Agricultural UAV operation path planning system and method based on Internet of Things

Abstract

The present application discloses an agricultural drone operation path planning system and method based on the Internet of Things, which belongs to the field of smart agriculture. The present application constructs an initial path planning model, imports the spray width and overlap coefficient into the initial path planning model to calculate the path spacing, constructs a droplet motion model, imports the initial droplet diameter and mass into the droplet motion model to evaluate the droplet settling velocity, constructs a droplet evaporation and drift model, imports environmental data, droplet diameter and settling velocity into the droplet evaporation and drift model to evaluate the droplet evaporation effect and the horizontal drift distance of the droplets, constructs a drone flight altitude and path correction model, imports the settling droplet data and real-time spray width into the drone flight altitude and path correction model to adjust the drone flight altitude and path, constructs a dosage control model, imports the degree of pests and diseases in each grid into the dosage control model to evaluate the drug spraying dosage of each grid, thereby improving the spraying accuracy.

Description

Technical Field

[0001] This application belongs to the field of smart agriculture, specifically an agricultural drone operation path planning system and method based on the Internet of Things. Background Art

[0002] With the development of intelligent agriculture, plant protection drones have been widely used in agricultural operations such as pest control and pesticide spraying. Traditional drone spraying solutions rely primarily on fixed altitudes, uniform dosages, and regular grid coverage, failing to fully consider key factors such as wind speed and direction disturbances, environmental factors affecting droplet movement, and the growth status of surface crops during the actual spraying process.

[0003] In complex environments with high temperature, low humidity or strong wind, droplets are prone to evaporation or diffusion. Existing systems lack droplet evaporation modeling and deposition trajectory prediction mechanisms, resulting in reduced pesticide landing effectiveness. Most use a constant nozzle opening and a fixed spray flow rate, failing to dynamically adjust the dosage according to the severity of crop diseases and pests, which is not conducive to precision agricultural pesticide application. This application combines wind speed and direction, droplet movement, real-time image recognition and dosage control to plan drone operations, thereby improving the accuracy of drug spraying. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, this application proposes an agricultural drone operation path planning system and method based on the Internet of Things.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] The agricultural drone operation path planning system and method based on the Internet of Things includes the following specific steps:

[0007] Construct an initial path planning model, import the spray width and overlap coefficient into the initial path planning model to calculate the path spacing;

[0008] Construct a droplet motion model and import the initial droplet diameter and mass into the droplet motion model to estimate the droplet settling velocity;

[0009] Construct a droplet evaporation and drift model, import environmental data, droplet diameter, and settling velocity into the droplet evaporation and drift model to evaluate the droplet evaporation effect and droplet horizontal drift distance;

[0010] Construct a UAV flight altitude and path correction model, import the precipitation droplet data and real-time spray width into the UAV flight altitude and path correction model to adjust the UAV flight altitude and path;

[0011] A dosage control model was constructed, and the degree of pests and diseases in each grid was imported into the dosage control model to evaluate the drug spraying dosage for each grid.

[0012] Preferably, the step of constructing an initial path planning model and importing the spray width and the overlap coefficient into the initial path planning model to calculate the path spacing comprises the following specific steps:

[0013] S11. Set the UAV operation area through the electronic map and rasterize the operation area into The grid diagram, where the coordinates of the center of the i-th unit grid are: , the distance between adjacent paths is: , where W is the width of the nozzle spray, is the overlap coefficient, where each grid unit represents the minimum operating unit that the UAV needs to cover during the spraying mission to ensure the overlapping coverage between the spraying areas;

[0014] S12. Obtain historical flight parameters and paths, obtain obstacle grid data through historical flight data, and use the A* algorithm to perform initial path planning in the grid. The spatial coordinate point set of the UAV flight path is: ,in, is the flight height of the UAV in the i-th grid, where the calculation formula for the nozzle spray width is: ,in, Fix the atomization angle for the nozzle.

