Intelligent control method and device for precise operation of aircraft in preventing and controlling agricultural and forestry pests and diseases

Abstract

The present invention provides an intelligent control method and device for precise operation of aircraft in preventing and controlling agricultural and forestry pests and diseases, including: during the operation process, acquiring the time series data of wind speed and direction, and acquiring the images of crops and forest trees in the next operation area; inputting the time series data of wind speed and direction into a preset LSTM model to output the target wind speed and direction data, inputting the images of crops and forest trees into a preset neural network model to output the density of crops and forest trees, and determining the growth trend of crops and forest trees; determining the ideal angle of the nozzle of the operation aircraft during operation based on the growth trend of crops and forest trees, and determining the nozzle angle and flight speed of the operation aircraft based on the target wind speed and direction data and the ideal angle, and then determining the flight height and nozzle flow rate of the operation aircraft based on the growth trend of crops and forest trees and the ideal angle; performing spraying operation on the next operation area based on the flight speed, flight height, nozzle angle and nozzle flow rate. The spraying parameters determined by this solution have better adaptability and higher dynamics.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and particularly to an intelligent control method and device for precise operation of aircraft in preventing and controlling agricultural and forestry pests and diseases. Background Art

[0002] With the rapid development of modern agriculture, the requirements for pest control and pesticide spraying efficiency of crops and forest trees are also getting higher and higher. Traditional pesticide spraying methods, such as manual spraying or ground machinery spraying, often have problems such as low efficiency, uneven spraying, and high labor costs. Therefore, a new type of pesticide spraying technology - manned aircraft and unmanned aircraft spraying technology - has gradually received extensive attention and application.

[0003] Manned aircraft and unmanned aircraft pesticide spraying technology, as the name implies, is to use manned aircraft and unmanned aircraft as carriers, equipped with pesticide spraying equipment, and spray pesticides on crops and forest trees. This technology combines the high mobility, flexibility, and precise positioning capabilities of manned aircraft and unmanned aircraft, as well as the intelligent and automated control of pesticide spraying equipment, making pesticide spraying more efficient, precise, and safe.

[0004] When using manned aircraft and unmanned aircraft for spraying operations, the key points lie in route planning and spraying parameter planning. Among them, the determination of spraying parameters largely determines whether crops and forest trees can be sprayed sufficiently and appropriately. These spraying parameters include the flight speed of the aircraft, flight altitude, as well as the nozzle angle and nozzle flow rate of the aircraft nozzle. In the prior art, most of the above spraying parameters are determined in advance according to experience, and there are the following problems: on the one hand, only the influence of the current wind speed is considered when determining the spraying parameters, and the adaptability is insufficient when the operation area is large; on the other hand, after the spraying parameters are determined, all areas are sprayed according to this, and the dynamic performance is poor when the operation area is large. Summary of the Invention

[0005] The present invention provides an intelligent control method and device for aircraft operations in preventing and controlling agricultural and forestry pests and diseases, so as to solve the problems of insufficient adaptability and poor dynamic performance of spraying parameters in the prior art.

[0006] On the one hand, the present invention provides an intelligent control method for precise operation of aircraft in preventing and controlling agricultural and forestry pests and diseases, including:

[0007] Step S101, during the flight operation of the operation aircraft, obtain the time series data of wind speed and wind direction, and obtain the images of crops and forest trees in the next operation area; the time series data of wind speed and wind direction are obtained by the operation aircraft collecting the wind speed and the corresponding wind direction at a specific sampling rate;

[0008] Step S102: Input the wind speed and wind direction time series data into a preset long short-term memory (LSTM) model to output the target wind speed and wind direction data. Input the images of crops and forest trees in the next operation area into a preset neural network model to output the density of crops and forest trees in the next operation area, and determine the growth conditions of crops and forest trees in the next operation area based on the density of crops and forest trees. The preset LSTM model is trained based on wind speed and wind direction time series data samples with wind speed and wind direction data labels, and the preset neural network model is trained based on crop and forest tree image samples with crop and forest tree density labels. The crops and forest trees in the crop and forest tree image samples are the same type of crops and forest trees as those in the crop and forest tree images.

[0009] Step S103: Determine the ideal angle between the liquid column and the vertical plane during the operation of the nozzle of the operation aircraft based on the growth conditions of crops and forest trees. Based on the target wind speed and wind direction data and the ideal angle, determine the nozzle angle and flight speed of the operation aircraft in the next operation area. Then, determine the flight height and nozzle flow rate of the operation aircraft based on the growth conditions of crops and forest trees and the ideal angle.

[0010] Step S104: Perform spraying operations on the next operation area based on the flight speed, flight height, nozzle angle, and nozzle flow rate.

[0011] In an alternative embodiment of the present invention, determining the ideal angle between the liquid column and the vertical plane during the operation of the nozzle of the operation aircraft based on the growth conditions of crops and forest trees includes:

[0012] If the growth conditions of crops and forest trees indicate that the crops and forest trees in the next operation area are in a stage before the fruiting stage, determine the ideal angle as the first angle;

[0013] If the growth conditions of crops and forest trees indicate that the crops and forest trees in the next operation area are in a stage after the fruiting stage, determine the ideal angle as the second angle;

[0014] If the growth conditions of crops and forest trees indicate that the crops and forest trees in the next operation area are in the fruiting stage, determine the ideal angle as the third angle;

[0015] Among them, the first angle, the second angle, and the third angle decrease in sequence.

[0016] In an alternative embodiment of the present invention, the method further includes:

[0017] Construct a simulation model of the angle change between the liquid column and the vertical plane of the operation aircraft affected by wind speed, wind direction, nozzle angle, flight direction, and flight speed. The simulation model includes a wind speed input interface, a wind direction input interface, a nozzle angle input interface, a flight direction input interface, and a flight speed input interface.

[0018] Determine the nozzle angle and flight speed of the operating aircraft in the next operation area based on the target wind speed and direction data and the ideal angle, including:

[0019] For each process of the operating aircraft flying in the same direction, input the flight direction into the simulation model through the flight direction input interface, input the current flight speed as the initial flight speed into the simulation model through the flight speed input interface, input the target wind speed and direction data into the simulation model through the wind direction input interface and the wind speed input interface, adjust the input value of the nozzle angle input interface multiple times until the angle between the liquid column and the vertical plane is close to the ideal angle, and determine the input value of the nozzle angle input interface at this time as the nozzle angle;

[0020] Fine-tune the input value of the flight speed input interface multiple times until the angle between the liquid column and the vertical plane is equal to the ideal angle, and determine the input value of the flight speed input interface at this time as the flight speed.

