CN112987793B - Spraying method and device based on unmanned aerial vehicle, electronic equipment and medium - Google Patents

Abstract

The present disclosure relates to a spraying method apparatus, an electronic device, and a medium based on an unmanned aerial vehicle; wherein, the method comprises the following steps: determining a first characteristic area and a second characteristic area in the unmanned aerial vehicle operation process; wherein the first characteristic area comprises a crop canopy vortex area and the second characteristic area comprises a droplet deposition area; determining the distance between the center point of the first characteristic region and the center point of the second characteristic region, and determining the area difference between the area of the first characteristic region and the area of the second characteristic region; and adjusting the operation parameters of the unmanned aerial vehicle according to the distance and the area difference. The utility model discloses the operation parameter that this disclosed embodiment can adjust unmanned aerial vehicle at the operation in-process is so that crop canopy vortex region and droplet deposition area overlap, and canopy vortex region initiative contains droplet deposition area to improve the spraying precision, obtain the best operation effect.

Description

Drone-based spraying method, apparatus, electronics and media

technical field

The present disclosure relates to the technical field of drones, in particular to a drone-based spraying method, device, electronic equipment and media.

Background technique

As a new type of pesticide application equipment, plant protection unmanned aerial vehicles are developing very rapidly. When the plant protection UAV is operating, it will generate a downward rotor wind field, which will disturb the crop canopy. The plants in the canopy vortex often swing and the leaves turn over, which has a great impact on the penetration of droplets. Influence.

When the UAV advances rapidly during the operation, both the droplet flow deposition area and the crop canopy vortex area will lag behind. At different operating speeds, due to the different degrees of attenuation of the movement speed of different particles, the airflow and fog droplets reach the crop canopy. The positions of the layers are different, which causes the rotor wind field to make it difficult for the droplets to deposit in the canopy vortex area in time after the blades are turned over, which seriously affects the spray effect; thus greatly reducing the operating efficiency of the plant protection drone.

Contents of the invention

In order to solve the above technical problems or at least partly solve the above technical problems, the present disclosure provides a drone-based spraying method, device, electronic equipment and media.

In a first aspect, the present disclosure provides a drone-based spraying method, the method comprising:

Determining a first characteristic area and a second characteristic area during the operation of the drone; wherein, the first characteristic area includes a crop canopy vortex area, and the second characteristic area includes a droplet deposition area;

determining a distance between a center point of the first characteristic region and a center point of the second characteristic region, and determining an area difference between an area of the first characteristic region and an area of the second characteristic region;

Adjusting the operating parameters of the drone according to the distance and the area difference.

Optionally, the determination of the first characteristic area and the second characteristic area during the operation of the UAV includes:

Obtain the operating parameters and meteorological parameters of the unmanned aerial vehicle during the operation; wherein, the operating parameters include at least one of flight speed, flight height, droplet size and model; the meteorological parameters include air temperature, humidity , at least one of wind speed and wind direction;

inputting the job parameters into the pre-trained first recognition model, and determining the first characteristic region according to the output of the first recognition model; wherein, the first recognition model is based on the first historical characteristic region and the historical job The parameters are trained;

Inputting the meteorological parameters into the pre-trained second recognition model, and determining the second characteristic region according to the output of the second recognition model; wherein, the second recognition model is based on the second historical characteristic region and the historical weather The parameters are trained.

Optionally, the determination of the first characteristic area and the second characteristic area during the operation of the UAV includes:

Obtain the operation image when the drone is operating;

Segmenting the operation image to obtain a first feature area and a second feature area; wherein, the segmentation process includes at least one of an optical flow method, an inter-frame difference method, and a background subtraction method.

Optionally, the determining the distance between the center point of the first feature area and the center point of the second feature area includes:

performing contour extraction on the first feature area to obtain an enclosing area of the first feature area; and performing contour extraction on the second feature area to obtain an enclosing area of the second feature area;

determining the coordinates of the center point of the first feature area according to the enclosing area of the first feature area; determining the coordinates of the center point of the second feature area according to the enclosing area of the second feature area;

calculating the distance between the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area.

