Maize canopy deformation analysis method under rotor wing wind field based on fluid-structure interaction

Abstract

The invention discloses a corn canopy deformation analysis method under a rotor wing wind field based on fluid-structure interaction, and the method comprises the following steps: S1, measuring key phenotypic parameters and physical and mechanical properties of high-stalk corn, and building a canopy parameterized three-dimensional model; s2, high-fidelity grids are divided, material attributes are given, boundary constraints are applied, and a finite element analysis model is constructed; s3, constructing a lattice Boltzmann fluid analysis model in combination with the unmanned aerial vehicle model and the virtual wind tunnel; s4, carrying out bidirectional fluid-solid coupling numerical simulation; and S5, based on a simulation result, extracting projection leaf area and stalk inclination angle change data. The method can accurately simulate and predict the dynamic response of the corn canopy under the action of the rotor wing wind field in the operation process of the plant protection unmanned aerial vehicle, and provides effective technical support for unmanned aerial vehicle pesticide application parameter optimization and crop damage evaluation.

Description

Technical Field

[0001] This invention relates to the field of agricultural aviation plant protection technology, and in particular to a method for analyzing maize canopy deformation under rotor wind fields based on fluid-structure interaction. Background Technology

[0002] For field application of pesticides on tall crops such as corn and sorghum, traditional ground spraying operations are difficult to achieve precise delivery and effective deposition of pesticides due to crop canopy shading and limitations in field access. In recent years, multi-rotor agricultural drones have become an important trend in the development of precision spraying technology in agriculture due to their flexible deployment, high operational efficiency, and good adaptability to complex terrain and different crops.

[0003] During pesticide application by agricultural drones, the downwash airflow generated by the drone rotors interacts in complex ways with the crop canopy, significantly affecting the trajectory and deposition distribution of pesticide droplets. This makes it difficult to guarantee the penetration and uniformity of pesticide droplets within the canopy. Therefore, understanding the interaction between the drone rotor airflow and the crop canopy is a crucial prerequisite for achieving precise targeted pesticide application within the canopy.

[0004] Currently, research methods in this field are mainly divided into two categories: field trials and numerical simulations. While field trials can reflect real-world pesticide application scenarios, they face limitations such as high costs, difficulties in data collection, and susceptibility to environmental interference, and they struggle to deeply analyze the intrinsic interaction mechanisms between wind fields and crop structure. Numerical simulations, as an effective supplementary method, are mostly based on computational fluid dynamics models. However, existing studies often simplify the crop canopy as a rigid porous medium, neglecting its dynamic deformation response under wind conditions. For tall maize with broad leaves and tall stems, significant dynamic responses such as leaf deformation and stem bending occur under wind conditions. Accurately quantifying these key canopy morphology parameters is crucial for a deep understanding of the interaction between rotor airflow and the canopy and for optimizing pesticide application effectiveness.

[0005] Therefore, this application proposes a method for analyzing the deformation of maize canopy under rotor wind fields based on fluid-structure interaction. Summary of the Invention

[0006] One objective of this invention is to propose a method for analyzing the deformation of maize canopy under rotor wind fields based on fluid-structure interaction. This invention can accurately reproduce the dynamic deformation process of tall maize canopy under the rotor wind fields of agricultural drones, quantify and extract key morphological parameters such as projected leaf area and stem inclination angle, overcome the limitations of traditional research that simplifies the crop canopy as a rigid medium, and complete multi-condition parameterized analysis in a low-cost and short-cycle manner. This provides accurate and reliable technical support for optimizing drone spraying parameters, assessing crop damage, and analyzing the wind field-canopy interaction mechanism.

[0007] A method for analyzing corn canopy deformation under a rotor wind field based on fluid-structure interaction according to an embodiment of the present invention includes the following steps:

[0008] S1. Based on the physiological characteristics of tall maize, measure its key phenotypic parameters and physical and mechanical properties, and establish a parameterized three-dimensional model of maize canopy;

[0009] S2. Based on the three-dimensional corn canopy, a high-fidelity mesh is generated, the physical and mechanical properties of the canopy material are assigned and boundary constraints are applied to construct a finite element analysis model of the corn canopy.

