A translation type sprinkler variable irrigation control system

Abstract

The present application relates to the technical field of crop irrigation, and particularly relates to a variable irrigation control system of a translation type sprinkler, comprising: an irrigation demand acquisition module, which is used for determining the irrigation demand of each grid unit in a target farmland according to remote sensing data and ground measured data corresponding to the grid units; and a control module, which is used for controlling the translation type sprinkler to perform an irrigation task on the target farmland according to the irrigation demand of each grid unit in the target farmland and the coordinate information of the translation type sprinkler in the current translation type sprinkler system. The translation type sprinkler system comprises the translation type sprinkler and a pump station connected with the translation type sprinkler. The variable irrigation control system of the translation type sprinkler significantly improves the calculation accuracy of the irrigation demand and avoids the problem of uneven irrigation caused by single data deviation.

Description

Technical Field

[0001] This invention relates to the field of crop irrigation technology, and in particular to a variable irrigation control system for a translational sprinkler irrigation machine. Background Technology

[0002] With the development of agricultural technology, modern intelligent irrigation technology is being gradually promoted and applied in agricultural production. Traditional agricultural irrigation methods mostly rely on manual operation or simple timed and quantitative irrigation equipment. This method often fails to provide precise irrigation according to the actual needs of farmland, leading to water waste and uneven crop growth, which restricts the further development of agriculture.

[0003] In recent years, variable rate irrigation (VRI) has gradually gained attention as an important component of precision agriculture. This technology can adjust irrigation volume according to the actual needs of different areas of farmland, significantly improving water resource utilization efficiency. However, current VRI technologies still have the following shortcomings: Existing VRI systems typically rely on a single data source, such as remote sensing data or limited ground-based measurement data, to calculate irrigation demand. In practical applications, this method may be affected by incomplete data or errors from a single information source, leading to significant deviations in the calculated irrigation demand and affecting irrigation accuracy. Existing sprinkler irrigation systems mostly use fixed irrigation rates or manually controlled movement speeds. This method cannot dynamically adjust irrigation parameters according to the needs of different areas, resulting in insufficient water in some areas and over-irrigation in others, which is detrimental to balanced crop growth. While some sprinkler irrigation equipment has automation functions, it still requires a high degree of human intervention in actual operation. For example, the movement trajectory and spraying parameters of the sprinkler machine need to be manually set, lacking the ability to dynamically optimize based on real-time data. Summary of the Invention

[0004] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a variable irrigation control system for a translational sprinkler irrigation machine.

[0005] To achieve the above objectives, the main technical solutions adopted by the present invention include:

[0006] This invention provides a variable irrigation control system for a translational sprinkler irrigation machine, comprising:

[0007] The irrigation demand acquisition module is used to determine the irrigation demand corresponding to each grid cell in the target farmland based on the remote sensing data and ground measurement data corresponding to the pre-defined grid cells in the target farmland.

[0008] The control module is used to control the translational sprinkler to perform irrigation tasks on the target farmland based on the irrigation needs corresponding to each grid unit in the target farmland and the coordinate information of the translational sprinkler in the current translational sprinkler system.

[0009] The translational sprinkler system includes a translational sprinkler machine and a pump station connected to the translational sprinkler machine.

[0010] Preferably,

[0011] The translational sprinkler system includes:

[0012] The sprinkler machine itself is mounted on a mobile platform;

[0013] The GPS module is installed on the sprinkler body and is used to obtain the coordinate information of the translational sprinkler and transmit the coordinate information to the control module.

[0014] A mobile platform, connected to the control module, is used to drive the sprinkler machine body to move along a set trajectory within the target farmland under the control of the control module.

[0015] The intelligent sprinkler head is installed on the sprinkler machine body and connected to the control module, and is used to sprinkle irrigation on the target farmland according to the working parameters controlled by the control module;

[0016] The operating parameters include: the spray width of the intelligent sprinkler head and the spray rate of the intelligent sprinkler head;

[0017] The pumping station includes:

[0018] A water pump is used to draw water from a water source and send it through a pipeline into the sprinkler machine body;

[0019] The flow control valve is used to regulate the water flow rate delivered to the sprinkler body under the control of the control module.

[0020] Preferably, the control module, based on the irrigation prescription map and the obtained coordinate information of the current translational sprinkler in the translational sprinkler system, controls the translational sprinkler to perform irrigation tasks on the target farmland, specifically including:

[0021] The coordinate information of the current translational sprinkler system is compared with the coordinate range of each grid cell in the target farmland in advance. If the coordinate information of the current translational sprinkler system belongs to the coordinate range of any grid cell, it is determined that the translational sprinkler is located in the grid cell. According to the irrigation requirements corresponding to the grid cell, the moving speed of the translational sprinkler and the working parameters of the intelligent sprinkler head are controlled so that the moving speed and working parameters meet the first preset condition.

