Unmanned aerial vehicle parameter optimization method with flow guiding side spraying mechanism and flow guiding side spraying mechanism

Abstract

The present application relates to the field of agricultural aviation plant protection and computer simulation technology, in particular to a UAV parameter optimization method with a guide flow side spraying mechanism and the guide flow side spraying mechanism, which comprises constructing a simulation analysis model; based on the simulation analysis model, a multiphase flow simulation model is constructed, simulation calculation is carried out with boundary conditions and UAV parameters as design variables, and a multiphase flow simulation data set is obtained; based on the multiphase flow simulation data set, a function relationship between the environmental wind speed and the droplet deposition center offset is obtained through quadratic regression fitting, and a droplet drift model is constructed; through the response surface method, the UAV parameters are iteratively optimized under the drift constraint based on the droplet drift model, and the best parameter combination is obtained. Through the present application, the droplet motion law under the action of complex downward washing airflow and side spraying mechanism can be accurately simulated, the problem of uneven crown layer droplet deposition caused by traditional downward spraying operation is solved, and reliable simulation data support is provided for parameter setting of the UAV variable pesticide application control system.

Description

Technical Field

[0001] This invention relates to the fields of agricultural aviation plant protection and computer simulation technology, specifically to a method for optimizing the parameters of a UAV with a flow-guiding side spray mechanism and the flow-guiding side spray mechanism. Background Technology

[0002] In recent years, agricultural drones have been increasingly used in precision agriculture. However, when applying pesticides to crops with three-dimensional canopies, such as trellis vineyards, the traditional downspraying mode of agricultural drones often results in uneven droplet deposition in the lower part of the canopy, poor penetration, and easy drift due to external wind conditions, leading to pesticide waste and environmental pollution.

[0003] To address these issues, existing research has begun to explore the introduction of lateral spray mechanisms. However, there is a lack of in-depth quantitative calculations regarding the physical mechanisms by which the guide structure guides and alters the momentum of the rotor downwash flow field. Due to the lack of numerical simulations of airflow constraints and momentum conversion processes, current guide ducts largely rely on experience, lack mechanistic support, and are difficult to optimize precisely in terms of structural parameters.

[0004] Furthermore, crosswinds significantly interfere with the deposition trajectory of spray droplets. Existing simulation assessment methods often only provide qualitative cloud map displays, lacking analytical modeling of the quantitative mapping relationship between environmental wind speed and droplet deposition offset. This makes it difficult for UAVs to achieve real-time drift compensation based on simulation data when operating in complex wind fields, thus hindering the development of precision variable-rate application technology. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for optimizing UAV parameters and a flow-guiding side-spraying mechanism. This method can accurately simulate the droplet movement patterns under the influence of complex downwash airflow and the side-spraying mechanism, solve the problem of uneven canopy droplet deposition caused by traditional downwashing operations, and provide reliable simulation data support for parameter tuning of the UAV variable spraying control system.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A method for optimizing UAV parameters with a flow-guiding side-jet mechanism and the flow-guiding side-jet mechanism, which includes: Construct a simulation analysis model; the simulation analysis model includes a 3D model of the UAV and a crop canopy model. The 3D UAV model includes a multi-rotor UAV model and a guide side spray mechanism model. A multiphase flow simulation model was constructed based on the simulation analysis model. The boundary conditions and UAV parameters were used as design variables for simulation calculation to obtain a multiphase flow simulation dataset. The UAV parameters include UAV structural parameters, guide side spray mechanism structural parameters, and UAV operation parameters. The multiphase flow simulation dataset includes the deposition distribution data of droplets in each layer of the crop canopy. Based on a multiphase flow simulation dataset, a functional relationship between ambient wind speed and droplet deposition center offset was obtained through quadratic regression fitting, and a droplet drift model was constructed. With the optimization objectives of minimizing the coefficient of variation of droplet deposition uniformity and maximizing the average deposition density in the target crop canopy, the UAV parameters are iteratively optimized using the response surface methodology under drift constraints based on a droplet drift model to obtain the optimal parameter combination. The specific formula is as follows: ; in, The coefficient of variation for depositional uniformity. The average sediment density, For flight altitude, For flight speed, To the flow rate of the pesticide, The droplet size is... For ambient wind speed Lateral offset of the droplet deposition center under the current This is the maximum allowed offset threshold.

[0007] As a preferred option, constructing a simulation analysis model specifically includes: A simulation computational domain was established, including a multi-rotor UAV model, a flow-guiding side-spraying mechanism model, and a crop canopy model. Within the simulation calculation domain, the area around each rotor of the multi-rotor UAV is divided into the rotation domain, and the area inside the nozzle of the flow guide side spray mechanism is divided into the internal flow field. The geometric model within the simulation computation domain is simplified, and the mesh region is divided. The crop canopy model is divided into a multi-layer structure, with each layer designed as a porous medium model. The resistance parameters of the porous media model were set based on crop plant height, leaf area index, and number of leaves. These resistance parameters included permeability. and inertial drag coefficient Resistance source term in porous media model satisfy: ; in, Indicates the coordinate direction, with values ​​ranging from 1 to 2. or for The momentum source term in the direction, in units of The dynamic viscosity of air, in units of Permeability of porous media, in units of for The velocity component in the direction, in units of This is the inertial drag coefficient, in units of... Air density, unit: The magnitude of the velocity vector, in units of .

