the irrigation unit in the Map stage to obtain the unit water supply; and summarizing and verifying the unit water supply in the Reduce stage to obtain the total water supply.

[0013] In one implementation of the present application, after irrigating the irrigation area, the method further includes: irrigating the irrigation area if verification is passed; after the irrigation is completed, obtaining the type and growth stage of the crop and the soil moisture content within a second preset time period to provide feedback on the unit water supply; and based on the feedback, simulating the water demand of the crop at different growth stages.

[0014] In one implementation of the present application, after obtaining image data of the irrigation area and meteorological data within a preset time period, the method also includes: aligning and removing noise from the three-dimensional point cloud data; and performing radiation correction, atmospheric correction, and geometric correction on the remote sensing images based on the remote sensing data including the remote sensing images.

[0015] In a second aspect, an embodiment of the present application also provides an irrigation computing device for low crops, the device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: obtain image data of the irrigation area and meteorological data within a first preset time period, the image data comprising remote sensing data and LIDAR data, the intelligent irrigation model comprising a crop sub-model, a soil sub-model and a terrain sub-model; input remote sensing data into the crop sub-model to obtain crop data, input remote sensing data into the soil sub-model to obtain soil data, input LIDAR data into the terrain sub-model to obtain terrain data; divide and calculate the crop data, soil data and meteorological data according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

[0016] In a third aspect, an embodiment of the present application also provides a non-volatile computer storage medium for irrigation calculations for low crops, storing computer executable instructions, and the computer executable instructions are set to: obtain image data of the irrigation area and meteorological data within a first preset time period, the image data including remote sensing data and LIDAR data, and the intelligent irrigation model including a crop sub-model, a soil sub-model and a terrain sub-model; input the remote sensing data into the crop sub-model to obtain crop data, input the remote sensing data into the soil sub-model to obtain soil data, and input the LIDAR data into the terrain sub-model to obtain terrain data; divide and calculate the crop data, soil data and meteorological data according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

[0017] The embodiments of the present application provide an irrigation calculation method, device, and medium for low-lying crops. After considering the terrain slope, more accurate irrigation strategies can be formulated to avoid water accumulation in low-lying areas due to poor drainage, while ensuring that highlands also receive sufficient water, thereby reducing water waste, improving irrigation efficiency, and lowering agricultural water costs. The terrain slope information calculated through LIDAR data can more accurately understand the terrain characteristics of the irrigation area, thereby formulating irrigation strategies that better meet actual needs. MapReduce can split image data into multiple small blocks and perform feature extraction, classification, and other operations in parallel on multiple nodes, thereby significantly reducing processing time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1A flowchart of an irrigation calculation method for short crops provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the internal structure of an irrigation computing device for short crops provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0022] The embodiments of the present application provide an irrigation calculation method, equipment, and medium for low-lying crops, which solve the problem that the irrigation scheme in the prior art ignores the terrain slope, and uses remote sensing data as a data source, which requires a lot of calculation and leads to water shortage or excessive water for crops during critical growth stages.

[0023] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0024] Figure 1 This is a flow chart of an irrigation calculation method for low-lying crops provided in an embodiment of the present application. Figure 1 As shown, an irrigation calculation method for short crops provided in an embodiment of the present application specifically includes the following steps:

[0025] Step 10: Acquire image data of the irrigation area and meteorological data within a first preset time period, where the image data includes remote sensing data and LIDAR data, and the intelligent irrigation model includes a crop sub-model, a soil sub-model, and a terrain sub-model.

[0026] In the embodiment of the present application, due to the consideration of the terrain slope factor, only the irrigation plan for short crops is optimized and calculated. Tall crops may affect the elevation information of the ground due to their own growth reasons. The amount of remote sensing data and LIDAR data is huge, and MapReduce is used for calculation. In the MapReduce calculation process, the calculation task is first decomposed, and each sub-model can be regarded as a Map. Then, the calculation results of the sub-model are merged and analyzed in the Reduce operation.

[0027] Step 20: Input the remote sensing data into the crop sub-model to obtain crop data, input the remote sensing data into the soil sub-model to obtain soil data, and input the LIDAR data into the terrain sub-model to obtain terrain data.

[0028] In this step, the amount of remote sensing data and LIDAR data is huge, so a distributed parallel computing method is adopted to enable the three sub-models (ie, three MAPs) to perform calculations simultaneously.

