CN113887085A - Plant protection unmanned aerial vehicle spraying quality evaluation method based on fogdrop deposition model - Google Patents
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Abstract
The invention provides a method for evaluating the spraying quality of a plant protection unmanned aerial vehicle based on a droplet deposition model, and relates to the field of spraying operation of plant protection unmanned aerial vehicles. The method comprises the following steps: acquiring data of the environmental information by using an environmental detection system; acquiring unmanned aerial vehicle operation parameter information in real time by using an unmanned aerial vehicle terminal; the cloud server generates a droplet deposition distribution result in the whole area by using the established droplet deposition distribution model; and the cloud server transmits the evaluation result to the user terminal. Collect plant protection unmanned aerial vehicle operation information through the unmanned aerial vehicle terminal, collect real-time environment data information through environment detecting system, through droplet deposition model, calculate the droplet deposition distribution condition in the operation region behind the operation of plant protection unmanned aerial vehicle for the quality is sprayed in the evaluation, and degree of automation is high, saves a large amount of manpower and materials, and the quantization result is comprehensive reliable, and the operation process can be traceed back, can optimize flight control with high-quality operation as the target.
Description
Technical Field
The invention relates to the field of spraying operation of a plant protection unmanned aerial vehicle, in particular to a method for evaluating the spraying quality of the plant protection unmanned aerial vehicle based on a droplet deposition model.
Background
In recent years, people pay attention to rapid development and wide application of a plant protection unmanned aerial vehicle which is one of important components of agricultural plant protection industry in China. Compare with traditional manual work and spray and ground plant protection machinery, plant protection unmanned aerial vehicle has that the flexibility is strong, the operating efficiency is high, the security is high, strong adaptability, with low costs, do not destroy advantages such as arable land. Compared with piloted fixed wing airplanes and helicopters, the plant protection unmanned aerial vehicle has the advantages of flexibility and no need of a special take-off and landing airport, and is particularly suitable for small and scattered fields in China and dense agricultural areas of residences. Compare with large-scale fixed wing aircraft, plant protection unmanned aerial vehicle adopts the low latitude operation usually, and the lower wind field that washes that the rotor produced has increased the penetrability of liquid drop to the crop, has improved the prevention and cure effect. The existence of the lower wind washing field enhances the penetrability of the fog drops, but also makes the motion process of the fog drops in the air more complex, the deposition rule of the fog drops is more difficult to study, and the operation quality is high and low and is difficult to evaluate. The basis for evaluating the operation quality of the plant protection unmanned aerial vehicle is mainly whether the sprayed droplets are uniformly distributed on target crops, whether a re-spraying and missing-spraying phenomenon exists or not, and whether a large amount of drift is generated or not, and the deposition amount of the fog drops is an important index and represents the volume of the deposited fog drops on a unit area. The distribution of the deposition amount of fog drops in the operation area is a quantitative index for measuring the operation quality of the unmanned aerial vehicle. The accurate distribution condition of the deposition amount of the fogdrops is obtained, so that reasonable spraying decision is facilitated to be made, the spraying quality is improved, and the pesticide consumption is reduced.
The most common method for detecting the droplet deposition in academic research is water-sensitive paper detection, and after the water-sensitive paper collects droplets, the droplet deposition information is analyzed by combining an image processing method. The method can directly observe the deposition effect. However, the accuracy of droplet deposition detection can be limited by the overlap of droplets on water-sensitive paper and the resolution of the scanner. In addition, the estimation of the droplet deposition distribution of the whole working area by using the droplet deposition rate at a limited number of sampling points is obviously inaccurate. Also similar to water sensitive paper is the indirect detection of droplet deposition by calculation and analysis of tracers added to the pesticide. The tracer method can sensitively detect the deposition of the fog drops, and the cost of raw materials is low. However, in order to obtain the droplet deposition data, subsequent data processing and complicated analysis are required. There are also commercial products currently used to detect the quality of droplet deposits, such as laser particle size analyzers and blade moisture sensors. Furthermore, analytical balances have been used to directly measure the mass of droplet deposition.
