CN116636518A - Unmanned aerial vehicle pesticide application control method and system - Google Patents
Unmanned aerial vehicle pesticide application control method and system Download PDFInfo
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- 239000000575 pesticide Substances 0.000 title claims abstract description 118
- 238000000034 method Methods 0.000 title claims abstract description 83
- 230000008021 deposition Effects 0.000 claims abstract description 129
- 238000012377 drug delivery Methods 0.000 claims abstract description 38
- 238000010586 diagram Methods 0.000 claims abstract description 31
- 230000008569 process Effects 0.000 claims abstract description 25
- 238000005507 spraying Methods 0.000 claims abstract description 21
- 239000003814 drug Substances 0.000 claims description 61
- 239000003595 mist Substances 0.000 claims description 47
- 229940079593 drug Drugs 0.000 claims description 45
- 241000607479 Yersinia pestis Species 0.000 claims description 21
- 201000010099 disease Diseases 0.000 claims description 21
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 6
- 239000007921 spray Substances 0.000 description 8
- 238000004891 communication Methods 0.000 description 6
- 230000001276 controlling effect Effects 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 5
- 241000196324 Embryophyta Species 0.000 description 4
- 241000238631 Hexapoda Species 0.000 description 4
- 230000008859 change Effects 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 4
- 238000004062 sedimentation Methods 0.000 description 4
- 241000209140 Triticum Species 0.000 description 3
- 235000021307 Triticum Nutrition 0.000 description 3
- 230000009471 action Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000005137 deposition process Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 239000007788 liquid Substances 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
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- 238000013135 deep learning Methods 0.000 description 2
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- 238000013528 artificial neural network Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000001647 drug administration Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
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- 238000002360 preparation method Methods 0.000 description 1
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- 238000012545 processing Methods 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
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- 238000006467 substitution reaction Methods 0.000 description 1
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- 238000005406 washing Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
- G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
- G05D1/0816—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability
- G05D1/0825—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability using mathematical models
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
- A01M7/0089—Regulating or controlling systems
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/106—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U2101/00—UAVs specially adapted for particular uses or applications
- B64U2101/40—UAVs specially adapted for particular uses or applications for agriculture or forestry operations
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U2101/00—UAVs specially adapted for particular uses or applications
- B64U2101/45—UAVs specially adapted for particular uses or applications for releasing liquids or powders in-flight, e.g. crop-dusting
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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- Life Sciences & Earth Sciences (AREA)
- Aviation & Aerospace Engineering (AREA)
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- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
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- Pure & Applied Mathematics (AREA)
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- Insects & Arthropods (AREA)
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- Wood Science & Technology (AREA)
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Abstract
The invention provides a method and a system for controlling unmanned aerial vehicle drug delivery, which belong to the technical field of agriculture and comprise the following steps: acquiring position information of an unmanned aerial vehicle, and acquiring operation parameters of spraying operation of the unmanned aerial vehicle on a target land block; inputting the operation parameters into a fog drop deposition prediction model of the unmanned aerial vehicle, and obtaining predicted fog drop density output by the fog drop deposition prediction model; based on the position information, comparing and analyzing the predicted fog drop density with a drug delivery prescription diagram of the target land block to determine a parameter adjustment amount of the unmanned aerial vehicle, wherein the parameter adjustment amount is used for adjusting operation parameters. According to the unmanned aerial vehicle pesticide application control method and system, the unmanned aerial vehicle operation parameters are utilized to predict the fog drop density in the pesticide application process, and the predicted quantity is compared with the prescription chart, so that the operation parameters are adjusted in real time according to the pesticide application quality in the unmanned aerial vehicle pesticide application process, the utilization rate of the pesticide is improved, the pesticide application quality is guaranteed, and accurate pesticide application is realized.
Description
Technical Field
The invention relates to the technical field of agriculture, in particular to an unmanned aerial vehicle pesticide application control method and system.
Background
Unmanned aerial vehicle pesticide application operation is one of the most commonly used modes in current plant protection machinery operation by virtue of the advantages of high operation efficiency, low operation cost, wide terrain adaptability and the like. At present, whether the unmanned aerial vehicle is applied medicine and accurately sprays the problem directly influences the pesticide utilization ratio, and the most important factor that influences unmanned aerial vehicle and accurately apply medicine is the decision-making implementation method of applying medicine.
In recent years, one research direction of drug delivery decision making is to use image information based on deep learning and a spectrum information fusion technology, so as to design a drug delivery auxiliary decision making system to provide data support for a drug delivery prescription of an unmanned aerial vehicle.
However, unmanned aerial vehicle dispensing in the above scheme cannot guarantee dispensing quality.
Disclosure of Invention
The unmanned aerial vehicle drug delivery control method and system provided by the invention are used for solving the defect that the drug delivery quality cannot be ensured in the prior art, realizing the real-time adjustment of the operation parameters according to the drug delivery quality in the unmanned aerial vehicle drug delivery process, improving the utilization rate of the drug, ensuring the drug delivery quality and realizing the accurate drug delivery.
The invention provides an unmanned aerial vehicle drug delivery control method, which comprises the following steps:
acquiring position information of an unmanned aerial vehicle and operation parameters of spraying operation of the unmanned aerial vehicle on a target land block, wherein the operation parameters comprise: flight speed and application flow rate;
inputting the operation parameters into a mist deposition prediction model of the unmanned aerial vehicle, and obtaining predicted mist density output by the mist deposition prediction model; the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model;
And comparing and analyzing the predicted fog drop density with the pesticide application prescription map of the target land block based on the position information to determine the parameter adjustment quantity of the unmanned aerial vehicle, wherein the parameter adjustment quantity is used for adjusting the operation parameters.