[0010] S3. Based on the finite element analysis model, the UAV 3D model and the virtual wind tunnel environment, construct a lattice Boltzmann fluid analysis model;

[0011] S4. Based on the aforementioned finite element analysis model of the maize canopy and the lattice Boltzmann fluid analysis model, conduct a two-way fluid-structure interaction numerical simulation.

[0012] S5. Based on the numerical simulation results, combined with image processing technology and mathematical analysis methods, the projected leaf area and stem inclination angle change data during the canopy deformation process are extracted to quantify the dynamic response characteristics of the canopy under the rotor wind field.

[0013] Furthermore, the key phenotypic parameters mentioned in step S1 include:

[0014] Corn plant height, stalk length, leaf thickness, and the length, width, angle, number, and height of the upper, middle, and lower layers of leaves;

[0015] The physical and mechanical properties include: the elastic modulus, Poisson's ratio, density, and moisture content of the stem; and the elastic modulus, Poisson's ratio, density, and moisture content of the leaves.

[0016] Furthermore, step S2 includes the following sub-steps:

[0017] S21. Use a regular hexahedral mesh to discretize the corn stalks and leaves, and divide them according to their geometric characteristics using different mesh scales;

[0018] S22. The boundary constraints include: linear elastic constraints applied to the bottom of the stem and the ground, and binding constraints applied to the base of the leaf and the surface of the stem.

[0019] S23. In the finite element analysis software ABAQUS, assign physical and mechanical properties to the stem and leaves respectively;

[0020] S24. In the finite element analysis software ABAQUS, a finite element model of the maize canopy with fixed plant spacing and row spacing is constructed based on the linear array method.

[0021] Furthermore, step S3 includes the following sub-steps:

[0022] S31. Export the corn canopy finite element analysis model from ABAQUS as an STL format file, and import it together with the UAV model into a 14m×10m×8m virtual wind tunnel to establish the lattice Boltzmann fluid analysis model.

[0023] S32. In the fluid solver, set the UAV rotor speed, forward speed, flight altitude and corresponding wall boundaries according to the actual plant protection operation conditions of the UAV.

[0024] Furthermore, step S4 includes the following sub-steps:

[0025] S41. In the initialization phase of coupled calculation, the finite element analysis model and the fluid analysis model are set to have the same total simulation time and time step, and the corresponding parameters are synchronously corrected in the XML configuration file to ensure that the simulation time parameters of the three are strictly consistent.

[0026] S42. Configure XML files according to different solver versions: For the dual 19 version solver, only the total simulation time parameter needs to be corrected; for the dual 22 or other version solvers, ensure that the total simulation time, STL file name and solution algorithm related parameters are all set correctly.

[0027] S43. Coupled calculations are solved using a co-simulation engine. The XML file output by the fluid solver and the INP file generated by the solid solver are imported into the system's saved file to build a data exchange environment for co-simulation. The co-simulation engine is started by entering the corresponding command code in the command line.

[0028] Furthermore, step S5 includes the following sub-steps:

[0029] S51. Post-process the numerical simulation results in Tecplot by acquiring continuous frames of canopy deformation images through vertical overhead shots at a fixed height; extract the canopy region based on image binarization technology and calculate its projected leaf area.

[0030] S52. Analyze the change in the inclination angle of the stem in the finite element analysis software. Set up a monitoring point at a height of 1.9m on the stem, extract the displacement change data of the monitoring point under the action of the wind field, and combine mathematical geometric analysis methods to invert the dynamic inclination angle change process of the stem.