In addition to the methods, a portable aerial pesticide application fog drop deposition amount detection device is also provided, when the device is used for detecting dense crops, the crops can be damaged, and in addition, after pesticide application, the device enters a field to collect the fog drop deposition amount, so that harm can be caused to human health; the output voltage of a used capacitive sensor is related to the conductivity of liquid, so that the accuracy of the system for detecting the deposition amount of the fog drops can be influenced when the conductivity of the liquid changes, meanwhile, the particle size of the fog drops in aerial pesticide application operation is small, the spraying amount is small, and the collection of trace deposition amount of the fog drops is difficult to support due to the fact that the capacitive sensor is adopted due to the fact that the sensitivity is insufficient.
In order to solve the problems, a droplet deposition distribution real-time monitoring method which is high in accuracy and convenient to operate is urgently needed to improve the operation quality of the plant protection unmanned aerial vehicle.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a spraying quality evaluation method of a plant protection unmanned aerial vehicle based on a droplet deposition model, and solves the problems in the background technology.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a plant protection unmanned aerial vehicle spraying quality evaluation method based on a fogdrop deposition model comprises the following steps:
step 1, an environment detection system is used for carrying out data acquisition on environment information, real-time environment data information of an operation area is obtained and transmitted to a cloud server;
step 3, the cloud server uses the established fogdrop deposition distribution model, uses the environment data information and the operation parameter information collected in real time as input, uses the fogdrop deposition distribution in the operation area as output, and generates a fogdrop deposition distribution result in the whole area;
and 4, evaluating the operation by the cloud server according to the droplet deposition distribution result, and transmitting the evaluation result to the user terminal.
Preferably, the method for constructing the droplet deposition distribution model comprises the following steps: on the basis of selecting different operation parameter information and different target environment data information of the sample unmanned aerial vehicle, fog drop deposition distribution conditions under different parameter combinations are obtained through a computational fluid dynamics numerical simulation mode, then fog drop deposition distribution conditions under any operation combination are obtained through an inverse distance weighted average mode, data obtained in actual operation are compared with model output, model parameters are further corrected, and finally a fog drop deposition model is obtained.
Preferably, the droplet deposition distribution model is obtained by training through the following steps: carrying out physical modeling on a sample unmanned aerial vehicle, carrying out physical modeling on an operation area, carrying out gridding treatment, introducing the established physical models of the unmanned aerial vehicle and the operation area into computational fluid dynamics software, and simulating different operation parameters and environment parameters by setting boundary conditions;
the method comprises the steps of injecting fog drops into a flow field by setting the type of a spray head and the spraying flow conditions, obtaining the distribution condition of the fog drops in an operation area through calculation, obtaining the distribution condition of the fog drops under any parameter combination through an inverse distance weighted average mode on the basis of the distribution condition of the fog drops under different operation combinations, obtaining a theoretical model of the fog drop deposition distribution, and further correcting model parameters through actual operation detection to obtain a more accurate fog drop deposition distribution model.
Preferably, the droplet deposition distribution model obtains data through CFD numerical simulation, various parameters are easy to control, and interference of random factors is avoided.
Preferably, the step 1 specifically includes: environmental information is collected through a meteorological sensing module to obtain environmental data information in an operation area, and the meteorological sensing module comprises a temperature and humidity sensor, a wind speed sensor and a GPS sensor.
Preferably, the step 3 further comprises the following steps: and preprocessing the operation parameter information and the environment data information of the unmanned aerial vehicle to obtain scaled data, wherein the preprocessing comprises normal distribution standardization and statistical distribution standardization.
Plant protection unmanned aerial vehicle sprays quality evaluation system based on droplet deposition model, including unmanned aerial vehicle terminal, environment detecting system, cloud server and user system, environment detecting system can acquire the real-time environmental data information of operation region and transmit for the cloud server, the unmanned aerial vehicle terminal can gather unmanned aerial vehicle operation parameter information in real time and transmit for the cloud server, the cloud server is including the storage module that has droplet deposition distribution prediction model, the cloud server can be according to droplet deposition distribution model, with the environmental data information and the operation parameter information of collecting in real time as the input, with droplet deposition distribution as the output in the operation region, generate the droplet deposition distribution result in the whole area.