[0021] In an alternative embodiment of the present invention, determining the flight height and nozzle flow rate of the operating aircraft based on the growth of crops and forest trees and the ideal angle includes:

[0022] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in a stage before the fruiting stage, determine the flight height as the first height;

[0023] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in a stage after the fruiting stage, determine the flight height as the second height;

[0024] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in the fruiting stage, determine the flight height as the third height;

[0025] Determine the average pesticide spraying amount per unit area in the next operation area based on the growth of crops and forest trees, and determine the nozzle flow rate based on the average pesticide spraying amount, the ideal angle, and the flight height;

[0026] Among them, the first height, the second height, and the third height decrease in sequence.

[0027] In an alternative embodiment of the present invention, the method further includes:

[0028] During the spraying operation of the operating aircraft in the next operation area, when the flight height changes due to obstacle avoidance, after re-determining the new nozzle flow rate based on the new flight height, the average pesticide spraying amount, and the ideal angle, execute step S104.

[0029] In an alternative embodiment of the present invention, determining the growth of crops and forest trees in the next operation area based on the density of crops and forest trees includes:

[0030] Determine the crop and forest density ranges corresponding to the growth conditions of different crops and forests based on the types of crops and forests in the next operation area;

[0031] By comparing the crop and forest density with the crop and forest density ranges corresponding to the growth conditions of different crops and forests, obtain the growth conditions of the crops and forests in the next operation area.

[0032] In an alternative embodiment of the present invention, the method further includes:

[0033] During the spraying operation of the operation aircraft in the next operation area, if the change in the wind speed and wind direction time series data collected within a preset duration exceeds a preset threshold, then based on the new wind speed and wind direction time series data, execute steps S102 to S103 to determine a new flight speed, a new flight height, a new nozzle angle, and a new nozzle flow rate;

[0034] Continue the spraying operation on the remaining area of the next operation area based on the new flight speed, new flight height, new nozzle angle, and new nozzle flow rate.

[0035] In a second aspect, the present invention further provides an intelligent control device for aircraft pest control operations in agriculture, including:

[0036] A data acquisition module, configured to obtain wind speed and wind direction time series data and obtain images of crops and forests in the next operation area during the flight operation of the operation aircraft; the wind speed and wind direction time series data are obtained by the operation aircraft collecting the wind speed and the corresponding wind direction at a specific sampling rate;

[0037] A crop and forest growth condition acquisition module, configured to input the wind speed and wind direction time series data into a preset long short-term memory network (LSTM) model to output target wind speed and wind direction data, input the images of crops and forests in the next operation area into a preset neural network model to output the crop and forest density in the next operation area, and determine the growth conditions of the crops and forests in the next operation area based on the crop and forest density; the preset LSTM model is trained based on wind speed and wind direction time series data samples with wind speed and wind direction data labels, the preset neural network model is trained based on crop and forest image samples with crop and forest density labels, and the crops and forests in the crop and forest image samples are the same type of crops and forests as those in the crop and forest images;

[0038] A spraying parameter acquisition module, configured to determine the ideal angle between the liquid column and the vertical plane during the operation of the nozzle of the operation aircraft based on the growth conditions of the crops and forests, and determine the nozzle angle and flight speed of the operation aircraft in the next operation area based on the target wind speed and wind direction data and the ideal angle, and then determine the flight height and nozzle flow rate of the operation aircraft based on the growth conditions of the crops and forests and the ideal angle;

[0039] A spraying operation module for performing a spraying operation on the next operation area based on the flight speed, flight altitude, nozzle angle, and nozzle flow rate.

[0040] In an alternative embodiment of the present invention, the spraying parameter acquisition module is further configured to:

[0041] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in a stage before the fruiting stage, determine the ideal angle as the first angle;

[0042] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in a stage after the fruiting stage, determine the ideal angle as the second angle;

[0043] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in the fruiting stage, determine the ideal angle as the third angle;

[0044] Wherein, the first angle, the second angle, and the third angle decrease in sequence.

[0045] In an alternative embodiment of the present invention, the device further includes a simulation module for:

[0046] Construct a simulation model of the angle change between the liquid column and the vertical plane affected by the wind speed, wind direction, nozzle angle, flight direction, and flight speed of the operation aircraft. The simulation model includes a wind speed input interface, a wind direction input interface, a nozzle angle input interface, a flight direction input interface, and a flight speed input interface;

[0047] The spraying parameter acquisition module is further configured to:

[0048] For each co-directional flight process of the operation aircraft, input the flight direction into the simulation model through the flight direction input interface, input the current flight speed as the initial flight speed into the simulation model through the flight speed input interface, input the target wind speed and wind direction data into the simulation model through the wind direction input interface and the wind speed input interface, adjust the input value of the nozzle angle input interface multiple times until the angle between the liquid column and the vertical plane is close to the ideal angle, and determine the input value of the nozzle angle input interface at this time as the nozzle angle;

[0049] Fine-tune the input value of the flight speed input interface multiple times until the angle between the liquid column and the vertical plane is equal to the ideal angle, and determine the input value of the flight speed input interface at this time as the flight speed.

[0050] In an alternative embodiment of the present invention, the spraying parameter acquisition module is further configured to:

[0051] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in a stage before the fruiting stage, determine the flight altitude as the first altitude;

[0052] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in a stage after the fruiting period, determine the flight altitude as the second altitude;

[0053] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in the fruiting period, determine the flight altitude as the third altitude;

[0054] Determine the average pesticide spraying amount per unit area of the next operation area based on the growth of crops and forest trees, and determine the nozzle flow rate based on the average pesticide spraying amount, the ideal angle, and the flight altitude;

[0055] Among them, the first altitude, the second altitude, and the third altitude decrease in sequence.

[0056] In an alternative embodiment of the present invention, the device further includes an obstacle avoidance module for:

[0057] During the spraying operation of the operation aircraft in the next operation area, when the flight altitude changes due to obstacle avoidance, after re-determining the new nozzle flow rate based on the new flight altitude, the average pesticide spraying amount, and the ideal angle, execute step S104.

[0058] In an alternative embodiment of the present invention, the crop and forest tree growth acquisition module is specifically used for:

[0059] Based on the types of crops and forest trees in the next operation area, determine the crop and forest tree density ranges corresponding to different crop and forest tree growths;

[0060] By comparing the crop and forest tree density with the crop and forest tree density ranges corresponding to different crop and forest tree growths, obtain the growth of crops and forest trees in the next operation area.