According to the unmanned aerial vehicle pesticide application control method provided by the invention, before the operation parameters are input into the unmanned aerial vehicle droplet deposition prediction model, the method further comprises the following steps of:
acquiring the sample fog drop point cloud quantity and the sample fog drop deposition quantity of the unmanned aerial vehicle in a plurality of sample areas under different sample operation parameters; the sample job parameters include: sample flight speed and sample application flow;
and constructing the mist drop deposition prediction model based on the sample mist drop point cloud quantity and the sample mist drop deposition quantity of each sample area of each sample operation parameter.
According to the unmanned aerial vehicle application control method provided by the invention, the construction of the droplet deposition prediction model based on the sample droplet point cloud quantity and the sample droplet deposition quantity of each sample area of each sample operation parameter comprises the following steps:
based on the number of sample fog point clouds and sample fog drop deposition amount of the unmanned aerial vehicle in each sample area under a plurality of sample operation parameters, determining a linear relation between the number of the fog point clouds and the fog drop deposition amount of the unmanned aerial vehicle operation and a deposition relation between the fog drop deposition amount, the flying speed and the application flow;
Determining a quantitative relationship between the flight speed, the application flow rate and the number of droplet point clouds based on the linear relationship and the deposition relationship;
determining a density relationship between the flight speed, the application flow rate and the fog drop density based on the number relationship and the area of the sample area;
and constructing the mist deposition prediction model based on the density relation.
According to the unmanned aerial vehicle drug delivery control method provided by the invention, the drug delivery prescription diagram is obtained based on the following steps:
acquiring a hyperspectral image of the target land block;
determining the pest and disease damage range and the pest and disease damage degree of the target land block based on the hyperspectral image;
determining a pesticide application prescription diagram of the target land according to the pest and disease range and the pest and disease extent; the prescription map includes a distribution of drug delivery requirements within the target plot.
According to the unmanned aerial vehicle pesticide application control method provided by the invention, the comparison analysis is carried out on the predicted fog drop density and the pesticide application prescription map of the target land parcel based on the position information so as to determine the parameter adjustment quantity of the unmanned aerial vehicle, and the method comprises the following steps:
determining a drug application amount requirement corresponding to the position information in the drug application prescription chart;
Comparing and analyzing the predicted fog drop density with the drug application amount requirement to generate a drug amount error;
and determining the parameter adjustment quantity according to the drug quantity error.
The invention also provides an unmanned aerial vehicle pesticide application control system, which comprises an unmanned aerial vehicle, an unmanned aerial vehicle real-time tracking platform, a digital radio station and an industrial personal computer;
the unmanned aerial vehicle is used for spraying operation;
the unmanned aerial vehicle real-time tracking platform is provided with a plurality of laser radars;
the laser radar is used for collecting fog drop point cloud data in the unmanned aerial vehicle spraying operation process, and the fog drop point cloud data are used for constructing a fog drop deposition prediction model;
the unmanned aerial vehicle is provided with a GPS antenna, and the GPS antenna is used for collecting the position information of the unmanned aerial vehicle and sending the position information to the industrial personal computer through the digital radio station;
the plurality of laser radars send the fog drop point cloud data to the industrial personal computer through the digital radio station;
the industrial personal computer is internally provided with a processor; the unmanned aerial vehicle control system further comprises a memory and a program or instructions stored on the memory and capable of running on the processor, wherein the program or instructions are executed by the processor to perform any one of the unmanned aerial vehicle administration control methods.
The invention also provides an industrial personal computer, which comprises:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring position information of an unmanned aerial vehicle and operation parameters of spraying operation of the unmanned aerial vehicle on a target land block, and the operation parameters comprise: flight speed and application flow rate;
the input module is used for inputting the operation parameters into a mist deposition prediction model of the unmanned aerial vehicle and obtaining predicted mist density output by the mist deposition prediction model; the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model;
and the analysis module is used for comparing and analyzing the predicted fog drop density with the pesticide application prescription map of the target land block based on the position information so as to determine the parameter adjustment quantity of the unmanned aerial vehicle, wherein the parameter adjustment quantity is used for adjusting the operation parameters.
The invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the unmanned aerial vehicle drug delivery control method when executing the program.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of unmanned aerial vehicle administration control as described in any one of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements a method of controlling the dispensing of a medicament by a drone as described in any one of the above.
According to the unmanned aerial vehicle pesticide application control method and system, the unmanned aerial vehicle operation parameters are utilized to predict the fog drop density in the pesticide application process, and the predicted quantity is compared with the prescription chart, so that the operation parameters are adjusted in real time according to the pesticide application quality in the unmanned aerial vehicle pesticide application process, the utilization rate of the pesticide is improved, the pesticide application quality is guaranteed, and accurate pesticide application is realized.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an unmanned aerial vehicle administration control method provided by the invention;
fig. 2 is a schematic structural diagram of an unmanned aerial vehicle drug delivery control system provided by the invention;
FIG. 3 is a schematic diagram of a droplet dot cloud distribution provided by the invention;
FIG. 4 is a schematic diagram of coordinate system conversion provided by the present invention;
FIG. 5 is a schematic view of the deposition amount of mist droplets provided by the present invention;
FIG. 6 is a schematic illustration of a prescription chart provided by the present invention;
FIG. 7 is a second flow chart of the unmanned aerial vehicle application control method provided by the invention;
FIG. 8 is a schematic diagram of the structure of the industrial personal computer provided by the invention;
fig. 9 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Currently, accurate pesticide application decisions formulated for plant diseases and insect pests are mainly influenced by five factors, namely crop information acquisition, pesticide application nozzle selection, variable pesticide application control, aircraft flight attitude control and pesticide application quality detection. The preparation of the pesticide application decision-making mainly utilizes technologies such as image processing, deep learning, artificial neural network, spectrum analysis and the like to acquire crop information, so that analysis decision is carried out on the crop information, a pesticide application prescription chart is constructed, then a pesticide application decision-making system is evaluated by using a pesticide application quality detection method according to the pesticide application nozzle selection, parameter setting of a pesticide application control system and the influence of a gas washing field on the liquid medicine deposition quality under the aircraft flight attitude, and a pesticide application operation decision-making system is finally constructed by correcting the pesticide application operation decision-making system. However, the pesticide application decision system can only adjust the operation parameters of the plant protection machinery after pesticide application operation, and then the operation is performed again.