Preferably, the unmanned aerial vehicle terminal includes orientation module, shower nozzle module, flies control module and wireless communication module, orientation module and shower nozzle module all link to each other with flying control module, fly control module and pass through wireless communication module and connect the cloud ware, environment detecting system includes meteorological sensing module, treater and wireless communication module, meteorological sensing module connects the treater, the treater passes through wireless communication module and connects the cloud ware.
Preferably, the meteorological sensing module comprises a temperature and humidity sensor and a wind speed sensor, and the processor is respectively connected with the temperature and humidity sensor and the wind speed sensor.
(III) advantageous effects
The invention provides a spraying quality evaluation method of a plant protection unmanned aerial vehicle based on a droplet deposition model. The method has the following beneficial effects:
according to the invention, the flight height, flight speed and other information in the operation process of the plant protection unmanned aerial vehicle are collected through the unmanned aerial vehicle terminal, the real-time environment data information of the operation area of the unmanned aerial vehicle is collected and obtained through the environment detection system, the information is input into the cloud server, and the droplet deposition distribution condition in the operation area after the plant protection unmanned aerial vehicle operates is calculated through the droplet deposition model for evaluating the spraying quality.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic diagram of the meshing between the working area and the unmanned aerial vehicle body according to the present invention;
FIG. 3 is a schematic view of a droplet deposition profile according to the present invention;
FIG. 4 is a schematic view of the ground droplet deposition of the present invention;
FIG. 5 is a schematic view of the distribution of droplet deposition in a work area under continuous operation conditions in accordance with the present invention;
FIG. 6 is a block diagram of an environmental detection system of the present invention;
fig. 7 is a block diagram of an unmanned aerial vehicle terminal of the present invention;
fig. 8 is a block diagram of the system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
as shown in fig. 1 to 5, an embodiment of the present invention provides a method for evaluating spraying quality of a plant protection unmanned aerial vehicle based on a droplet deposition model, including the following steps:
step 1, an environment detection system is used for carrying out data acquisition on environment information, real-time environment information data of an operation area are obtained, and the real-time environment information data are transmitted to a fog droplet deposition distribution model of a cloud server;
and 3, the cloud server uses the established fogdrop deposition distribution model, uses the environment information, flight parameters and position information collected in real time as input, uses the fogdrop deposition distribution in the operation area as output, and generates a fogdrop deposition distribution result in the whole area.
And 4, evaluating the operation by the cloud server according to the droplet deposition distribution result, and transmitting the evaluation result to the user terminal.
In the embodiment of the invention, in step 1, the environment detection system is a ground monitoring device and can detect environment information, the environment detection system adopts a wireless sensor to acquire environment temperature information and wind speed information to acquire environment information data, and can acquire wind speed information, wind direction information and environment information, wherein the wind speed information is information of average wind speed and the wind direction information, and the environment information comprises temperature and humidity. The unmanned aerial vehicle operation parameters comprise the type of a spray head, the particle size of spray flow fog drops, the pesticide consumption and the like which are set in advance. Information such as unmanned aerial vehicle flying height, speed owing to receive the environmental impact and have certain volatility, so fly to control the module and the positioning module real-time data information of gathering by unmanned aerial vehicle carries on and passes to cloud ware.
In the step 2, the transmission from the unmanned aerial vehicle terminal to the cloud server is generally carried out by adopting a mobile internet GPRS/4G/5G. The program is mainly realized by using C language on an embedded flight control module of the unmanned aerial vehicle terminal, and comprises communication between flight control and a sensor, communication between the flight control and a cloud server and the like.