[0008] Preferably, an Eulerian-Lagrange framework is used to construct a multiphase flow model. The Eulerian method is used to describe the airflow field of the continuous phase, and the Lagrange method is used to track the drug droplets of the discrete phase. A two-way coupling is adopted between the continuous phase and the discrete phase, and the following parameters are set: The continuous phase parameters include at least the ambient gas type, ambient wind speed, rotation center and rotation speed of the rotating domain, computational domain boundary conditions, internal flow field boundary conditions, and UAV flight parameters; Discrete phase parameters were used for droplet tracking using a discrete phase model. The atomization model was set as a linearized unstable breakup model, and the secondary breakup model was set as a TAB model. The spray pressure, particle type, jet flow rate, and spray particle size were also set.

[0009] Preferably, the boundary conditions include at least one of the following: ambient wind speed, temperature conditions, and crop plant development; the UAV parameters include the structural dimensions of the guide side spray mechanism, the nozzle spray angle parameters, the spray boom tilt angle parameters, the UAV flight speed, and the UAV flight altitude.

[0010] As a preferred option, based on the multiphase flow simulation dataset, the droplet trajectory coordinate data of the nozzle at the near and far wind inlets under different environmental wind speeds are extracted; Using ambient wind speed as the independent variable and the lateral offset of the droplet deposition center determined based on droplet trajectory coordinate data as the dependent variable, a droplet drift model is constructed by least squares quadratic regression fitting: ; in, Ambient wind speed, unit: ; The lateral offset of the droplet deposition center, in units of ; These are the regression coefficients; For the left nozzle, the drift model under near-airflow conditions is: ; The drift model under the condition of far-end wind is: ; For the right nozzle, the drift model under near-airflow conditions is: ; The drift model under the condition of far-end wind is: ;; in, All units are The unit is .

[0011] As a preferred option, the optimization objectives are to minimize the coefficient of variation of the uniformity of droplet deposition in the target crop canopy and maximize the average deposition density. The coefficient of variation of the uniformity of deposition is the ratio of the standard deviation of the deposition amount at each sampling point in the target crop canopy to the average deposition amount, and the average deposition density is the mass or number of droplets deposited per unit area. Select design variables from the UAV parameters and set the value range for each selected design variable; The Box-Behnken design method was used to generate an experimental scheme consisting of multiple sets of design variable parameters. The multiphase flow simulation model was used to perform simulation calculations on each set of design variable parameters. The deposition uniformity variation coefficient and average deposition density corresponding to each set of design variable parameters were extracted from the simulation results. Using the design variables as independent variables and the coefficient of variation of sedimentary uniformity and average sedimentary density as response variables, a quadratic regression mathematical model was established through quadratic regression fitting. ; in, For the first The encoded values ​​of each design variable; The regression coefficient is the coefficient of variation of sedimentation uniformity; The regression coefficient for the average sediment density; The number of design variables was determined; and the significance and goodness-of-fit tests were performed on the quadratic regression mathematical model. Under the optimization objectives of minimizing the coefficient of variation of sedimentation uniformity and maximizing the average sedimentation density, the validated quadratic regression mathematical model is solved through multi-objective optimization to obtain the optimal combination of UAV parameters; The optimal combination of UAV parameters is re-input into the multiphase flow simulation model for verification simulation. The verification simulation results are compared with the predicted values ​​of the quadratic regression mathematical model to confirm the reliability of the optimization results and obtain the final optimal parameter combination.

[0012] A flow-guiding side-spraying mechanism, disposed below the rotor of a multi-rotor UAV, is used for liquid spraying operations of the multi-rotor UAV. It is obtained by optimizing the above method and includes: The device includes a main body, which is suspended from a guide tube below the rotor via a mounting bracket. A nozzle is provided at the end of the guide tube away from the rotor. A guide plate is provided below the guide tube along its length, and a concave guide groove is provided inside the guide plate along its length. The guide plate includes a straight section and a bent section. The end of the straight section is located below the nozzle, and the end of the bent section extends towards the rotor and bends upward. The bent section of the guide plate is used to capture the rotor downwash airflow and guide it into a lateral airflow horizontal to the straight section.

[0013] Preferably, the other end of the guide tube is connected to the underside of the rotor via a support adjustment frame; the support adjustment frame includes a guide tube mounting plate for fixing the guide tube and a fixing plate for fixing it to the multi-rotor UAV, the fixing plate and the guide tube mounting plate are fixedly connected and the included angle between them is adjustable.

[0014] Preferably, a connector is provided between the guide tube and the guide plate, with the upper and lower ends of the connector respectively fitted onto the guide tube and the guide plate; the end of the connector fitted onto the guide plate is provided with a guide hole for the airflow guided by the guide plate to pass through; the included angle between the guide tube and the guide plate is adjustable. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the parameter optimization method for a UAV with a flow-guiding side spray mechanism in this embodiment.

[0016] Figure 2 This is a cloud map of the droplet trajectory from the simulation results in this embodiment.

[0017] Figure 3 These are the droplets after data extraction in this embodiment.

[0018] Figure 4 This is a schematic diagram of the multi-rotor UAV with a flow-guiding side-jet structure in this embodiment.

[0019] Figure 5 This is a schematic diagram of the flow-guiding side spray structure in this embodiment.