The spray nozzle type selection decision system is constructed from the spray nozzle type selection angle, a large amount of experiments are needed, spray nozzle type selection is carried out according to specific conditions of crop diseases and insect pests, but in an actual operation environment, whether the pesticide is applied accurately or not is related to the fact that the pesticide liquid leaves the spray nozzle to be atomized into mist drops which are spatially changed by wind fields, so that the accurate prevention and control of the unmanned aerial vehicle pesticide application on the diseases and insect pests cannot be met by the spray nozzle type selection decision system.
The following describes a method and a system for controlling the drug delivery of a unmanned aerial vehicle according to an embodiment of the present invention with reference to fig. 1 to 9.
According to the unmanned aerial vehicle pesticide application control method provided by the embodiment of the invention, the execution main body can be electronic equipment or software or a functional module or a functional entity capable of realizing the unmanned aerial vehicle pesticide application control method in the electronic equipment, and the electronic equipment in the embodiment of the invention comprises but is not limited to an industrial personal computer. The execution body is not limited to the present invention.
Fig. 1 is one of the flow charts of the unmanned aerial vehicle drug delivery control method provided by the invention, as shown in fig. 1, including but not limited to the following steps:
first, in step S1, position information of an unmanned aerial vehicle and operation parameters of spraying operation of the unmanned aerial vehicle on a target land parcel are acquired, where the operation parameters include: flight speed and application flow rate.
The unmanned aerial vehicle can be a plant protection unmanned aerial vehicle with a pesticide application function.
In the process of spraying operation of the unmanned aerial vehicle on the target land, the industrial control machine monitors operation parameters of the unmanned aerial vehicle in real time, wherein the operation parameters can comprise the flight speed of the unmanned aerial vehicle and the pesticide spraying flow rate to the land.
The target plot is an area to be applied with a pesticide, for example, wheat is planted in the target plot, and the unmanned aerial vehicle is required to apply the pesticide to the wheat canopy in the target plot.
The industrial personal computer is loaded with SOPAS, EXCEL, MATLAB and other software to assist data analysis.
Optionally, before the operation parameter is input to the droplet deposition prediction model of the unmanned aerial vehicle, the method further includes:
acquiring the sample fog drop point cloud quantity and the sample fog drop deposition quantity of the unmanned aerial vehicle in a plurality of sample areas under different sample operation parameters; the sample job parameters include: sample flight speed and sample application flow;
and constructing the mist drop deposition prediction model based on the sample mist drop point cloud quantity and the sample mist drop deposition quantity of each sample area of each sample operation parameter.
Fig. 2 is a schematic structural diagram of an unmanned aerial vehicle drug delivery control system provided by the present invention, as shown in fig. 2, including:
The system comprises an unmanned aerial vehicle, an unmanned aerial vehicle real-time tracking platform, a digital radio station and an industrial personal computer; the unmanned aerial vehicle operator can intervene in unmanned aerial vehicle's application process through the industrial computer.
The unmanned aerial vehicle is provided with a GPS antenna for collecting the position information of the unmanned aerial vehicle;
the digital radio station comprises a receiving end and a transmitting end, so that communication between the digital radio station and the unmanned aerial vehicle real-time tracking platform is realized;
the unmanned aerial vehicle real-time tracking platform is a cuboid frame, the initial position of the unmanned aerial vehicle real-time tracking platform is taken as a reference, the X axis is perpendicular to the guide rails and is located on the same plane with the two guide rails, the Y axis is along the direction of the guide rails, and the Z axis is perpendicular to the plane where the two guide rails are located, so that an XYZ rectangular coordinate system is constructed. 101 laser radars (LiDAR) and a distance sensor are mounted on the cuboid frame, the distance sensor is located on the LiDAR, the LiDAR can move along the Z axis, the height of the LiDAR can be adjusted in real time according to the height of the crop canopy, and the height of the LiDAR is determined by actual working conditions. When the unmanned aerial vehicle real-time tracking platform moves on the sliding rail, the LiDAR is used for collecting sample fog drop point clouds in the process of applying the unmanned aerial vehicle to crop canopy; the distance sensor is used for collecting the distance between the unmanned aerial vehicle real-time tracking platform and the unmanned aerial vehicle, guaranteeing that 101 LiDARs are under the same vertical height, and can guarantee that the distance between the LiDAR and the unmanned aerial vehicle is always kept d meters under different flight speeds of the unmanned aerial vehicle through the distance sensor, so that the real-time tracking detection function is achieved, the change process of fog drops in space of the unmanned aerial vehicle under the whole course is reflected in real time, and the work of the real-time monitoring of pesticide delivery and the real-time regulation and control of operation parameters of the unmanned aerial vehicle in the later stage is guaranteed.
The monitoring of fogdrop deposition quality, the decision of giving medicine to poor free of charge and the accurate regulation and control of giving medicine to poor free of charge are one key point of unmanned aerial vehicle variable spraying of wheat, and unmanned aerial vehicle is in the medicine application in-process, and the wind field that lower rotor produced causes serious effect to liquid medicine deposition, and the LiDAR detector model can select TIM351-2134001, and scanning angle is 270 degrees, and measuring range is 0.05 meter ~ 10 meters, and the parameter setting is 15Hz.