In step 3, the cloud server droplet deposition model is built by mainly using a numerical simulation mode of Computational Fluid Dynamics (CFD) to collect data, and the model is built according to the following steps:
(1) establishing a physical model of a sample plant protection unmanned aerial vehicle, and performing meshing of a working area and an unmanned aerial vehicle body, wherein the working area and the unmanned aerial vehicle body are subjected to meshing as shown in fig. 2;
(2) and calculating flow fields under different flying heights, different flying speeds and different environmental wind speeds by setting boundary conditions. The operating parameter combinations are as follows:
| operating parameters | Value taking |
| Flying height/(m) | 2，2.1，2.3，2.5，2.7，3 |
| Flying speed/(m/s) | 0，0.5，1，1.5，2，2.5，3 |
| Side wind speed/(m/s) | 0，0.5，1，1.5，2，2.5，3 |
(3) The flow field calculation adopts an SST k-omega turbulence model, and the mathematical form of the model is as follows:
wherein: mu, v and w are components psi of the velocity vector in the directions of x, y and z, zeta is a generalized diffusion coefficient, and S is a generalized source term.
(4) After the flow field is calculated and converged, a discrete phase model is introduced to set parameters such as the type of a spray head, the spray flow, the droplet size and the like according to a spraying system of a sample model, a proper amount of droplets are injected in consideration of calculation accuracy and calculation amount, and the moving position of the droplets in the flow field is tracked.
(5) The method comprises the steps of performing grid division on the ground of a working area, describing the deposition rate of liquid drops at different positions in the range of the working area by counting the number of the liquid drops falling in each grid on the ground, and thus obtaining the distribution condition of the liquid drops in the whole working area, wherein the distribution condition is the mist deposition distribution condition of the flying height of 3m, the flying speed of 0-3m/s and the crosswind speed of 0m/s as shown in figure 3.
(6) And calculating the fog drop deposition distribution conditions under all the parameter combinations in the table, and forming a matrix X by using the obtained fog drop deposition rates in each grid under all the parameter combinations, wherein the jth column of the X represents the fog drop deposition rate in each grid under the jth parameter combination, and the ith row represents the fog drop deposition rate in different operation parameters in the ith grid.
(7) In actual operation, the preset nozzle type, spray flow and droplet particle size in an experiment are adopted, and the flying height, flying speed and vector for crosswind speed of the unmanned aerial vehicle at any moment are mainly recorded
It is shown that h is the flying height, v is the flying speed, w is the crosswind speed (perpendicular to the flying direction), and if the crosswind direction is not perpendicular to the flying direction in actual flight, the crosswind speed and the flying direction need to be decomposed in two directions. Then using a normalization formula
Normalizing P to obtain normalized vector
(8) And (3) predicting the droplet deposition distribution in real time by adopting an inverse distance weighted average method according to the measured flying height, flying speed and distance between the wind speed and the parameters in the experiment through the calculated deposition rate matrix to obtain a real-time droplet deposition distribution vector Y, wherein the calculation formula is as follows:
wherein
And epsilon is 0.00001 as a default parameter. As shown in FIG. 4, the ground droplet deposition condition is shown, the flight direction is the positive half axis direction of the y axis, the crosswind direction is the negative half axis direction of the x axis, the flight height is 3m, the crosswind speed is-2 m/s, and the flight speed is 2 m/s.
(9) In the operation process of the plant protection unmanned aerial vehicle, according to a certain sampling frequency, the flying heights, the flying speeds and the crosswind speeds of different spraying positions are collected, and the droplet deposition distribution condition in the whole operation area is obtained, as shown in fig. 5, the droplet deposition distribution condition in the operation area under the continuous operation condition is obtained.
In step 4, the intelligent mobile phone end can obtain the quality evaluation report of the plant protection operation by accessing the cloud server, or obtain the quality evaluation report in real time by installing an app.