[0031] Furthermore, the method for calculating the projected leaf area in step S51 is as follows:

[0032] S511. In the fluid analysis model, a 4m×4m square area is set on the ground as a reference surface;

[0033] S512. When extracting the canopy region, the square region is captured simultaneously. Based on the known area of ​​the reference region, the projected leaf area is calculated using the spatial ratio conversion between image pixels and actual physical size. The calculation formula is as follows:

[0034]

[0035] in, Represents the projected leaf area; This represents the total number of pixels in the image that represent the canopy region extracted through image processing; This represents the total number of pixels occupied by the square reference plane in the image; This represents the actual area of ​​the reference surface, with a value of 16 m².

[0036] Furthermore, the method for calculating the stem inclination angle in step S52 is as follows:

[0037] S521. In the finite element analysis model, a monitoring point is set at the center of the upper surface of the stem, and the initial coordinate data and continuous frame coordinate data of the monitoring point are collected.

[0038] S522. Based on the current frame coordinate data and the initial coordinate data, calculate the displacement increments ΔX and ΔZ of the stalk along the longitudinal direction (X-axis) and transverse direction (Z-axis) of the UAV's forward direction. Using inverse trigonometric functions, obtain the change in the stalk's tilt angle in the corresponding directions. The calculation formula is as follows:

[0039]

[0040]

[0041] in, Indicates the longitudinal angle of inclination of the stem; Indicates the angle of inclination of the stem in the lateral direction; This indicates the displacement increment of the monitoring point in the X-axis direction; This represents the displacement increment of the monitoring point in the Z-axis direction. This represents the initial vertical height of the monitoring point.

[0042] The beneficial effects of this invention are:

[0043] 1. This invention employs parametric 3D modeling and high-fidelity mesh generation technology, combined with the real phenotypic parameters of maize and the physical and mechanical properties of materials, which significantly improves the geometric accuracy and mechanical realism of the canopy model, laying a reliable foundation for carrying out high-precision fluid-structure interaction simulation.

[0044] 2. This invention, by constructing a virtual wind tunnel environment and a joint simulation platform, realizes the full-process simulation of rotor wind field and canopy deformation under UAV spraying conditions. It can complete multi-scenario parametric analysis in a low cost and short cycle, effectively making up for the shortcomings of high cost and difficult data collection in field trials.

[0045] 3. This invention uses two-way fluid-structure interaction numerical simulation to accurately reproduce the dynamic deformation process of the tall corn canopy under the action of the wind field of the plant protection drone rotor. It realizes the quantitative extraction of key morphological parameters such as projected leaf area and stem inclination angle, overcomes the limitation of traditional rigid models that cannot reflect the dynamic response of the canopy, and provides a new method for accurately evaluating the wind field-canopy interaction mechanism.

[0046] 4. The standardized simulation process established by this invention has good versatility and scalability. It is not only applicable to the corn canopy, but also provides a technical paradigm for wind field response research of tall crops such as sorghum and sugarcane, promoting the digitalization and precision development of agricultural aviation plant protection. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 This is a schematic diagram of the process for analyzing corn canopy deformation under a rotor wind field based on fluid-structure interaction, as proposed in this invention.

[0049] Figure 2 This is a schematic diagram of maize plant stem and leaf samples for a method of analyzing maize canopy deformation under a rotor wind field based on fluid-structure interaction proposed in this invention.

[0050] Figure 3 This is a schematic diagram of the measurement of the physical and mechanical properties of maize plants in a method for analyzing maize canopy deformation under a rotor wind field based on fluid-structure interaction, as proposed in this invention.

[0051] Figure 4 This is a schematic diagram of a single maize plant finite element analysis model for a method for analyzing maize canopy deformation under a rotor wind field based on fluid-structure interaction proposed in this invention.

[0052] Figure 5 This is a schematic diagram of the row spacing and plant spacing of maize plant populations in a method for analyzing maize canopy deformation under a rotor wind field based on fluid-structure interaction proposed in this invention.

[0053] Figure 6 This is a schematic diagram of the initial boundary condition setting of the fluid computation domain for a method for analyzing corn canopy deformation under a rotor wind field based on fluid-structure interaction proposed in this invention.