[0061] In an alternative embodiment of the present invention, the device further includes an adjustment module for:

[0062] During the spraying operation of the operation aircraft in the next operation area, if the change in the wind speed and wind direction time series data collected within a preset duration exceeds a preset threshold, execute steps S102 to S103 based on the new wind speed and wind direction time series data to determine a new flight speed, a new flight altitude, a new nozzle angle, and a new nozzle flow rate;

[0063] Continue the spraying operation on the remaining area of the next operation area based on the new flight speed, the new flight altitude, the new nozzle angle, and the new nozzle flow rate.

[0064] In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements any one of the above-mentioned intelligent control methods for precise operation of aircraft for preventing and controlling agricultural and forestry pests and diseases.

[0065] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements any one of the above-mentioned intelligent control methods for aircraft operation in preventing and controlling agricultural pests and diseases.

[0066] In a fifth aspect, the present invention further provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements any one of the above-mentioned intelligent control methods for precise operation of aircraft for preventing and controlling agricultural and forestry pests and diseases.

[0067] An intelligent control method and device for precise operation of aircraft for preventing and controlling agricultural and forestry pests and diseases provided by the present invention, before spraying operation on the next operation area, that is, during the operation in the current operation area, first obtains the time series data of wind speed and wind direction, then predicts the wind speed and wind direction data of the next operation area through a preset LSTM model, and at the same time obtains the images of crops and forest trees in the next operation area, and then obtains the density of crops and forest trees in the next operation area, and further obtains the growth trend of crops and forest trees in the next operation area; then determines the ideal angle based on the growth trend of crops and forest trees, and obtains the nozzle angle and flight speed based on this ideal angle, the ideal angle during spraying, and finally determines the corresponding flight height and nozzle flow rate based on the growth trend of crops and forest trees; thus, spraying operation is carried out on the next operation area according to the above-mentioned flight speed, flight height, nozzle angle, and nozzle flow rate. On the one hand, this solution considers the influence of wind speed and wind direction and also considers the density of crops and forest trees and the growth trend of crops and forest trees when determining the spraying parameters, so the adaptability is better. On the other hand, this solution can dynamically adjust the spraying parameters according to the wind speed and wind direction and the growth trend of crops and forest trees in different operation areas, and the dynamic nature is stronger. In short, this solution can ensure a better spraying effect through adaptability and dynamic nature, and achieve a better effect of preventing and controlling pests and diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0069] Figure 1 It is a schematic flow chart of an intelligent control method for precise operation of aircraft for preventing and controlling agricultural and forestry pests and diseases provided by the present invention;

[0070] Figure 2 The structural block diagram of an intelligent control device for precise operation of aircraft in preventing and controlling agricultural and forestry pests and diseases provided by the present invention;

[0071] Figure 3 It is the structural schematic diagram of the electronic device provided by the present invention. Specific embodiments

[0072] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0073] The embodiment of the present invention provides a system on which an intelligent control method for precise operation of aircraft in preventing and controlling agricultural and forestry pests and diseases depends. The system may include an intelligent control background server, one or more working aircraft (unmanned aircraft), and corresponding user terminals. The user terminal sends spraying tasks to the working aircraft through the intelligent control background server. After receiving the instructions from the intelligent control port server, the working aircraft starts spraying operations. Among them, the working aircraft is equipped with various sensors, including wind direction and speed sensors, image acquisition sensors, etc. It will collect various parameters in real time according to the configuration and feedback these collected parameters to the intelligent control background server in real time. Various network models or simulation models are set on the intelligent control background server to process these parameters, obtain route parameters, spraying parameters, etc. required for the next-stage operation of the working aircraft, and send these spraying parameters to the working aircraft to achieve fully automatic spraying operations of the working aircraft. The specific implementation process of the intelligent control method for aircraft in preventing and controlling agricultural and forestry pests and diseases operations in the embodiment of the present invention will be described below.

[0074] Figure 1 It is the flow schematic diagram of an intelligent control method for precise operation of aircraft in preventing and controlling agricultural and forestry pests and diseases provided by the embodiment of the present invention. As Figure 1 shown, the method may include:

[0075] Step S101, during the flight operation of the working aircraft, obtain wind speed and wind direction time series data, and obtain images of crops and forest trees in the next operation area; the wind speed and wind direction time series data are obtained by the working aircraft collecting wind speed and corresponding wind direction at a specific sampling rate.

[0076] Specifically, the intelligent control background server will pre-divide the entire area to be operated into multiple operation areas through route planning, and then specify the operation aircraft to perform spraying operations on each divided operation area in a certain order. Then, when the operation aircraft is performing the spraying operation on the current operation area, it will continuously collect wind speed and wind direction data in real time, and at the same time, it will collect images of crops and forest trees in the next operation area. The subsequent steps will construct wind speed and wind direction time series data through the wind speed and wind direction data for predicting the target wind speed and wind direction data when performing the spraying operation in the next operation area, and use the images of crops and forest trees to determine the density of crops and forest trees and the growth trend of crops and forest trees in the next operation area.

[0077] Among them, the wind speed and wind direction time series data includes a series of data in time series. For example, the wind speed and wind direction corresponding to time t1, the wind speed and wind direction corresponding to time t2, and the wind speed and wind direction corresponding to time t3. The length and time interval of the time series data can be set according to requirements, and the embodiments of the present invention do not make limitations.

[0078] Step S102: Input the wind speed and wind direction time series data into a preset long short-term memory network (LSTM) model to output the target wind speed and wind direction data, input the images of crops and forest trees in the next operation area into a preset neural network model to output the density of crops and forest trees in the next operation area, and determine the growth trend of crops and forest trees in the next operation area based on the density of crops and forest trees; the preset LSTM model is trained based on the wind speed and wind direction time series data samples with wind speed and wind direction data labels, and the preset neural network model is trained based on the crop and forest tree image samples with crop and forest tree density labels. The crops and forest trees in the crop and forest tree image samples are the same type of crops and forest trees as those in the crop and forest tree images.

[0079] Among them, the density of crops and forest trees includes the plant density of crops and forest trees and the leaf density of crops and forest trees, and these parameters can be used to judge the growth trend of crops and forest trees.