[0020] Figure 6 Comparison of the drift distances of the left / right nozzles at different wind speeds and the quadratic polynomial fitting. Detailed Implementation

[0021] To further understand the content of this invention, the invention will be described in detail with reference to the embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0022] The technical solution of this invention first constructs a simulation analysis model including the structural parameters, flight operation parameters, guide-flow side-spray mechanism parameters, nozzle spray angle, spray boom tilt angle, and crop canopy. It simulates the trajectory of pesticide droplets, deposition amount, drift amount, and coefficient of variation at specific locations in the crop canopy under preset combinations of environmental wind speed and guide-flow side-spray parameters, generating a multi-dimensional simulation dataset. Droplet trajectory coordinate data from nozzles at different lateral wind speeds near and far from the wind outlet are extracted, fitted into a quadratic equation, and a droplet drift model is constructed. Then, based on the model, the droplet distribution of the drone under specific flight parameters, environmental wind speed, and side-spray parameters is calculated and predicted, including deposition amount and drift amount, achieving accurate prediction of pesticide application effect.

[0023] The UAV spray simulation analysis model constructed by this technical solution quantifies the coupling mechanism of the rotor downwash air field and the lateral induced air field by simulating the constraint effect of the funnel-shaped guide structure. It introduces a multiphase flow bidirectional coupling mechanism and strict droplet dynamics assumptions, accurately simulates the flow field changes under complex guide side spray structure, and the calculation results are reliable, truly reflecting the influence of airflow on droplet motion. It also incorporates the effect of grapevine characteristics on droplet penetration and deposition, supplements the continuous phase-discrete phase coupling mechanism, and improves the efficiency of UAV operation.

[0024] Example 1 Seen in Figure 1 In one specific embodiment, this disclosure proposes a method for optimizing the parameters of a UAV with a flow-guiding side spray mechanism, which includes: Construct a simulation analysis model; the simulation analysis model includes a 3D model of the UAV and a crop canopy model. The 3D UAV model includes a multi-rotor UAV model and a guide side spray mechanism model. A multiphase flow simulation model was constructed based on the simulation analysis model. The boundary conditions and UAV parameters were used as design variables for simulation calculation to obtain a multiphase flow simulation dataset. The UAV parameters include UAV structural parameters, guide side spray mechanism structural parameters, and UAV operation parameters. The multiphase flow simulation dataset includes the deposition distribution data of droplets in each layer of the crop canopy. Based on a multiphase flow simulation dataset, a functional relationship between ambient wind speed and droplet deposition center offset was obtained through quadratic regression fitting, and a droplet drift model was constructed. With the optimization objectives of minimizing the coefficient of variation of droplet deposition uniformity and maximizing the average deposition density in the target crop canopy, the UAV parameters are iteratively optimized using the response surface methodology under drift constraints based on a droplet drift model to obtain the optimal parameter combination. The specific formula is as follows: ; in, The coefficient of variation for depositional uniformity. The average sediment density, For flight altitude, For flight speed, To the flow rate of the pesticide, The droplet size is... For ambient wind speed Lateral offset of the droplet deposition center under the current This is the maximum allowed offset threshold.

[0025] In this embodiment, the preferred multi-rotor UAV configuration is a quadcopter, which is the mainstream model for agricultural plant protection UAVs in China. It features a symmetrical layout, simple structure, and a relatively regular downwash airflow field, facilitating simulation calculations. Furthermore, under the same load, the quadcopter has a larger single rotor diameter, resulting in a concentrated downwash airflow in an inverted funnel shape, which is beneficial for the guide jet mechanism to capture the downwash airflow below the rotor and guide it laterally. Simulation comparisons show that compared to other types of multi-rotor UAVs, the quadcopter UAV has a symmetrical downwash airflow field, moderate rotor wind speed, and superior airflow guidance effect compared to other multi-rotor configurations. In addition, the UAV and parameter optimization method of this embodiment is applicable to all types of multi-rotor UAVs.

[0026] This disclosure constructs a multiphase flow simulation model that includes a guide-side spray mechanism model, a crop canopy model, and a gas-liquid bidirectional coupling model. This model accurately simulates the flow direction and acceleration process of the rotor downwash airflow within the guide structure, as well as the penetration and deposition behavior of droplets in different layers of the crop canopy. Through response surface methodology, multi-parameter collaborative optimization is achieved, calculating the optimal parameter combination. A droplet drift model is used as a constraint to ensure that the drift amount of the obtained optimal parameter combination does not exceed the maximum allowable offset threshold under a given environmental wind speed. Compared to traditional empirical design methods, the simulation analysis method in this embodiment can quantitatively output the optimal parameter combination, significantly improving the uniformity and penetration of pesticide application in three-dimensional canopy crops. Furthermore, by introducing drift constraints from the droplet drift model, the optimized parameter combination not only achieves good deposition effects in actual operations but also allows for controllable drift, avoiding the problem of solely pursuing deposition effects while ignoring drift risks.