[0015] Preferably, constructing a droplet motion model and importing the initial droplet diameter and mass into the droplet motion model to evaluate the droplet settling velocity comprises the following specific steps:

[0016] S21. Obtain real-time wind speed and direction through wind speed and direction sensors, obtain UAV nozzle parameters, and calculate the droplet settling velocity by substituting the droplet mass into the droplet motion equation, where the droplet motion equation is: , where m is the mass of the droplet, g is the acceleration of gravity, ρ is the air density, C is the air resistance coefficient, which can be obtained from the table, A is the frontal area of the droplet, and v is the droplet velocity. The droplet mass calculation formula is: , the calculation formula for the windward area of the droplet is: ,in, is the pesticide density, is the initial droplet diameter, and the droplet settling velocity is calculated as: , the droplet motion equation is established through Newton's second law, taking into account the influence of gravity, air resistance and wind force on the actual movement of droplets.

[0017] Preferably, the construction of the droplet evaporation and drift model, importing environmental data, droplet diameter and settling velocity into the droplet evaporation and drift model to evaluate the droplet evaporation effect and droplet horizontal drift distance includes the following specific steps:

[0018] S31. Obtain the ambient humidity and temperature. When the droplets are sprayed onto the surface of crops, they will be affected by the environment. The square of the droplet diameter decays linearly with time. The droplet diameter at time t is calculated as follows: , where K is the evaporation coefficient, and the calculation formula for the evaporation coefficient is: ,in, is the latent heat, D is the water vapor diffusion coefficient, c is the specific heat capacity of air, P is the saturated vapor pressure, T is the ambient temperature, RH is the relative humidity of air, and the evaporation rate constant is determined by the air temperature, humidity, water vapor diffusion coefficient, latent heat, specific heat capacity of air and saturated vapor pressure.

[0019] S32. Substituting the crosswind velocity into the horizontal drift distance calculation formula of the droplet to calculate the horizontal drift distance of the droplet, wherein the calculation formula of the horizontal drift distance of the droplet is: ,in, is the crosswind speed, is the settling time, where the calculation formula for the settling time is: ,in, is the height of crops in the i-th grid.

[0020] Preferably, the step of constructing a UAV flight altitude and path correction model, importing the settling droplet data and the real-time spray width into the UAV flight altitude and path correction model to adjust the UAV flight altitude and path comprises the following specific steps:

[0021] S41. Substitute the droplet horizontal drift data and the droplet diameter after settling into the altitude adjustment formula to adjust the UAV flight altitude. The altitude adjustment formula is: ,in, is the adjusted flight height of the i-th grid, is the offset flight height adjustment for the i-th grid, is the flight height adjustment of the spray diameter of the i-th grid. In order to make the offset not exceed the maximum allowable distance, the calculation formula for the offset flight height adjustment of the i-th grid is: , where d max The maximum distance allowed for horizontal drift of droplets is substituted into the droplet diameter calculation formula to calculate the droplet diameter after settling. The droplet diameter after settling is compared with the droplet diameter spraying threshold. If it is within the threshold range, the aircraft will fly at the original altitude. If it is not within the threshold range, the altitude will be adjusted. The calculation formula for the spray diameter flight altitude adjustment of the i-th grid is: ,in, is the spray threshold of droplet diameter;

[0022] S42. Substituting the adjusted flight altitude into the real-time spray width calculation formula to calculate the real-time spray width, wherein the real-time spray width calculation formula is: , automatically adjust the flight path grid spacing according to the preset overlap coefficient, where the flight path grid spacing adjustment formula is: ,in, is the overlap coefficient, and the flight path is offset by the same distance in the crosswind direction. The calculation formula for the offset abscissa is: , so that the flight path is shifted by an equal amount toward the upstream direction of the wind, maximizing the accuracy of the droplet landing point and preventing missed spraying and repeated spraying.