[0080] Specifically, in the case where the overall operation area is large, the growth trends of crops and forest trees between different divided operation areas may be different. At this time, if the same spraying parameters are still used, the spraying effect will not be ideal. At the same time, the wind speed and wind direction during the operation of different divided operation areas may change compared with the wind speed and wind direction during the operation of the previous operation area. At this time, if the same spraying parameters are still used, the spraying effect will also not be ideal. Therefore, the embodiments of the present invention will predict the wind speed and wind direction in the next operation area, determine the growth trend of crops and forest trees in the next operation area, and then reconfigure the spraying parameters to ensure the adaptability and dynamics of the spraying parameters, and thus ensure the spraying effect of different operation areas.

[0081] Specifically, before performing the spraying operation in the next operation area (at this time, the current operation area is in operation), the obtained time series data of wind speed and wind direction is input into a preset long short-term memory network (LSTM) model to predict the wind speed and wind direction data of the next operation area, that is, the target wind speed and wind direction data. Among them, the preset LSTM model may include an input layer, an LSTM layer, a hidden layer, and an output layer. For the trained preset LSTM model, the time series data of wind speed and wind direction is input through the input layer after being normalized, and then the long-term dependence relationship in the time series data of wind speed and wind direction is captured in the LSTM layer. The hidden layer processes this long-term relationship and maps the output through the output layer to obtain the final prediction result, that is, the final target wind speed and wind direction data.

[0082] For the images of crops and forest trees in the next operation area, they are input into a preset neural network model, and the density of crops and forest trees in this operation area is output. The preset neural network model performs image preprocessing, target detection, image segmentation, feature extraction on the images of crops and forest trees, and then uses a regression model or a density regression algorithm to obtain the density of crops and forest trees.

[0083] Among them, the types and principles of the models used above are not limited in the embodiments of the present invention, as long as the corresponding functions can be realized through training.

[0084] Furthermore, after obtaining the density of crops and forest trees, according to the types and historical data of the crops and forest trees, evaluate the growth stage they are in, and then determine the growth trend of the crops and forest trees. It should be noted that different growth trends of crops and forest trees can be divided according to historical experience, or different growth trends of crops and forest trees can be divided according to the requirements of the scheme. The principle is that the average pesticide spraying amounts corresponding to different growth trends of crops and forest trees are different, and the spraying angles (related to the ideal angle between the liquid column and the vertical plane mentioned later) required to achieve specific spraying effects are different.

[0085] Step S103, determine the ideal angle between the liquid column and the vertical plane when the nozzle of the operation aircraft operates based on the growth trend of crops and forest trees, and determine the nozzle angle and flight speed of the operation aircraft in the next operation area based on the target wind speed and wind direction data and the ideal angle. Then determine the flight height and nozzle flow rate of the operation aircraft based on the growth trend of crops and forest trees and the ideal angle.

[0086] Specifically, after determining the growth trend of crops and forest trees, considering that the leaf density and plant density of crops and forest trees are different under different growth trends of crops and forest trees, when spraying pesticides, the spraying angle of pesticides will have a greater impact on the agricultural penetration and uniformity. Therefore, the solution of the present invention will give the corresponding spraying angle, that is, the ideal angle, according to the division of the growth trend of crops and forest trees. The ideal angle is the ideal angle when the pesticide is sprayed out of the nozzle without being affected by the wind and other factors.

[0087] Further, after determining the required ideal angle and the wind speed and direction data, the flight speed and nozzle angle of the operating aircraft can be determined according to the required ideal angle.

[0088] Still further, after determining the flight speed and nozzle angle in the spraying parameters, and then determining the average pesticide spraying amount per unit area according to the growth of crops and forest trees, the flight altitude and nozzle flow rate of the operating aircraft can be further determined.

[0089] Step S104, perform spraying operations on the next operation area based on the flight speed, flight altitude, nozzle angle, and nozzle flow rate.

[0090] Specifically, after determining the spraying parameters required for the operation through the above steps, the spraying operation can be carried out according to the above spraying parameters.

[0091] It should be noted that the flight route is planned before operating on the entire operation area. Then the flight direction of the operating aircraft is consistent with the flight route, that is, the flight direction is determined during the operation. Therefore, the influence of the flight direction needs to be adaptively considered in the above steps to obtain the corresponding spraying parameters. The principle of obtaining the spraying parameters in different directions is the same and will not be elaborated here.

[0092] Further, repeat the above steps S101 - S104 for each divided operation area to complete the spraying operation on the entire operation area.

[0093] The solution provided by the present invention, before performing spraying operations on the next operation area, that is, during the operation in the current operation area, first obtains the time series data of wind speed and direction, then predicts the wind speed and direction data of the next operation area through a preset LSTM model, and at the same time obtains the images of crops and forest trees in the next operation area, and then obtains the density of crops and forest trees in the next operation area, and further obtains the growth of crops and forest trees in the next operation area; then determines the ideal angle through the growth of crops and forest trees, and obtains the nozzle angle and flight speed based on this ideal angle, the ideal angle during spraying, and finally determines the corresponding flight altitude and nozzle flow rate through the growth of crops and forest trees; thus, perform spraying operations on the next operation area according to the above flight speed, flight altitude, nozzle angle, and nozzle flow rate. On the one hand, this solution considers the influence of wind speed and direction on the basis of also considering the density of crops and forest trees and the growth of crops and forest trees when determining the spraying parameters, with better adaptability. On the other hand, this solution can dynamically adjust the spraying parameters according to the wind speed and direction as well as the growth of crops and forest trees in different operation areas, with stronger dynamics. In short, this solution can ensure better spraying effects through adaptability and dynamics, and achieve better pest control effects.

[0094] In an alternative embodiment of the present invention, determining an ideal angle between the liquid column and the vertical plane during the operation of the nozzle of the working aircraft based on the growth of crops and forest trees includes:

[0095] If the growth of crops and forest trees indicates that the crops and forest trees in the next working area are in a stage before the fruiting stage, then determine the ideal angle as the first angle;

[0096] If the growth of crops and forest trees indicates that the crops and forest trees in the next working area are in a stage after the fruiting stage, then determine the ideal angle as the second angle;

[0097] If the growth of crops and forest trees indicates that the crops and forest trees in the next working area are in the fruiting stage, then determine the ideal angle as the third angle;

[0098] Wherein, the first angle, the second angle and the third angle decrease in sequence.