[0027] In this embodiment, constructing the simulation analysis model specifically includes: A simulation computational domain was established, including a multi-rotor UAV model, a flow-guiding side-spraying mechanism model, and a crop canopy model. Within the simulation calculation domain, the area around each rotor of the multi-rotor UAV is divided into the rotation domain, and the area inside the nozzle of the flow guide side spray mechanism is divided into the internal flow field. The geometric model within the simulation computation domain is simplified, and the mesh region is divided. The crop canopy model is divided into a multi-layer structure, with each layer designed as a porous medium model. The resistance parameters of the porous media model were set based on crop plant height, leaf area index, and number of leaves. These resistance parameters included permeability. and inertial drag coefficient Resistance source term in porous media model satisfy: ; in, Indicates the coordinate direction, with values ​​ranging from 1 to 2. or for The momentum source term in the direction, in units of The dynamic viscosity of air, in units of Permeability of porous media, in units of for The velocity component in the direction, in units of This is the inertial drag coefficient, in units of... Air density, unit: The magnitude of the velocity vector, in units of .

[0028] Unlike conventional vertical spraying simulation scenarios, in this embodiment, the internal flow field is used to simulate the flow direction and acceleration process of the rotor downwash airflow inside the guide structure; simplification includes removing structural details that have little impact on the airflow, such as bolts and clamps, to improve mesh generation quality and computational efficiency; the crop canopy model is used to simulate the obstruction effect of crop leaves on airflow and the interception and capture effect on droplets, dividing the crop canopy into a multi-layer structure, which can evaluate the deposition effect of each layer separately.

[0029] The rotating domain is used to simulate the air turbulence caused by the high-speed rotation of the rotor. The cylindrical area around each rotor is divided into rotating domains, and the grid within the rotating domain rotates as a whole with the set rotor speed. The internal flow field simulates the airflow channel area inside the guide side spray mechanism, including the internal cavities of the air inlet, bending section and air outlet, where the airflow undergoes turning and acceleration processes. Through grid region division, a high-quality boundary layer grid is generated inside the guide structure to accurately capture the flow separation and secondary flow phenomena near the wall. The formula used in the porous medium model describes the viscosity loss and inertial loss of the airflow in the crop canopy, which can accurately simulate the penetration and deposition behavior of droplets in each layer of the crop canopy.

[0030] In this disclosure, a multiphase flow model is constructed using an Eulerian-Lagrange framework. The Eulerian method is used to describe the airflow field of the continuous phase, and the Lagrange method is used to track the drug droplets of the discrete phase. A two-way coupling is adopted between the continuous and discrete phases, and the following parameters are set: The continuous phase parameters include at least the ambient gas type, ambient wind speed, rotation center and rotation speed of the rotating domain, computational domain boundary conditions, internal flow field boundary conditions, and UAV flight parameters; Discrete phase parameters were used for droplet tracking using a discrete phase model. The atomization model was set as a linearized unstable breakup model, and the secondary breakup model was set as a TAB model. The spray pressure, particle type, jet flow rate, and spray particle size were also set.

[0031] Specifically, this embodiment employs an Eulerian-Lagrange framework. The continuous phase airflow field is described using the Eulerian method, while the discrete phase droplets are tracked using the Lagrange method. A two-way coupling is used between the continuous and discrete phases; that is, the airflow affects droplet motion, and the droplets, in turn, affect the airflow. Therefore, for the guide-side spray mechanism, a gas-liquid two-way coupling model must be established because the high-speed airflow at the guide structure outlet shears, stretches, and breaks up the droplets, completely altering their initial motion state. The linearized unstable fragmentation model serves as the atomization model, describing the initial atomization process when the liquid is ejected from the nozzle. The TAB model serves as the secondary fragmentation model, describing the secondary fragmentation process of droplets moving in the airflow. In the guide-side spray scenario, the high-speed airflow at the guide structure outlet causes the droplets to undergo intense secondary fragmentation; therefore, the coupled fragmentation model is crucial for the accuracy of deposition prediction.

[0032] In this disclosure, the boundary conditions include at least one of environmental wind speed, temperature conditions, and crop plant development. The drone parameters include the structural dimensions of the guide side spray mechanism, the nozzle spray angle, the spray boom tilt angle, the drone flight speed, and the drone flight altitude.

[0033] Specifically, temperature conditions affect the evaporation rate of droplets, and crop plant development corresponds to different leaf area indices and canopy structure parameters, which can be simulated for crops at different growth stages; the structural parameters of the guide side spray mechanism include the inlet diameter, outlet diameter, bending angle, radius of curvature, arc length, and wall thickness of the guide pipe; the nozzle spray angle parameters include the spray fan angle and the nozzle installation tilt angle; the spray bar tilt angle is the tilt angle of the spray bar relative to the horizontal plane.

[0034] In this disclosure, based on a multiphase flow simulation dataset, the droplet trajectory coordinate data of the nozzle at the near-wind outlet and the far-wind outlet under different environmental wind speeds are extracted; Using ambient wind speed as the independent variable and the lateral offset of the droplet deposition center determined based on droplet trajectory coordinate data as the dependent variable, a droplet drift model is constructed by least squares quadratic regression fitting: ; in, Ambient wind speed, unit: ; The lateral offset of the droplet deposition center, in units of ; These are the regression coefficients; For the left nozzle, the drift model under near-airflow conditions is: ; The drift model under the condition of far-end wind is: ; For the right nozzle, the drift model under near-airflow conditions is: ; The drift model under the condition of far-end wind is: ; in, All units are The unit is .

[0035] The drift characteristics of nozzles at different locations differ. The near-air inlet is the side closest to the airflow inlet, where the droplets are more strongly propelled by the ambient wind; the far-air inlet is the side furthest from the airflow inlet, where the droplets are relatively less affected by the ambient wind.