[0023] Preferably, the step of constructing a dosage control model and importing the pest and disease severity of each grid into the dosage control model to evaluate the drug spraying dosage of each grid comprises the following specific steps:

[0024] S51. Use a camera mounted on a drone to collect crop images, import the crop images taken for each grid into a pre-trained image model, output a pest and disease score value, and substitute the pest and disease score value into the spray dosage calculation formula to calculate the spray dosage. The spray dosage calculation formula for the i-th grid is: ,in, The lowest dose of the drug, The highest dose of the drug, is the pest and disease score value of the i-th grid;

[0025] S52. Substitute the spraying dose into the UAV spraying flow rate calculation formula to calculate the UAV spraying flow rate per unit time, wherein the UAV spraying flow rate calculation formula is: , where V is the flight speed of the UAV, and the flow rate of each grid is adjusted by adjusting the opening of the nozzle valve.

[0026] The agricultural drone operation path planning system based on the Internet of Things is implemented based on the above-mentioned agricultural drone operation path planning method based on the Internet of Things, and specifically includes:

[0027] The initial path planning module is used to calculate the path spacing based on the spray width and overlap coefficient;

[0028] Droplet motion evaluation module, used to estimate droplet settling velocity based on initial droplet diameter and mass;

[0029] The droplet evaporation module is used to evaluate the droplet evaporation effect through environmental data and droplet diameter;

[0030] Drift assessment module, used to assess the horizontal drift distance of droplets based on crosswind velocity and settling time;

[0031] The flight altitude correction module is used to adjust the UAV's flight altitude based on the horizontal drift data of the droplets and the diameter of the droplets after settling;

[0032] Path correction module, used to adjust the UAV flight path through real-time spray width;

[0033] The dosage control module is used to evaluate the drug spraying dosage of each grid according to the degree of pests and diseases in each grid.

[0034] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0035] The processor executes the above-mentioned agricultural drone operation path planning method based on the Internet of Things by calling the computer program stored in the memory.

[0036] A computer-readable storage medium is characterized in that it stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned agricultural drone operation path planning method based on the Internet of Things.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] This application constructs an initial path planning model, imports the spray width and overlap coefficient into the initial path planning model to calculate the path spacing, constructs a droplet motion model, imports the initial droplet diameter and mass into the droplet motion model to evaluate the droplet settling velocity, constructs a droplet evaporation and drift model, imports environmental data, droplet diameter and settling velocity into the droplet evaporation and drift model to evaluate the droplet evaporation effect and the horizontal drift distance of the droplets, constructs a drone flight altitude and path correction model, imports the settling droplet data and real-time spray width into the drone flight altitude and path correction model to adjust the drone flight altitude and path, constructs a dosage control model, imports the degree of pests and diseases in each grid into the dosage control model to evaluate the drug spraying dosage of each grid, and this application plans the drone operation in combination with wind speed and direction, droplet motion, real-time image recognition and dosage control, thereby improving the accuracy of drug spraying. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a schematic diagram of the overall process of the agricultural drone operation path planning method based on the Internet of Things in this application;

[0040] Figure 2 A flowchart for adjusting the drone flight path for this application;

[0041] Figure 3 This is a schematic diagram of the overall framework of the agricultural drone operation path planning system based on the Internet of Things in this application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0043] Example 1

[0044] See also Figure 1-2 , an embodiment provided by this application: an agricultural drone operation path planning method based on the Internet of Things, which includes the following specific steps:

[0045] Construct an initial path planning model, import the spray width and overlap coefficient into the initial path planning model to calculate the path spacing;

[0046] Construct a droplet motion model and import the initial droplet diameter and mass into the droplet motion model to estimate the droplet settling velocity;

[0047] Construct a droplet evaporation and drift model, import environmental data, droplet diameter, and settling velocity into the droplet evaporation and drift model to evaluate the droplet evaporation effect and droplet horizontal drift distance;

[0048] Construct a UAV flight altitude and path correction model, import the precipitation droplet data and real-time spray width into the UAV flight altitude and path correction model to adjust the UAV flight altitude and path;

[0049] A dosage control model was constructed, and the degree of pests and diseases in each grid was imported into the dosage control model to evaluate the drug spraying dosage for each grid.