Example two:
as shown in fig. 6 to 8, an embodiment of the invention provides a plant protection unmanned aerial vehicle spraying quality evaluation system based on a droplet deposition model, which comprises an unmanned aerial vehicle terminal, an environment detection system, a cloud server and a user system, wherein the environment detection system can acquire real-time environment data information of a working area and transmit the real-time environment data information to the cloud server, the unmanned aerial vehicle terminal can acquire the working parameter information of the unmanned aerial vehicle in real time and transmit the working parameter information to the cloud server, the cloud server comprises a storage module storing a droplet deposition distribution prediction model, and the cloud server can take the environment data information and the working parameter information collected in real time as input according to the droplet deposition distribution model, take the droplet deposition distribution in the working area as output, and generate a droplet deposition distribution result in the whole area.
The unmanned aerial vehicle terminal includes orientation module, shower nozzle module, flies control module and wireless communication module, and orientation module and shower nozzle module all link to each other with flying control module, and fly control module and pass through wireless communication module and connect the cloud ware, and environment detecting system includes meteorological sensing module, treater and wireless communication module, and meteorological sensing module connects the treater, and the treater passes through wireless communication module and connects the cloud ware.
The weather sensing module comprises a temperature and humidity sensor and a wind speed sensor, and the processor is connected with the temperature and humidity sensor and the wind speed sensor respectively.
Collect the flight height in the plant protection unmanned aerial vehicle operation process through the unmanned aerial vehicle terminal, flying speed, the environment wind speed, information such as wind direction, collect the real-time environmental data information who acquires the unmanned aerial vehicle operation region through environment detecting system, with above information input cloud ware, through droplet deposition model, calculate the droplet deposition distribution condition in the operation region behind the plant protection unmanned aerial vehicle operation, be used for the evaluation to spray the quality, degree of automation is high, save a large amount of manpower and materials, the quantization result is comprehensive reliable, the complete record of operation process, the operation process can be traceed back, be convenient for manage, can optimize flight control for the target with high-quality operation, lay good basis for really realizing full automatization flight.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. Plant protection unmanned aerial vehicle sprays quality evaluation method based on droplet deposition model, its characterized in that: the method comprises the following steps:
step 1, an environment detection system is used for carrying out data acquisition on environment information, real-time environment data information of an operation area is obtained and transmitted to a cloud server;
step 2, the unmanned aerial vehicle terminal is used for collecting unmanned aerial vehicle operation parameter information in real time and transmitting the information to the cloud server;
step 3, the cloud server uses the established fogdrop deposition distribution model, uses the environment data information and the operation parameter information collected in real time as input, uses the fogdrop deposition distribution in the operation area as output, and generates a fogdrop deposition distribution result in the whole area;
and 4, evaluating the operation by the cloud server according to the droplet deposition distribution result, and transmitting the evaluation result to the user terminal.
2. The plant protection unmanned aerial vehicle spraying quality evaluation method based on the droplet deposition model according to claim 1, characterized in that: the method for constructing the droplet deposition distribution model comprises the following steps: on the basis of selecting different operation parameter information and different target environment data information of the sample unmanned aerial vehicle, fog drop deposition distribution conditions under different parameter combinations are obtained through a computational fluid dynamics numerical simulation mode, then fog drop deposition distribution conditions under any operation combination are obtained through an inverse distance weighted average mode, data obtained in actual operation are compared with model output, model parameters are further corrected, and finally a fog drop deposition model is obtained.
3. The plant protection unmanned aerial vehicle spraying quality evaluation method based on the droplet deposition model according to claim 1, characterized in that: the droplet deposition distribution model is obtained by training the following steps: carrying out physical modeling on a sample unmanned aerial vehicle, carrying out physical modeling on an operation area, carrying out gridding treatment, introducing the established physical models of the unmanned aerial vehicle and the operation area into computational fluid dynamics software, and simulating different operation parameters and environment parameters by setting boundary conditions;
the method comprises the steps of injecting fog drops into a flow field by setting the type of a spray head and the spraying flow conditions, obtaining the distribution condition of the fog drops in an operation area through calculation, obtaining the distribution condition of the fog drops under any parameter combination through an inverse distance weighted average mode on the basis of the distribution condition of the fog drops under different operation combinations, obtaining a theoretical model of the fog drop deposition distribution, and further correcting model parameters through actual operation detection to obtain a more accurate fog drop deposition distribution model.