[0099] Specifically, there can be various classification criteria for the growth of crops and forest trees. It can be classified according to agricultural techniques. For example, the germination stage, the growth stage, the flowering stage, the fruiting stage and the maturity stage. It can also be classified according to other criteria according to needs. For example, the first density stage, the second density stage and the third density stage, etc. The growth of crops and forest trees is different, and their plant density and leaf density are also different. During the spraying operation, the ideal angle of the liquid column needs to be different to have corresponding penetrability and uniformity. In addition, the growth of crops and forest trees is different, and the ideal angle of the liquid column needs to be well controlled to avoid damage to the plants and the flowers, fruits, etc. on the plants.

[0100] In the solution of the present invention, for the crops and forest trees before the fruiting stage, the corresponding ideal angle is smaller than that of the crops and forest trees after the fruiting stage, and is further smaller than that of the crops and forest trees in the fruiting stage. For example, before the fruiting stage, an ideal angle of 60 degrees is adopted. After the fruiting stage, an ideal angle of 30 degrees is adopted. During the fruiting stage, an ideal angle of 0 degrees is adopted, that is, the liquid column can be perpendicular to the working area during the fruiting stage.

[0101] It should be noted that the growth of crops and forest trees can be further subdivided into more levels according to the above principles (considering penetrability and destructiveness). For example, it can be divided into 8 or 10 different growths of crops and forest trees. At the same time, each growth of crops and forest trees corresponds to an ideal angle.

[0102] In an alternative embodiment of the present invention, the method may further include:

[0103] Construct a simulation model of the angle change between the liquid column and the vertical plane of the working aircraft affected by wind speed, wind direction, nozzle angle, flight direction and flight speed. The simulation model includes a wind speed input interface, a wind direction input interface, a nozzle angle input interface, a flight direction input interface and a flight speed input interface;

[0104] Based on the target wind speed and direction data and the ideal angle, determine the nozzle angle and flight speed of the working aircraft in the next working area, including:

[0105] For each co-directional flight process of the working aircraft, input the flight direction into the simulation model through the flight direction input interface, input the current flight speed as the initial flight speed into the simulation model through the flight speed input interface, input the target wind speed and direction data into the simulation model through the wind direction input interface and the wind speed input interface, adjust the input value of the nozzle angle input interface multiple times until the angle between the liquid column and the vertical plane is close to the ideal angle, and determine the input value of the nozzle angle input interface at this time as the nozzle angle;

[0106] Fine-tune the input value of the flight speed input interface multiple times until the angle between the liquid column and the vertical plane is equal to the ideal angle, and determine the input value of the flight speed input interface at this time as the flight speed.

[0107] In an alternative embodiment of the present invention, based on the growth of crops and forest trees and the ideal angle, determine the flight height and nozzle flow rate of the working aircraft, including:

[0108] If the growth of crops and forest trees indicates that the crops and forest trees in the next working area are in a stage before the fruiting stage, determine the flight height as the first height;

[0109] If the growth of crops and forest trees indicates that the crops and forest trees in the next working area are in a stage after the fruiting stage, determine the flight height as the second height;

[0110] If the growth of crops and forest trees indicates that the crops and forest trees in the next working area are in the fruiting stage, determine the flight height as the third height;

[0111] Based on the growth of crops and forest trees, determine the average pesticide spraying amount per unit area in the next working area, and determine the nozzle flow rate based on the average pesticide spraying amount, the ideal angle, and the flight height;

[0112] Among them, the first height, the second height, and the third height decrease in sequence.

[0113] Specifically, similar to the principle of determining the ideal angle, after determining the growth of crops and forest trees, when determining the flight height based on the growth of crops and forest trees, it is also necessary to consider the density of crops and forest trees to adapt to the corresponding height to ensure the penetration of spraying. At the same time, it is also necessary to consider the destructiveness of spraying in different growth cycles to avoid damage to flowers, fruits, etc. Since the fruiting stage has the highest leaf density and is the least likely to be damaged, its height is the lowest. After the fruiting stage, the leaf density is relatively high and is less likely to be damaged, so its height is medium. Before the fruiting stage, the leaf density is relatively low and is easily damaged, so its height is the highest.

[0114] Similarly, the growth conditions of crops and forest trees can be further subdivided into more levels according to the above principles (considering penetrability and destructiveness). For example, they can be divided into 8 or 10 different growth conditions of crops and forest trees. At the same time, each growth condition of crops and forest trees corresponds to a flight altitude.

[0115] Specifically, determining the growth conditions of crops and forest trees in the next operation area based on the density of crops and forest trees includes:

[0116] Based on the types of crops and forest trees in the next operation area, determining the density ranges of crops and forest trees corresponding to different growth conditions of crops and forest trees;

[0117] By comparing the density of crops and forest trees with the density ranges of crops and forest trees corresponding to different growth conditions of crops and forest trees, the growth conditions of crops and forest trees in the next operation area are obtained.

[0118] In an alternative embodiment of the present invention, the method further includes:

[0119] During the spraying operation of the operation aircraft in the next operation area, when the flight altitude changes due to obstacle avoidance, after re-determining the new nozzle flow rate based on the new flight altitude, average pesticide spraying amount, and ideal angle, step S104 is executed.

[0120] Specifically, the operation aircraft has an automatic obstacle avoidance function. Since the flight altitude changes due to obstacle avoidance, it is necessary to re-determine the nozzle flow rate based on the new flight altitude and then continue the spraying operation. This further improves the dynamic performance and adaptability and ensures the spraying effect.

[0121] In an alternative embodiment of the present invention, the method further includes:

[0122] During the spraying operation of the operation aircraft in the next operation area, if the change in the wind speed and wind direction time series data collected within a preset time duration exceeds a preset threshold, then steps S102 to S103 are executed based on the new wind speed and wind direction time series data to determine a new flight speed, a new flight altitude, a new nozzle angle, and a new nozzle flow rate;

[0123] Continuing the spraying operation on the remaining area of the next operation area based on the new flight speed, new flight altitude, new nozzle angle, and new nozzle flow rate.

[0124] Specifically, since the wind speed and wind direction have changed suddenly, that is, they exceed the preset threshold, which means that the previously predicted wind speed and wind direction data are no longer accurate. To ensure the accuracy of the spraying parameters, it is necessary to re-predict the new wind speed and wind direction data and then continue to complete the spraying operation. This also further improves the dynamic performance and adaptability and ensures the spraying effect.