[0050] In this embodiment, it should be specifically explained that constructing an initial path planning model and importing the spray width and overlap coefficient into the initial path planning model to calculate the path spacing includes the following specific steps:

[0051] S11. Set the UAV operation area through the electronic map and rasterize the operation area into The grid diagram, where the coordinates of the center of the i-th unit grid are: , the distance between adjacent paths is: , where W is the width of the nozzle spray, is the overlap coefficient, where each grid unit represents the minimum operating unit that the UAV needs to cover during the spraying mission to ensure the overlapping coverage between the spraying areas;

[0052] For example, in this embodiment, the overlap coefficient The default value is 0.8;

[0053] S12. Obtain historical flight parameters and paths, obtain obstacle grid data through historical flight data, and use the A* algorithm to perform initial path planning in the grid. The spatial coordinate point set of the UAV flight path is: ,in, is the flight height of the UAV in the i-th grid, where the calculation formula for the nozzle spray width is: ,in, Fix the atomization angle for the nozzle.

[0054] In this embodiment, it should be specifically explained that constructing a droplet motion model and importing the initial droplet diameter and mass into the droplet motion model to evaluate the droplet settling velocity includes the following specific steps:

[0055] S21. Obtain real-time wind speed and direction through wind speed and direction sensors, obtain UAV nozzle parameters, and calculate the droplet settling velocity by substituting the droplet mass into the droplet motion equation, where the droplet motion equation is: , where m is the mass of the droplet, g is the acceleration of gravity, ρ is the air density, C is the air resistance coefficient, which can be obtained from the table, A is the frontal area of the droplet, and v is the droplet velocity. The droplet mass calculation formula is: , the calculation formula for the frontal area of the droplet is: ,in, is the pesticide density, is the initial droplet diameter, and the droplet settling velocity is calculated as: , the droplet motion equation is established through Newton's second law, taking into account the influence of gravity, air resistance and wind force on the actual movement of droplets.

[0056] It should be noted that the selection of the droplet particle size in this embodiment is based on the particle size distribution parameters provided by the nozzle manufacturer, and the volume median particle size is used as a representative value for droplet motion calculation.

[0057] In this embodiment, it should be specifically explained that constructing a droplet evaporation and drift model, importing environmental data, droplet diameter, and settling velocity into the droplet evaporation and drift model to evaluate the droplet evaporation effect and droplet horizontal drift distance includes the following specific steps:

[0058] S31. Obtain the ambient humidity and temperature. When the droplets are sprayed onto the surface of crops, they will be affected by the environment. The square of the droplet diameter decays linearly with time. The droplet diameter at time t is calculated as follows: , where K is the evaporation coefficient, and the calculation formula for the evaporation coefficient is: ,in, is the latent heat ( ), D is the water vapor diffusion coefficient ( ), c is the specific heat capacity of air, P is the saturated vapor pressure, the specific heat capacity and saturated vapor pressure of air are obtained by looking up the table according to the ambient temperature, T is the ambient temperature, RH is the relative humidity of air, and the evaporation rate constant is determined by the air temperature, humidity, water vapor diffusion coefficient, latent heat, specific heat capacity of air and saturated vapor pressure.

[0059] For example, in this embodiment, the latent heat is , the water vapor diffusion coefficient is ;

[0060] S32. Substituting the crosswind velocity into the horizontal drift distance calculation formula of the droplet to calculate the horizontal drift distance of the droplet, wherein the calculation formula of the horizontal drift distance of the droplet is: ,in, is the crosswind speed, is the settling time, where the calculation formula for the settling time is: ,in, is the height of crops in the i-th grid.

[0061] In this embodiment, it should be specifically explained that constructing a UAV flight altitude and path correction model, importing the settling droplet data and the real-time spray width into the UAV flight altitude and path correction model to adjust the UAV flight altitude and path includes the following specific steps:

[0062] S41. Substitute the droplet horizontal drift data and the droplet diameter after settling into the altitude adjustment formula to adjust the UAV flight altitude. The altitude adjustment formula is: ,in, is the adjusted flight height of the i-th grid, is the offset flight height adjustment for the i-th grid, is the flight height adjustment of the spray diameter of the i-th grid. In order to make the offset not exceed the maximum allowable distance, the calculation formula for the offset flight height adjustment of the i-th grid is: , where d max The maximum distance allowed for horizontal drift of droplets is substituted into the droplet diameter calculation formula to calculate the droplet diameter after settling. The droplet diameter after settling is compared with the droplet diameter spraying threshold. If it is within the threshold range, the aircraft will fly at the original altitude. If it is not within the threshold range, the altitude will be adjusted. The calculation formula for the spray diameter flight altitude adjustment of the i-th grid is: ,in, is the spray threshold of droplet diameter;