Optionally, the determining the area difference between the area of the first characteristic region and the area of the second characteristic region includes:

calculating an area difference between the area of the first characteristic region and the area of the second characteristic region according to the coordinates of the center point of the first characteristic region and the coordinates of the center point of the second characteristic region.

Optionally, the determining the distance between the center point of the first feature area and the center point of the second feature area includes:

determining a first flight angle of the first characteristic area and a second flight angle of the second characteristic area according to the operation image;

Determine the first lag distance according to the first flight angle and the flying height of the drone; determine the second lag distance according to the second flight angle and the flying height of the drone;

A distance difference between the first hysteresis distance and the second hysteresis distance is used as the distance between the center point of the first feature area and the center point of the second feature area.

Optionally, the adjusting the operating parameters of the UAV according to the distance and the area difference includes:

Detecting whether the area difference is greater than a preset threshold, and whether the distance is equal to the preset threshold;

If not, at least one of the flying speed, flying height and droplet size of the drone is adjusted so that the area difference is greater than a preset threshold and the distance is equal to the preset threshold.

In a second aspect, the present disclosure also provides a drone-based spraying device, including:

The first determination module is used to determine the first characteristic area and the second characteristic area during the operation of the drone; wherein, the first characteristic area includes the crop canopy vortex area, and the second characteristic area includes fog droplets deposition area;

A second determination module, configured to determine the distance between the center point of the first feature area and the center point of the second feature area, and determine the difference between the area of the first feature area and the area of the second feature area area difference;

A parameter adjustment module, configured to adjust the operating parameters of the drone according to the distance and the area difference.

Optionally, the first determination module is specifically used for:

Obtain the operating parameters and meteorological parameters of the unmanned aerial vehicle during the operation; wherein, the operating parameters include at least one of flight speed, flight height, droplet size and model; the meteorological parameters include air temperature, humidity , at least one of wind speed and wind direction;

inputting the job parameters into the pre-trained first recognition model, and determining the first characteristic region according to the output of the first recognition model; wherein, the first recognition model is based on the first historical characteristic region and the historical job The parameters are trained;

Inputting the meteorological parameters into the pre-trained second recognition model, and determining the second characteristic region according to the output of the second recognition model; wherein, the second recognition model is based on the second historical characteristic region and the historical weather The parameters are trained.

Optionally, the first determination module is specifically used for:

Obtain the operation image when the drone is operating;

Segmenting the operation image to obtain a first feature area and a second feature area; wherein, the segmentation process includes at least one of an optical flow method, an inter-frame difference method, and a background subtraction method.

Optionally, the second determining module is specifically used for:

performing contour extraction on the first feature area to obtain an enclosing area of the first feature area; and performing contour extraction on the second feature area to obtain an enclosing area of the second feature area;

determining the coordinates of the center point of the first feature area according to the enclosing area of the first feature area; determining the coordinates of the center point of the second feature area according to the enclosing area of the second feature area;

calculating the distance between the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area.

Optionally, the second determining module is specifically used for:

calculating an area difference between the area of the first characteristic region and the area of the second characteristic region according to the coordinates of the center point of the first characteristic region and the coordinates of the center point of the second characteristic region.

Optionally, the second determining module is specifically used for:

determining a first flight angle of the first characteristic area and a second flight angle of the second characteristic area according to the operation image;

Determine the first lag distance according to the first flight angle and the flying height of the drone; determine the second lag distance according to the second flight angle and the flying height of the drone;

A distance difference between the first hysteresis distance and the second hysteresis distance is used as the distance between the center point of the first feature area and the center point of the second feature area.