[0029] As an optional embodiment, based on LIDAR data as three-dimensional point cloud data of the irrigation area, the LIDAR data is input into the terrain sub-model to obtain terrain data, which may specifically include: Step 2011: preprocessing the three-dimensional point cloud data and converting it into grid data; calculating the slope value of the grid unit based on the elevation information of the grid data; and statistically analyzing the slope values ​​of the grid units to obtain the slope value of the irrigation area.

[0030] In this step, LIDAR technology uses laser beams to accurately measure the surface, which can obtain three-dimensional coordinate information of the surface and provide more accurate and detailed terrain data. LIDAR data contains a large amount of discrete three-dimensional point cloud data. Directly calculating the slope of such a large amount of three-dimensional point cloud data will be very time-consuming. Therefore, the three-dimensional point cloud data can be converted into regular grid data. Each grid cell contains an elevation value. The slope value of the grid cell can then be calculated by the elevation difference between adjacent grid cells and the distance between them. The slope values ​​of the grid cells are counted, the highest and lowest values ​​are removed, and the remaining data is averaged to obtain the slope value of the target area, which simplifies the data structure and calculation complexity.

[0031] For example, Point 1: (x = 0, y = 0, z = 100), Point 2: (x = 10, y = 0, z = 101), Point 3: (x = 0, y = 10, z = 99), Point 4: (x = 10, y = 10, z = 100), 10x10 meter grid, Grid Cell 1 (0-10m, 0-10m): z = 100 (based on Point 1), Grid Cell 2 (10-20m, 0-10m): z = 101 (based on Point 2), Grid Cell 3 (0-10m, 10-20m): z = 99 (based on Point 3), Grid Cell 4 (10-20m, 10-20m): z = 100 (based on Point 4); calculate the distance from Grid Cell 1 to Grid Cell 2 (along the x-axis): Slope = arctan (Δz / Δx), where Δz = 101-100 = 1 meter, Δx = 10 meters, and Slope = arctan (101) ≈ 5.71 degrees. The slope value of the target area can be obtained by performing the calculations in sequence.

[0032] Furthermore, after obtaining the slope value of the irrigation area, the method may further include: Step 2012: if the slope value is lower than a first preset threshold, adopting a directional irrigation method;

[0033] In this step, for example, if the slope value is less than 30 degrees, the water outlet of the water pipe can be fixed on the ground and flow directly to the roots of the plants.

[0034] Step 2014: When the slope value is higher than the first preset threshold, a spraying method is adopted.

[0035] In this step, for example, if the slope value is higher than 30 degrees, the water outlet of the water pipe can be set to a spraying shape, with one set at a certain distance to form a more uniform surface coverage.

[0036] As another optional embodiment, remote sensing data is input into the soil sub-model to obtain soil data, which may specifically include: step 2021: converting the remote sensing data into surface temperature values; step 2022: extracting the surface temperature values ​​during the day and at night respectively and calculating to obtain the surface temperature difference value; step 2023: obtaining the soil thermal inertia based on the full-band albedo and the surface temperature difference value; step 2024: obtaining the soil moisture content based on the inversion of the soil thermal inertia.

[0037] In this step, the formula for soil thermal inertia is P = (KQc)^1 / 2, where P is thermal inertia, K is thermal conductivity, Q is soil density, and c is specific heat. However, since these parameters are difficult to obtain directly, apparent thermal inertia (ATI) is usually used instead. The formula for apparent thermal inertia is ATI = (1–A) / (Td-Tn), where A is the full-band albedo, Td and Tn are the surface temperatures during the day and night, respectively. Using the known difference in surface temperature ΔT between day and night and the full-band albedo A, in this embodiment of the application, the remote sensing image is a corrected remote sensing image, and the pixel value corresponding to the atmospherically corrected image is the reflectivity. In this way, the ATI value can be calculated. Soil with large thermal inertia has high water content, while soil with small thermal inertia has low water content. Therefore, the soil water content is obtained by inverting the soil thermal inertia. It is worth noting that this method is applicable to bare soil or soil with low vegetation cover.

[0038] For example, the daytime surface temperature Td = 30 degrees, the nighttime surface temperature Tn = 20 degrees, the full-band albedo A = 0.2, the surface temperature difference value is 10 degrees, ATI = (1–A) / (Td-Tn) = 0.08, and the ATI value is 0.08, which is relatively low compared to a lower ATI value, that is, the soil moisture content is relatively low.

[0039] As another optional embodiment, remote sensing data is input into a crop sub-model to obtain crop data, which may specifically include: step 2031: extracting color features, texture features, and shape features of crops in remote sensing data to obtain the type and growth stage of the crops; step 2032: counting the number of pixels of the same crop type and growth stage to obtain the actual area of ​​the crops.

