A method for calculating irrigation water demand in irrigation districts based on digital twin technology

Abstract

This invention discloses a method for calculating irrigation water demand in irrigation districts based on digital twin technology. This invention relates to the field of irrigation technology and solves the problem that humidity data within an irrigation district is related to soil moisture and other factors, as well as the corresponding light environment, resulting in specific differences in the accuracy of the actual water demand. This invention confirms the actual area of ​​the irrigation district image and, based on this confirmed area, determines the corresponding irrigation water volume, making the confirmed irrigation water volume value more accurate and ensuring sufficient precision. It also assesses the humidity changes within the corresponding irrigation district based on the monitored humidity data for a given period, and then determines the corresponding water demand based on these actual changes. Subsequently, based on the confirmed water volume data and corresponding solar radiation data, the elevation angle of the spray gun is adjusted accordingly, enabling the corresponding irrigation district to achieve better spray irrigation treatment effects and making the calculation of irrigation water demand more accurate.

Description

Technical Field

[0001] This invention relates to the field of irrigation technology, specifically a method for calculating irrigation water demand in irrigation districts based on digital twin technology. Background Technology

[0002] Crops cannot grow without water. Irrigation provides the water needed for crop growth, maintains cell turgor pressure, and ensures the normal functioning of crop physiological activities. For example, water is one of the raw materials in photosynthesis; it participates in the light and dark reactions, providing hydrogen atoms for the production of organic matter. At the same time, proper irrigation can also regulate crop temperature. In hot weather, it removes heat through transpiration, preventing crops from suffering heat damage.

[0003] Irrigation is also crucial for improving crop yield and quality. In arid and semi-arid regions, effective irrigation can help crops that would otherwise struggle due to water shortages to thrive and increase yields. For example, in some water-scarce wheat-growing areas, proper irrigation can increase the number of grains per ear and produce fuller grains, thereby improving wheat yield and quality.

[0004] Digital twins are a technology that tightly integrates a physical entity with its digital model. In irrigation districts, by constructing a digital twin model of the irrigation district, its actual status can be reflected in real time. This model includes a digital representation of physical elements such as the topography, soil type, crop distribution, and irrigation system layout. The crop distribution and related topography require corresponding low-altitude drones to conduct actual surveys, acquire and confirm relevant image data of the designated irrigation district.

[0005] Patent application CN111626892A discloses a method and system for measuring and monitoring water demand in irrigation areas, relating to agricultural irrigation water demand measurement. The method includes: dynamically and automatically calculating the water release volume for each canal system based on the water demand of fields at different times and for different crops; dynamically monitoring irrigation water volume using water metering devices installed on each canal system, promptly reminding managers when to close gates, and forming reasonable irrigation plans for different times, crops, and plots; guiding irrigation operations in the irrigation area, and forming a big data warehouse for automated irrigation in the irrigation area; this invention enables real-time sharing of irrigation area information resources, improves the operational efficiency of the irrigation area, reduces operating costs, provides scientific decision-making basis for irrigation area management departments, and ultimately achieves informatization of irrigation area management, rationalization of water allocation, accurate water measurement, and standardized charging, providing technical support for achieving efficient modern agriculture.

[0006] In traditional irrigation systems, the amount of water required is typically determined based on the area of ​​the irrigation district, and this amount remains fixed once determined. However, in actual irrigation operations, this is not the case. The actual amount of water needed varies due to a variety of factors. For example, the humidity within the irrigation district is closely related to soil moisture, other relevant factors, and sunlight conditions. The interaction of these complex factors leads to a significant difference between the actual water required and the pre-set value, making it impossible to achieve the desired irrigation results when irrigating according to a fixed amount and cycle.

[0007] This problem can be effectively solved by introducing digital twin technology. Digital twin technology can construct a virtual model that closely matches the actual irrigation area. This model not only includes information on the irrigation area's area but also multi-dimensional data such as soil moisture and light conditions. By collecting and analyzing this data in real time, the actual irrigation water required by the irrigation area at different times and under different conditions can be accurately calculated, no longer limited to a fixed value determined by a single area factor. Furthermore, irrigation operations based on this dynamically changing irrigation water data can significantly improve irrigation precision, thereby optimizing irrigation effects and ensuring the precise water requirements for crop growth. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a method for calculating irrigation water demand in irrigation districts based on digital twin technology. This method solves the problem that humidity data in irrigation districts is related to soil moisture and other factors, as well as the corresponding light environment, resulting in specific differences in the accuracy of the actual water demand.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for calculating irrigation water demand in irrigation districts based on digital twin technology, comprising the following steps:

[0010] Step 1: Use a low-altitude UAV to acquire associated images of the irrigation area, and perform edge contour processing on the acquired associated images to confirm the associated irrigation area images. Then, based on the acquisition altitude of the low-altitude UAV when acquiring the corresponding associated images, confirm the actual area parameters of the irrigation area images. The specific method is as follows:

[0011] S11. The Sobel algorithm is used to perform edge detection on the acquired associated image. The coordinates of each pixel in the associated image are labeled as (i, j). Based on the confirmed coordinates, the gradient values ​​associated with the corresponding pixel in the horizontal and vertical directions are determined. The gradient value associated with the corresponding pixel in the horizontal direction is labeled as Gx, and the gradient value associated with the corresponding pixel in the vertical direction is labeled as Gy. Confirm the gradient magnitude G associated with the corresponding pixel. x-y ;

[0012] S12. The gradient magnitude G corresponding to the different pixels associated with each different point coordinate (i, j) in the associated image. x-y The process is repeated sequentially, and the magnitude difference Cz between adjacent pixels is identified, where Cz = |G1|. x-y -G2 x-y |, of which G1 x-y G2 represents the gradient magnitude associated with a group of adjacent pixels. x-y This represents the gradient magnitude associated with another group of pixels in the adjacent pixel group. The magnitude difference Cz is compared with the preset value Y1, where Y1 is a pre-set threshold. If Cz≤Y1, the two groups of adjacent pixels are marked as the same type of points. If Cz>Y1, the two groups of adjacent pixels are marked as different types of points.

[0013] S13. Based on the several sets of similar points identified in this associated image, identify the areas covered by the several sets of similar points and mark them as similar areas. Then mark the similar area with the largest area parameter as the irrigation area and determine the irrigation area image.

[0014] S14. The area parameter of the currently confirmed irrigation area image is calibrated as M. Based on the acquisition altitude H of the low-altitude UAV corresponding to the current image acquisition time, the actual total area parameter Mz of the corresponding irrigation area is confirmed by the formula: Mz = H × C1 × M, where C1 is a preset fixed coefficient factor.

[0015] Step 2: Based on the determined irrigation district image and the associated actual area parameters, determine the irrigation water demand associated with this irrigation district. The specific sub-steps are as follows:

[0016] S21. Based on the actual area parameter Mz confirmed by the corresponding irrigation area image, the irrigation water demand GS is determined by Mz×C2=GS, where C2 is a preset fixed coefficient factor.

[0017] Step 3: Based on the confirmed irrigation water demand of the corresponding irrigation district, and based on the humidity monitoring sensors installed in the corresponding irrigation district to identify the humidity change data generated in the corresponding irrigation district during past irrigation cycles, the irrigation water demand associated with the next irrigation cycle is readjusted based on the humidity change data of past irrigation cycles. The specific method is as follows:

[0018] S31. When this irrigation district is irrigated for the first time, after the irrigation process of three irrigation cycles is executed, the irrigation cycle is a preset cycle. When the irrigation cycle arrives, irrigation is carried out directly. The irrigation amount associated in the first three irrigation cycles is the confirmed irrigation water demand. When the fourth irrigation cycle or other subsequent irrigation cycles are executed, the humidity change data associated with the previous irrigation cycles are confirmed.

[0019] S32. Based on the confirmed irrigation cycle and the humidity change data associated with the corresponding time, generate a humidity change curve belonging to the corresponding irrigation cycle. The initial time of the corresponding humidity change curve is calibrated to time 0. Several sets of humidity change curves after calibration are placed in the same two-dimensional coordinate system. Based on the different humidity data associated with the same time, the mean value of several humidity data at the same time is confirmed, the mean value point is locked, and the determined mean value points are connected to determine the humidity change curve to be processed for the next set of irrigation cycles.

[0020] S33. Based on the confirmed humidity change curve to be processed and the preset humidity threshold (the humidity threshold is a preset value), the relevant curves above the humidity threshold are marked as upper curves, and the relevant curves below the humidity threshold are marked as lower curves. The sum of several sets of humidity data associated with the upper curve is used to confirm the upper total parameter ZS, and the sum of several sets of humidity data associated with the lower curve is used to confirm the lower total parameter ZX. The correlation difference Zc is confirmed by Zc = ZX - ZS.