Optionally, the parameter adjustment module is specifically used for:

Detecting whether the area difference is greater than a preset threshold, and whether the distance is equal to the preset threshold;

If not, at least one of the flying speed, flying height and droplet size of the drone is adjusted so that the area difference is greater than a preset threshold and the distance is equal to the preset threshold.

In a third aspect, the present disclosure also provides an electronic device, which includes:

one or more processors;

storage means for storing one or more programs,

When the one or more programs are executed by the one or more processors, the one or more processors implement any one of the drone-based spraying methods in the embodiments of the present invention.

In a fourth aspect, the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the drone-based spray described in any one of the embodiments of the present invention is realized method.

Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages: the operating parameters of the UAV can be adjusted during the operation so that the crop canopy vortex area and the droplet deposition area overlap, and the canopy vortex The area actively includes the droplet deposition area, so as to improve the spray accuracy and obtain the best operation effect.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description serve to explain the principles of the disclosure.

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, In other words, other drawings can also be obtained from these drawings without paying creative labor.

Fig. 1 is a schematic flow chart of a drone-based spraying method provided by an embodiment of the present disclosure;

2 is a schematic flow diagram of another drone-based spraying method provided by an embodiment of the present disclosure;

3 is a schematic flow diagram of another drone-based spraying method provided by an embodiment of the present disclosure;

Fig. 4 is the working diagram of unmanned aerial vehicle;

Fig. 5 is a schematic structural diagram of a spraying device based on a drone provided by an embodiment of the present disclosure;

Fig. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.

Detailed ways

In order to more clearly understand the above objects, features and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that, in the case of no conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other.

In the following description, many specific details are set forth in order to fully understand the present disclosure, but the present disclosure can also be implemented in other ways than described here; obviously, the embodiments in the description are only some of the embodiments of the present disclosure, and Not all examples.

Fig. 1 is a schematic flow chart of a drone-based spraying method provided by an embodiment of the present disclosure. This embodiment is applicable to the situation of utilizing drones for plant protection operations. The method of this embodiment can be executed by a spraying device based on a drone, which can be implemented in hardware/or software, and can be configured in electronic equipment. The drone-based spraying method described in any embodiment of the present application can be realized. As shown in Figure 1, the method specifically includes the following:

S110. Determine a first characteristic area and a second characteristic area during the operation of the drone; wherein, the first characteristic area includes a crop canopy vortex area, and the second characteristic area includes a droplet deposition area.

Since the UAV ignores natural wind factors when the UAV is in operation, when the UAV is hovering, the rotor wind field will cause certain disturbances to the crop canopy, and the vortex area of the crop canopy will appear, which will affect the penetration of fog droplets sex. Both the crop canopy vortex area and the droplet deposition area are under the drone, if during the spraying process, the crop canopy vortex area and the droplet deposition area overlap and the crop canopy vortex area actively includes the droplet deposition area , the mist droplets can be effectively deposited on the middle and lower layers of the plant and the back of the leaves to achieve effective absorption of the spray.

In this embodiment, the first characteristic region and the second characteristic region are two regions formed on the crop when the drone is operating; effective analysis of these two regions can make the first characteristic region and the second characteristic region It tends to overlap on the vertical plane to improve the operating efficiency of the drone.

S120. Determine the distance between the center point of the first feature area and the center point of the second feature area, and determine the area difference between the area of the first feature area and the area of the second feature area.

In this embodiment, the distance between the center point of the first feature area and the center point of the second feature area can effectively reflect the position deviation between the first feature area and the second feature area; the area of the first feature area and the second feature area The area difference of the areas of the regions can directly reflect the size difference between the two regions, so as to effectively determine the coverage relationship between the first characteristic region and the second characteristic region.

S130. Adjust the operating parameters of the drone according to the distance and the area difference.