[0040] In this step, the spectral information of crops in remote sensing images, namely the color characteristics, texture characteristics and shape characteristics of the crops, is used to identify different crop types and their growth stages. Through spectral analysis, different crops and different growth stages have specific reflectance characteristics in different bands; the crop types such as wheat, corn, rice, etc. are obtained, and their growth stages such as seedling stage, growth stage, maturity stage, etc. are estimated. The pixels are counted to obtain the area, which is more conducive to calculating the total water supply.

[0041] For example, wheat typically appears lighter green in its early stages of growth, gradually darkening as it grows. In remote sensing images, this can be determined by calculating the reflectance intensity of the green band (G) to determine whether it belongs to wheat, corn, or another category. Counting the number of pixels classified as wheat and corn, assuming the remote sensing image resolution is 1 meter x 1 meter, each pixel represents an actual area of ​​1 square meter. Multiplying the number of pixels belonging to the same crop type by the area represented by each pixel yields the actual area of ​​that crop type. Assuming the classification results show 10,000 wheat pixels and 8,000 corn pixels, and the remote sensing image resolution is 1 meter x 1 meter, the actual area of ​​wheat = 10,000 x 1 square meter = 10,000 square meters, and the actual area of ​​corn = 8,000 x 1 square meter = 8,000 square meters.

[0042] Step 30: Use MapReduce to divide and calculate the crop data, soil data, and meteorological data according to the preset dimensions to obtain the total water supply of the irrigation area.

[0043] In this step, the amount of crop data, soil data, and meteorological data is huge, so distributed parallel computing is performed again and divided according to preset dimensions. The preset dimensions can be the size of the crop area, date, or temperature, which is further explained below.

[0044] As another optional embodiment, crop data, soil data, and meteorological data are divided and calculated according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area. Specifically, the following may be included: Step 301: Divide the actual area according to a preset ratio to obtain irrigation units; Step 302: Obtain the type and growth stage of the crop, soil moisture content, and meteorological data of the irrigation unit in the Map stage to obtain the unit water supply; Step 303: Summarize and verify the unit water supply in the Reduce stage.

[0045] In this step, the preset dimension is a preset proportion of the actual area. That is, in order to reduce the amount of calculation, a large irrigation area is divided into small irrigation units, which are then summarized. At the same time, the unit water supply of similar irrigation units can be compared to verify the accuracy of the water supply.

[0046] Furthermore, after irrigating the irrigation area, the method may further include:

[0047] Step 304: If the verification is successful, irrigate the irrigation area. Step 305: After the irrigation is completed, obtain the type and growth stage of the crop and the soil moisture content within the second preset time period to provide feedback on the water supply. Step 306: Based on the feedback, simulate the water demand of the crop at different growth stages.

[0048] In this step, the collected data is compared with the preset soil moisture to evaluate the irrigation effect. If the irrigation effect is not good, such as the soil moisture is too low or too high, the water supply needs to be adjusted.

[0049] It is understandable that after obtaining the image data of the irrigation area and the meteorological data within the preset time period, the method may further include: Step 01: registering and removing noise from the three-dimensional point cloud data;

[0050] In this step, 3D point cloud data registration is the process of unifying point cloud data obtained from different perspectives, different times, or different sensors into the same coordinate system. Distance-based filtering can be used to identify noise points, and based on the identification results, the noise points are deleted from the 3D point cloud data or replaced with the average value of the neighboring points.

[0051] Step 02: Based on the remote sensing data including remote sensing images, perform radiation correction, atmospheric correction and geometric correction on the remote sensing images.

[0052] In this step, the remote sensing image can include the main remote sensing image and auxiliary remote sensing image of the target irrigation area. The auxiliary image is used to assist in correction. The radiation correction is to eliminate the radiation error caused by the sensor or external environmental factors in the remote sensing image. The atmospheric correction is to eliminate the influence of the atmosphere on the remote sensing image, such as gas absorption and scattering, to obtain the true reflectivity or radiance information of the surface. The geometric correction is to eliminate the geometric distortion in the remote sensing image. Further cropping can be performed to determine the image of the irrigation area.

[0053] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides an irrigation calculation device for low-lying crops, the structure of which is as follows: Figure 2 shown.