101 LiDAR detectors are fixedly carried on an unmanned aerial vehicle real-time tracking platform, each LiDAR is 0.6m away from a crop canopy in the Z-axis direction, a distance sensor is arranged above each LiDAR, the distance between a LiDAR scanning center and the unmanned aerial vehicle in the Z-axis direction is guaranteed to be 0.6m, the flying speed v of the unmanned aerial vehicle, the pesticide application flow q and the flying height are 1.2m, and the flying height is specifically the distance between a spray head and the crop canopy.
Optionally, the constructing the droplet deposition prediction model based on the sample droplet point cloud quantity and the sample droplet deposition quantity of each sample region according to each sample operation parameter includes:
based on the number of sample fog point clouds and sample fog drop deposition amount of the unmanned aerial vehicle in each sample area under a plurality of sample operation parameters, determining a linear relation between the number of the fog point clouds and the fog drop deposition amount of the unmanned aerial vehicle operation and a deposition relation between the fog drop deposition amount, the flying speed and the application flow;
Determining a quantitative relationship between the flight speed, the application flow rate and the number of droplet point clouds based on the linear relationship and the deposition relationship;
determining a density relationship between the flight speed, the application flow rate and the fog drop density based on the number relationship and the area of the sample area;
and constructing the mist deposition prediction model based on the density relation.
Fig. 3 is a schematic diagram of distribution of droplet point cloud, as shown in fig. 3, an unmanned aerial vehicle is used for carrying out experiments under different flight speeds v and application flow rates q, and an application decision database model is constructed, and can be used for correspondingly estimating flight speed and application flow rates of unmanned aerial vehicle application operation according to parameters such as droplet deposition quantity, deposition density and the like in a range of 5×5m of each small square according to the droplet point cloud distribution range, deposition quantity and deposition density, and the actual distribution range, deposition quantity and deposition density can be analyzed due to the fitting linear relation between traditional measurement and LiDAR scanning, the deposition density is the density distribution of droplet deposited on an XOY plane, and the droplet deposition quantity and deposition density in a range of 5×5m of each small square can be estimated according to a droplet deposition prediction model.
In the actual operation process, combining the pesticide application prescription diagram of crop diseases and insect pests, generating a specific pesticide application decision database by combining the cloud droplet deposition range and the deposition density distribution diagram of the points, comparing the data of the real-time monitoring of the scanned mist droplets of LiDAR with the pesticide application decision database, and regulating and controlling the flight speed and pesticide application flow of the unmanned aerial vehicle in real time if the data are deviated from the pesticide application decision database, thereby ensuring the accurate pesticide application to the greatest extent.
In the pesticide application prescription diagram, the distribution data of pesticide application requirement is determined according to the actual pesticide application requirement of each part in the target land, the point density is linearly related to the pesticide application quantity, the region with larger point density is a serious pest and disease damage region, and the corresponding pesticide application quantity is larger. In addition, the dosage can be flexibly adjusted according to the type and actual effect of the medicine.
In order to explore the distribution situation of fog drop points in different areas in a space, 101 (i=1, 2 … …, 101) single-thread LiDARs can be used for synchronously scanning the point cloud data of the fog drop in the area (height is 1.2 m) between a nozzle below an unmanned plane and a crop canopy in the direction parallel to a YOZ plane, the distance between scanning planes of each LiDAR is 0.05m, the point cloud number in a spray width of 5m can be completely scanned, and the three-dimensional fog drop point group distribution is constructed.
All the point clouds obtained by 1 to 101 LiDAR scans are divided into cuboid sections of 5X 1.2 (unit: m) at intervals of 5 meters along the Y-axis direction. Each cuboid interval scanning time t is:
under the same operation parameters, selecting LiDAR scanning fog drop point cloud time t as follows:
t2-t1＝t (2)
wherein t1 is the start scanning time of any cuboid interval; t2 is the end scanning time of any cuboid interval.
FIG. 4 is a schematic diagram of coordinate system conversion according to the present invention, wherein as shown in FIG. 4, a three-dimensional polar coordinate system is established with the initial position of each LiDAR as the origin of coordinates, and the polar coordinate of the ith LiDAR point is (. Alpha. i ，r i ) The calculation formula is as follows:
wherein RangeValue i (j) Is the ith LiDAR jth data; scaleFactor is the scaling factor; startAngle i Is the initial scan angle of the ith LiDAR; angular resolution is the scan angle resolution.
Converting any space coordinate of the LiDAR scanning fog drop point cloud in the time period from t1 to t2 from a polar coordinate system to a rectangular coordinate system, wherein the origin of the rectangular coordinate system is also the starting position of each LiDAR, and if the rectangular coordinate (y, z) of the point R is:
wherein alpha represents the elevation angle at which the LiDAR detector scans particles; r represents the radial distance from the observation point to the LiDAR detector; i represents the ith LiDAR.
As can be seen from the formula (2), the accumulated operation time in the cuboid interval of 5×5×1.2 (unit: m) is t, and the cloud count of the single LiDAR scanned fogdrops is P i (i=1, 2, …, 101), then during the unmanned aerial vehicle administration of the nth set of operating parameters, the sample number of foggy point clouds P of the entire course of the unmanned aerial vehicle n The calculation is as follows:
the method can calculate the number of the cloud points of the fogdrops in the space between the unmanned aerial vehicle and the crop canopy in the whole route, and the data measured by the method can reproduce the sedimentation change process of the fogdrops when the unmanned aerial vehicle applies the pesticide, so that a visual quantitative analysis method is provided for researching the pesticide application deposition quality.