[0054] Figure 7 This is a schematic diagram of the canopy projection leaf area extraction method for a method for analyzing maize canopy deformation under a rotor wind field based on fluid-structure interaction proposed in this invention.

[0055] Figure 8 This is a schematic diagram illustrating the arrangement and calculation method of stalk tilt angle monitoring points for a method for analyzing corn canopy deformation under a rotor wind field based on fluid-structure interaction, as proposed in this invention.

[0056] In the diagram: 1. Universal mechanical test bench; 2. Pressure head; 3. Support base; 4. Dryer; 5. UAV model; 6. Corn canopy model; 7. Fluid computation domain. Detailed Implementation

[0057] To make the technical means and objectives and effects of the present invention easier to understand, the embodiments of the present invention will be described in detail below with reference to specific illustrations.

[0058] like Figure 1 As shown, this invention discloses a method for analyzing corn canopy deformation under rotor wind fields based on fluid-structure interaction, including the following implementation steps:

[0059] S1. Based on the physiological characteristics of tall maize, measure its key phenotypic parameters and physical and mechanical properties, and establish a parameterized three-dimensional model of the maize canopy. The key phenotypic parameters mentioned in step S1 include: maize plant height, stem length, leaf thickness, and the length, width, inclination angle, number, and attachment height of the upper, middle, and lower layers of leaves; the physical and mechanical properties include: stem elastic modulus, Poisson's ratio, density, and water content; and leaf elastic modulus, Poisson's ratio, density, and water content.

[0060] This embodiment uses a universal mechanical testing bench and a dryer to measure the physical and mechanical properties of corn canopy materials. The specific process is as follows:

[0061] like Figure 2 and Figure 3 As shown, 10 corn plants with the same growth status and in the grain-filling stage were randomly selected and divided into upper canopy layer (2.3-1.5m), middle canopy layer (1.5-0.8m) and lower canopy layer (0.8-0.4m) in the vertical direction.

[0062] In each canopy, the stem was divided into three equal segments, and a cylindrical sample with a length of 140 mm was cut from each segment; for the leaf part, three complete leaves were randomly selected, and rectangular samples with a length of 70 mm and a width of 20 mm were cut from the leaf base along the midrib.

[0063] During testing, the stem or leaf sample is placed on a support frame, and the indenter moves vertically downward at a rate of 1 mms⁻¹. The deformation and load increment of the sample are collected synchronously at a frequency of 100 Hz, and the load-displacement curve is plotted.

[0064] The elastic modulus of the stem is calculated using the following formula:

[0065]

[0066]

[0067] The elastic modulus of the blade is calculated using the following formula:

[0068]

[0069]

[0070] in, It is the span of the support frame. It is the load increment. It is the displacement increment. It refers to the stem diameter, which can be measured using a vernier caliper with an accuracy of 0.01 mm. It is the stem wall that is thick. It is the Poisson's ratio of corn leaves. and These are the moments of inertia of the stem and the leaves, respectively. and These are the width and thickness of the blade, respectively.

[0071] After the mechanical test was completed, the sample was placed in a dryer and dried continuously at 105°C until constant weight. The moisture content was calculated based on the dry weight and fresh weight.

[0072] S2. Based on the three-dimensional corn canopy, a high-fidelity mesh is generated, the canopy material is assigned physical and mechanical properties and boundary constraints are applied, and a finite element analysis model of the corn canopy is constructed. Step S2 includes the following specific steps:

[0073] S21. Use a regular hexahedral mesh to discretize the corn stalks and leaves, and divide them according to their geometric characteristics using different mesh scales;

[0074] S22. The boundary constraints include: linear elastic constraints applied to the bottom of the stem and the ground, and binding constraints applied to the base of the leaf and the surface of the stem.

[0075] S23. In the finite element analysis software ABAQUS, assign physical and mechanical properties to the stem and leaves respectively;

[0076] S24. In the finite element analysis software ABAQUS, a finite element model of the maize canopy with fixed plant spacing and row spacing is constructed based on the linear array method.