[0063] S42. Substituting the adjusted flight altitude into the real-time spray width calculation formula to calculate the real-time spray width, wherein the real-time spray width calculation formula is: , automatically adjust the flight path grid spacing according to the preset overlap coefficient, where the flight path grid spacing adjustment formula is: ,in, is the overlap coefficient, and the flight path is offset by the same distance in the crosswind direction. The calculation formula for the offset abscissa is: , so that the flight path is shifted by an equal amount toward the upstream direction of the wind, maximizing the accuracy of the droplet landing point and preventing missed spraying and repeated spraying.

[0064] In this embodiment, it should be specifically explained that constructing a dosage control model and importing the pest and disease severity of each grid into the dosage control model to evaluate the drug spraying dosage of each grid includes the following specific steps:

[0065] S51. Use a camera mounted on a drone to collect crop images, import the crop images taken for each grid into a pre-trained image model, output a pest and disease score value, and substitute the pest and disease score value into the spray dosage calculation formula to calculate the spray dosage. The spray dosage calculation formula for the i-th grid is: ,in, The lowest dose of the drug, The highest dose of the drug, is the pest and disease score value of the i-th grid;

[0066] For example, in this embodiment, the i-th grid pest score value is , 0–0.3 indicates minor pests and diseases, 0.3–0.6 indicates moderate pests and diseases, and 0.6–1.0 indicates severe pests and diseases;

[0067] S52. Substitute the spraying dose into the UAV spraying flow rate calculation formula to calculate the UAV spraying flow rate per unit time, wherein the UAV spraying flow rate calculation formula is: , where V is the flight speed of the UAV, and the flow rate of each grid is adjusted by adjusting the opening of the nozzle valve.

[0068] It should be noted that the flow sensor monitors the actual flow rate of the pesticide in real time and feeds the data back to the controller. The controller compares the actual flow rate with the preset target flow rate, and then adjusts the flow rate by adjusting the opening of the nozzle valve and changing the pump speed to reach the preset value, thereby accurately controlling the spraying dosage.

[0069] It should be noted that the values of the various setting parameters in this embodiment are determined by obtaining representative historical drone spraying process data and historical spraying effect data, hiring experts to manually determine whether the drone spraying effect meets the requirements, and substituting the obtained historical data into the calculation results and judgment results of each step in this embodiment into the fitting software to output the values of the various setting parameters that meet the highest judgment accuracy.

[0070] The advantages of this embodiment over the prior art are:

[0071] This embodiment constructs an initial path planning model, imports the spray width and overlap coefficient into the initial path planning model to calculate the path spacing, constructs a droplet motion model, imports the initial droplet diameter and mass into the droplet motion model to evaluate the droplet settling velocity, constructs a droplet evaporation and drift model, imports environmental data, droplet diameter and settling velocity into the droplet evaporation and drift model to evaluate the droplet evaporation effect and the horizontal drift distance of the droplets, constructs a drone flight altitude and path correction model, imports the settling droplet data and real-time spray width into the drone flight altitude and path correction model to adjust the drone flight altitude and path, constructs a dosage control model, imports the degree of pests and diseases in each grid into the dosage control model to evaluate the drug spraying dosage of each grid, and this application plans the drone operation in combination with wind speed and direction, droplet motion, real-time image recognition and dosage control, thereby improving the accuracy of drug spraying.