[0125] Figure 2 This is a structural block diagram of an intelligent control device for precise operation of an aircraft in preventing and controlling agricultural and forestry pests and diseases provided by an embodiment of the present invention. The device includes:

[0126] The data acquisition module 201 is used to obtain wind speed and wind direction time-series data during the flight operation of the operation aircraft, and obtain images of crops and forest trees in the next operation area; the wind speed and wind direction time-series data are obtained by the operation aircraft collecting the wind speed and the corresponding wind direction at a specific sampling rate.

[0127] The crop and forest tree growth condition acquisition module 202 is used to input the wind speed and wind direction time-series data into a preset long short-term memory network (LSTM) model to output target wind speed and wind direction data, input the images of crops and forest trees in the next operation area into a preset neural network model to output the density of crops and forest trees in the next operation area, and determine the growth condition of crops and forest trees in the next operation area based on the density of crops and forest trees; the preset LSTM model is trained based on wind speed and wind direction time-series data samples with wind speed and wind direction data labels, the preset neural network model is trained based on crop and forest tree image samples with crop and forest tree density labels, and the crops and forest trees in the crop and forest tree image samples are the same type of crops and forest trees as those in the crop and forest tree images.

[0128] The spraying parameter acquisition module 203 is used to determine the ideal angle between the liquid column and the vertical plane during the operation of the nozzle of the operation aircraft based on the growth condition of crops and forest trees, and determine the nozzle angle and flight speed of the operation aircraft in the next operation area based on the target wind speed and wind direction data and the ideal angle, and then determine the flight height and nozzle flow rate of the operation aircraft based on the growth condition of crops and forest trees and the ideal angle.

[0129] The spraying operation module 204 is used to perform spraying operation on the next operation area based on the flight speed, flight height, nozzle angle, and nozzle flow rate.

[0130] The solution provided by the present invention, before performing spraying operations on the next operation area, that is, during the operation in the current operation area, first obtains the time series data of wind speed and wind direction, then predicts the wind speed and wind direction data of the next operation area through a preset LSTM model, and at the same time obtains the images of crops and forest trees in the next operation area, and further obtains the density of crops and forest trees in the next operation area, and further obtains the growth conditions of crops and forest trees in the next operation area; then determines the ideal angle based on the growth conditions of crops and forest trees, and obtains the nozzle angle and flight speed based on this ideal angle, the ideal angle during spraying, and finally determines the corresponding flight height and nozzle flow rate based on the growth conditions of crops and forest trees; thereby performing spraying operations on the next operation area according to the above flight speed, flight height, nozzle angle, and nozzle flow rate. On the one hand, when determining the spraying parameters, this solution takes into account not only the influence of wind speed and wind direction, but also the density of crops and forest trees and the growth conditions of crops and forest trees, with better adaptability. On the other hand, this solution can dynamically adjust the spraying parameters according to the wind speed and wind direction of different operation areas and the growth conditions of crops and forest trees, with stronger dynamics. In short, this solution can ensure better spraying effects through adaptability and dynamics, achieving better pest control effects.

[0131] In an alternative embodiment of the present invention, the spraying parameter acquisition module is further configured to:

[0132] If the growth conditions of crops and forest trees indicate that the crops and forest trees in the next operation area are in a stage before the fruiting stage, then determine the ideal angle as the first angle;

[0133] If the growth conditions of crops and forest trees indicate that the crops and forest trees in the next operation area are in a stage after the fruiting stage, then determine the ideal angle as the second angle;

[0134] If the growth conditions of crops and forest trees indicate that the crops and forest trees in the next operation area are in the fruiting stage, then determine the ideal angle as the third angle;

[0135] Wherein, the first angle, the second angle, and the third angle decrease in sequence.

[0136] In an alternative embodiment of the present invention, the device further includes a simulation module for:

[0137] Construct a simulation model of the angle change between the liquid column and the vertical plane under the influence of wind speed, wind direction, nozzle angle, flight direction, and flight speed of the operation aircraft. The simulation model includes a wind speed input interface, a wind direction input interface, a nozzle angle input interface, a flight direction input interface, and a flight speed input interface;

[0138] The spraying parameter acquisition module is further configured to:

[0139] For each process of the aircraft flying in the same direction during an operation, input the flight direction into the simulation model through the flight direction input interface, input the current flight speed as the initial flight speed into the simulation model through the flight speed input interface, input the target wind speed and direction data into the simulation model through the wind direction input interface and the wind speed input interface, adjust the input value of the nozzle angle input interface multiple times until the angle between the liquid column and the vertical plane is close to the ideal angle, and determine the input value of the nozzle angle input interface at this time as the nozzle angle;

[0140] Fine-tune the input value of the flight speed input interface multiple times until the angle between the liquid column and the vertical plane is equal to the ideal angle, and determine the input value of the flight speed input interface at this time as the flight speed.

[0141] In an alternative embodiment of the present invention, the spraying parameter acquisition module is further configured to:

[0142] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in a stage before the fruiting stage, determine the flight height as the first height;

[0143] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in a stage after the fruiting stage, determine the flight height as the second height;

[0144] If the growth of crops and forest trees indicates that the crops and forest trees in the next operation area are in the fruiting stage, determine the flight height as the third height;

[0145] Determine the average pesticide spraying amount per unit area of the next operation area based on the growth of crops and forest trees, and determine the nozzle flow rate based on the average pesticide spraying amount, the ideal angle, and the flight height;

[0146] Wherein, the first height, the second height, and the third height decrease in sequence.

[0147] In an alternative embodiment of the present invention, the device further includes an obstacle avoidance module for:

[0148] During the spraying operation of the operation aircraft in the next operation area, when the flight height changes due to obstacle avoidance, after re-determining the new nozzle flow rate based on the new flight height, the average pesticide spraying amount, and the ideal angle, execute step S104.

[0149] In an alternative embodiment of the present invention, the crop and forest tree growth acquisition module is specifically configured to:

[0150] Based on the types of crops and forest trees in the next operation area, determine the range of crop and forest tree density corresponding to different crop and forest tree growth;

[0151] By comparing the density of crops and forest trees with the range of crop and forest tree density corresponding to different growth conditions of crops and forest trees, the growth conditions of crops and forest trees in the next operation area are obtained.

[0152] In an alternative embodiment of the present invention, the device further includes an adjustment module for:

[0153] During the spraying operation of the operation aircraft in the next operation area, if the change in the wind speed and wind direction time series data collected within a preset duration exceeds a preset threshold, steps S102 to S103 are executed based on the new wind speed and wind direction time series data to determine a new flight speed, a new flight height, a new nozzle angle, and a new nozzle flow rate;

[0154] Continue the spraying operation on the remaining area of the next operation area based on the new flight speed, the new flight height, the new nozzle angle, and the new nozzle flow rate.