The area directly under the unmanned aerial vehicle application was divided into several 5×5×1.2 meters square grids under the single route, and the deposition amount was collected under the route using 5×70 meters coated paper. And under different flight speeds v and application flow q, the unmanned aerial vehicle uses LiDAR scanning to acquire corresponding space point clouds, converts the space point clouds in the cuboid interval into fog drop deposition quantity of 5X 5 meters square grid on the XOY plane according to the cuboid interval of 5X 1.2 meters.
FIG. 5 is a schematic diagram of the deposition amount of mist droplets, as shown in FIG. 5, 5×5 meters of coated paper is placed under the whole navigation line, and the correlation model of the number of point clouds and the deposition density corresponding to each plane 5×5 meters of point cloud images and the deposition amount and the deposition density of 5×5 meters of coated paper is calculated. And obtaining an application model database of the operation parameters, namely the deposition quantity, the deposition density, the deposition range and the plane point cloud picture.
Under the condition of no natural wind and the same temperature and humidity conditions, the flying height of the unmanned aerial vehicle is far from the crop canopy H and the flying speed v 0 Flow rate q of application 0 Under the action of rotor wing wind field obtained by LiDAR real-time scanning, the point cloud number of the space fog drop point cloud in the corresponding XOY area of 5X 5 meters is P 0 The corresponding fogdrop deposition amount of the coated paper is Q 0 . Under different application operation parameters: v 0 、v 1 、…、v n And q 0 、q 1 、…、q n 。
V under different operation parameters n 、q n The number of the corresponding sample fog drop point clouds in the 5X 5m XOY plane square is P n The deposition amount of the corresponding sample fog drops is Q n Sample mist drop point cloud quantity P under each set of operation parameters n With sample droplet deposition quantity Q n There is a linear correlation between:
wherein P is the number of mist drop point clouds; q is the deposition amount of fog drops; k is a correlation coefficient; z is Z 0 Is constant.
Under different flying speeds v and application flow rates Q, the deposition relation between the deposition quantity Q of the fog drops and v and Q can be obtained as follows:
Q＝cq+dv+G (7)
wherein c and d represent coefficients; g represents a constant.
And (3) combining the steps (6) and (7) to obtain the number relation among the flying speed, the application flow and the cloud quantity of the fogdrops:
P＝K(cq+dv+G)-Z 0 (8)
the number P of fog drop points is 5X 5m of fog drop points of the XOY plane square grid, so that the density of fog drops is calculated by taking 5X 5m in the XOY plane, and the density relation among the flying speed, the medicine application flow and the density of the fog drops is as follows:
and (3) taking the formula (9) as a mist deposition prediction model of the unmanned aerial vehicle, and calculating a corresponding predicted mist density by the mist deposition prediction model after inputting the flight speed and the pesticide application flow of the unmanned aerial vehicle. The unmanned aerial vehicle model corresponds to the fog drop deposition prediction model one by one.
According to the unmanned aerial vehicle pesticide application control method provided by the invention, the change rule of the fog drops in the actual deposition process can be reduced to the greatest extent by extracting the relation between the operation parameters and the fog drop density in the unmanned aerial vehicle pesticide application process, the fog drop space distribution and the deposition distribution density under the action of the rotor wing wind fields under different unmanned aerial vehicles are intuitively embodied in a visual mode, a foundation is laid for constructing pesticide application operation parameters, and data support is provided for decision implementation of unmanned aerial vehicle pesticide application flight attitude adjustment and pesticide application flow control.
Further, in step S2, the operation parameters are input to a droplet deposition prediction model of the unmanned aerial vehicle, and a predicted droplet density output by the droplet deposition prediction model is obtained; the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model.
The flight speed of the unmanned aerial vehicle and the pesticide application flow sprayed to the ground are input into a droplet deposition prediction model of the unmanned aerial vehicle, the droplet deposition prediction model carries out mathematical relation calculation on the flight speed and the pesticide application flow to obtain predicted droplet density corresponding to the current flight speed and the pesticide application flow, namely the unmanned aerial vehicle adopts the operation parameter, and the droplet density in the process of spraying to the ground is the predicted droplet density.
Further, in step S3, based on the position information, the predicted droplet density and the drug delivery prescription map of the target land are compared and analyzed to determine a parameter adjustment amount of the unmanned aerial vehicle, where the parameter adjustment amount is used for adjusting the operation parameter.
Comparing and analyzing the predicted fog drop density with the fog drop density to be applied at the current position in the application prescription diagram, if the predicted fog drop density and the fog drop density are inconsistent, adjusting parameters of the unmanned aerial vehicle, calculating the parameter adjustment quantity of the unmanned aerial vehicle, and adjusting operation parameters of the unmanned aerial vehicle according to the parameter adjustment quantity; if the parameter adjustment quantity is 0, the unmanned aerial vehicle keeps the current state.
According to the unmanned aerial vehicle pesticide application control method, the unmanned aerial vehicle operation parameters are utilized to predict the fog drop density in the pesticide application process, and the predicted quantity is compared with the prescription chart, so that the operation parameters are adjusted in real time according to the pesticide application quality in the unmanned aerial vehicle pesticide application process, the utilization rate of the pesticide is improved, the pesticide application quality is guaranteed, and the accurate pesticide application is realized.
Optionally, the administration prescription map is obtained based on the following steps:
acquiring a hyperspectral image of the target land block;
Determining the pest and disease damage range and the pest and disease damage degree of the target land block based on the hyperspectral image;
determining a pesticide application prescription diagram of the target land according to the pest and disease range and the pest and disease extent; the prescription map includes a distribution of drug delivery requirements within the target plot.