[0036] The droplet drift model disclosed herein can predict the lateral offset of droplet deposition centers at near-wind and far-wind positions of the left and right nozzles under different environmental wind speeds, elevating drift assessment from qualitative cloud map judgment to quantitative equation calculation. Before actual UAV operation, the predicted drift offset can be calculated by substituting the real-time environmental wind speed into the corresponding drift model. If the predicted offset exceeds the maximum allowable offset threshold yt, it indicates a high risk of pesticide damage under the current wind speed, and operation should be suspended or operational parameters adjusted.

[0037] In this disclosure, the optimization objectives are to minimize the coefficient of variation of the uniformity of droplet deposition in the target crop canopy and maximize the average deposition density. The coefficient of variation of the uniformity of deposition is the ratio of the standard deviation of the deposition amount at each sampling point in the target crop canopy to the average deposition amount, which is used to measure the uniformity of pesticide application by the spraying device. The smaller the value, the more uniform the deposition. The average deposition density is the mass or number of droplets deposited per unit area, which is used to directly measure the pesticide application efficiency of the spraying device. The larger the value, the more complete the deposition. Select design variables from the UAV parameters and set the value range for each selected design variable; The Box-Behnken design method was used to generate an experimental scheme consisting of multiple sets of design variable parameters. The multiphase flow simulation model was used to perform simulation calculations on each set of design variable parameters. The deposition uniformity variation coefficient and average deposition density corresponding to each set of design variable parameters were extracted from the simulation results. Using the design variables as independent variables and the coefficient of variation of sedimentary uniformity and average sedimentary density as response variables, a quadratic regression mathematical model was established through quadratic regression fitting. ; in, For the first The encoded values ​​of each design variable; The regression coefficient is the coefficient of variation of sedimentation uniformity; The regression coefficient for the average sediment density; The number of design variables was determined; and the significance and goodness-of-fit tests were performed on the quadratic regression mathematical model. Under the optimization objectives of minimizing the coefficient of variation of sedimentation uniformity and maximizing the average sedimentation density, the validated quadratic regression mathematical model is solved through multi-objective optimization to obtain the optimal combination of UAV parameters; The optimal combination of UAV parameters is re-input into the multiphase flow simulation model for verification simulation. The verification simulation results are compared with the predicted values ​​of the quadratic regression mathematical model to confirm the reliability of the optimization results and obtain the final optimal parameter combination. If the deviation between the verification simulation results and the predicted values ​​is within the allowable range, the parameter combination is confirmed as the final optimal parameter combination; if the deviation is large, the quadratic regression mathematical model needs to be modified or additional experimental points need to be added before re-optimization.

[0038] Box-Behnken design is a response surface methodology that uses three levels for each factor. Experimental points are selected at the midpoints and center points of each side of a cube. It exhibits rotational property, meaning that the variance of predicted values ​​is the same at all points equidistant from the center point. Compared to full factorial experiments, Box-Behnken design can fit a reliable quadratic regression mathematical model with fewer trials. Multi-objective optimization uses the expectation function method to combine multiple optimization objectives into a single comprehensive expectation value, seeking the parameter combination that maximizes this comprehensive expectation value to obtain the optimal combination of UAV parameters.

[0039] In practice: Using the grape canopy as the simulation object, the grape height was set to 1.5m and the row spacing to 3.5m. The crop canopy was divided into three regions: upper, middle, and lower, each approximately 0.4m deep. Based on the measured leaf area index (LAI) of 2.5, the permeability α and inertial drag coefficient C2 of each porous media model were set. The bottom layer of the crop canopy started at a height of approximately 0.3m above the ground, the middle layer ranged from 0.3 to 1.05m, and the top layer ranged from 1.05 to 1.7m. The canopy thickness between rows was approximately 0.5m, and the canopy was approximately 0.7m from the bottom edge of the UAV rotor.

[0040] The simulation conditions were changed, with the ambient wind speed set to three levels: 1 m / s, 2 m / s, and 3 m / s. The temperature was set to a normal temperature of 25°C. The crop plant development was referenced to the canopy structure from the grape flowering stage to the fruit enlargement stage. The nozzle spray angle was set to three different initial parameters: 50°, 80°, and 110°. The spray bar tilt angle was set to three levels: 10°, 20°, and 30°. Multiple simulation tasks were performed to obtain the deposition and drift of droplets in the upper, middle, and lower layers of the grape canopy, forming the original dataset.

[0041] The flight speed is set to a range of 3 m / s to 5 m / s, and the flight altitude is set to a range of 2.5 m to 3.5 m. The design variable values ​​are set as follows: 2.5 m ≤ H ≤ 3.5 m, 3 m / s ≤ V ≤ 5 m / s, 60 L / hm² ≤ F ≤ 90 L / hm², and 100 μm ≤ Dp ≤ 300 μm. In the continuous phase parameters, the spray pressure is set to 0.3 MPa, the spray flow rate is 0.8 L / min, the spray particle size is set to 150 μm, the particle type is an aqueous solution, and the density is 1000 kg / m³.

[0042] Simulation results of airflow and droplet trajectory show that... Figure 2 The powerful downwash airflow generated by the rotor enters the air inlet of the guide assembly, accelerates after passing through the bending section, and is ejected from the horn nozzle, generating a horizontally outward induced airflow. Under the action of this airflow, the droplets ejected from the nozzle gain significant horizontal momentum, effectively offsetting some of the premature droplet settling caused by the downwash airflow, allowing the droplets to penetrate the grape leaf canopy more deeply.