[0155] Figure 3 An example of a schematic physical structure diagram of an electronic device is shown in Figure 3As shown in the figure, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340. Among them, the processor 310, the communication interface 320, and the memory 330 complete communication with each other through the communication bus 340. The processor 310 can call the logical instructions in the memory 330 to execute the intelligent control method for precise operation of preventing and controlling agricultural and forestry pests and diseases by an aircraft. The method includes: Step S101, during the flight operation of the operation aircraft, obtain the time series data of wind speed and wind direction, and obtain the images of crops and forest trees in the next operation area; the time series data of wind speed and wind direction is obtained by the operation aircraft collecting the wind speed and the corresponding wind direction at a specific sampling rate; Step S102, input the time series data of wind speed and wind direction into a preset long short-term memory network (LSTM) model to output the target wind speed and wind direction data, input the images of crops and forest trees in the next operation area into a preset neural network model to output the density of crops and forest trees in the next operation area, and determine the growth trend of crops and forest trees in the next operation area based on the density of crops and forest trees; the preset LSTM model is trained based on the time series data samples of wind speed and wind direction with wind speed and wind direction data labels, and the preset neural network model is trained based on the crop and forest tree image samples with crop and forest tree density labels. The crops and forest trees in the crop and forest tree image samples are the same type of crops and forest trees as those in the crop and forest tree images; Step S103, determine the ideal angle between the liquid column and the vertical plane during the spraying operation of the nozzle of the operation aircraft based on the growth trend of crops and forest trees, and determine the nozzle angle and flight speed of the operation aircraft in the next operation area based on the target wind speed and wind direction data and the ideal angle. Then, determine the flight height and nozzle flow rate of the operation aircraft based on the growth trend of crops and forest trees and the ideal angle; Step S104, perform spraying operation on the next operation area based on the flight speed, flight height, nozzle angle, and nozzle flow rate.

[0156] In addition, when the logical instructions in the above-mentioned memory 330 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0157] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the intelligent control method for aircraft to prevent and control agricultural pests and diseases provided by the above-mentioned various methods. The method includes: Step S101, during the flight operation of the operation aircraft, obtain the wind speed and wind direction time series data, and obtain the images of crops and forest trees in the next operation area; the wind speed and wind direction time series data are obtained by the operation aircraft collecting the wind speed and the corresponding wind direction at a specific sampling rate; Step S102, input the wind speed and wind direction time series data into a preset long short-term memory network (LSTM) model to output the target wind speed and wind direction data, input the images of crops and forest trees in the next operation area into a preset neural network model to output the density of crops and forest trees in the next operation area, and determine the growth trend of crops and forest trees in the next operation area based on the density of crops and forest trees; the preset LSTM model is trained based on the wind speed and wind direction time series data samples with wind speed and wind direction data labels, and the preset neural network model is trained based on the crop and forest tree image samples with crop and forest tree density labels. The crops and forest trees in the crop and forest tree image samples are the same type of crops and forest trees as those in the crop and forest tree images; Step S103, determine the ideal angle between the liquid column and the vertical plane during the spraying operation of the nozzle of the operation aircraft based on the growth trend of crops and forest trees, and determine the nozzle angle and flight speed of the operation aircraft in the next operation area based on the target wind speed and wind direction data and the ideal angle. Then, determine the flight height and nozzle flow rate of the operation aircraft based on the growth trend of crops and forest trees and the ideal angle; Step S104, perform spraying operations on the next operation area based on the flight speed, flight height, nozzle angle, and nozzle flow rate.

[0158] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the intelligent control method for aircraft pest control in agricultural operations provided by the above-mentioned various methods. The method includes: Step S101, during the flight operation of the operation aircraft, obtain the time-series data of wind speed and wind direction, and obtain the images of crops and forest trees in the next operation area; the time-series data of wind speed and wind direction is obtained by the operation aircraft collecting the wind speed and the corresponding wind direction at a specific sampling rate; Step S102, input the time-series data of wind speed and wind direction into a preset long short-term memory network (LSTM) model to output the target wind speed and wind direction data, input the images of crops and forest trees in the next operation area into a preset neural network model to output the density of crops and forest trees in the next operation area, and determine the growth trend of crops and forest trees in the next operation area based on the density of crops and forest trees; the preset LSTM model is trained based on the time-series data samples of wind speed and wind direction with wind speed and wind direction data labels, and the preset neural network model is trained based on the crop and forest tree image samples with crop and forest tree density labels. The crops and forest trees in the crop and forest tree image samples are the same type of crops and forest trees as those in the crop and forest tree images; Step S103, determine the ideal angle between the liquid column and the vertical plane when the nozzle of the operation aircraft is operating based on the growth trend of crops and forest trees, and determine the nozzle angle and flight speed of the operation aircraft in the next operation area based on the target wind speed and wind direction data and the ideal angle. Then, determine the flight height and nozzle flow rate of the operation aircraft based on the growth trend of crops and forest trees and the ideal angle; Step S104, perform spraying operations on the next operation area based on the flight speed, flight height, nozzle angle, and nozzle flow rate.

[0159] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

[0160] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent control method for aircraft control of agricultural and forestry pests and diseases, characterized in that: include: Step S101, during the flight operation of the operating aircraft, obtaining time series data of wind speed and direction, and obtaining images of crops and trees in the next operating area; the time series data of wind speed and direction is obtained by the operating aircraft collecting wind speed and corresponding wind direction at a specific sampling rate; Step S102, inputting the wind speed and direction time series data into a preset long short-term memory network LSTM model, outputting target wind speed and direction data, inputting the crop and tree images of the next operation area into a preset neural network model, outputting the crop and tree density of the next operation area, and determining the growth of crops and trees in the next operation area based on the crop and tree density; The preset LSTM model is obtained by training based on wind speed and wind direction time series data samples with wind speed and wind direction data labels, and the preset neural network model is obtained by training based on crop and tree image samples with crop and tree density labels, and the crops and trees in the crop and tree image samples are the same type of crops and trees as the crops and trees in the crop and tree images; Step S103, determining the ideal angle between the liquid column and the vertical plane of the nozzle of the operating aircraft during operation based on the growth of the crops and trees, and determining the nozzle angle and flight speed of the operating aircraft in the next operating area based on the target wind speed and direction data and the ideal angle, and then determining the flight altitude and nozzle flow rate of the operating aircraft based on the growth of the crops and trees and the ideal angle; Step S104, performing a spraying operation on the next operation area based on the flight speed, the flight altitude, the nozzle angle, and the nozzle flow rate; Wherein, the determining of the flight altitude and nozzle flow rate of the operating aircraft based on the growth of the crops and trees and the ideal angle includes: If the growth conditions of the crops and trees indicate that the crops and trees in the next operation area are in a stage before the fruiting stage, determining the flight altitude to be the first altitude; If the growth conditions of the crops and trees indicate that the crops and trees in the next operation area are in a stage after the fruiting period, determining the flight altitude to be the second altitude; If the growth conditions of the crops and trees indicate that the crops and trees in the next operation area are in the fruit-bearing period, determining the flight altitude to be the third altitude; Determine the average pesticide spraying amount per unit area of ​​the next operation area based on the growth of the crops and trees, and determine the nozzle flow rate based on the average pesticide spraying amount, the ideal angle and the flight altitude; Among them, the first height, the second height and the third height decrease in sequence.