[0021] S34. Based on the confirmed correlation difference Zc, confirm the irrigation water demand GG associated with the next irrigation cycle: GG = Zc × C3 + GS, where C3 is a preset fixed coefficient factor, and GS is the irrigation water demand confirmed in the previous irrigation cycle.

[0022] Step 4: Based on the determined irrigation water demand, confirm the corresponding irrigation duration, and begin irrigation at the start of the next irrigation cycle. Based on the corresponding irrigation duration, confirm the corresponding time period, lock the solar radiation data associated with this time period, and based on the confirmed solar radiation data, determine the elevation angle of the spray gun during the irrigation process to complete the irrigation process for the corresponding irrigation cycle. The specific method is as follows:

[0023] S41. Based on the confirmed irrigation water demand GG and the corresponding spray volume Ps of the spray gun per unit time, where Ps is a preset value, the irrigation duration SS is confirmed by using GG÷Ps=SS.

[0024] S42. Starting from the initial moment of the next irrigation cycle, continue the confirmed irrigation duration SS, lock a time cycle, and extract the solar radiation data associated with this time cycle from the cloud.

[0025] S43. Based on the solar radiation data associated with the time period, confirm the solar radiation parameter FS associated with the corresponding time. k Where k represents different moments within the time period, using YD k =FS k ×A1 confirms the elevation angle YD associated with the corresponding time. k Where A1 is a preset fixed coefficient factor;

[0026] S44. Different elevation angles YD confirmed at different times k It can control the elevation angle of the spray gun in real time at a specified time to complete the watering work for the corresponding time period.

[0027] This invention provides a method for calculating irrigation water demand in irrigation districts based on digital twin technology. Compared with existing technologies, it has the following advantages:

[0028] This invention uses a low-altitude drone to collect images of the irrigation area, and performs contour correlation checks based on the collected images to determine the corresponding irrigation area image. Then, based on the flight altitude of the low-altitude drone, the actual area of ​​the irrigation area image is confirmed, and based on the confirmed actual area, the corresponding irrigation amount is locked, making the confirmed irrigation amount value more accurate and ensuring its precision.

[0029] Based on the humidity data monitored in the corresponding cycle, the humidity changes in the corresponding irrigation area are assessed, and the corresponding water demand is determined based on these actual changes. Subsequently, based on the confirmed water volume data and the corresponding solar radiation data, the elevation angle of the spray gun is adjusted accordingly to enable the corresponding irrigation area to achieve better water spraying treatment effect and to make the calculation of irrigation water demand in the irrigation area more accurate. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0031] Figure 2 This is a schematic diagram illustrating the determination of the mean point location in this invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] First Embodiment

[0034] Please see Figure 1 This application provides a method for calculating irrigation water demand in irrigation districts based on digital twin technology, including the following steps:

[0035] Step 1: Use a low-altitude UAV to acquire associated images of the irrigation area, and perform edge contour processing on the acquired associated images to confirm the associated irrigation area images. Then, based on the acquisition altitude of the low-altitude UAV when acquiring the corresponding associated images, confirm the actual area parameters of the irrigation area images. Specifically, determine the required water treatment volume by confirming the overall area parameters of the corresponding irrigation area images. Since the image ratios acquired by the low-altitude UAV at different altitudes are inconsistent, the overall area is comprehensively confirmed based on the confirmed image ratios. The specific sub-steps for confirming the area parameters are as follows:

[0036] S11. The Sobel algorithm is used to perform edge detection on the acquired associated image. The coordinates of each pixel in the associated image are labeled as (i, j). Based on the confirmed coordinates, the gradient values ​​associated with the corresponding pixel in the horizontal and vertical directions are determined. The gradient value associated with the corresponding pixel in the horizontal direction is labeled as Gx, and the gradient value associated with the corresponding pixel in the vertical direction is labeled as Gy. Confirm the gradient magnitude G associated with the corresponding pixel. x-y ;

[0037] S12. The gradient magnitude G corresponding to the different pixels associated with each different point coordinate (i, j) in the associated image. x-y The process is repeated sequentially, and the magnitude difference Cz between adjacent pixels is identified, where Cz = |G1|. x-y -G2 x-y |, of which G1 x-y G2 represents the gradient magnitude associated with a group of adjacent pixels. x-y This represents the gradient magnitude associated with another group of pixels in an adjacent pixel group. The magnitude difference Cz is compared with the preset value Y1, where Y1 is a pre-set threshold determined in advance by relevant operators based on experience. If Cz > Y1, the two groups of adjacent pixels are marked as outliers; otherwise, the two groups of adjacent pixels are marked as the same type.