Since the process of the crop canopy vortex area and the droplet deposition area generated by the drone during operation is dynamic, it is difficult to directly measure its position; therefore, this embodiment uses the distance between the two areas during the drone operation Adjust the operation parameters in real time according to the area deviation, so that the crop canopy vortex area and the droplet deposition area overlap, and the crop canopy vortex area actively includes the droplet deposition area, thereby improving the spraying accuracy and making the drone's operation effect reach optimal.

The embodiment of the present disclosure determines the first characteristic region and the second characteristic region during the operation of the drone; wherein, the first characteristic region includes the crop canopy vortex region, and the second characteristic region includes the droplet deposition region; the first characteristic is determined The distance between the center point of the area and the center point of the second feature area, and the area difference between the area of the first feature area and the area of the second feature area are determined; the operating parameters of the drone are adjusted according to the distance and the area difference. The embodiment of the present disclosure can adjust the operating parameters of the drone during the operation so that the crop canopy vortex area and the droplet deposition area overlap, and the canopy vortex area actively includes the droplet deposition area, thereby improving the spraying accuracy. Get the best job results.

Fig. 2 is a schematic flowchart of another drone-based spraying method provided by an embodiment of the present disclosure. This embodiment is further expanded and optimized on the basis of the above embodiments, and can be combined with any optional solution in the above technical solutions. As shown in Figure 2, the method includes:

S210. Obtain operating parameters and meteorological parameters of the UAV during the operation process.

In this embodiment, the operating parameters include at least one of flight speed, flight altitude, droplet size and aircraft type; the meteorological parameters include at least one of air temperature, humidity, wind speed and wind direction.

Among them, the operating parameters of the drone can be manually set in advance. When obtaining the operating parameters, the preset operating parameters of the drone can be read through the control system of the drone; and the local meteorological data can be obtained based on the cloud server.

S220. Input operation parameters into the pre-trained first recognition model, and determine the first characteristic region according to the output of the first recognition model; wherein, the first recognition model is trained according to the first historical characteristic region and historical operation parameters.

In this embodiment, the pre-trained first recognition model may be a mathematical model of the operating parameters of the drone and the vortex area of the crop canopy obtained through experiments and computational fluid dynamics (Computational Fluid Dynamics, CFD) research.

S230. Input meteorological parameters into the pre-trained second recognition model, and determine the second characteristic region according to the output of the second recognition model; wherein, the second recognition model is obtained by training according to the second historical characteristic region and historical meteorological parameters.

In this embodiment, the pre-trained second recognition model may be a mathematical model of the drone's operating parameters and droplet deposition area obtained through experiments and computational fluid dynamics (Computational Fluid Dynamics, CFD) research.

In this embodiment, the pre-trained recognition model is used to effectively and quickly determine the crop canopy vortex area and the droplet deposition area according to the input information of the model, which solves the problem that it is difficult to directly obtain the accurate area manually.

S240. Perform contour extraction on the first feature area to obtain an enclosing area of the first feature area; and perform contour extraction on the second feature area to obtain an enclosing area of the second feature area.

In this embodiment, the enclosing area of the first feature area is a boundary shape composed of boundary points of the first feature area; the enclosing area of the second feature area is a boundary shape composed of boundary points of the second feature area.

Specifically, contour extraction can be performed on the first feature region and the second feature region by the contour extraction method, boundary tracking method, region growing method or region splitting and merging method to obtain the boundary shape of the first feature region and the shape of the second feature region. border shape.

S250. Determine the coordinates of the central point of the first characteristic region according to the surrounding area of the first characteristic region; determine the coordinates of the central point of the second characteristic region according to the surrounding region of the second characteristic region; calculate the coordinates of the central point of the first characteristic region and the distance between the coordinates of the center point of the second feature area.

In this embodiment, a distance formula between two points can be used to calculate the distance between the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area; for details, refer to the following formula (1).

Among them, x _a is the abscissa of the center point of the first feature area; y _a is the ordinate of the center point of the first feature area; x _b is the abscissa of the center point of the second feature area; y _b is the second feature The ordinate of the center point of the region.