2. The method according to claim 1, characterized in that: The method of determining the ideal angle between the liquid column and the vertical plane when the nozzle of the operation aircraft is operating based on the growth of the crops and trees includes: If the growth conditions of the crops and trees indicate that the crops and trees in the next operation area are in a stage before the fruiting stage, determining the ideal angle as the first angle; If the growth conditions of the crops and trees indicate that the crops and trees in the next operation area are in a stage after the fruiting period, determining the ideal angle to be the second angle; If the growth conditions of the crops and trees indicate that the crops and trees in the next operation area are in the fruit-bearing period, determining the ideal angle to be the third angle; Among them, the first angle, the second angle and the third angle decrease in sequence.

3. The method according to claim 1, characterized in that The method further comprises: Constructing a simulation model of the change in angle between the liquid column and the vertical plane under the influence of wind speed, wind direction, nozzle angle, flight direction and flight speed of the operating aircraft, wherein the simulation model includes a wind speed input interface, a wind direction input interface, a nozzle angle input interface, a flight direction input interface and a flight speed input interface; The step of determining the nozzle angle and flight speed of the operating aircraft in the next operating area based on the target wind speed and direction data and the ideal angle includes: For each co-directional flight process of the operating aircraft, the flight direction is input into the simulation model through the flight direction input interface, the current flight speed is used as the initial flight speed and input into the simulation model through the flight speed input interface, the target wind speed and direction data are input into the simulation model through the wind direction input interface and the wind speed input interface, the input value of the nozzle angle input interface is adjusted multiple times until the angle between the liquid column and the vertical plane is close to the ideal angle, and the input value of the nozzle angle input interface at this time is determined as the nozzle angle; The input value of the flight speed input interface is fine-tuned multiple times until the angle between the liquid column and the vertical plane is equal to the ideal angle, and the input value of the flight speed input interface at this time is determined as the flight speed.

4. The method according to claim 1, characterized in that: The method further comprises: When the operating aircraft is performing spraying operations in the next operating area and the flight altitude changes due to obstacle avoidance, a new nozzle flow rate is re-determined based on the new flight altitude, the average pesticide spraying amount and the ideal angle, and then step S104 is executed.

5. The method according to claim 1, characterized in that: The step of determining the growth of crops and trees in the next operation area based on the density of crops and trees includes: Based on the types of crops and trees in the next operation area, determining the range of crop and tree density corresponding to the growth of different crops and trees; By comparing the crop and tree density with the crop and tree density ranges corresponding to the growth conditions of different crops and trees, the growth conditions of crops and trees in the next operation area can be obtained.

6. The method according to claim 1, characterized in that The method further comprises: During the spraying operation of the operating aircraft in the next operating area, if the change of the wind speed and wind direction time series data collected within the preset time period exceeds the preset threshold, steps S102 to S103 are executed based on the new wind speed and wind direction time series data to determine a new flight speed, a new flight altitude, a new nozzle angle, and a new nozzle flow rate; The spraying operation is continued on the remaining area of ​​the next operation area based on the new flight speed, the new flight altitude, the new nozzle angle and the new nozzle flow rate.

7. An intelligent control device for aircraft control of agricultural pests and diseases, characterized in that: include: The data acquisition module is used to obtain the wind speed and wind direction time series data and the crop and tree images in the next operation area during the operation of the operation aircraft; the wind speed and wind direction time series data is obtained by the operation aircraft collecting the wind speed and the corresponding wind direction at a specific sampling rate; A crop and tree growth acquisition module is used to input the wind speed and direction time series data into a preset long short-term memory network LSTM model, output target wind speed and direction data, input the crop and tree image of the next operation area into a preset neural network model, output the crop and tree density of the next operation area, and determine the crop and tree growth in the next operation area based on the crop and tree density; the LSTM model is trained based on wind speed and direction time series data samples with wind speed and direction data labels, the preset neural network model is trained based on crop and tree image samples with crop and tree density labels, and the crops and trees in the crop and tree image samples are the same type of crops and trees as the crops and trees in the crop and tree images; A spraying parameter acquisition module, for determining the ideal angle between the liquid column and the vertical plane of the nozzle of the operating aircraft during operation based on the growth of the crops and trees, and determining the nozzle angle and flight speed of the operating aircraft in the next operating area based on the target wind speed and direction data and the ideal angle, and then determining the flight altitude and nozzle flow rate of the operating aircraft based on the growth of the crops and trees and the ideal angle; A spraying operation module, used for performing a spraying operation on the next operation area based on the flight speed, the flight altitude, the nozzle angle and the nozzle flow rate; Wherein, the spraying parameter acquisition module is further used for: The method of determining the flight altitude and nozzle flow rate of the operating aircraft based on the growth of the crops and trees and the ideal angle includes: If the growth conditions of the crops and trees indicate that the crops and trees in the next operation area are in a stage before the fruiting stage, determining the flight altitude to be the first altitude; If the growth conditions of the crops and trees indicate that the crops and trees in the next operation area are in a stage after the fruiting period, determining the flight altitude to be the second altitude; If the growth conditions of the crops and trees indicate that the crops and trees in the next operation area are in the fruit-bearing period, determining the flight altitude to be the third altitude; Determine the average pesticide spraying amount per unit area of ​​the next operation area based on the growth of the crops and trees, and determine the nozzle flow rate based on the average pesticide spraying amount, the ideal angle and the flight altitude; Among them, the first height, the second height and the third height decrease in sequence.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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