[0038] S13. Based on the several sets of similar points confirmed in this associated image, confirm the areas covered by the several sets of similar points and mark them as similar areas. Then mark the similar area with the largest area parameter as the irrigation area (in the actual shooting process, when the drone is controlled, it can take pictures when it can completely cover the corresponding irrigation area to obtain the corresponding associated image. Therefore, the similar area associated with the largest area can be selected to lock the corresponding irrigation area). Determine the irrigation area image (the irrigation area image is a part of the area inside the associated image). Mark the area parameter of the currently confirmed irrigation area image as M.

[0039] S14. Based on the acquisition altitude H of the low-altitude UAV corresponding to the current associated image acquisition time, the actual total area parameter Mz of the corresponding irrigation area is determined by the following formula: Mz = H × C1 × M. C1 is a preset fixed coefficient factor, which is determined by the relevant operators based on experience. The UAV has different image scaling ratios at different altitudes, so a callback is performed based on this parameter to determine the actual area parameter.

[0040] Step 2: Based on the determined irrigation district image and the associated actual area parameters, determine the irrigation water demand associated with this irrigation district. Specifically, in the actual irrigation process, the corresponding area parameters correspond to different irrigation water volumes, thereby determining the irrigation water demand associated with the corresponding irrigation district. The specific sub-steps for determining the irrigation water demand are as follows:

[0041] S21. Based on the actual area parameter Mz confirmed by the corresponding irrigation area image, the irrigation water demand GS is determined by Mz×C2=GS, where C2 is a preset fixed coefficient factor, the specific value of which is determined by the operator based on experience, and is the theoretical irrigation water demand per unit area.

[0042] Specifically, based on the total area parameters associated with the corresponding irrigation district and the irrigation volume required per unit area, the actual irrigation water demand associated with the corresponding irrigation cycle is determined.

[0043] Second Embodiment

[0044] The corresponding irrigation cycle is a preset time cycle. Within the past time cycle, based on the monitoring sensors set in the corresponding area, the correlation changes in humidity data and specific changes in solar radiation values ​​within the corresponding area are identified, and the actual associated irrigation water demand is readjusted to lock in the actual associated water demand.

[0045] Step 3, Combining Figure 2 Based on the confirmed irrigation water demand of the corresponding irrigation district, and based on the humidity change data of the corresponding irrigation district identified by the humidity monitoring sensors installed in the corresponding irrigation district during past irrigation cycles, the irrigation water demand associated with the next irrigation cycle is readjusted based on the humidity change data of past irrigation cycles. The specific method of readjustment is as follows:

[0046] S31. When this irrigation area is irrigated for the first time, after three irrigation cycles are executed (that is, at the beginning of irrigation, the humidity change data cannot be confirmed, so the corresponding value can only be confirmed after irrigation), the irrigation cycle is a preset cycle. When the irrigation cycle arrives, irrigation is carried out directly. The interval between each irrigation cycle is 12 hours. That is, the irrigation amount corresponding to the value can be irrigated within this cycle. It is determined in advance by relevant personnel based on experience. The irrigation amount associated in the first three irrigation cycles is the confirmed irrigation water demand. When the fourth irrigation cycle or other subsequent irrigation cycles are executed, the humidity change data associated with the previous irrigation cycles are confirmed.

[0047] S32. Based on the confirmed irrigation cycle and the humidity change data associated with the corresponding time, generate a humidity change curve belonging to the corresponding irrigation cycle. The initial time of the corresponding humidity change curve is calibrated to time 0. Several sets of humidity change curves after calibration are placed in the same two-dimensional coordinate system (after time calibration, the same time corresponds to humidity data associated with different cycles, so several sets of humidity data can be placed in the same two-dimensional coordinate system). Based on the different humidity data associated with the same time, confirm the mean of several humidity data at the same time, lock the mean point, and then connect the determined mean points to determine the humidity change curve to be processed for the next irrigation cycle.

