Irrigation area irrigation water demand calculation method based on digital twinborn technology

Abstract

The invention discloses an irrigation area irrigation water demand calculation method based on a digital twinborn technology, relates to the technical field of irrigation area irrigation, and solves the problem that humidity data in an irrigation area is associated with soil humidity and other factors and is also associated with a corresponding illumination environment. According to the method, the actual area of an irrigation area image is confirmed, and the corresponding irrigation amount is locked based on the confirmed actual area, so that the confirmed irrigation amount value is more accurate, the accuracy can be fully guaranteed, and the humidity data monitored in the corresponding period can be compared with the humidity data monitored in the corresponding period. The humidity change condition in the corresponding irrigation area is evaluated, then the corresponding water demand is determined based on the actual change condition, and subsequently, the elevation angle of the spray gun is subjected to associated adjustment based on the confirmed water quantity data and the corresponding solar radiation data, so that the corresponding irrigation area can achieve a better water spray irrigation treatment effect. And the irrigation water demand calculation of the irrigation area is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of irrigation technology for irrigation areas, and in particular to a method for calculating irrigation water demand for irrigation areas based on digital twin technology. Background Art

[0002] Crop growth is inseparable from water. Irrigation can provide the water needed for crop growth, maintain cell turgor pressure, and ensure the normal physiological activities of crops. For example, in the process of photosynthesis, water is one of the raw materials. It participates in light and dark reactions and provides hydrogen atoms for the production of organic matter. At the same time, appropriate irrigation can also regulate the body temperature of crops. In hot weather, transpiration can remove heat and prevent crops from being damaged by high temperatures.

[0003] Irrigation is also crucial to improving crop yield and quality. In arid and semi-arid areas, effective irrigation can make crops that originally grew poorly due to water shortage thrive and increase yield. For example, in some water-scarce wheat-growing areas, reasonable irrigation can increase the number of wheat grains and make the grains fuller, thereby improving wheat yield and quality.

[0004] Digital twin is a technology that closely combines physical entities with their digital models. In irrigation, by building a digital twin model of the irrigation area, the actual status of the irrigation area can be reflected in real time. This model includes the digital presentation of physical elements such as the topography, soil type, crop distribution, and irrigation system layout of the irrigation area. Its crop distribution and related topography require corresponding low-altitude drones to conduct actual surveys to obtain and confirm relevant image data of the designated irrigation area.

[0005] The patent application with the publication number CN111626892A discloses a method and system for measuring and monitoring water demand in irrigation areas, which involves agricultural irrigation water demand measurement. The method includes: dynamically and automatically calculating the amount of water that should be released from each canal system through the water demand of fields of different crops at different periods; dynamically monitoring the irrigation water situation through water metering equipment installed on each canal system, and promptly reminding management personnel when to close the gates, so as to form a reasonable irrigation plan for different periods, different crops and different plots; guiding irrigation operations in irrigation areas, and forming a big data warehouse for automated irrigation in irrigation areas; the invention realizes real-time sharing of irrigation area information resources, improves the operating efficiency of irrigation areas, and reduces the operating costs of irrigation areas; provides scientific decision-making basis for irrigation area management departments, and ultimately realizes the informatization of irrigation area management, rationalization of water allocation, precision of water measurement, and standardization of charges, providing technical guarantee for the realization of efficient modern agriculture.

[0006] In the traditional irrigation process, the amount of water is usually determined based on the area of ​​the irrigation area, and once determined, the amount remains fixed. However, this is not the case in actual irrigation operations. The actual amount of water required will change due to a variety of factors. For example, the humidity data in the irrigation area is closely related to soil moisture, other related factors, and the light environment. The interaction of these complex factors leads to a significant difference in the accuracy of the water required for actual irrigation and the pre-set value, which makes it impossible to achieve the ideal irrigation effect when irrigating according to a fixed amount and cycle.

[0007] If digital twin technology is introduced, this problem can be effectively solved. Digital twin technology can build a virtual model that is highly consistent with the actual irrigation area. The model not only covers the irrigation area information, but also includes multi-dimensional data such as soil moisture and light environment. By collecting and analyzing these data in real time, the actual amount of irrigation water required for the irrigation area at different times and under different conditions can be accurately calculated, and it is no longer limited to a fixed value determined based on a single area factor. At the same time, irrigation operations based on these dynamically changing irrigation water volume data can significantly improve irrigation accuracy, thereby optimizing irrigation effects and ensuring the precise water needs of crop growth. Summary of the invention