S260. Calculate an area difference between the area of the first feature area and the area of the second feature area according to the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area.

In this embodiment, if the first characteristic region and the second characteristic region are regular figures, the area calculation of the first characteristic region and the second characteristic region can be performed according to the area calculation method of the regular figure, and the area difference between the two can be obtained ; If the first feature area and the second feature area are irregular figures, their areas can be predicted to find the area difference between them.

In this embodiment, according to the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area, the distance between the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area can be accurately calculated. The distance, and the area difference between the area of the first characteristic region and the area of the second characteristic region.

S270. Adjust the operating parameters of the drone according to the distance and the area difference.

Fig. 3 is a schematic flowchart of another drone-based spraying method provided by an embodiment of the present disclosure. This embodiment is further expanded and optimized on the basis of the above embodiments, and can be combined with any optional solution in the above technical solutions. As shown in Figure 3, the method includes:

S310. Obtain an operation image during the operation of the drone.

In this embodiment, by installing an image information collection component on the UAV, the image information of the first characteristic area and the second characteristic area can be collected and returned to the UAV during the operation of the UAV; specifically, the image information collection The components can be installed directly above and beside the UAV to collect image information.

The graphic types of the operation images acquired in this embodiment may include red-green-blue (GRB) images, panchromatic images, depth images, thermal infrared images, hyperspectral images, multispectral images, and point cloud images.

S320. Perform segmentation processing on the job image to obtain a first feature area and a second feature area; wherein, the segmentation process includes at least one of an optical flow method, an inter-frame difference method, and a background subtraction method.

In this embodiment, the operation image may be segmented based on the boundary points in it, so as to distinguish the first feature area from the second feature area.

This embodiment also provides a method that can determine the first characteristic area and the second characteristic area according to the operation image of the drone, and provides more identification methods for determining the first characteristic area and the second characteristic area.

S330. Determine a first flight angle of the first feature area and a second flight angle of the second feature area according to the operation image.

In this embodiment, the first flight angle of the first characteristic area is the angle between the drone and the first characteristic area with the direction perpendicular to the ground as the reference line; the second flight angle of the second characteristic area is The UAV takes the direction perpendicular to the ground as the reference line, and the angle between the second characteristic area; for details, please refer to Figure 4, which is a schematic diagram of the operation of the UAV; in Figure 4, the first flight angle is α; The second flight angle is β.

S340. Determine the first lag distance according to the first flight angle and the flight height of the drone; determine the second lag distance according to the second flight angle and the flight height of the drone; combine the first lag distance with the second lag distance The distance difference of is taken as the distance between the center point of the first feature area and the center point of the second feature area.

In this embodiment, referring to Fig. 4; the flight height of the drone is a preset parameter value H; the first lag distance is L; the second lag distance is B; then the center point of the first feature area and the second lag distance The distance between the center points of the two feature regions can be referred to the following formula (2).

d=L-B=tanα·H-tanβ·H (2)

This embodiment also provides that the angle between the feature area and the calculation of the operation image can be used to calculate the distance between the center points of the two feature areas, and the distance between the center point of the first feature area and the center point of the second feature area is increased. An optional method for calculation.

S350. Calculate an area difference between the area of the first feature area and the area of the second feature area according to the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area.

S360. Adjust the operating parameters of the drone according to the distance and the area difference.

In this embodiment, optionally, the operating parameters of the drone are adjusted according to the distance and area difference, including:

Detecting whether the area difference is greater than a preset threshold, and whether the distance is equal to a preset threshold;

If not, at least one of the flight speed, flight height and droplet size of the drone is adjusted so that the area difference is greater than the preset threshold and the distance is equal to the preset threshold.

Wherein, the preset threshold in this embodiment may be close to zero. When the area difference between the first characteristic area and the second characteristic area is greater than zero, and the distance is equal to zero, the droplet deposition area can be overlapped in the crop canopy vortex area to achieve effective penetration of the droplet.