Before the target land parcels are applied, hyperspectral images of the target land parcels are acquired, then the crop in the target land parcels are subjected to disease and pest diagnosis according to the hyperspectral images, the occurrence range of the disease and pest and the severity of the disease and pest in the occurrence range are determined, the application rate of the position is determined according to the severity, and further the deviation error of the fogdrops in the deposition process is caused according to the action of a rotor wing wind field under the unmanned aerial vehicle, so that a medicine application prescription diagram of the whole target land parcels, which is expected in consideration of deviation, is obtained, the deviation target of the fogdrops is reduced, the utilization rate of medicines is improved, and the accurate regulation and control of the application of the unmanned aerial vehicle to the target area are realized.
Fig. 6 is a schematic diagram of a prescription provided by the invention, and as shown in fig. 6, the dot density is linearly related to the application rate, the region with larger dot density is the region with serious disease and pest damage, and the corresponding application rate is larger.
According to the invention, multiple experiments are carried out on the unmanned aerial vehicle at different flight speeds, so that a mist deposition prediction model is constructed, and in the actual operation of the unmanned aerial vehicle, the pesticide application strategy can be adjusted in real time according to the mist deposition prediction model, so that the deviation target of the pesticide application atomized mist is reduced to the maximum extent, and the pesticide utilization rate is improved to the maximum extent.
Optionally, the comparing and analyzing the predicted fog drop density with the drug delivery prescription map of the target land based on the position information to determine the parameter adjustment amount of the unmanned aerial vehicle includes:
determining a drug application amount requirement corresponding to the position information in the drug application prescription chart;
comparing and analyzing the predicted fog drop density with the drug application amount requirement to generate a drug amount error;
and determining the parameter adjustment quantity according to the drug quantity error.
The drug application requirement and the specific drug application coordinate are known from the drug application prescription diagram, a drug application decision database of drug application error-parameter adjustment quantity is built by combining the drug application density output by the drug application prescription diagram and the specific drug application coordinate, in actual operation, the drug application is accurately regulated and controlled by directly matching operation parameters through the drug application decision database if drug application deviation exists due to real-time matching comparison between the drug application density output by the drug application prescription diagram and the drug application prescription diagram.
Specifically, determining the drug application requirement of the current position of the unmanned aerial vehicle in a drug application prescription chart, wherein the drug application can be in the form of fog drop density;
the fogdrop density output by the fogdrop deposition prediction model is poor with the drug application amount requirement to obtain a drug application amount error, if the drug application amount error is positive, the drug application amount exceeds the expected value, and the parameter adjustment amount can be the increment of the flight speed of the unmanned aerial vehicle or the reduction of the drug application flow; if the dosage error is negative, the dosage is lower than expected, and the parameter adjustment amount may be a reduced amount of the flight speed of the unmanned aerial vehicle or an increased amount of the dosage flow.
In addition, the drug delivery quality of the unmanned aerial vehicle can be evaluated, the fog drop density output by the fog drop deposition prediction model in the whole drug delivery process of the unmanned aerial vehicle is analyzed, an actual fog drop deposition diagram is obtained, the actual fog drop deposition diagram and the drug delivery prescription diagram are compared and analyzed, the overall drug dosage error of the two is obtained, and a final drug delivery quality evaluation result is obtained according to the drug dosage error.
According to the unmanned aerial vehicle pesticide application control method provided by the invention, pesticide application deviation is reduced by jointly adjusting the flight speed and pesticide application flow of the unmanned aerial vehicle, a pesticide application decision implementation database of the unmanned aerial vehicle is constructed, and accurate pesticide application operation of the unmanned aerial vehicle is completed for different crop objects.
Fig. 7 is a second flow chart of the unmanned aerial vehicle drug delivery control method provided by the invention, as shown in fig. 7, including:
in the construction stage of the mist deposition prediction model, a plurality of groups of operation parameters of the unmanned aerial vehicle are subjected to experiments, including the flying speed v and the pesticide application flow q of the unmanned aerial vehicle, and LiDAR point cloud and deposition amount (P) are obtained n And Q is equal to n Linear correlation); collecting the deposition quantity and the deposition density of the plane small square, and further obtaining the quantity relation among the flying speed, the application flow and the cloud quantity of the fogdrops: p=k (cq+dv+g) -Z 0 To build a visual deposition range model, i.eThe method comprises the steps of obtaining a medicine application prescription diagram of a target land block, wherein the medicine application prescription diagram comprises a medicine application requirement and a medicine application coordinate area;
in the unmanned aerial vehicle pesticide application stage, acquiring operation parameters of the unmanned aerial vehicle, and inputting the operation parameters into a droplet deposition prediction model to obtain predicted droplet density;
and comparing the predicted fog drop density with the drug application amount requirement of the current position of the unmanned aerial vehicle, further obtaining a drug amount error, and matching corresponding parameter adjustment amounts in a drug application decision database according to the drug amount error.
In addition, the actual real-time operation LiDAR scanning obtains deposition parameters, and the deposition parameters are compared with the drug administration decision database to determine whether the deposition parameters are consistent with the expected deposition parameters or not, so that real-time regulation and control are realized.
According to the unmanned aerial vehicle pesticide application control method provided by the invention, the whole process visualization of pesticide application fog drop sedimentation is realized under a single route of the unmanned aerial vehicle, a data support model is also provided for unmanned aerial vehicle pesticide application, and the pesticide application fog drops are obtained in space distribution, sedimentation distribution range and sedimentation density through LiDAR real-time tracking. The drug delivery deposition prediction model can provide data support for accurate drug delivery, and LiDAR real-time detection tracking is matched with the prescription map and the deposition amount model for inspection. The unmanned aerial vehicle pesticide application real-time monitoring and regulation function can be realized.