[0022] The first preset condition is: Irrigation demand = moving speed × spray width of the smart sprinkler head × spray rate of the smart sprinkler head.

[0023] Preferably,

[0024] Remote sensing data is generated by drones equipped with visible light cameras and multispectral cameras to collect surface image data of grid cells through aerial photography, and based on the collected surface image data.

[0025] The remote sensing data includes raw vegetation indices;

[0026] The ground measurement data corresponding to the grid cell includes soil data collected from multiple sampling points set in the grid cell;

[0027] The soil data includes soil moisture and soil texture.

[0028] Preferably, the irrigation demand acquisition module determines the irrigation demand corresponding to each grid cell in the target farmland based on remote sensing data and ground-measured data corresponding to pre-defined grid cells in the target farmland, specifically including:

[0029] Based on the ground measurement data corresponding to the pre-delineated grid units in the target farmland, the corresponding initial vegetation index is obtained;

[0030] The final vegetation index is obtained based on the initial spectral data and the original vegetation index in the remote sensing data;

[0031] The initial soil moisture is obtained based on the original vegetation index in the remote sensing data;

[0032] The final soil moisture is obtained based on the initial soil moisture and the original soil moisture from the ground measurement data;

[0033] The original soil moisture is the average value of the soil moisture collected from multiple sampling points set in the grid cell;

[0034] Based on the final vegetation index and final soil moisture, the irrigation requirements corresponding to this grid cell are obtained.

[0035] Preferably, obtaining the corresponding initial vegetation index based on the ground measurement data corresponding to the pre-delineated grid cells in the target farmland specifically includes:

[0036] Based on the ground measurement data corresponding to the pre-delineated grid units in the target farmland, the corresponding initial vegetation index is obtained using formula (1);

[0037] The formula (1) is:

[0038]

[0039] in,

[0040] A = R NIR，0 -R Red，0 B = R NIR，0 +R Red，0 ;

[0041] C = β NIN R NIR，0 -β Red R Red，0 D = β NIN R NIR，0 +β Red R Red，0 ;

[0042] R Red，0 The red light reflectance of the soil under waterless conditions was obtained in advance;

[0043] β Red The sensitivity coefficient of soil moisture to the red light band is obtained in advance;

[0044] R NIR，0 The near-infrared reflectance of the soil under waterless conditions was obtained in advance;

[0045] β NIN The sensitivity coefficient of soil moisture to the near-infrared band is obtained in advance;

[0046] SM s This refers to the original soil moisture from ground-measured data;

[0047] NDVI s This represents the initial vegetation index.

[0048] Preferably, obtaining the final vegetation index based on the initial spectral data and the original vegetation index in the remote sensing data specifically includes:

[0049] Based on the initial spectral data and the original vegetation index in the remote sensing data, the final vegetation index is obtained using formula (2);

[0050] The formula (2) is:

[0051] NDVI f =w1·NDVI d +w2·NDVI s ;

[0052] NDVI f This is the final vegetation index;

[0053] NDVI d The original vegetation index;

[0054]

[0055] Q d The minimum value between the resolution of the image captured by the visible light camera of the UAV and the resolution of the image captured by the multispectral camera;

[0056] Q s The number of sampling points set in the grid cell.

[0057] Preferably, obtaining the initial soil moisture based on the original vegetation index in the remote sensing data specifically includes:

[0058] The initial soil moisture is obtained using formula (3) based on the original vegetation index in the remote sensing data.

[0059] Formula (3) includes:

[0060]

[0061] SM g This represents the initial soil moisture value;

[0062] 'a' is the first empirical parameter;

[0063] b is the second empirical parameter;

[0064] c is the third empirical parameter.

[0065] Preferably, the final soil moisture is obtained based on the initial soil moisture and the original soil moisture from the ground-measured data, specifically including:

[0066] Based on the initial soil moisture and the original soil moisture in the ground measured data, the final soil moisture is obtained by formula (4);

[0067] Formula (4) includes:

[0068]

[0069] SM t This represents the final soil moisture value;

[0070] α is the first preset weight value;

[0071] β is the second preset weight value;

[0072] γ is the third preset weight value;

[0073] tanh() is the hyperbolic tangent function;

[0074] E corr =k1·P+k2·T+k3·W d;

[0075] P represents the current rainfall value;

[0076] T represents the current ambient temperature.

[0077] W d This is the current wind speed value;

[0078] k1 is the first preset adjustment parameter;

[0079] k2 is the second preset adjustment parameter;

[0080] k3 is the third preset adjustment parameter;

[0081] β g This represents the difference between the maximum and minimum initial soil moisture values ​​for that grid cell within a historical time period.

[0082] β s This represents the difference between the maximum and minimum original soil moisture values ​​for that grid cell during the historical time period.