In this embodiment, the area difference between the first characteristic region and the second characteristic region is monitored in real time during the operation of the drone, and whether the distance from the center point of the first characteristic region to the center point of the second characteristic region satisfies the preset condition , so as to judge whether it is necessary to adjust the operation parameters, so as to realize effective monitoring during the operation of the UAV.

Fig. 5 is a schematic structural diagram of a drone-based spraying device provided by an embodiment of the present disclosure; the device is configured in an electronic device, and can implement the drone-based spraying method described in any embodiment of the present application. The device specifically includes the following:

The first determination module 510 is used to determine the first characteristic area and the second characteristic area during the operation of the drone; wherein, the first characteristic area includes the crop canopy vortex area, and the second characteristic area includes fog Droplet deposition area;

The second determination module 520 is configured to determine the distance between the center point of the first feature area and the center point of the second feature area, and determine the area of the first feature area and the area of the second feature area the area difference;

A parameter adjustment module 530, configured to adjust the operating parameters of the drone according to the distance and the area difference.

In this embodiment, optionally, the first determining module 510 is specifically configured to:

Obtain the operating parameters and meteorological parameters of the unmanned aerial vehicle during the operation; wherein, the operating parameters include at least one of flight speed, flight height, droplet size and model; the meteorological parameters include air temperature, humidity , at least one of wind speed and wind direction;

inputting the job parameters into the pre-trained first recognition model, and determining the first characteristic region according to the output of the first recognition model; wherein, the first recognition model is based on the first historical characteristic region and the historical job The parameters are trained;

Inputting the meteorological parameters into the pre-trained second recognition model, and determining the second characteristic region according to the output of the second recognition model; wherein, the second recognition model is based on the second historical characteristic region and the historical weather The parameters are trained.

In this embodiment, optionally, the first determining module 510 is specifically configured to:

Obtain the operation image when the drone is operating;

Segmenting the operation image to obtain a first feature area and a second feature area; wherein, the segmentation process includes at least one of an optical flow method, an inter-frame difference method, and a background subtraction method.

In this embodiment, optionally, the second determination module 520 is specifically configured to:

performing contour extraction on the first feature area to obtain an enclosing area of the first feature area; and performing contour extraction on the second feature area to obtain an enclosing area of the second feature area;

determining the coordinates of the center point of the first feature area according to the enclosing area of the first feature area; determining the coordinates of the center point of the second feature area according to the enclosing area of the second feature area;

calculating the distance between the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area.

In this embodiment, optionally, the second determination module 520 is specifically configured to:

calculating an area difference between the area of the first characteristic region and the area of the second characteristic region according to the coordinates of the center point of the first characteristic region and the coordinates of the center point of the second characteristic region.

In this embodiment, optionally, the second determination module 520 is specifically configured to:

determining a first flight angle of the first characteristic area and a second flight angle of the second characteristic area according to the operation image;

Determine the first lag distance according to the first flight angle and the flying height of the drone; determine the second lag distance according to the second flight angle and the flying height of the drone;

A distance difference between the first hysteresis distance and the second hysteresis distance is used as the distance between the center point of the first feature area and the center point of the second feature area.

In this embodiment, optionally, the parameter adjustment module 530 is specifically used for:

Detecting whether the area difference is greater than a preset threshold, and whether the distance is equal to the preset threshold;

If not, at least one of the flying speed, flying height and droplet size of the drone is adjusted so that the area difference is greater than a preset threshold and the distance is equal to the preset threshold.

Through the spraying device based on the drone of the embodiment of the present invention, the operating parameters of the drone can be adjusted during the operation so that the crop canopy vortex area and the droplet deposition area overlap, and the canopy vortex area actively contains fog Droplet deposition area, thereby improving spray accuracy and obtaining the best operating results.