[0077] like Figure 4 As shown, the mesh discretization of corn stalks and leaves, the setting of physical and mechanical properties, and the application of boundary constraints were all completed in Hypermesh software.

[0078] The mesh size for the stem was set to 0.5 mm; the leaves were first meshed using 0.1 mm shell elements, and then stretched along both sides of the normal direction using these shell elements to generate a double-layer hexahedral element with a total thickness of 0.4 mm. After meshing, the quality was checked using the software's built-in tools, requiring a distortion rate ≤5% and a Jacobian determinant ≥0.7. Unacceptable meshes were locally refined or reconstructed.

[0079] This mesh discretization strategy can accurately reflect the mechanical properties of materials, effectively capture structural deformation under wind load, and improve the overall accuracy of the simulation.

[0080] Based on the physical and mechanical properties measured above, corresponding material parameters were assigned to the blades and stems in Hypermesh. A linear elastic constraint was applied between the bottom of the stem and the ground, and the stiffness of the foundation spring was set to 1×106 N / m3; a binding constraint was set between the base of the blade and the outer surface of the stem to ensure the effective transmission of force and displacement.

[0081] like Figure 5 As shown, the finite element preprocessing model of a single maize plant was imported into Abaqus. A maize canopy population model was constructed by setting the narrow row spacing of 450mm, the wide row spacing of 750mm, and the plant spacing of 300mm using a linear array. There is no overlap or penetration between the leaves of the maize canopy population.

[0082] S3. Based on the finite element analysis model, the UAV 3D model, and the virtual wind tunnel environment, construct the lattice Boltzmann fluid analysis model. Step S3 includes the following specific steps:

[0083] S31. Export the corn canopy finite element analysis model from ABAQUS as an STL format file, and import it together with the UAV model into a 14m×10m×8m virtual wind tunnel to establish the lattice Boltzmann fluid analysis model.

[0084] S32. In the fluid solver, set the UAV rotor speed, forward speed, flight altitude and corresponding wall boundaries according to the actual plant protection operation conditions of the UAV.

[0085] like Figure 6As shown, in this embodiment, the length Q of the computational domain 7 is 14m, the width R is 10m, and the height P is 8m. Its bottom surface is set as the wall boundary, the top surface is set as the pressure inlet boundary, and the other four sides are set as the pressure outlet boundary.

[0086] The drone model was obtained by scanning an actual agricultural drone using a 3D laser scanner. After importing, it was simplified. Drone 5 was set to forced motion, with a forward speed of 3 m / s and a flight altitude of 3 m. The rotational speeds of the eight rotors were set based on measured data: 925 r / min for the four front rotors (R1, R2, R5, R6) and 1150 r / min for the four rear rotors (R3, R4, R7, R8). To balance the torque, the rotation directions of adjacent rotors on the same side were set to opposite directions.

[0087] The spatial discretization resolution of the fluid domain is set to 0.1m, with the region within 0.5m of the rotor blade surface and from 1m above to 0.5m below the canopy being refined to 0.0125m, and the airflow wake region set to 0.00625m. With these resolution settings, the stability coefficient can be maintained at approximately 0.86, thus ensuring both numerical stability and computational efficiency.

[0088] Meanwhile, the co-simulation time of the finite element solver and the LB solver is kept synchronized and set to 3 seconds.

[0089] S4. Based on the finite element analysis model of the maize canopy and the lattice Boltzmann fluid analysis model, conduct two-way fluid-structure interaction numerical simulation.

[0090] Specifically, step S4 includes the following sub-steps:

[0091] S41. In the initialization phase of coupled calculation, the finite element analysis model and the fluid analysis model are set to have the same total simulation time and time step, and the corresponding parameters are synchronously corrected in the XML configuration file to ensure that the simulation time parameters of the three are strictly consistent.

[0092] S42. Configure XML files according to different solver versions: For the dual 19 version solver, only the total simulation time parameter needs to be corrected; for the dual 22 or other version solvers, ensure that the total simulation time, STL file name and solution algorithm related parameters are all set correctly.