[0083] Preferably, the irrigation requirement corresponding to the grid cell is obtained based on the final vegetation index and the final soil moisture, specifically including:

[0084] Based on the final vegetation index and final soil moisture, the irrigation demand corresponding to the grid cell is obtained by using an inversion model.

[0085] The inversion model is as follows:

[0086]

[0087] WD represents the irrigation requirement corresponding to this grid cell;

[0088] NDVI max This refers to the maximum vegetation index corresponding to the crops in the target farmland during the historical time period;

[0089] SM opt The optimal soil moisture value corresponding to the crops in the target farmland is obtained in advance;

[0090] ET represents the current evapotranspiration rate;

[0091]

[0092] ε is the first preset contribution value;

[0093] σ is the second preset contribution value;

[0094] μ is the calibration coefficient;

[0095] δ is the evapotranspiration correction factor.

[0096] The beneficial effects of this invention are as follows: The variable irrigation control system for a translational sprinkler system combines remote sensing data and ground-based measured data. It utilizes multispectral images of farmland collected by drones to generate the original vegetation index; simultaneously, it combines measured data such as soil moisture and texture to calculate the irrigation demand for each grid unit. This complementary dual-data source approach significantly improves the accuracy of irrigation demand calculation and avoids uneven irrigation caused by deviations in a single data source.

[0097] This invention discloses a variable irrigation control system for a translational sprinkler irrigation machine. The control module dynamically adjusts the sprinkler's moving speed and the spraying parameters (including spray width and spray rate) of the intelligent sprinkler head based on the sprinkler's real-time coordinate information and the irrigation needs of the target grid unit. This real-time control method ensures that the sprinkler can flexibly respond to the irrigation needs of different areas, achieving precise variable irrigation.

[0098] This invention discloses a variable irrigation control system for a translational sprinkler irrigation machine. Through the coordinated operation of a GPS module and a control module, the system automatically acquires the current position of the sprinkler irrigation machine and compares it with the coordinate range of farmland grid cells. This allows for automatic path adjustment and irrigation task execution without manual intervention. The system is intelligent, efficient, and easy to operate, significantly reducing labor costs and improving the automation level of irrigation operations. Attached Figure Description

[0099] Figure 1 This is a schematic diagram of the variable irrigation control system for a translational sprinkler irrigation machine according to the present invention. Detailed Implementation

[0100] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0101] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present invention can be understood more clearly and thoroughly, and that the scope of the present invention can be fully conveyed to those skilled in the art.

[0102] Example 1

[0103] See Figure 1 This embodiment provides a variable irrigation control system for a translational sprinkler irrigation machine, including:

[0104] The irrigation demand acquisition module is used to determine the irrigation demand corresponding to each grid cell in the target farmland based on the remote sensing data and ground measurement data corresponding to the pre-defined grid cells in the target farmland.

[0105] In this embodiment, the irrigation needs of each grid unit in the target farmland are accurately calculated using remote sensing data and ground-based measured data to avoid over- or under-irrigation.

[0106] The control module is used to control the translational sprinkler to perform irrigation tasks on the target farmland based on the irrigation needs corresponding to each grid unit in the target farmland and the coordinate information of the translational sprinkler in the current translational sprinkler system.

[0107] The translational sprinkler system includes a translational sprinkler machine and a pump station connected to the translational sprinkler machine.

[0108] Specifically, the translational sprinkler system includes:

[0109] The sprinkler machine itself is mounted on a mobile platform;

[0110] The GPS module is installed on the sprinkler body and is used to obtain the coordinate information of the translational sprinkler and transmit the coordinate information to the control module.

[0111] In this embodiment, the GPS location information of the sprinkler machine body can be obtained in real time through the GPS module and matched with the grid unit coordinates of the farmland to ensure accurate positioning of irrigation operations.

[0112] A mobile platform, connected to the control module, is used to drive the sprinkler machine body to move along a set trajectory within the target farmland under the control of the control module.

[0113] The intelligent sprinkler head is installed on the sprinkler machine body and connected to the control module, and is used to sprinkle irrigation on the target farmland according to the working parameters controlled by the control module;

[0114] The operating parameters include: the spray width of the intelligent sprinkler head and the spray rate of the intelligent sprinkler head;

[0115] The pumping station includes:

[0116] A water pump is used to draw water from a water source and send it through a pipeline into the sprinkler machine body;

[0117] The flow control valve is used to regulate the water flow rate delivered to the sprinkler body under the control of the control module.

[0118] In this embodiment, remote sensing data is acquired by a UAV (equipped with a visible light camera and a multispectral camera) and transmitted to the irrigation demand acquisition module; ground-based measured data is collected by on-site sampling equipment and input into the irrigation demand acquisition module. The irrigation demand acquisition module calculates the irrigation demand of each grid unit in the target farmland based on the input data and sends the calculation results to the control module.