[0043] like Figure 6 As shown, during the drift model fitting process, simulations were run under environmental wind speeds of 1 m / s, 2 m / s, and 3 m / s respectively. The deposition center coordinates of the droplets ejected from the left and right nozzles at the near and far wind positions at each wind speed were recorded at the canopy height. The least squares method was used to perform quadratic curve fitting with the environmental wind speed as the x-axis and the lateral offset distance of the deposition center relative to the windless condition as the y-axis.

[0044] The specific drift model obtained is as follows: For the left nozzle, under the condition of near the air outlet... Under the condition of far wind For the right nozzle, under conditions near the air outlet Under the condition of far wind The units of y1, y2, y3, and y4 are all in meters, and the unit of x is in meters per second.

[0045] In response surface methodology optimization, a multi-factor, multi-level orthogonal experimental scheme was constructed using Box-Behnken response surface analysis. A quadratic regression mathematical model was established using the statistical analysis software Design-Expert, and significance and goodness-of-fit tests were performed. The model's p-value was less than 0.001, indicating extremely significant results. Figure 3 As shown, the deposition effect reaches its peak when the nozzle angle is 110° and the boom tilt angle is 10°. Analysis of the influence of various factors on the coefficient of variation of deposition uniformity and average deposition density using response surface methodology and contour plots revealed a significant interaction between the nozzle spray angle and the boom tilt angle. Optimization results indicate that, under the parameter combination of a flight speed of 3 m / s, a flight altitude of 2.5 m, a pesticide flow rate of 75 L / hm², and a droplet size of 300 μm, the average deposition density in the lower and middle canopy of grapevines increases by 24%, and the coefficient of variation of deposition uniformity decreases to 26%, significantly improving the pesticide application quality for three-dimensional canopy crops.

[0046] Seen in Figure 4 , Figure 5 In one specific embodiment, this disclosure proposes a flow-guiding side-spraying mechanism, which is disposed below the rotor 120 of a multi-rotor UAV for liquid spraying operations of the multi-rotor UAV. The relevant parameters of the flow-guiding side-spraying mechanism are optimized by the above-described method, including: The device includes a main body 110, which is suspended from a guide pipe 210 below the rotor 120 by a mounting bracket. A nozzle 220 is provided at the end of the guide pipe 210 away from the rotor 120. A guide plate 230 is provided below the guide pipe 210 along its length, and a concave guide groove is provided inside the guide plate 230 along its length. The guide plate 230 includes a straight section 231 and a bent section 232. The end of the straight section 231 is located below the nozzle 220, and the end of the bent section 232 extends towards the rotor 120 and bends upward. The bent section 232 of the guide plate 230 is used to capture the downwash airflow of the rotor and guide it into a lateral airflow horizontal to the straight section 231. The guide-flow side-jet mechanism captures the rotor downwash airflow through the bend section 232, transforming the vertically downward airflow into a horizontally lateral auxiliary airflow. This gives the droplets ejected from the nozzle 220 horizontal momentum, effectively offsetting some of the premature droplet settling caused by the downwash airflow and allowing the droplets to penetrate the crop leaf canopy more deeply. In this embodiment, the guide-flow side-jet mechanism transforms the flow field structure from the traditional "vertical downwash" to a composite form of "vertical downwash and horizontal jet," and the droplet transport mechanism changes from gravity-dominated to aerodynamically assisted.

[0047] In specific implementation, for example, in terms of specific structural parameters, the inlet diameter of the guide pipe 210 is 400mm, the outlet diameter is 380mm, the cross-sectional diameter slightly narrows from the inlet to the outlet, and the overall shape is similar to a curved semi-cylindrical funnel; the bending angle is 67°, the radius of curvature is 304mm, the arc length is 477mm, the wall thickness is 3mm, and the material is ABS engineering plastic; the inlet of the guide pipe 210 extends to below the rotor 120 to capture the downwash airflow of the rotor, and the outlet diffuses in a funnel shape to reduce the attenuation of the outlet airflow velocity.

[0048] Nozzle 220 is a fan-shaped pressure nozzle, installed at the air outlet, 300mm horizontally from the center of the rotor, outside the horizontal projection range of the outer edge of rotor 120. This arrangement avoids the nozzle 220 being directly in the strong impact zone of the rotor's downwash airflow. Simultaneously, the lateral airflow guided by the guide tube 210 provides auxiliary horizontal momentum to the droplets. The nozzle 220 has a nozzle diameter of 2.5mm, an operating pressure of 0.35MPa, an installation tilt angle (angle with the vertical direction) of 10° to 30°, and a spray fan angle of 50° to 110°. The spray angle determines the initial direction of the droplets entering the lateral airflow, and the spray fan angle determines the lateral distribution width of the droplets. The matching of these two parameters has a significant impact on the uniform deposition of droplets in each layer of the crop canopy. Through the above optimization method, the optimized installation tilt angle of nozzle 220 is 10°, and the optimized spray fan angle is 110°, achieving the optimal balance between deposition penetration and coverage uniformity.

[0049] In this disclosure, the other end of the guide tube 210 is connected to the lower part of the rotor 120 via a support adjustment frame 240; the support adjustment frame 240 includes a guide tube mounting plate 241 for fixing the guide tube 210 and a fixing plate 242 for fixing to the multi-rotor UAV, the fixing plate 242 is fixedly connected to the guide tube mounting plate 241 and the included angle between the two is adjustable.