[0093] S43. Coupled calculations are solved using a co-simulation engine. The XML file output by the fluid solver and the INP file generated by the solid solver are imported into the system's saved file to build a data exchange environment for co-simulation. The co-simulation engine is started by entering the corresponding command code in the command line.

[0094] In this embodiment, the time step in step S41 is uniformly set to 1e-4s, and the time step parameter needs to be synchronously marked in the XML configuration file.<timestep> 1e-4< / timestep> .

[0095] In step S42, the solution algorithm parameters of the dual 22 version solver need to be explicitly set to... <negotiationmethod> MAX< / negotiationmethod> Meanwhile, the residual convergence criterion remains set to [value]. <residualcriterion> 1e-6< / residualcriterion> This is to ensure the stability of the solution process and the accuracy of the calculation results, and to meet the requirements of two-way fluid-structure interaction numerical simulation for algorithm adaptability and convergence.

[0096] In step S43, ensure the total simulation time and STL file name, and ensure the accuracy of data interaction in the fluid-structure interaction model. The specific configuration is as follows:

[0097] Total simulation time configuration: via XML tags <duration> 3< / duration> The total simulation time was set to 3 seconds, which is strictly consistent with the total simulation time of the corn canopy finite element analysis model and the lattice Boltzmann fluid analysis model, to avoid loss of synchronization in coupled calculations due to deviations in time parameters;

[0098] STL file name association configuration: Define the STL format file name exported from ABAQUS of the corn canopy finite element analysis model as "yumi_STL", and synchronously update the physical quantity related variable labels in the XML file, replacing the original... <variable> XXX.ASSEMBLY_SURF-1< / variable> The format is:

[0099] <variable> force.yumi_STL< / variable> (Force signal interaction variable)

[0100] <variable> coordinates.yumi_STL< / variable> (Coordinate data interaction variables)

[0101] <variable> displacement.yumi_STL< / variable> (Displacement data interaction variables)

[0102] <variable> velocity.yumi_STL< / variable> (Speed ​​data interaction variable)

[0103] This configuration ensures that the fluid solver and solid solver accurately identify the corn canopy model, enabling the effective transfer of data such as wind load and canopy deformation.

[0104] The coupling data exchange frequency is set to exchange data once every 10 time steps to ensure the synchronization of fluid-structure interaction.

[0105] S5. Based on the numerical simulation results, combined with image processing technology and mathematical analysis methods, the projected leaf area and stem inclination angle change data during the canopy deformation process are extracted to quantify the dynamic response characteristics of the canopy under the rotor wind field.

[0106] Step S5 includes the following specific steps:

[0107] S51. Post-process the numerical simulation results in Tecplot by acquiring continuous frames of canopy deformation images through vertical overhead shots at a fixed height; extract the canopy region based on image binarization technology and calculate its projected leaf area.

[0108] S52. Analyze the change in the inclination angle of the stem in the finite element analysis software. Set up a monitoring point at a height of 1.9m on the stem, extract the displacement change data of the monitoring point under the action of the wind field, and combine mathematical geometric analysis methods to invert the dynamic inclination angle change process of the stem.

[0109] The method for calculating the projected leaf area in step S51 above is as follows:

[0110] S511. In the fluid analysis model, a 4m × 4m square area is set on the ground as a reference surface, such as... Figure 7 As shown;

[0111] S512. When extracting the canopy region, the square region is captured simultaneously. Based on the known area of ​​the reference region, the projected leaf area is calculated using the spatial ratio conversion between image pixels and actual physical size. The calculation formula is as follows:

[0112]

[0113] in, Represents the projected leaf area; This represents the total number of pixels in the image that represent the canopy region extracted through image processing; This represents the total number of pixels occupied by the square reference plane in the image; This represents the actual area of ​​the reference surface, with a value of 16 m².