[0048] S33. Based on the confirmed humidity change curve to be processed and the preset humidity threshold, the humidity threshold is a preset value determined by the operator based on experience. From the humidity change curve to be processed, the relevant curves that are higher than the humidity threshold are marked as upper curves, and the relevant curves that are lower than the humidity threshold are marked as lower curves. The sum of several sets of humidity data associated with the upper curve is used to confirm the upper total parameter ZS, and the sum of several sets of humidity data associated with the lower curve is used to confirm the lower total parameter ZX. The correlation difference Zc is confirmed by Zc = ZX - ZS.

[0049] S34. Based on the confirmed correlation difference Zc, confirm the irrigation water demand GG associated with the next irrigation cycle: GG = Zc × C3 + GS, where C3 is a preset fixed coefficient factor, the specific value of which is determined by the operator based on experience, and GS is the irrigation water demand confirmed in the previous irrigation cycle.

[0050] Step 4: Based on the determined irrigation water demand, confirm the corresponding irrigation duration, and begin irrigation at the start of the next irrigation cycle. Based on the corresponding irrigation duration, confirm the corresponding time period, lock the solar radiation data associated with this time period, and based on the determined solar radiation data, confirm the elevation angle of the spray gun during the irrigation process to complete the irrigation process for the corresponding irrigation cycle. The specific sub-steps for real-time adjustment and confirmation are as follows:

[0051] S41. Based on the confirmed irrigation water demand GG and the corresponding spray volume Ps of the spray gun per unit time, where Ps is a preset value, preset by the relevant operators in advance, the irrigation duration SS is confirmed by GG÷Ps=SS.

[0052] S42. Starting from the initial moment of the next irrigation cycle, continue the confirmed irrigation duration SS, lock a time cycle, and extract the solar radiation data associated with this time cycle from the cloud.

[0053] S43. Based on the solar radiation data associated with the time period, confirm the solar radiation parameter FS associated with the corresponding time. k Where k represents different moments within the time period, using YD k =FS k ×A1 confirms the elevation angle YD associated with the corresponding time. k A1 is a preset fixed coefficient factor. This factor means that when the solar radiation intensity is high, its elevation angle is low, and when the solar radiation intensity is low, its elevation angle is high. This factor is determined in advance by relevant operators based on experience.

[0054] S44. Different elevation angles YD confirmed at different times k It can control the elevation angle of the spray gun in real time at a specified time to complete the watering work for the corresponding time period.

[0055] When solar radiation is high, a high spray gun angle can cause water to evaporate. To reduce excessive evaporation, the spray gun angle should be lowered to achieve better irrigation results.

[0056] Third Embodiment

[0057] In its specific implementation, this embodiment includes all the implementation processes of the two sets of embodiments described above.

[0058] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0059] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for calculating irrigation water demand in an irrigation district based on digital twin technology, characterized in that, Includes the following steps: Step 1: Use a low-altitude UAV to acquire associated images of the irrigation area, and perform edge contour processing on the acquired associated images to confirm the associated irrigation area images. Then, based on the acquisition altitude of the low-altitude UAV when acquiring the corresponding associated images, confirm the actual area parameters of the irrigation area images. The specific method is as follows: S11. The Sobel algorithm is used to perform edge detection on the acquired associated image. The coordinates of each pixel in the associated image are labeled as (i, j). Based on the confirmed coordinates, the gradient values ​​associated with the corresponding pixel in the horizontal and vertical directions are determined. The gradient value associated with the corresponding pixel in the horizontal direction is labeled as Gx, and the gradient value associated with the corresponding pixel in the vertical direction is labeled as Gy. Confirm the gradient magnitude G associated with the corresponding pixel. x-y ; S12. The gradient magnitude G corresponding to the different pixels associated with each different point coordinate (i, j) in the associated image. x-y The process is repeated sequentially to confirm and identify the magnitude difference Cz between adjacent pixels, where Cz = |G1|. x-y -G2 x-y |, of which G1 x-y G2 represents the gradient magnitude associated with a group of adjacent pixels. x-y The gradient magnitude associated with another group of pixels in the adjacent pixels is represented by the gradient difference Cz. The difference in magnitude is compared with the preset value Y1, where Y1 is a pre-set threshold. If Cz≤Y1, the two groups of adjacent pixels are marked as the same type of points. S13. Based on the several sets of similar points identified in this associated image, identify the areas covered by the several sets of similar points and mark them as similar areas. Then mark the similar area with the largest area parameter as the irrigation area and determine the irrigation area image. S14. The area parameter of the currently confirmed irrigation area image is calibrated as M. Based on the acquisition altitude H of the low-altitude UAV corresponding to the current image acquisition time, the actual total area parameter Mz of the corresponding irrigation area is confirmed by Mz=H×C1×M, where C1 is a preset fixed coefficient factor. Step 2: Based on the determined irrigation district image and the associated actual area parameters, determine the irrigation water demand associated with this irrigation district. The specific sub-steps are as follows: Based on the actual area parameter Mz confirmed by the corresponding irrigation area image, the irrigation water requirement GS is determined by Mz×C2=GS, where C2 is a preset fixed coefficient factor. Step 3: Based on the confirmed irrigation water demand of the corresponding irrigation area, and based on the humidity monitoring sensors set up in the corresponding irrigation area, identify the humidity change data generated by the corresponding irrigation area in the past irrigation cycle. Based on the humidity change data of the past irrigation cycle, adjust the irrigation water demand associated with the next irrigation cycle again. Step 4: Based on the determined irrigation water demand, confirm the corresponding irrigation duration, start irrigation at the beginning of the next irrigation cycle, confirm the corresponding time period based on the corresponding irrigation duration, lock the solar radiation data associated with this time period, confirm the elevation angle of the corresponding spray gun during the irrigation process based on the determined solar radiation data, and complete the irrigation process of the corresponding irrigation cycle.