[0072] Example 2

[0073] like Figure 3 As shown, the agricultural drone operation path planning system based on the Internet of Things is implemented based on the above-mentioned agricultural drone operation path planning based on the Internet of Things. It specifically includes an initial path planning module, a droplet motion evaluation module, a droplet evaporation module, a drift evaluation module, a flight altitude correction module, a path correction module and a dosage control module. The initial path planning module is used to calculate the path spacing through the spray width and the overlap coefficient; the droplet motion evaluation module is used to evaluate the droplet settling velocity through the initial droplet diameter and mass; the droplet evaporation module is used to evaluate the droplet evaporation effect through environmental data and droplet diameter; the drift evaluation module is used to evaluate the droplet horizontal drift distance through crosswind speed and settling time; the flight altitude correction module is used to adjust the drone flight altitude through the droplet horizontal drift data and the droplet diameter after settling; the path correction module is used to adjust the drone flight path through the real-time spray width; the dosage control module is used to evaluate the drug spraying dosage of each grid through the degree of pests and diseases in each grid.

[0074] Example 3

[0075] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0076] The processor executes the above-mentioned agricultural drone operation path planning method based on the Internet of Things by calling the computer program stored in the memory.

[0077] This electronic device can vary significantly depending on its configuration or performance. It can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the IoT-based agricultural drone path planning method provided in the above-mentioned method embodiment. The electronic device can also include other components for implementing the device's functions. For example, the electronic device can also include components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment is not described in detail here.

[0078] Example 4

[0079] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0080] When the computer program runs on a computer device, the computer device executes the above-mentioned agricultural drone operation path planning method based on the Internet of Things.

[0081] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0082] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

Claims

1. The agricultural UAV operation path planning method based on the Internet of Things is characterized by: It includes the following specific steps: Construct an initial path planning model, import the spray width and overlap coefficient into the initial path planning model to calculate the path spacing; Construct a droplet motion model and import the initial droplet diameter and mass into the droplet motion model to estimate the droplet settling velocity; A droplet evaporation and drift model was constructed. Environmental data, droplet diameter, and settling velocity were imported into the droplet evaporation and drift model to evaluate the droplet evaporation effect and droplet horizontal drift distance. The model included the following specific steps: obtaining the ambient humidity and temperature, substituting the initial droplet diameter and environmental parameters into the droplet diameter calculation formula to calculate the droplet diameter after a period of time. The droplet diameter calculation formula at time t is: , where K is the evaporation coefficient, is the initial droplet diameter, and the evaporation coefficient is calculated as: ,in, is the latent heat, D is the water vapor diffusion coefficient, c is the specific heat capacity of air, P is the saturated vapor pressure, T is the ambient temperature, is the pesticide density, RH is the relative humidity of air, and the crosswind speed is substituted into the formula for calculating the horizontal drift distance of the droplets to calculate the horizontal drift distance of the droplets. The calculation formula for the horizontal drift distance of the droplets is: ,in, is the crosswind speed, is the settling time, where the calculation formula for the settling time is: ,in, is the height of crops in the grid, is the flight altitude within the grid, is the droplet settling velocity; Construct a UAV flight altitude and path correction model, adjust the UAV flight altitude based on the diameter of the settled droplets and the horizontal drift distance of the droplets, and calculate the real-time spray width based on the adjusted flight altitude to correct the flight path; A dosage control model was constructed, and the degree of pests and diseases in each grid was imported into the dosage control model to evaluate the drug spraying dosage for each grid.

2. The method for planning the path of an agricultural drone based on the Internet of Things according to claim 1, wherein: The construction of the initial path planning model and the importation of the spray width and the overlap coefficient into the initial path planning model to calculate the path spacing include the following specific steps: The UAV operation area is set through the electronic map, and the operation area is rasterized into The grid diagram is used to calculate the spacing G between adjacent paths based on the nozzle spray width and overlap coefficient; Obtain historical flight parameters and paths, obtain obstacle grid data through historical flight data, mark the obstacle grids in the operating area, use the A* algorithm to perform initial path planning in the grid, generate a set of flight path coordinate points from the starting point to the end point of the drone, and assign a flight altitude value to each grid.