[0119] The control module receives irrigation demands from the grid cells of the irrigation demand acquisition module and real-time location information from the GPS module on the translational sprinkler. It sends control commands to the mobile platform of the translational sprinkler to adjust its movement speed and trajectory. It also sends control commands to the intelligent sprinkler heads on the sprinkler body to adjust the spray width and spray rate. Finally, it sends adjustment commands to the flow control valves of the pump station to control the water flow.

[0120] The sprinkler unit includes an intelligent sprinkler head that connects to the control module and receives operating parameters (spray width, spray rate) for variable irrigation. It also integrates a GPS module that connects to the control module, providing real-time coordinate information for the sprinkler unit.

[0121] The mobile platform connects to the sprinkler unit, carrying it and moving along a set trajectory according to instructions from the control module. It receives control signals for movement speed and trajectory. A water pump draws water from an external source (such as a canal or reservoir) and delivers it to the sprinkler unit via pipelines. A flow control valve connects to the control module, receiving adjustment commands to dynamically adjust the water flow rate to ensure irrigation needs are met. The target farmland is divided into grid cells, each corresponding to a set of irrigation demand data. The translational sprinkler unit can complete the irrigation task cell by cell, according to instructions from the control module.

[0122] In the practical application of this embodiment, the control module, based on the irrigation prescription map and the obtained coordinate information of the current translational sprinkler in the translational sprinkler system, controls the translational sprinkler to perform irrigation tasks on the target farmland, specifically including:

[0123] The coordinate information of the current translational sprinkler system is compared with the coordinate range of each grid cell in the target farmland in advance. If the coordinate information of the current translational sprinkler system belongs to the coordinate range of any grid cell, it is determined that the translational sprinkler is located in the grid cell. According to the irrigation requirements corresponding to the grid cell, the moving speed of the translational sprinkler and the working parameters of the intelligent sprinkler head are controlled so that the moving speed and working parameters meet the first preset condition.

[0124] The first preset condition is: Irrigation demand = moving speed × spray width of the smart sprinkler head × spray rate of the smart sprinkler head.

[0125] In this embodiment, the control module can dynamically adjust the sprinkler parameters, ensuring that each grid unit accurately meets its irrigation needs. This avoids the problem of excessive or insufficient water flow in traditional irrigation methods, thereby improving crop yield and quality. Simultaneously, the coordinated control of the moving speed and spraying parameters ensures efficient use of irrigation water and prevents water waste. This embodiment achieves a fully automated variable irrigation process through real-time coordinate positioning and dynamic parameter adjustment. It eliminates the tediousness of traditional manual operation, improves irrigation efficiency, and reduces labor costs.

[0126] Compared to the traditional method of uniform irrigation using a fixed program, this solution uses coordinate positioning combined with the irrigation needs of grid units to form a dynamic adjustment mechanism, which significantly improves irrigation accuracy.

[0127] In this embodiment, the remote sensing data is generated by a drone equipped with a visible light camera and a multispectral camera to collect surface image data of grid cells through aerial photography.

[0128] The remote sensing data includes raw vegetation indices;

[0129] The ground measurement data corresponding to the grid cell includes soil data collected from multiple sampling points set in the grid cell;

[0130] The soil data includes soil moisture and soil texture.

[0131] For example, this embodiment uses a drone equipped with a visible light camera and a multispectral camera to acquire farmland surface image data through aerial photography. For instance, the surface images acquired by the visible light camera can reflect the overall distribution of farmland and the state of plant growth. The multispectral camera captures spectral information in different bands (such as red light and near-infrared bands) to generate a raw vegetation index (NDVI). This index can be used to assess vegetation cover, plant health, etc.

[0132] For example, a farmland is divided into multiple 10×10 meter grid units. The surface image data taken by the drone during its flight is processed to generate a vegetation index for each grid unit. For example, grid unit A: original vegetation index = 0.6, indicating good vegetation growth; grid unit B: original vegetation index = 0.4, indicating weak vegetation growth, which may require supplemental irrigation.

[0133] Several sampling points were set up within each grid cell to collect soil-related data, such as: Soil moisture: measured using a portable moisture meter; Soil texture: the proportions of sand, loam, and clay were obtained through particle size analysis. For example, at sampling point A in grid cell A, the soil moisture content was measured to be 25%; the soil texture was mainly sandy with low water retention. At sampling point B in grid cell B, the soil moisture content was measured to be 15%; the soil texture was mainly loam with moderate water retention.

[0134] In this embodiment, remote sensing data provides a macroscopic view of vegetation health status, while ground-based measured data provides microscopic soil information. By comprehensively analyzing these two types of data, the specific irrigation needs of each grid cell can be determined. For example: Grid cell A: the soil is relatively dry and the original vegetation index is low, requiring increased irrigation; Grid cell B: the soil moisture content is good, but the original vegetation index shows weak plant growth, which may require appropriate supplemental irrigation and attention to other agronomic measures.