[0008] In response to the shortcomings of the prior art, the present invention provides a method for calculating the irrigation water demand of an irrigation area based on digital twin technology, which solves the problem that the humidity data in the irrigation area is related to the humidity of the soil and other factors, and is also related to the corresponding lighting environment, so there are specific differences in the accuracy of the actual required water volume.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for calculating irrigation water demand in an irrigation area based on digital twin technology, comprising the following steps:

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

[0011] S11, using the Sobel algorithm to perform edge detection on the acquired associated image, calibrating the point coordinates of each pixel in the associated image as (i, j), and based on the confirmed point coordinates, confirming the gradient values ​​associated with the corresponding pixel in the horizontal direction and the vertical direction, calibrating the gradient value associated with the corresponding pixel in the horizontal direction as Gx, calibrating the gradient value associated with the corresponding pixel in the vertical direction as Gy, and using Confirm the gradient magnitude G associated with the corresponding pixel point x-y ;

[0012] S12, the gradient amplitude G corresponding to the different pixel points associated with each different point coordinate (i, j) in the associated image x-y Confirm in turn and identify the amplitude difference Cz between adjacent pixels, where Cz = |G1 x-y -G2 x-y |, where G1 x-y Represents the gradient magnitude associated with a group of pixels in adjacent pixels, G2 x-y Represents the gradient amplitude associated with another group of pixel points in the adjacent pixel points, and checks the amplitude difference Cz with the preset value Y1, where Y1 is a threshold set in advance. If Cz≤Y1, the two groups of adjacent pixel points are marked as similar points, and if Cz>Y1, the two groups of adjacent pixel points are marked as different points;

[0013] S13, based on the groups of similar points confirmed in the associated image, confirm the areas covered by the groups 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, calibrating the area parameter of the currently confirmed irrigation area image as M, based on the acquisition height H of the low-altitude drone corresponding to the current associated image acquisition time, using: Mz = H × C1 × M to confirm the actual total area parameter Mz of the corresponding irrigation area, where C1 is a preset fixed coefficient factor;

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

[0016] S21, based on the actual area parameter Mz confirmed by the corresponding irrigation area image, the irrigation water requirement GS is confirmed by: Mz×C2=GS, where C2 is a preset fixed coefficient factor;

[0017] Step 3: Based on the irrigation water demand confirmed by the corresponding irrigation area, the humidity monitoring sensor set in the corresponding irrigation area identifies the humidity change data of the corresponding irrigation area in the past irrigation cycle, and based on the humidity change data of the past irrigation cycle, the irrigation water demand associated with the next group of irrigation cycles is adjusted again. The specific method is as follows:

[0018] S31, when the irrigation area is irrigated for the first time, after executing the irrigation process of three irrigation cycles, the irrigation cycle is the preset cycle, and the irrigation process is directly carried out when the irrigation cycle arrives. The irrigation amount associated with 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 past irrigation cycles is 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, calibrate the initial time of the corresponding humidity change curve to time 0, and place the calibrated groups of humidity change curves in the same two-dimensional coordinate system, based on the different humidity data associated with the same time, confirm the mean of the 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 related to the next group of irrigation cycles;

[0020] S33, based on the confirmed humidity change curve to be processed and the preset humidity threshold, wherein the humidity threshold is a preset value, the relevant curve above the humidity threshold is calibrated as an upper curve from the humidity change curve to be processed, and the relevant curve below the humidity threshold is calibrated as a lower curve, the sum of several groups of humidity data associated with the upper curve is used to confirm the upper total parameter ZS, and the sum of several groups of humidity data associated with the lower curve is used to confirm the lower total parameter ZX, and the associated difference Zc is confirmed by using Zc=ZX-ZS;

[0021] S34, based on the confirmed correlation difference Zc, confirm the irrigation water demand GG associated with the next set of irrigation cycles: GG = Zc × C3 + GS, where C3 is a preset fixed coefficient factor, and GS is the irrigation water demand confirmed by the previous set of irrigation cycles;

[0022] Step 4: Based on the determined irrigation water demand, the corresponding irrigation duration is confirmed, irrigation is carried out at the start time of the next irrigation cycle, and the corresponding time period is confirmed based on the corresponding irrigation duration, the solar radiation data associated with this time period is locked, and the elevation angle of the corresponding spray gun during the irrigation process is confirmed based on the determined solar radiation data, and the irrigation process of the corresponding irrigation cycle is completed. 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 time SS is confirmed by adopting GG÷Ps=SS;

[0024] S42, starting from the initial moment of the next irrigation cycle, continuing the confirmed irrigation time SS, locking a set of time periods, and extracting the solar radiation data associated with this time period 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 moment k , where k represents different moments in the time period, using YD k =FS k ×A1 confirms the elevation angle YD associated with the corresponding moment k , where A1 is the preset fixed coefficient factor;

[0026] S44, based on different elevation angles YD confirmed at different times k , the elevation angle of the spray gun at the specified time is controlled in real time to complete the watering work in the corresponding time period.