[0114] In this embodiment, ImageJ 1.54f software was used for image post-processing. The continuous frame acquisition frequency was set to 10 frames / second (consistent with the simulation time step), and the image resolution was 1920×1080 pixels. Image binarization was performed using the Otsu automatic thresholding method, with a threshold range of 0-255, achieved through the software's "Image→Adjust→Threshold" function. After extracting the canopy region, the total number of pixels was automatically counted.

[0115] The method for calculating the stem inclination angle in step S52 above is as follows:

[0116] S521. In the finite element analysis model, a monitoring point is set at the center of the upper surface of the stem, and the initial coordinate data and continuous frame coordinate data of this monitoring point are collected, such as... Figure 8 As shown;

[0117] S522. Based on the current frame coordinate data and the initial coordinate data, calculate the displacement increments ΔX and ΔZ of the drone in the forward direction (longitudinal, X-axis) and laterally (Z-axis). Combining inverse trigonometric function calculations, obtain the change in the stalk's tilt angle in the corresponding directions. The calculation formula is as follows:

[0118]

[0119]

[0120] in, Indicates the longitudinal angle of inclination of the stem; Indicates the angle of inclination of the stem in the lateral direction; This indicates the displacement increment of the monitoring point in the X-axis direction; This represents the displacement increment of the monitoring point in the Z-axis direction; This represents the initial vertical height of the monitoring point.

[0121] The coordinate data of the monitoring points were extracted using the Abaqus "Result→Field Output→Query" function, with a coordinate recording accuracy of ±0.001m. The tilt angle calculation results were converted from radians to degrees (1 radian ≈ 57.3°), retained to one decimal place, and the final output was a tilt angle curve that changed over time.

[0122] In summary, this invention obtains the deformation parameters of tall crops in a wind field through two-way fluid-structure interaction numerical simulation between UAVs and the corn canopy. It can deeply reveal the interaction mechanism between rotor airflow and canopy structure, providing theoretical basis and technical support for optimizing UAV pesticide application parameters and achieving precision pesticide application.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing corn canopy deformation under rotor wind fields based on fluid-structure interaction, characterized in that, Includes the following steps: S1. Based on the physiological characteristics of tall maize, measure its key phenotypic parameters and physical and mechanical properties, and establish a parameterized three-dimensional model of maize canopy; S2. Based on the three-dimensional corn canopy, a high-fidelity mesh is generated, the physical and mechanical properties of the canopy material are assigned and boundary constraints are applied to construct a finite element analysis model of the corn canopy. S3. Based on the finite element analysis model, the UAV 3D model and the virtual wind tunnel environment, construct a lattice Boltzmann fluid analysis model; S4. Based on the aforementioned finite element analysis model of the maize canopy and the lattice Boltzmann fluid analysis model, conduct a two-way fluid-structure interaction numerical simulation. S5. Based on the numerical simulation results, combined with image processing technology and mathematical analysis methods, the projected leaf area and stem inclination angle change data during the canopy deformation process are extracted to quantify the dynamic response characteristics of the canopy under the rotor wind field.

2. The method for analyzing corn canopy deformation under rotor wind fields based on fluid-structure interaction as described in claim 1, characterized in that, The key phenotypic parameters mentioned in step S1 include: Corn plant height, stalk length, leaf thickness, and the length, width, angle, number, and height of the upper, middle, and lower layers of leaves; The physical and mechanical properties include: the elastic modulus, Poisson's ratio, density, and moisture content of the stem; and the elastic modulus, Poisson's ratio, density, and moisture content of the leaves.

3. The method for analyzing corn canopy deformation under rotor wind fields based on fluid-structure interaction as described in claim 1, characterized in that, Step S2 includes the following sub-steps: S21. Use a regular hexahedral mesh to discretize the corn stalks and leaves, and divide them according to their geometric characteristics using different mesh scales; S22. The boundary constraints include: linear elastic constraints applied to the bottom of the stem and the ground, and binding constraints applied to the base of the leaf and the surface of the stem. S23. In the finite element analysis software ABAQUS, assign physical and mechanical properties to the stem and leaves respectively; S24. In the finite element analysis software ABAQUS, a finite element model of the maize canopy with fixed plant spacing and row spacing is constructed based on the linear array method.