[0135] In this embodiment, remote sensing data provides macro-scale information on surface vegetation, facilitating the rapid identification of differences in irrigation needs within farmland. Ground-measured data provides specific soil condition information, supplementing the limitations of remote sensing data and ensuring the accuracy of irrigation decisions. Traditional methods rely on manual measurement, which is inefficient and difficult to cover large areas of farmland; utilizing UAV remote sensing technology combined with ground sampling allows for rapid and comprehensive acquisition of farmland condition data, significantly reducing manpower and improving efficiency. Precise gridded data provides a scientific basis for variable irrigation, enabling each grid unit to adjust irrigation volume according to actual needs, avoiding over-irrigation or under-irrigation, and improving water resource utilization efficiency.

[0136] In the practical application of this embodiment, the irrigation demand acquisition module determines the irrigation demand corresponding to each grid cell in the target farmland based on the remote sensing data and ground measurement data corresponding to the pre-defined grid cells in the target farmland. Specifically, this includes:

[0137] Based on the ground measurement data corresponding to the pre-delineated grid cells in the target farmland, the corresponding initial vegetation indices are obtained; specifically including:

[0138] Based on the ground measurement data corresponding to the pre-delineated grid units in the target farmland, the corresponding initial vegetation index is obtained using formula (1);

[0139] The formula (1) is:

[0140]

[0141] in,

[0142] A = R NIR，0 -R Red，0 B = R NIR，0+R Red，0 ;

[0143] C = β NIN R NIR，0 -β Red R Red，0 D = β NIN R NIR，0 +β Red R Red，0 ;

[0144] R Red，0 The red light reflectance of the soil under waterless conditions was obtained in advance; β Red R represents the sensitivity coefficient of soil moisture to the red light band, obtained in advance. NIR，0 The near-infrared reflectance of the soil under waterless conditions was obtained in advance; β NIN The sensitivity coefficient of soil moisture to the near-infrared band, obtained in advance; SM s This refers to the raw soil moisture from ground-measured data; NDVI s This represents the initial vegetation index.

[0145] In this embodiment, formula (1) can more accurately calculate the vegetation index by considering the sensitivity coefficient of soil moisture to red light and near-infrared bands, thereby improving the accuracy of monitoring farmland vegetation conditions. In addition, formula (1) also introduces the sensitivity coefficient of soil moisture to red light and near-infrared bands, which can effectively correct the influence of soil moisture on the vegetation index, making the calculation results more reliable. This is an innovation of the traditional NDVI calculation method, which can more accurately reflect the influence of soil moisture on the vegetation index. At the same time, formula (1) is applicable to the calculation of vegetation index under different soil types and moisture conditions, enhancing the adaptability and generalization ability of the model.

[0146] The final vegetation index is obtained based on the initial spectral data and the original vegetation index in the remote sensing data;

[0147] Based on the initial spectral data and the original vegetation index in the remote sensing data, the final vegetation index is obtained using formula (2);

[0148] The formula (2) is:

[0149] NDVI f =w1·NDVI d +w2·NDVI s ;

[0150] NDVI f The final vegetation index; NDVI d The original vegetation index;

[0151]

[0152] Q d Q is the minimum value between the resolution of the image captured by the UAV's visible light camera and the resolution of the image captured by the multispectral camera; s The number of sampling points set in the grid cell.

[0153] In this embodiment, by comprehensively considering the resolution of the UAV visible light camera and the multispectral camera, as well as the number of sampling points in the grid cell, the final vegetation index can be calculated more accurately. Formula (2) uses w1 and w2 to balance the influence of the original vegetation index and the initial vegetation index, ensuring that the final vegetation index is more reliable. w1 and w2 are calculated using resolution and the number of sampling points, ensuring a reasonable allocation of weights for different data sources. Therefore, the final vegetation index NDVI is more reliable. f It is obtained by combining the original vegetation index and the initial vegetation index, and then performing a weighted average based on the weighting coefficients, resulting in a more comprehensive and accurate calculation.

[0154] The initial soil moisture is obtained based on the original vegetation index in the remote sensing data; specifically, the initial soil moisture is obtained using formula (3) based on the original vegetation index in the remote sensing data.

[0155] Formula (3) includes:

[0156]

[0157] SM g denoted as the initial soil moisture value; a is the first empirical parameter; b is the second empirical parameter; and c is the third empirical parameter.

[0158] Formula (3) in this embodiment, through the combination of exponential functions and empirical parameters, can more accurately reflect the nonlinear relationship between soil moisture and vegetation index, thereby improving the accuracy of the calculation. The empirical parameters a, b, and c in the formula can be adjusted according to different environmental conditions, making the formula more flexible and adaptable. In formula (3), the exponential function... This describes the relationship between soil moisture and vegetation indices, allowing for better capture of nonlinear changes.