[0054] Figure 2This is a schematic diagram of the internal structure of an irrigation computing device for low-lying crops provided in an embodiment of the present application. Figure 2 As shown, the equipment includes:

[0055] at least one processor 201;

[0056] and, a memory 202 communicatively coupled to the at least one processor;

[0057] In which, the memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 to enable the at least one processor 201 to: obtain image data of the irrigation area and meteorological data within a first preset time period, the image data includes remote sensing data and LIDAR data, and the intelligent irrigation model includes a crop sub-model, a soil sub-model and a terrain sub-model; input the remote sensing data into the crop sub-model to obtain crop data, input the remote sensing data into the soil sub-model to obtain soil data, and input the LIDAR data into the terrain sub-model to obtain terrain data; and divide and calculate the crop data, soil data and meteorological data according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

[0058] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for irrigation calculation of low crops stores computer-executable instructions, wherein the computer-executable instructions are configured to: obtain image data of an irrigation area and meteorological data within a first preset time period, wherein the image data includes remote sensing data and LIDAR data, and the intelligent irrigation model includes a crop sub-model, a soil sub-model, and a terrain sub-model; input the remote sensing data into the crop sub-model to obtain crop data, input the remote sensing data into the soil sub-model to obtain soil data, and input the LIDAR data into the terrain sub-model to obtain terrain data; and divide and calculate the crop data, soil data, and meteorological data according to preset dimensions through MapReduce to obtain the total water supply of the irrigation area.

[0059] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0060] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0061] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0062] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may 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, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0065] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0066] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0067] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0068] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0069] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for calculating irrigation for low-lying crops, characterized in that: The method comprises: Acquire image data of the irrigation area and meteorological data within a first preset time period, wherein the image data includes remote sensing data and LIDAR data, and the intelligent irrigation model includes a crop sub-model, a soil sub-model, and a terrain sub-model; Inputting the remote sensing data into the crop sub-model to obtain crop data, inputting the remote sensing data into the soil sub-model to obtain soil data, and inputting the LIDAR data into the terrain sub-model to obtain terrain data; By using MapReduce, the crop data, soil data, and meteorological data are divided and calculated according to preset dimensions to obtain the total water supply of the irrigation area; Based on the LIDAR data being three-dimensional point cloud data of the irrigation area, inputting the LIDAR data into the terrain sub-model to obtain terrain data specifically includes: Preprocessing the three-dimensional point cloud data and converting it into grid data; Calculating the slope value of the grid cell according to the elevation information of the grid data; Counting the slope values ​​of the grid cells to obtain the slope value of the irrigation area; When the slope value is lower than a first preset threshold, a directional irrigation method is adopted; When the slope value is higher than a first preset threshold, a spraying mode is adopted; Inputting the remote sensing data into the soil sub-model to obtain soil data specifically includes: converting the remote sensing data into surface temperature values; Extract the surface temperature values ​​during the day and night respectively and calculate them to obtain the surface temperature difference value; The soil thermal inertia is obtained according to the full-band albedo and the surface temperature difference value; The soil moisture content is obtained by inverting the soil thermal inertia; Inputting the remote sensing data into the crop sub-model to obtain crop data specifically includes: Extracting color features, texture features, and shape features of crops from the remote sensing data to obtain the type and growth stage of the crops; Count the number of pixels of the same crop type and growth stage to obtain the actual area of ​​the crop; The method of calculating the crop data, soil data, and meteorological data according to preset dimensions by MapReduce to obtain the total water supply of the irrigation area specifically includes: Dividing the actual area according to a preset ratio to obtain irrigation units; In the Map stage, the type and growth stage of the crop, soil moisture content and meteorological data of the irrigation unit are obtained to obtain the unit water supply; In the Reduce phase, the unit water supply is aggregated and verified to obtain the total water supply.

2. The irrigation calculation method for low-lying crops according to claim 1, characterized in that: After obtaining the total water supply, the method further includes: If the verification is passed, irrigating the irrigation area; After the irrigation is completed, obtaining the type and growth stage of the crop and the soil moisture content within a second preset time period to provide feedback on the water supply of the unit; Based on the feedback, the water requirements of the crops at different growth stages are simulated.

3. The irrigation calculation method for low-lying crops according to claim 1, characterized in that: After acquiring the image data of the irrigation area and the meteorological data within a preset time period, the method further includes: Performing registration and noise removal on the three-dimensional point cloud data; Based on the remote sensing data including remote sensing images, radiation correction, atmospheric correction and geometric correction are performed on the remote sensing images.

4. An irrigation calculation device for low-lying crops, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Execute the steps of the irrigation calculation method for low-lying crops as described in any one of claims 1 to 3.

5. A non-volatile computer storage medium for irrigation calculations for low-lying crops, storing computer-executable instructions, characterized in that: The computer executable instructions are configured to: Execute the steps of the irrigation calculation method for low-lying crops as described in any one of claims 1 to 3.

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