The drone-based spraying device provided in the embodiments of the present invention can execute the drone-based spraying method provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

Fig. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. As shown in Figure 6, the electronic device includes a processor 610, a memory 620, an input device 630, and an output device 640; the number of processors 610 in the electronic device can be one or more, and one processor 610 is taken as an example in Figure 6 ; The processor 610, the memory 620, the input device 630 and the output device 640 in the electronic device can be connected through a bus or in other ways. In FIG. 6, the connection through a bus is taken as an example.

The memory 620, as a computer-readable storage medium, can be used to store software programs, computer-executable programs and modules, such as program instructions/modules corresponding to the drone-based spraying method in the embodiment of the present invention. The processor 610 runs the software programs, instructions and modules stored in the memory 620 to execute various functional applications and data processing of the electronic device, that is, to realize the drone-based spraying method provided by the embodiment of the present invention.

The memory 620 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of the terminal, and the like. In addition, the memory 620 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage devices. In some examples, the memory 620 may further include memory located remotely relative to the processor 610, and these remote memories may be connected to electronic devices through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The input device 630 can be used to receive input numbers or character information, and generate key signal input related to user settings and function control of electronic equipment, and can include a keyboard, a mouse, and the like. The output device 640 may include a display device such as a display screen.

An embodiment of the present disclosure also provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to implement the drone-based spraying method provided by the embodiment of the present invention when executed by a computer processor.

Certainly, a storage medium containing computer-executable instructions provided by an embodiment of the present invention, the computer-executable instructions are not limited to the method operations described above, and may also execute the UAV-based drone provided by any embodiment of the present invention. Relevant operations in the spray method.

Through the above description about the implementation mode, those skilled in the art can clearly understand that the present invention can be realized by means of software and necessary general-purpose hardware, and of course it can also be realized by hardware, but in many cases the former is a better implementation mode . Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a floppy disk of a computer , read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disc, etc., including several instructions to make a computer device (which can be a personal computer, A server, or a network device, etc.) executes the methods described in various embodiments of the present invention.

It is worth noting that in the embodiments of the search device above, the units and modules included are only divided according to functional logic, but are not limited to the above-mentioned divisions, as long as the corresponding functions can be realized; in addition, each function The specific names of the units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present invention.

It should be noted that in this article, relative terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these No such actual relationship or order exists between entities or operations. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

The above descriptions are only specific implementation manners of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (6)