4. The method for analyzing corn canopy deformation under rotor wind fields based on fluid-structure interaction as described in claim 1, characterized in that, Step S3 includes the following sub-steps: S31. Export the corn canopy finite element analysis model from ABAQUS as an STL format file, and import it together with the UAV model into a 14m×10m×8m virtual wind tunnel to establish the lattice Boltzmann fluid analysis model. S32. In the fluid solver, set the UAV rotor speed, forward speed, flight altitude and corresponding wall boundaries according to the actual plant protection operation conditions of the UAV.

5. The method for analyzing corn canopy deformation under rotor wind fields based on fluid-structure interaction according to claim 1, characterized in that, Step S4 includes the following sub-steps: S41. In the initialization phase of coupled calculation, the finite element analysis model and the fluid analysis model are set to have the same total simulation time and time step, and the corresponding parameters are synchronously corrected in the XML configuration file to ensure that the simulation time parameters of the three are strictly consistent. S42. Configure XML files according to different solver versions: For the dual 19 version solver, only the total simulation time parameter needs to be corrected; for the dual 22 or other version solvers, ensure that the total simulation time, STL file name and solver algorithm related parameters are all set correctly. S43. Coupled calculations are solved using a co-simulation engine. The XML file output by the fluid solver and the INP file generated by the solid solver are imported into the system's saved file to build a data exchange environment for co-simulation. The co-simulation engine is started by entering the corresponding command code in the command line.

6. The method for analyzing corn canopy deformation under rotor wind fields based on fluid-structure interaction as described in claim 1, characterized in that, Step S5 includes the following sub-steps: S51. Post-process the numerical simulation results in Tecplot by acquiring continuous frames of canopy deformation images through vertical overhead shots at a fixed height; extract the canopy region based on image binarization technology and calculate its projected leaf area. S52. Analyze the change in the inclination angle of the stem in the finite element analysis software. Set up a monitoring point at a height of 1.9m on the stem, extract the displacement change data of the monitoring point under the action of the wind field, and combine mathematical geometric analysis methods to invert the dynamic inclination angle change process of the stem.

7. The method for analyzing corn canopy deformation under rotor wind fields based on fluid-structure interaction as described in claim 6, characterized in that, The method for calculating the projected leaf area in step S51 is as follows: S511. In the fluid analysis model, a 4m×4m square area is set on the ground as a reference surface; S512. When extracting the canopy region, the square region is captured simultaneously. Based on the known area of ​​the reference region, the projected leaf area is calculated using the spatial ratio conversion between image pixels and actual physical size. The calculation formula is as follows: in, Represents the projected leaf area; This represents the total number of pixels in the image that represent the canopy region extracted through image processing; This represents the total number of pixels occupied by the square reference plane in the image; This represents the actual area of ​​the reference surface, with a value of 16 m².

8. The method for analyzing corn canopy deformation under rotor wind fields based on fluid-structure interaction according to claim 6, characterized in that, The method for calculating the stem inclination angle in step S52 is as follows: S521. In the finite element analysis model, a monitoring point is set at the center of the upper surface of the stem, and the initial coordinate data and continuous frame coordinate data of the monitoring point are collected. S522. Based on the current frame coordinate data and the initial coordinate data, calculate the displacement increments ΔX and ΔZ of the stalk along the longitudinal direction (X-axis) and transverse direction (Z-axis) of the UAV's forward direction. Using inverse trigonometric functions, obtain the change in the stalk's tilt angle in the corresponding directions. The calculation formula is as follows: in, Indicates the longitudinal angle of inclination of the stem; Indicates the angle of inclination of the stem in the lateral direction; This represents the displacement increment of the monitoring point in the X-axis direction; This represents the displacement increment of the monitoring point in the Z-axis direction. This represents the initial vertical height of the monitoring point.

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