[0159] The final soil moisture is obtained based on the initial soil moisture and the original soil moisture from the ground measurement data;

[0160] The original soil moisture is the average value of the soil moisture collected from multiple sampling points set in the grid cell;

[0161] Based on the initial soil moisture and the original soil moisture in the ground measured data, the final soil moisture is obtained by formula (4);

[0162] Formula (4) includes:

[0163]

[0164] SM t α represents the final soil moisture value; β represents the first preset weight value; γ represents the second preset weight value; and tanh() represents the hyperbolic tangent function.

[0165] E corr =k1·P+k2·T+k3·W d ;

[0166] P represents the current rainfall value; T represents the current ambient temperature value; W represents the current ambient temperature value. d The current wind speed value; k1 is the first preset adjustment parameter; k2 is the second preset adjustment parameter; k3 is the third preset adjustment parameter; β g β represents the difference between the maximum and minimum initial soil moisture values ​​for this grid cell over a historical time period. s This represents the difference between the maximum and minimum original soil moisture values ​​for that grid cell during the historical time period.

[0167] In this embodiment, formula (4) comprehensively considers the initial soil moisture, the original soil moisture from ground-measured data, and current environmental factors (rainfall, temperature, wind speed), enabling a more accurate calculation of the final soil moisture. Weight values ​​α, β, and γ are used to balance the influence of different data sources, ensuring a more reliable final soil moisture calculation. Furthermore, formula (4) introduces weighting coefficients and a hyperbolic tangent function to balance the influence of different data sources, thus more accurately reflecting the contributions of different data sources. The hyperbolic tangent function tanh() can capture the nonlinear relationship between soil moisture and different data sources.

[0168] For example, assuming the current rainfall is 10mm, then P = 10; the current ambient temperature is 20℃, then T = 20; the current wind speed is 5m / s, then W d =5; the first preset adjustment parameter k1 is 0.5; the second preset adjustment parameter k2 is 0.3; the third preset adjustment parameter k3 is 0.2; then E corr The specific value was calculated to be 12.

[0169] Assuming the initial soil moisture value SM g The value is 0.4; the difference β between the maximum and minimum initial soil moisture values ​​corresponding to this grid cell during the historical time period is 0.4. g The value is 0.2; the difference β between the maximum and minimum original soil moisture values ​​corresponding to this grid cell during the historical time period. sThe value is 0.1; the original soil moisture value SM in the ground measurement data is... s If it is 0.3, then Assuming α = 0.6, β = 0.3, and γ = 0.1, then the final soil moisture value SM is calculated using formula (4). t It is approximately 2.0769.

[0170] Based on the final vegetation index and final soil moisture, the irrigation requirement corresponding to this grid cell is obtained, specifically including:

[0171] Based on the final vegetation index and final soil moisture, the irrigation demand corresponding to the grid cell is obtained by using an inversion model.

[0172] The inversion model is as follows:

[0173]

[0174] WD represents the irrigation requirement corresponding to this grid cell; NDVI max The maximum vegetation index corresponding to the crops in the target farmland over a historical period; SM opt ET is the pre-obtained optimal soil moisture value corresponding to the crops in the target farmland; ET is the current evapotranspiration rate (reflecting water evaporation and evaporation loss (e.g., in mm / day)).

[0175]

[0176] ε is the first preset contribution value; σ is the second preset contribution value; μ is the calibration coefficient (used to adjust the formula result to the actual unit of water requirement (such as mm or m)). 3 / mu); δ is the evapotranspiration correction coefficient (reflecting the dynamic adjustment of water demand under current meteorological conditions).

[0177] In this embodiment, the inversion model, by comprehensively considering the final vegetation index and the final soil moisture, can more accurately calculate irrigation demand. Multiple parameters and correction coefficients are introduced into the inversion model, making the calculation results more accurate. The inversion model uses weighting coefficients ε and σ to balance the influence of vegetation index and soil moisture, ensuring that the final irrigation demand calculation is more reliable.

[0178] Example 2

[0179] This embodiment relates to a variable irrigation control system for a translational sprinkler irrigation machine, designed to achieve efficient and precise irrigation of each grid unit in a target farmland through accurate irrigation demand assessment and intelligent control. This system is particularly suitable for large-scale agricultural areas, significantly improving water resource utilization efficiency, reducing waste, and optimizing the crop growth environment.

[0180] This embodiment two provides a variable irrigation control system for a translational sprinkler irrigation machine, including:

[0181] The irrigation demand acquisition module is responsible for determining the irrigation demand of each grid cell based on remote sensing data and ground measurement data corresponding to the pre-defined grid cells in the target farmland.