[0027] The present invention provides a method for calculating irrigation water demand in an irrigation area based on digital twin technology. Compared with the prior art, it has the following beneficial effects:

[0028] The present invention uses a corresponding low-altitude drone to collect images of the irrigation area, and performs a contour correlation test based on the collected related images to determine the corresponding irrigation area image, and then confirms the actual area of ​​the irrigation area image based on the flight altitude of the corresponding low-altitude drone, and then locks the corresponding irrigation volume based on the confirmed actual area, so that the confirmed irrigation volume value is more accurate, and its accuracy can be fully guaranteed;

[0029] Based on the humidity data monitored in the corresponding period, the humidity change in the corresponding irrigation area is evaluated, and then the corresponding water demand is determined based on such 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 in a correlated manner, so that the corresponding irrigation area can achieve a better water spray irrigation treatment effect and the irrigation water demand calculation of the irrigation area can be more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the process of the present invention;

[0031] Figure 2 It is a schematic diagram for determining the mean point of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] First embodiment

[0034] See also Figure 1 , the present application provides a method for calculating irrigation water demand in an irrigation area based on digital twin technology, comprising the following steps:

[0035] Step 1: Use a low-altitude drone to acquire the associated image of the irrigation area, and perform edge contour processing on the acquired associated image to confirm the associated irrigation area image, and then confirm the actual area parameter of the irrigation area image based on the acquisition height of the low-altitude drone when the corresponding associated image is acquired. Specifically, the required treatment water volume is determined by confirming the overall area parameter of the corresponding irrigation area image. The image proportions acquired by the low-altitude drone at different altitudes are inconsistent, so based on the confirmed related image proportions, a comprehensive confirmation of the overall area is performed, wherein the specific sub-steps for confirming the area parameters are:

[0036] S11, using the Sobel algorithm to perform edge detection on the acquired associated image, calibrating the point coordinates of each pixel in the associated image as (i, j), and based on the confirmed point coordinates, confirming the gradient values ​​associated with the corresponding pixel in the horizontal direction and the vertical direction, calibrating the gradient value associated with the corresponding pixel in the horizontal direction as Gx, calibrating the gradient value associated with the corresponding pixel in the vertical direction as Gy, and using Confirm the gradient magnitude G associated with the corresponding pixel point x-y ;

[0037] S12, the gradient amplitude G corresponding to the different pixel points associated with each different point coordinate (i, j) in the associated image x-y Confirm in turn and identify the amplitude difference Cz between adjacent pixels, where Cz = |G1 x-y -G2 x-y |, where G1 x-y Represents the gradient magnitude associated with a group of pixels in adjacent pixels, G2 x-y Represents the gradient amplitude associated with another group of pixel points in the adjacent pixel points. The amplitude difference Cz is checked with the preset value Y1, where Y1 is a pre-set threshold, which is prepared by the relevant operators in advance based on experience. If Cz>Y1, the two groups of adjacent pixel points are marked as heterogeneous points, otherwise, the two groups of adjacent pixel points are marked as homogeneous points.

[0038] S13, based on the confirmed groups of similar points in the associated image, confirm the areas covered by the groups of similar points and mark them as similar areas, and 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 be photographed when it can completely cover the corresponding irrigation area, so as to obtain the corresponding associated image, so the similar area associated with the largest area can be selected to lock the corresponding irrigation area), and determine the irrigation area image (the irrigation area image is a part of the internal area of ​​the associated image), and mark the area parameter of the currently confirmed irrigation area image as M;

[0039] S14. Based on the acquisition height 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: Mz = H × C1 × M, where C1 is a preset fixed coefficient factor, which is formulated by 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 area image and the associated actual area parameters, the irrigation water demand associated with the irrigation area is locked. 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 area. The specific sub-steps of locking the irrigation water demand are:

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

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

[0043] Second embodiment

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

[0045] Step 3: Combination Figure 2 Based on the irrigation water demand confirmed by the corresponding irrigation area, and based on the humidity monitoring sensor set in the corresponding irrigation area to identify the humidity change data generated by the corresponding irrigation area in the past irrigation cycle, the irrigation water demand associated with the next group of irrigation cycles is readjusted based on the humidity change data of the past irrigation cycle, wherein the specific method of readjustment is:

[0046] S31. When the irrigation area is irrigated for the first time, after executing the irrigation process of three irrigation cycles (that is, when the irrigation starts, the humidity change data cannot be confirmed, so the corresponding value can be confirmed only after the irrigation), the irrigation cycle is the preset cycle, and the irrigation process is directly carried out when the irrigation cycle arrives. The interval between each irrigation cycle is 12 hours, that is, the irrigation amount corresponding to the value in this cycle can be irrigated. It is prepared in advance by relevant personnel based on experience. The irrigation amount associated with 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 past 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, calibrate the initial time of the corresponding humidity change curve to time 0, and place the calibrated groups of humidity change curves in the same two-dimensional coordinate system (after the time calibration, the same time corresponds to humidity data associated with different cycles, and the groups 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 the several humidity data at the same time, lock the mean point, and then connect the determined several mean points to determine the humidity change curve to be processed related to the next group of irrigation cycles;

[0048] S33, based on the confirmed humidity change curve to be processed and the preset humidity threshold, the humidity threshold is a preset value, which is formulated by the operator based on experience, the relevant curve above the humidity threshold in the humidity change curve to be processed is calibrated as the upper curve, and the relevant curve below the humidity threshold is calibrated as the lower curve, the sum of several groups of humidity data associated with the upper curve is used to confirm the upper total parameter ZS, and the sum of several groups of humidity data associated with the lower curve is used to confirm the lower total parameter ZX, and the associated difference Zc is confirmed by using Zc=ZX-ZS;

[0049] S34, based on the confirmed correlation difference Zc, confirm the irrigation water demand GG associated with the next set of irrigation cycles: 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 by the previous set of irrigation cycles;

[0050] Step 4: Based on the determined irrigation water demand, confirm the corresponding irrigation time, irrigate at the start time of the next irrigation cycle, and confirm the corresponding time period based on the corresponding irrigation time, lock the solar radiation data associated with this time period, and confirm the elevation angle of the corresponding spray gun during the irrigation process based on the determined solar radiation data to complete the irrigation process of 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 spray volume Ps of the corresponding spray gun per unit time, where Ps is a preset value preset in advance by the relevant operator, the irrigation time SS is confirmed by adopting GG÷Ps=SS;

[0052] S42, starting from the initial moment of the next irrigation cycle, continuing the confirmed irrigation time SS, locking a set of time periods, and extracting the solar radiation data associated with this time period 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 moment k , where k represents different moments in the time period, using YD k =FS k ×A1 confirms the elevation angle YD associated with the corresponding moment k , where A1 is a preset fixed coefficient factor, which means that when the solar radiation intensity is high, the elevation angle is low, and when the solar radiation intensity is low, the elevation angle is high. This factor is prepared in advance by relevant operators based on experience;

[0054] S44, based on different elevation angles YD confirmed at different times k , to control the elevation angle of the spray gun in real time at a specified time, and complete the irrigation work in the corresponding time period;

[0055] When the solar radiation intensity is high, if the spray gun is at a high elevation angle, water will evaporate. In order to reduce excessive evaporation of water, the corresponding elevation angle should be lowered to achieve better irrigation treatment effects.

[0056] Third embodiment

[0057] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0058] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0059] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for calculating irrigation water demand in an irrigation area based on digital twin technology, characterized in that: The following steps are involved: Step 1: Use a low-altitude drone to acquire the associated image of the irrigation area, perform edge contour processing on the acquired associated image to confirm the associated irrigation area image, and then confirm the actual area parameters of the irrigation area image based on the acquisition height of the low-altitude drone when the corresponding associated image is acquired; Step 2: Based on the determined irrigation area image and the associated actual area parameters, the irrigation water demand associated with the irrigation area is locked; Step 3: Based on the irrigation water demand confirmed by the corresponding irrigation area, the humidity change data of the corresponding irrigation area in the past irrigation cycle is identified based on the humidity monitoring sensor set in the corresponding irrigation area, and based on the humidity change data of the past irrigation cycle, the irrigation water demand associated with the next group of irrigation cycles is adjusted again; Step 4: Based on the determined irrigation water demand, confirm the corresponding irrigation duration, irrigate at the beginning of the next irrigation cycle, and confirm the corresponding time period based on the corresponding irrigation duration, lock the solar radiation data associated with this time period, and confirm the corresponding spray gun elevation angle during the irrigation process based on the determined solar radiation data to complete the irrigation process of the corresponding irrigation cycle.