[0050] By adjusting the angle between the fixed plate 242 and the guide pipe mounting plate 241, the installation angle of the guide pipe 210 and the guide plate 230 relative to the drone body can be changed, thereby adjusting the airflow guidance direction and the spray direction of the nozzle 220.

[0051] In this disclosure, a connector is provided between the guide pipe 210 and the guide plate, and the upper and lower ends of the connector are respectively sleeved on the guide pipe 210 and the guide plate 230; the end of the connector sleeved on the guide plate is provided with a guide hole for the airflow guided by the guide plate 230 to pass through; the included angle between the guide pipe 210 and the guide plate 230 is adjustable.

[0052] By setting up connecting parts, the relative angle between the guide pipe 210 and the guide plate 230 can be adjusted independently, thereby enabling fine adjustment of the matching relationship between the spray direction of the nozzle 220 and the direction of the guided airflow. Through the independent adjustment of the included angle between the guide pipe 210 and the guide plate 230, combined with the adjustment of the overall installation angle by the support adjustment frame 240, the multi-degree-of-freedom parameter adjustment of the guide side spray mechanism is realized, providing a structural basis for parameter optimization under different operating scenarios.

[0053] In practical implementation, the connector serves as a transitional connection between the guide pipe 210 and the guide plate 230. Its upper and lower ends are respectively provided with fitting holes that match the outer diameters of the guide pipe 210 and the guide plate 230. The connector is fixed to the guide pipe 210 and the guide plate 230 by clamps or bolts. The shape of the guide hole matches the cross-section of the concave guide groove in the guide plate 230, ensuring that the airflow captured and guided by the guide plate 230 can smoothly pass through the connector and flow out along the straight section 231 of the guide plate 230.

[0054] It is readily understood that those skilled in the art can combine, split, or reorganize the embodiments provided in this application to obtain other embodiments, all of which do not exceed the protection scope of this application.

[0055] The present invention and its embodiments have been described above illustratively. This description is not restrictive, and the embodiments shown are only part of the embodiments of the present invention. The actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for optimizing the parameters of a UAV with a flow-guiding side-jet mechanism, characterized in that, include: Construct a simulation analysis model; The simulation analysis model includes a 3D model of the UAV and a crop canopy model. The 3D UAV model includes a multi-rotor UAV model and a guide side spray mechanism model. A multiphase flow simulation model was constructed based on the simulation analysis model. The boundary conditions and UAV parameters were used as design variables for simulation calculation to obtain a multiphase flow simulation dataset. The UAV parameters include UAV structural parameters, guide side spray mechanism structural parameters, and UAV operation parameters. The multiphase flow simulation dataset includes the deposition distribution data of droplets in each layer of the crop canopy. Based on a multiphase flow simulation dataset, a functional relationship between ambient wind speed and droplet deposition center offset was obtained through quadratic regression fitting, and a droplet drift model was constructed. With the optimization objectives of minimizing the coefficient of variation of droplet deposition uniformity and maximizing the average deposition density in the target crop canopy, the UAV parameters are iteratively optimized using the response surface methodology under drift constraints based on a droplet drift model to obtain the optimal parameter combination. The specific formula is as follows: ； in, The coefficient of variation for depositional uniformity. The average sediment density, For flight altitude, For flight speed, To the flow rate of the pesticide, The droplet size is... For ambient wind speed Lateral offset of the droplet deposition center under the current This is the maximum allowed offset threshold.

2. The method for optimizing UAV parameters with a flow-guiding side-jet mechanism according to claim 1, wherein, The construction of the simulation analysis model specifically includes: A simulation computational domain was established, including a multi-rotor UAV model, a flow-guiding side-spraying mechanism model, and a crop canopy model. Within the simulation calculation domain, the area around each rotor of the multi-rotor UAV is divided into the rotation domain, and the area inside the nozzle of the flow guide side spray mechanism is divided into the internal flow field. The geometric model within the simulation computation domain is simplified, and the mesh region is divided. The crop canopy model is divided into a multi-layer structure, with each layer designed as a porous medium model. The resistance parameters of the porous media model were set based on crop plant height, leaf area index, and number of leaves. These resistance parameters included permeability. and inertial drag coefficient Resistance source term in porous media model satisfy: ； in, Indicates the coordinate direction, with values ​​ranging from 1 to 2. or for The momentum source term in the direction, in units of The dynamic viscosity of air, in units of Permeability of porous media, in units of for The velocity component in the direction, in units of This is the inertial drag coefficient, in units of... Air density, unit: The magnitude of the velocity vector, in units of .

3. The method for optimizing UAV parameters with a flow-guiding side-jet mechanism according to claim 2, wherein, The multiphase flow simulation model is constructed based on the simulation analysis model. Different boundary conditions and different UAV parameters are set for simulation calculations to obtain a multiphase flow simulation dataset, specifically including: A multiphase flow model was constructed using the Eulerian-Lagrange framework. The Eulerian method was used to describe the airflow field of the continuous phase, while the Lagrange method was used to track the drug droplets of the discrete phase. A two-way coupling was adopted between the continuous and discrete phases, and the following parameters were set: The continuous phase parameters include at least the ambient gas type, ambient wind speed, rotation center and rotation speed of the rotating domain, computational domain boundary conditions, internal flow field boundary conditions, and UAV flight parameters; Discrete phase parameters were used for droplet tracking using a discrete phase model. The atomization model was set as a linearized unstable breakup model, and the secondary breakup model was set as a TAB model. The spray pressure, particle type, jet flow rate, and spray particle size were also set.