[0182] Remote sensing data was collected by UAVs equipped with visible light and multispectral cameras to generate information such as the raw vegetation index (NDVI). Ground-based measured data, including parameters such as soil moisture and soil texture, were obtained through multiple sampling points set within each grid cell. The initial vegetation index, final vegetation index, initial soil moisture, and final soil moisture were calculated using formulas (1) to (4) to determine the irrigation requirements for each grid cell.

[0183] The control module receives data from the irrigation demand acquisition module and the current position coordinates of the translational sprinkler provided by the GPS module. It compares the current sprinkler coordinates with the coordinate range of each grid cell. When the sprinkler enters a grid cell, the control module adjusts the sprinkler's operating parameters to meet the irrigation needs of that grid cell. These operating parameters include the spray width and spray rate of the intelligent sprinkler head, ensuring that the preset condition "irrigation demand = movement speed × intelligent sprinkler head spray width × intelligent sprinkler head spray rate" is met.

[0184] The translational sprinkler irrigation system comprises a sprinkler unit, a GPS module, a mobile platform, and intelligent sprinkler heads. The sprinkler unit is mounted on the mobile platform, and the GPS module provides real-time location information to the control module. The mobile platform moves along a pre-set trajectory according to commands from the control module, ensuring coverage of the entire target farmland. The intelligent sprinkler heads execute irrigation tasks according to the operating parameters set by the control module.

[0185] The pumping station includes a water source pump and a flow control valve. The water source pump draws water from a water source and delivers it to the sprinkler irrigation machine through pipelines. The flow control valve, under the command of the control module, regulates the water flow delivered to the sprinkler irrigation machine to ensure the stability and accuracy of water volume during irrigation.

[0186] Specific implementation steps

[0187] Drones are used for aerial photography to collect surface image data and generate remote sensing data.

[0188] Several sampling points were set up in the target farmland, and soil samples were collected regularly to analyze soil moisture, texture and other characteristics.

[0189] Combining remote sensing data and ground-based measured data, the irrigation requirements of each grid cell are calculated using formulas (1) to (4).

[0190] The GPS module continuously monitors the location of the sprinkler and sends the coordinate information to the control module.

[0191] The control module dynamically plans the optimal path based on the irrigation prescription map and the current location of the sprinkler, ensuring that the sprinkler travels along the predetermined route.

[0192] When the sprinkler enters a specific grid cell, the control module adjusts the sprinkler's moving speed and the working parameters of the smart sprinkler head according to the irrigation needs of that grid cell.

[0193] For example, in arid areas with high water demand, the spraying rate of sprinkler heads can be increased; in humid areas or areas with low water demand, the spraying rate can be reduced to save water resources.

[0194] This second embodiment provides a highly efficient variable irrigation control system for a translational sprinkler irrigation machine. During operation, it continuously collects feedback on actual irrigation effects, such as changes in soil moisture and crop growth. Based on this feedback, it further optimizes the irrigation strategy, improving overall irrigation efficiency and crop yield. It combines advanced remote sensing technology with ground-based measured data analysis to achieve precision irrigation. By dynamically adjusting the sprinkler irrigation machine's operating parameters, it not only improves water resource utilization but also provides a scientific basis and technical support for modern agricultural production.

[0195] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0196] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0197] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that they are in indirect contact through an intermediate medium. Furthermore, "above," "over," or "on top" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," or "beneath" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0198] In the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0199] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A variable irrigation control system for a translational sprinkler irrigation machine, characterized in that, include: Irrigation demand acquisition module and control module; An irrigation demand acquisition module is used to determine the irrigation demand for each grid cell in the target farmland based on remote sensing data and ground-measured data corresponding to pre-defined grid cells. Specifically, this includes: acquiring an initial vegetation index based on the ground-measured data corresponding to the pre-defined grid cells; obtaining a final vegetation index based on the initial vegetation index and the original vegetation index in the remote sensing data; obtaining initial soil moisture based on the original vegetation index in the remote sensing data; obtaining final soil moisture based on the initial soil moisture and the original soil moisture in the ground-measured data; the original soil moisture is the average of soil moisture collected from multiple sampling points within the grid cell; and acquiring the irrigation demand for that grid cell based on the final vegetation index and the final soil moisture; the grid cell is 10×10 meters. The remote sensing data is generated by a drone equipped with a visible light camera and a multispectral camera to collect surface image data of grid cells through aerial photography, and the remote sensing data includes the original vegetation index. The ground measurement data corresponding to the grid cell includes soil data collected from multiple sampling points set in the grid cell; the soil data includes soil moisture and soil texture. The control module is used to control the translating sprinkler to perform irrigation tasks on the target farmland based on the irrigation needs corresponding to each grid unit in the target farmland and the obtained coordinate information of the translating sprinkler in the current translating sprinkler system. Specifically, it includes: comparing the coordinate information of the translating sprinkler in the current translating sprinkler system with the coordinate range of each grid unit in the target farmland in advance; if the coordinate information of the translating sprinkler in the current translating sprinkler system belongs to the coordinate range of any grid unit, it is determined that the translating sprinkler is located in that grid unit; and according to the irrigation needs corresponding to that grid unit, controlling the moving speed of the translating sprinkler and the working parameters of the intelligent sprinkler head, so that the moving speed and working parameters meet a first preset condition; the first preset condition is: irrigation needs = moving speed × spraying width of intelligent sprinkler head × spraying rate of intelligent sprinkler head; the translating sprinkler system includes the translating sprinkler and a pump station connected to the translating sprinkler.