2. According to claim 1, a method for calculating irrigation water demand in an irrigation area based on digital twin technology is characterized in that: In step 1, the specific method of confirming the irrigation area image is: S11, using the Sobel algorithm to perform edge detection on the acquired associated image, calibrating the point coordinates of each pixel in the associated image as (i, j), and based on the confirmed point coordinates, confirming the gradient values ​​associated with the corresponding pixel in the horizontal direction and the vertical direction, calibrating the gradient value associated with the corresponding pixel in the horizontal direction as Gx, calibrating the gradient value associated with the corresponding pixel in the vertical direction as Gy, and using Confirm the gradient magnitude G associated with the corresponding pixel point x-y ; S12, the gradient amplitude G corresponding to the different pixel points associated with each different point coordinate (i, j) in the associated image x-y Confirm in turn and identify the amplitude difference Cz between adjacent pixels, where Cz = |G1 x-y -G2 x-y |, where G1 x-y Represents the gradient magnitude associated with a group of pixels in adjacent pixels, G2 x-y Represents the gradient amplitude associated with another group of pixel points in the adjacent pixel points, and compares the amplitude difference Cz with the preset value Y1, where Y1 is a threshold set in advance. If Cz≤Y1, the two groups of adjacent pixel points are marked as the same type of points; S13. Based on the groups of similar points confirmed in the associated image, the areas covered by the groups of similar points are confirmed and marked as similar areas, and then the similar areas with the largest area parameters are marked as irrigation areas, and the irrigation area images are determined.

3. According to claim 2, a method for calculating irrigation water demand in an irrigation area based on digital twin technology is characterized in that: In the step S12, if Cz>Y1, two groups of adjacent pixel points are marked as heterogeneous points.

4. The method for calculating irrigation water demand in an irrigation area based on digital twin technology according to claim 2 is characterized in that: In step 1, the specific method of confirming the actual area parameter of the irrigation area image is: S14. Calibrate the area parameter of the currently confirmed irrigation area image as M. Based on the acquisition height H of the low-altitude drone corresponding to the current associated image acquisition moment, use: Mz=H×C1×M to confirm the actual total area parameter Mz of the corresponding irrigation area, where C1 is a preset fixed coefficient factor.

5. According to the method for calculating irrigation water demand in an irrigation area based on digital twin technology according to claim 1, it is characterized in that: In step 2, the specific sub-steps of locking the irrigation water requirement are: S21. Based on the actual area parameter Mz confirmed by the corresponding irrigation area image, the irrigation water requirement GS is confirmed using: Mz×C2=GS, where C2 is a preset fixed coefficient factor.

6. The method for calculating irrigation water demand in an irrigation area based on digital twin technology according to claim 1 is characterized in that: In step 3, the specific method of re-adjusting the irrigation water demand associated with the next set of irrigation cycles is: S31, when the irrigation area is irrigated for the first time, after executing the irrigation process of three irrigation cycles, the irrigation cycle is the preset cycle, and the irrigation process is directly carried out when the irrigation cycle arrives. The irrigation amount associated with 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 past irrigation cycles is 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, calibrate the initial time of the corresponding humidity change curve to time 0, and place the calibrated groups of humidity change curves in the same two-dimensional coordinate system, based on the different humidity data associated with the same time, confirm the mean of the 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 related to the next group of irrigation cycles; S33, based on the confirmed humidity change curve to be processed and the preset humidity threshold, wherein the humidity threshold is a preset value, the relevant curve above the humidity threshold is calibrated as an upper curve from the humidity change curve to be processed, and the relevant curve below the humidity threshold is calibrated as a lower curve, the sum of several groups of humidity data associated with the upper curve is used to confirm the upper total parameter ZS, and the sum of several groups of humidity data associated with the lower curve is used to confirm the lower total parameter ZX, and the associated difference Zc is confirmed by using Zc=ZX-ZS; S34. Based on the confirmed associated difference Zc, confirm the irrigation water demand GG associated with the next set of irrigation cycles: GG = Zc × C3 + GS, where C3 is a preset fixed coefficient factor, and GS is the irrigation water demand confirmed by the previous set of irrigation cycles.

7. The method for calculating irrigation water demand in an irrigation area based on digital twin technology according to claim 1 is characterized in that: In step 4, the specific method of confirming the elevation angle of the corresponding spray gun during the irrigation process is: 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 time SS is confirmed by adopting GG÷Ps=SS; S42, starting from the initial moment of the next irrigation cycle, continuing the confirmed irrigation time SS, locking a set of time periods, and extracting the solar radiation data associated with this time period 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 moment k , where k represents different moments in the time period, using YD k =FS k ×A1 confirms the elevation angle YD associated with the corresponding moment k , where A1 is the preset fixed coefficient factor; S44, based on different elevation angles YD confirmed at different times k , the elevation angle of the spray gun at the specified time is controlled in real time to complete the watering work in the corresponding time period.

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