The unmanned aerial vehicle drug delivery control system provided by the invention is described below, and the unmanned aerial vehicle drug delivery control system described below and the unmanned aerial vehicle drug delivery control method described above can be correspondingly referred to each other.
The invention also provides an unmanned aerial vehicle pesticide application control system, which comprises an unmanned aerial vehicle, an unmanned aerial vehicle real-time tracking platform, a digital radio station and an industrial personal computer;
the unmanned aerial vehicle is used for spraying operation;
the unmanned aerial vehicle real-time tracking platform is provided with a plurality of laser radars;
the laser radar is used for collecting fog drop point cloud data in the unmanned aerial vehicle spraying operation process, and the fog drop point cloud data are used for constructing a fog drop deposition prediction model;
the unmanned aerial vehicle is provided with a GPS antenna, and the GPS antenna is used for collecting the position information of the unmanned aerial vehicle and sending the position information to the industrial personal computer through the digital radio station;
the plurality of laser radars send the fog drop point cloud data to the industrial personal computer through the digital radio station;
the industrial personal computer is internally provided with a processor; the unmanned aerial vehicle drug delivery control system further comprises a memory and a program or instructions stored on the memory and capable of running on the processor, wherein the program or instructions are executed by the processor to perform the unmanned aerial vehicle drug delivery control method according to any of the embodiments.
The laser radar (LiDAR) is responsible for monitoring the actual deposition area of the applied mist drops in real time and the deposition process change rule.
According to the unmanned aerial vehicle pesticide application control system provided by the invention, the unmanned aerial vehicle operation parameters are utilized to predict the fog drop density in the pesticide application process, and the predicted quantity is compared with the prescription chart, so that the operation parameters are adjusted in real time according to the pesticide application quality in the unmanned aerial vehicle pesticide application process, the utilization rate of the pesticide is improved, the pesticide application quality is ensured, and the accurate pesticide application is realized. The industrial personal computer provided by the invention is described below, and the industrial personal computer described below and the unmanned aerial vehicle pesticide application control method described above can be correspondingly referred to each other.
Fig. 8 is a schematic structural diagram of an industrial personal computer according to the present invention, as shown in fig. 8, including:
the acquiring module 801 is configured to acquire location information of an unmanned aerial vehicle, and an operation parameter of a spraying operation of the unmanned aerial vehicle on a target land parcel, where the operation parameter includes: flight speed and application flow rate;
the input module 802 is configured to input the operation parameter to a droplet deposition prediction model of the unmanned aerial vehicle, and obtain a predicted droplet density output by the droplet deposition prediction model; the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model;
And the analysis module 803 is configured to compare and analyze the predicted fog drop density with the drug delivery prescription map of the target land parcel based on the position information, so as to determine a parameter adjustment amount of the unmanned aerial vehicle, where the parameter adjustment amount is used for adjusting the operation parameter.
In the process of operating the industrial personal computer, the acquiring module 801 acquires position information of the unmanned aerial vehicle and operation parameters of the unmanned aerial vehicle spraying operation on the target land, wherein the operation parameters comprise: flight speed and application flow rate; the input module 802 inputs the operation parameters to a droplet deposition prediction model of the unmanned aerial vehicle, and obtains a predicted droplet density output by the droplet deposition prediction model; the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model; the analysis module 803 compares and analyzes the predicted fog drop density with the drug delivery prescription map of the target land parcel based on the position information to determine a parameter adjustment amount of the unmanned aerial vehicle, wherein the parameter adjustment amount is used for adjusting the operation parameter.
According to the industrial personal computer provided by the invention, the unmanned aerial vehicle operation parameters are utilized to predict the fog drop density in the pesticide application process, and the predicted quantity is compared with the prescription chart, so that the operation parameters are adjusted in real time according to the pesticide application quality in the pesticide application process of the unmanned aerial vehicle, the utilization rate of the pesticide is improved, the pesticide application quality is ensured, and the accurate pesticide application is realized.
Fig. 9 is a schematic structural diagram of an electronic device provided by the present invention, and as shown in fig. 9, the electronic device may include: processor 910, communication interface (Communications Interface), memory 930, and communication bus 940, wherein processor 910, communication interface 920, and memory 930 communicate with each other via communication bus 940. The processor 910 may invoke logic instructions in the memory 930 to execute a method of controlling the dispensing of a drone, the method comprising: acquiring position information of an unmanned aerial vehicle and operation parameters of spraying operation of the unmanned aerial vehicle on a target land block, wherein the operation parameters comprise: flight speed and application flow rate; inputting the operation parameters into a mist deposition prediction model of the unmanned aerial vehicle, and obtaining predicted mist density output by the mist deposition prediction model; the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model; and comparing and analyzing the predicted fog drop density with the pesticide application prescription map of the target land block based on the position information to determine the parameter adjustment quantity of the unmanned aerial vehicle, wherein the parameter adjustment quantity is used for adjusting the operation parameters.