2. The variable irrigation control system for a translational sprinkler irrigation machine according to claim 1, characterized in that, The translational sprinkler system includes: The sprinkler machine itself is mounted on a mobile platform; The GPS module is installed on the sprinkler body and is used to obtain the coordinate information of the translational sprinkler and transmit the coordinate information to the control module. A mobile platform, connected to the control module, is used to drive the sprinkler machine body to move along a set trajectory within the target farmland under the control of the control module. The intelligent sprinkler head is installed on the sprinkler machine body and connected to the control module, and is used to sprinkle irrigation on the target farmland according to the working parameters controlled by the control module; The operating parameters include: the spray width of the intelligent sprinkler head and the spray rate of the intelligent sprinkler head; The pumping station includes: A water pump is used to draw water from a water source and send it through a pipeline into the sprinkler machine body; The flow control valve is used to regulate the water flow rate delivered to the sprinkler body under the control of the control module.

3. The variable irrigation control system for a translational sprinkler irrigation machine according to claim 2, characterized in that, The process of obtaining the corresponding initial vegetation index based on ground measurement data corresponding to pre-defined grid units in the target farmland specifically includes: Based on the ground measurement data corresponding to the pre-delineated grid units in the target farmland, the corresponding initial vegetation index is obtained using formula (1); The formula (1) is: ； in, ； ； ； ； The red light reflectance of the soil under waterless conditions was obtained in advance; The sensitivity coefficient of soil moisture to the red light band is obtained in advance; The near-infrared reflectance of the soil under waterless conditions was obtained in advance; The sensitivity coefficient of soil moisture to the near-infrared band is obtained in advance; This refers to the original soil moisture from ground-measured data; This represents the initial vegetation index.

4. The variable irrigation control system for a translational sprinkler irrigation machine according to claim 3, characterized in that, The process of obtaining the final vegetation index based on the initial vegetation index and the original vegetation index in the remote sensing data specifically includes: The final vegetation index is obtained by formula (2) based on the initial vegetation index and the original vegetation index in the remote sensing data. The formula (2) is: ； This is the final vegetation index; The original vegetation index; ； ； The minimum value between the resolution of the image captured by the visible light camera of the UAV and the resolution of the image captured by the multispectral camera; The number of sampling points set in the grid cell.

5. The variable irrigation control system for a translational sprinkler irrigation machine according to claim 4, characterized in that, The step of obtaining initial soil moisture based on the original vegetation index in the remote sensing data specifically includes: The initial soil moisture is obtained using formula (3) based on the original vegetation index in the remote sensing data. Formula (3) includes: ； This represents the initial soil moisture value; 'a' is the first empirical parameter; b is the second empirical parameter; c is the third empirical parameter.

6. The variable irrigation control system for a translational sprinkler irrigation machine according to claim 5, characterized in that, Based on the initial soil moisture and the original soil moisture from ground-measured data, the final soil moisture is obtained, specifically including: Based on the initial soil moisture and the original soil moisture in the ground measured data, the final soil moisture is obtained by formula (4); Formula (4) includes: ； This represents the final soil moisture value; The first preset weight value; The second preset weight value; The third preset weight value; It is the hyperbolic tangent function; ； P represents the current rainfall value; T represents the current ambient temperature. This is the current wind speed value; Adjust the parameters for the first preset; Adjust the parameters for the second preset; Adjust the parameters for the third preset; This represents the difference between the maximum and minimum initial soil moisture values ​​for that grid cell within a historical time period. This represents the difference between the maximum and minimum original soil moisture values ​​for that grid cell during the historical time period.

7. The variable irrigation control system for a translational sprinkler irrigation machine according to claim 6, characterized in that, Based on the final vegetation index and final soil moisture, the irrigation requirement corresponding to this grid cell is obtained, specifically including: Based on the final vegetation index and final soil moisture, the irrigation demand corresponding to the grid cell is obtained by using an inversion model. The inversion model is as follows: ； WD represents the irrigation requirement corresponding to this grid cell; This refers to the maximum vegetation index corresponding to the crops in the target farmland during the historical time period; The optimal soil moisture value corresponding to the crops in the target farmland is obtained in advance; This is the current evapotranspiration rate; ； The first preset contribution value; This is the second preset contribution value; These are calibration coefficients; This is the evapotranspiration correction factor.

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