2. The method for calculating irrigation water demand in an irrigation district based on digital twin technology according to claim 1, characterized in that, In step S12, if Cz > Y1, the two groups of adjacent pixels are marked as outliers.

3. The method for calculating irrigation water demand in an irrigation district based on digital twin technology according to claim 1, characterized in that, In step three, the specific method for readjusting the irrigation water demand associated with the next irrigation cycle is as follows: S31. When this irrigation district is irrigated for the first time, after the irrigation process of three irrigation cycles is executed, the irrigation cycle is a preset cycle. When the irrigation cycle arrives, irrigation is carried out directly. The irrigation amount associated in the first three irrigation cycles is the confirmed irrigation water demand. When the fourth irrigation cycle or other subsequent irrigation cycles are executed, the humidity change data associated with the previous irrigation cycles are confirmed. S32. Based on the confirmed irrigation cycle and the humidity change data associated with the corresponding time, generate a humidity change curve belonging to the corresponding irrigation cycle. The initial time of the corresponding humidity change curve is calibrated to time 0. Several sets of humidity change curves after calibration are placed in the same two-dimensional coordinate system. Based on the different humidity data associated with the same time, the mean value of several humidity data at the same time is confirmed, the mean value point is locked, and the determined mean value points are connected to determine the humidity change curve to be processed for the next set of irrigation cycles. S33. Based on the confirmed humidity change curve to be processed and the preset humidity threshold (the humidity threshold is a preset value), the relevant curves above the humidity threshold are marked as upper curves, and the relevant curves below the humidity threshold are marked as lower curves. The sum of several sets of humidity data associated with the upper curve is used to confirm the upper total parameter ZS, and the sum of several sets of humidity data associated with the lower curve is used to confirm the lower total parameter ZX. The correlation difference Zc is confirmed by Zc=ZX-ZS. S34. Based on the confirmed correlation difference Zc, confirm the irrigation water demand GG associated with the next irrigation cycle: GG = Zc × C3 + GS, where C3 is a preset fixed coefficient factor, and GS is the irrigation water demand confirmed in the previous irrigation cycle.

4. The method for calculating irrigation water demand in an irrigation district based on digital twin technology according to claim 1, characterized in that, In step four, the specific method for confirming the elevation angle of the corresponding spray gun during irrigation is as follows: S41. Based on the confirmed irrigation water demand GG and the corresponding spray volume Ps of the spray gun per unit time, where Ps is a preset value, the irrigation duration SS is confirmed by using GG÷Ps=SS. S42. Starting from the initial moment of the next irrigation cycle, continue the confirmed irrigation duration SS, lock a time cycle, and extract the solar radiation data associated with this time cycle from the cloud. S43. Based on the solar radiation data associated with the time period, confirm the solar radiation parameter FS associated with the corresponding time. k Where k represents different moments within the time period, using YD k =FS k ×A1 confirms the elevation angle YD associated with the corresponding time. k Where A1 is a preset fixed coefficient factor; S44. Different elevation angles YD confirmed at different times k It can control the elevation angle of the spray gun in real time at a specified time to complete the watering work for the corresponding time period.

Images