Further, the logic instructions in the memory 930 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of executing the method of controlling the dispensing of a unmanned aerial vehicle provided by the above methods, the method comprising: acquiring position information of an unmanned aerial vehicle and operation parameters of spraying operation of the unmanned aerial vehicle on a target land block, wherein the operation parameters comprise: flight speed and application flow rate; inputting the operation parameters into a mist deposition prediction model of the unmanned aerial vehicle, and obtaining predicted mist density output by the mist deposition prediction model; the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model; and comparing and analyzing the predicted fog drop density with the pesticide application prescription map of the target land block based on the position information to determine the parameter adjustment quantity of the unmanned aerial vehicle, wherein the parameter adjustment quantity is used for adjusting the operation parameters.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method of controlling dispensing of a drone provided by the above methods, the method comprising: acquiring position information of an unmanned aerial vehicle and operation parameters of spraying operation of the unmanned aerial vehicle on a target land block, wherein the operation parameters comprise: flight speed and application flow rate; inputting the operation parameters into a mist deposition prediction model of the unmanned aerial vehicle, and obtaining predicted mist density output by the mist deposition prediction model; the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model; and comparing and analyzing the predicted fog drop density with the pesticide application prescription map of the target land block based on the position information to determine the parameter adjustment quantity of the unmanned aerial vehicle, wherein the parameter adjustment quantity is used for adjusting the operation parameters.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An unmanned aerial vehicle administration control method, characterized by comprising:
acquiring position information of an unmanned aerial vehicle and operation parameters of spraying operation of the unmanned aerial vehicle on a target land block, wherein the operation parameters comprise: flight speed and application flow rate;
inputting the operation parameters into a mist deposition prediction model of the unmanned aerial vehicle, and obtaining predicted mist density output by the mist deposition prediction model; the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model;
and comparing and analyzing the predicted fog drop density with the pesticide application prescription map of the target land block based on the position information to determine the parameter adjustment quantity of the unmanned aerial vehicle, wherein the parameter adjustment quantity is used for adjusting the operation parameters.
2. The unmanned aerial vehicle dosing control method of claim 1, wherein before inputting the operating parameters to the unmanned aerial vehicle's droplet deposition prediction model, obtaining the predicted droplet density output by the droplet deposition prediction model, further comprises:
acquiring the sample fog drop point cloud quantity and the sample fog drop deposition quantity of the unmanned aerial vehicle in a plurality of sample areas under different sample operation parameters; the sample job parameters include: sample flight speed and sample application flow;
And constructing the mist drop deposition prediction model based on the sample mist drop point cloud quantity and the sample mist drop deposition quantity of each sample area of each sample operation parameter.
3. The unmanned aerial vehicle dosing control method of claim 2, wherein the constructing the droplet deposition prediction model based on the sample droplet point cloud quantity and the sample droplet deposition quantity for each sample region for each sample operating parameter comprises:
based on the number of sample fog point clouds and sample fog drop deposition amount of the unmanned aerial vehicle in each sample area under a plurality of sample operation parameters, determining a linear relation between the number of the fog point clouds and the fog drop deposition amount of the unmanned aerial vehicle operation and a deposition relation between the fog drop deposition amount, the flying speed and the application flow;
determining a quantitative relationship between the flight speed, the application flow rate and the number of droplet point clouds based on the linear relationship and the deposition relationship;
determining a density relationship between the flight speed, the application flow rate and the fog drop density based on the number relationship and the area of the sample area;
and constructing the mist deposition prediction model based on the density relation.
4. The unmanned aerial vehicle dispensing control method of claim 1, wherein the dispensing prescription map is obtained based on the steps of:
Acquiring a hyperspectral image of the target land block;
determining the pest and disease damage range and the pest and disease damage degree of the target land block based on the hyperspectral image;
determining a pesticide application prescription diagram of the target land according to the pest and disease range and the pest and disease extent; the prescription map includes a distribution of drug delivery requirements within the target plot.
5. The unmanned aerial vehicle dosing control method of claim 4, wherein comparing the predicted fog drop density with the dosing prescription map of the target plot based on the location information to determine the parameter adjustment amount of the unmanned aerial vehicle comprises:
determining a drug application amount requirement corresponding to the position information in the drug application prescription chart;
comparing and analyzing the predicted fog drop density with the drug application amount requirement to generate a drug amount error;
and determining the parameter adjustment quantity according to the drug quantity error.
6. The unmanned aerial vehicle pesticide application control system is characterized by comprising an unmanned aerial vehicle, an unmanned aerial vehicle real-time tracking platform, a digital radio station and an industrial personal computer;
the unmanned aerial vehicle is used for spraying operation;
the unmanned aerial vehicle real-time tracking platform is provided with a plurality of laser radars;
The laser radar is used for collecting fog drop point cloud data in the unmanned aerial vehicle spraying operation process, and the fog drop point cloud data are used for constructing a fog drop deposition prediction model;
the unmanned aerial vehicle is provided with a GPS antenna, and the GPS antenna is used for collecting the position information of the unmanned aerial vehicle and sending the position information to the industrial personal computer through the digital radio station;
the plurality of laser radars send the fog drop point cloud data to the industrial personal computer through the digital radio station;
the industrial personal computer is internally provided with a processor; further comprising a memory and a program or instructions stored on the memory and executable on the processor, which program or instructions when executed by the processor perform the unmanned aerial vehicle administration control method of any one of claims 1-5.
7. An industrial personal computer, characterized by comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring position information of an unmanned aerial vehicle and operation parameters of spraying operation of the unmanned aerial vehicle on a target land block, and the operation parameters comprise: flight speed and application flow rate;
the input module is used for inputting the operation parameters into a mist deposition prediction model of the unmanned aerial vehicle and obtaining predicted mist density output by the mist deposition prediction model; the predicted fog drop density is obtained by calculating the fog drop density application amount of the operation parameters by the fog drop deposition prediction model;
And the analysis module is used for comparing and analyzing the predicted fog drop density with the pesticide application prescription map of the target land block based on the position information so as to determine the parameter adjustment quantity of the unmanned aerial vehicle, wherein the parameter adjustment quantity is used for adjusting the operation parameters.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the unmanned aerial vehicle administration control method of any one of claims 1-5 when the program is executed.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the unmanned aerial vehicle administration control method of any one of claims 1-5.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the unmanned aerial vehicle administration control method according to any one of claims 1 to 5.
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