3. The method for planning the path of an agricultural UAV based on the Internet of Things according to claim 2, wherein: The construction of the droplet motion model and the introduction of the initial droplet diameter and mass into the droplet motion model to evaluate the droplet settling velocity include the following specific steps: The real-time wind speed and direction are obtained through the wind speed and direction sensor, the parameters of the drone nozzle are obtained, and the droplet mass is substituted into the droplet motion equation to calculate the droplet settling velocity. The droplet motion equation is: , where m is the mass of the droplet, g is the acceleration of gravity, ρ is the air density, C is the air resistance coefficient, A is the frontal area of the droplet, and v is the droplet velocity. The formula for calculating the droplet mass is: , the calculation formula for the windward area of the droplet is: ,in, is the pesticide density, is the initial droplet diameter, and the droplet settling velocity is calculated as: .

4. The method for planning the path of an agricultural UAV based on the Internet of Things according to claim 3, wherein: The method of constructing a UAV flight altitude and path correction model, adjusting the UAV flight altitude according to the diameter of the settled droplets, and calculating the real-time spray width according to the adjusted flight altitude to correct the flight path includes the following specific steps: Substitute the diameter of the settled droplets into the height adjustment formula to adjust the flight altitude of the drone. The height adjustment formula is: ,in, is the adjusted flight height of the i-th grid, Offset fly height adjustment for the grid, is the grid spray diameter flight height adjustment, where the grid offset flight height adjustment calculation formula is: , where d max is the maximum horizontal drift distance of the droplets. Substitute the settling time into the droplet diameter calculation formula to calculate the droplet diameter after settling. Compare the droplet diameter after settling with the droplet diameter spraying threshold. If it is within the threshold range, fly at the original altitude. If it is not within the threshold range, adjust the altitude. The calculation formula for the grid spray diameter flight altitude adjustment is: ,in, is the spray threshold of droplet diameter; Substitute the adjusted flight altitude into the real-time spray width calculation formula to calculate the real-time spray width. The real-time spray width calculation formula is: , automatically adjust the flight path grid spacing according to the preset overlap coefficient, where the flight path grid spacing adjustment formula is: ,in, Fixed atomization angle for the nozzle, is the overlap coefficient, and the flight path is offset by the same distance in the crosswind direction. The calculation formula for the offset abscissa is: , is the horizontal coordinate of the center of the i-th unit grid.

5. The method for planning the path of an agricultural UAV based on the Internet of Things according to claim 4, wherein: The construction of the dosage control model and the importation of the pest and disease severity of each grid into the dosage control model to evaluate the drug spraying dosage of each grid include the following specific steps: Crop images are collected using drone-mounted cameras. The crop images captured for each grid are imported into a pre-trained image model, and pest and disease score values are output. The pest and disease score values are then substituted into the spray dosage calculation formula to calculate the spray dosage. The grid spray dosage calculation formula is: ,in, The lowest dose of the drug, The highest dose of the drug, Score values for grid pests and diseases; Substitute the spraying dose into the UAV spraying flow rate calculation formula to calculate the UAV spraying flow rate per unit time. The UAV spraying flow rate calculation formula is: , where V is the flight speed of the UAV, and the flow rate of each grid is adjusted by adjusting the opening of the nozzle valve.

6. An agricultural drone operation path planning system based on the Internet of Things, which is implemented based on the agricultural drone operation path planning method based on the Internet of Things according to any one of claims 1 to 5, characterized in that: Specifically include: The initial path planning module is used to calculate the path spacing based on the spray width and overlap coefficient; Droplet motion evaluation module, used to estimate droplet settling velocity based on initial droplet diameter and mass; The droplet evaporation module is used to evaluate the droplet evaporation effect through environmental data and droplet diameter; Drift assessment module, used to assess the horizontal drift distance of droplets based on crosswind velocity and settling time; The flight altitude correction module is used to adjust the flight altitude of the drone based on the diameter of the settled droplets; Path correction module, used to adjust the UAV flight path through real-time spray width; The dosage control module is used to evaluate the drug spraying dosage of each grid according to the degree of pests and diseases in each grid.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the agricultural drone operation path planning method based on the Internet of Things as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer executes the agricultural drone operation path planning method based on the Internet of Things as described in any one of claims 1 to 5.

Images