4. The method for optimizing UAV parameters with a flow-guiding side-jet mechanism according to claim 1, wherein: Boundary conditions include at least one of environmental wind speed, temperature conditions, and crop plant development. The drone parameters include the structural dimensions of the guide side spray mechanism, the nozzle spray angle, the spray boom tilt angle, the drone flight speed, and the drone flight altitude.

5. The method for optimizing UAV parameters with a flow-guiding side-jet mechanism according to claim 4, wherein, The process involves obtaining a functional relationship between environmental wind speed and droplet deposition center offset through quadratic regression fitting, and constructing a droplet drift model, specifically including: Based on the multiphase flow simulation dataset, the droplet trajectory coordinate data of the nozzle at the near and far wind inlets under different environmental wind speeds were extracted; Using ambient wind speed as the independent variable and the lateral offset of the droplet deposition center determined based on droplet trajectory coordinate data as the dependent variable, a droplet drift model is constructed by least squares quadratic regression fitting: ； in, Ambient wind speed, unit: ; The lateral offset of the droplet deposition center, in units of ; These are the regression coefficients; For the left nozzle, the drift model under near-airflow conditions is: ； The drift model under the condition of far-end wind is: ； For the right nozzle, the drift model under near-airflow conditions is: ； The drift model under the condition of far-end wind is: ；； in, All units are The unit is .

6. The method for optimizing UAV parameters with a flow-guiding side-jet mechanism according to claim 4, wherein, The optimization objective is to minimize the coefficient of variation of droplet deposition uniformity in the target crop canopy and maximize the average deposition density. Iterative optimization of UAV parameters is performed using the response surface methodology under drift constraints based on a droplet drift model. This includes: The optimization objectives are to minimize the coefficient of variation of the uniformity of droplet deposition in the target crop canopy and maximize the average deposition density. The coefficient of variation of the uniformity of deposition is the ratio of the standard deviation of the deposition amount at each sampling point in the target crop canopy to the average deposition amount, and the average deposition density is the mass or number of droplets deposited per unit area. Select design variables from the UAV parameters and set the value range for each selected design variable; The Box-Behnken design method was used to generate an experimental scheme consisting of multiple sets of design variable parameters. The multiphase flow simulation model was used to perform simulation calculations on each set of design variable parameters. The deposition uniformity variation coefficient and average deposition density corresponding to each set of design variable parameters were extracted from the simulation results. Using the design variables as independent variables and the coefficient of variation of sedimentary uniformity and average sedimentary density as response variables, a quadratic regression mathematical model was established through quadratic regression fitting. ； in, For the first The encoded values ​​of each design variable; The regression coefficient is the coefficient of variation of sedimentation uniformity. The regression coefficient for the average sediment density; The number of design variables was determined; and the significance and goodness-of-fit tests were performed on the quadratic regression mathematical model. Under the optimization objectives of minimizing the coefficient of variation of sedimentation uniformity and maximizing the average sedimentation density, the validated quadratic regression mathematical model is solved through multi-objective optimization to obtain the optimal combination of UAV parameters; The optimal combination of UAV parameters is re-input into the multiphase flow simulation model for verification simulation. The verification simulation results are compared with the predicted values ​​of the quadratic regression mathematical model to confirm the reliability of the optimization results and obtain the final optimal parameter combination.

7. A flow-guiding side-spraying mechanism, disposed below the rotor (120) of a multi-rotor unmanned aerial vehicle (UAV), for liquid spraying operations of the UAV, wherein the flow-guiding side-spraying mechanism is optimized from the method described in any one of claims 1 to 6, comprising: The device includes a main body (110), which is suspended by a mounting bracket from a guide pipe (210) below the rotor (120). A nozzle (220) is provided at the end of the guide pipe (210) away from the rotor (120). A guide plate (230) is provided below the guide pipe (210) along its length. A concave guide groove is provided inside the guide plate (230) along its length. The guide plate (230) includes a straight section (231) and a bent section (232). The end of the straight section (231) is located below the nozzle (220). The end of the bent section (232) extends towards the rotor (120) and bends upward. The bent section (232) of the guide plate (230) is used to capture the downwash airflow of the rotor and guide it into a lateral airflow horizontal to the straight section (231).

8. The flow-guiding side spray mechanism according to claim 7, wherein: The other end of the guide tube (210) is connected to the rotor (120) via a support adjustment frame (240); the support adjustment frame (240) includes a guide tube mounting plate (241) for fixing the guide tube (210) and a fixing plate (242) for fixing to the multi-rotor UAV. The fixing plate (242) is fixedly connected to the guide tube mounting plate (241) and the included angle between the two is adjustable.

9. The flow-guiding side spray mechanism according to claim 7, wherein: A connector is provided between the guide tube (210) and the guide plate. The upper and lower ends of the connector are respectively sleeved on the guide tube (210) and the guide plate (230). The end of the connector sleeved on the guide plate is provided with a guide hole for the airflow guided by the guide plate (230) to pass through. The included angle between the guide tube (210) and the guide plate (230) is adjustable.

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