1. A spraying method based on unmanned aerial vehicle, it is characterized in that, described method comprises: Determining a first characteristic area and a second characteristic area during the operation of the drone; wherein, the first characteristic area includes a crop canopy vortex area, and the second characteristic area includes a droplet deposition area; determining a distance between a center point of the first characteristic region and a center point of the second characteristic region, and determining an area difference between an area of the first characteristic region and an area of the second characteristic region; adjusting the operating parameters of the UAV according to the distance and the area difference; The determination of the first characteristic area and the second characteristic area in the process of unmanned aerial vehicle operation includes: Obtain the operating parameters and meteorological parameters of the unmanned aerial vehicle during the operation; wherein, the operating parameters include at least one of flight speed, flight height, droplet size and model; the meteorological parameters include air temperature, humidity , at least one of wind speed and wind direction; inputting the job parameters into the pre-trained first recognition model, and determining the first characteristic region according to the output of the first recognition model; wherein, the first recognition model is based on the first historical characteristic region and the historical job The parameters are trained; Inputting the meteorological parameters into the pre-trained second recognition model, and determining the second characteristic region according to the output of the second recognition model; wherein, the second recognition model is based on the second historical characteristic region and the historical weather The parameters are trained; The determining the distance between the center point of the first feature area and the center point of the second feature area includes: performing contour extraction on the first feature area to obtain an enclosing area of the first feature area; and performing contour extraction on the second feature area to obtain an enclosing area of the second feature area; determining the coordinates of the center point of the first feature area according to the enclosing area of the first feature area; determining the coordinates of the center point of the second feature area according to the enclosing area of the second feature area; calculating the distance between the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area; The determining the area difference between the area of the first characteristic region and the area of the second characteristic region includes: calculating an area difference between the area of the first feature area and the area of the second feature area according to the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area; The adjustment of the operating parameters of the drone according to the distance and the area difference includes: Detecting whether the area difference is greater than a preset threshold, and whether the distance is equal to the preset threshold; If not, at least one of the flying speed, flying height and droplet size of the drone is adjusted so that the area difference is greater than a preset threshold and the distance is equal to the preset threshold. 2. The method according to claim 1, wherein said determining the first characteristic area and the second characteristic area in the unmanned aerial vehicle operation process includes: Obtain the operation image when the drone is operating; Segmenting the operation image to obtain a first feature area and a second feature area; wherein, the segmentation process includes at least one of an optical flow method, an inter-frame difference method, and a background subtraction method. 3. The method according to claim 2, wherein the determining the distance between the center point of the first feature area and the center point of the second feature area comprises: determining a first flight angle of the first characteristic area and a second flight angle of the second characteristic area according to the operation image; Determine the first lag distance according to the first flight angle and the flying height of the drone; determine the second lag distance according to the second flight angle and the flying height of the drone; A distance difference between the first hysteresis distance and the second hysteresis distance is used as the distance between the center point of the first feature area and the center point of the second feature area. 4. A spraying device based on unmanned aerial vehicle, it is characterized in that, described device comprises: The first determination module is used to determine the first characteristic area and the second characteristic area during the operation of the drone; wherein, the first characteristic area includes the crop canopy vortex area, and the second characteristic area includes fog droplets deposition area; A second determination module, configured to determine the distance between the center point of the first feature area and the center point of the second feature area, and determine the difference between the area of the first feature area and the area of the second feature area area difference; A parameter adjustment module, configured to adjust the operating parameters of the drone according to the distance and the area difference; The first determination module determines the first characteristic area and the second characteristic area during the operation of the drone, specifically for: Obtain the operating parameters and meteorological parameters of the unmanned aerial vehicle during the operation; wherein, the operating parameters include at least one of flight speed, flight height, droplet size and model; the meteorological parameters include air temperature, humidity , at least one of wind speed and wind direction; inputting the job parameters into the pre-trained first recognition model, and determining the first characteristic region according to the output of the first recognition model; wherein, the first recognition model is based on the first historical characteristic region and the historical job The parameters are trained; Inputting the meteorological parameters into the pre-trained second recognition model, and determining the second characteristic region according to the output of the second recognition model; wherein, the second recognition model is based on the second historical characteristic region and the historical weather The parameters are trained; The second determination module determines the distance between the center point of the first feature area and the center point of the second feature area, specifically for: performing contour extraction on the first feature area to obtain an enclosing area of the first feature area; and performing contour extraction on the second feature area to obtain an enclosing area of the second feature area; determining the coordinates of the center point of the first feature area according to the enclosing area of the first feature area; determining the coordinates of the center point of the second feature area according to the enclosing area of the second feature area; calculating the distance between the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area; The second determination module determines the area difference between the area of the first characteristic region and the area of the second characteristic region, specifically for: calculating an area difference between the area of the first feature area and the area of the second feature area according to the coordinates of the center point of the first feature area and the coordinates of the center point of the second feature area; The parameter adjustment module adjusts the operating parameters of the drone according to the distance and the area difference, specifically for: Detecting whether the area difference is greater than a preset threshold, and whether the distance is equal to the preset threshold; If not, at least one of the flying speed, flying height and droplet size of the drone is adjusted so that the area difference is greater than a preset threshold and the distance is equal to the preset threshold. 5. An electronic device, characterized in that it comprises: one or more processors; storage means for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the drone-based spraying method according to any one of claims 1-3. 6. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the program is executed by a processor, the drone-based spraying method according to any one of claims 1-3 is realized.

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