4. The plant protection unmanned aerial vehicle spraying quality evaluation method based on the droplet deposition model according to claim 1, characterized in that: and the droplet deposition distribution model obtains data through CFD numerical simulation.
5. The plant protection unmanned aerial vehicle spraying quality evaluation method based on the droplet deposition model according to claim 1, characterized in that: the step 1 specifically comprises: environmental information is collected through a meteorological sensing module, and environmental data information in an operation area is obtained, wherein the meteorological sensing module comprises a temperature and humidity sensor and a wind speed sensor.
6. The plant protection unmanned aerial vehicle spraying quality evaluation method based on the droplet deposition model according to claim 1, characterized in that: the step 3 is preceded by the following steps: and preprocessing the operation parameter information and the environment data information of the unmanned aerial vehicle to obtain scaled data, wherein the preprocessing comprises normal distribution standardization and statistical distribution standardization.
7. Plant protection unmanned aerial vehicle sprays quality evaluation system based on droplet deposition model, its characterized in that: including unmanned aerial vehicle terminal, environment detecting system, cloud ware and user system, environment detecting system can acquire the real-time environment data information of operation region and transmit for the cloud ware, the unmanned aerial vehicle terminal can gather unmanned aerial vehicle operation parameter information in real time and transmit for the cloud ware, the cloud ware is including the storage module that has droplet deposit distribution prediction model, the cloud ware can be according to droplet deposit distribution model, with the environmental data information and the operation parameter information of real-time collection as the input, with droplet deposit distribution in the operation region as output, generates the droplet deposit distribution result in the whole area.
8. The plant protection unmanned aerial vehicle spraying quality evaluation system based on droplet deposition model of claim 7, characterized in that: the unmanned aerial vehicle terminal includes orientation module, flies control module and wireless communication module, orientation module links to each other with flying control module, fly control module and pass through wireless communication module and connect the cloud ware, environment detecting system includes meteorological sensing module, treater and wireless communication module, meteorological sensing module connects the treater, the treater passes through wireless communication module and connects the cloud ware.
9. The plant protection unmanned aerial vehicle spraying quality evaluation system based on droplet deposition model of claim 8, characterized in that: the weather sensing module comprises a temperature and humidity sensor and a wind speed sensor, and the processor is connected with the temperature and humidity sensor and the wind speed sensor respectively.
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| CN116087042A (en) * | 2023-02-16 | 2023-05-09 | 中国矿业大学 | Real-time monitoring system and method for dust deposition volume spatial distribution |
| CN116797106A (en) * | 2023-08-29 | 2023-09-22 | 山东孟子居生态农业股份有限公司 | Plant protection unmanned aerial vehicle operation effect evaluation system |
| CN117929215A (en) * | 2024-03-21 | 2024-04-26 | 北京市农林科学院智能装备技术研究中心 | Aviation pesticide application liquid deposition distribution monitoring method and device and electronic equipment |
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| 张瑞瑞;李龙龙;文瑶;陈立平;唐青;伊铜川;宋佳星;: "植保无人机喷施雾滴沉积特性的荧光示踪分析", 农业工程学报, no. 06, 23 March 2020 (2020-03-23) * |
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| CN116087042A (en) * | 2023-02-16 | 2023-05-09 | 中国矿业大学 | Real-time monitoring system and method for dust deposition volume spatial distribution |
| CN116087042B (en) * | 2023-02-16 | 2023-09-08 | 中国矿业大学 | Real-time monitoring system and method for dust deposition volume spatial distribution |
| CN116797106A (en) * | 2023-08-29 | 2023-09-22 | 山东孟子居生态农业股份有限公司 | Plant protection unmanned aerial vehicle operation effect evaluation system |
| CN116797106B (en) * | 2023-08-29 | 2023-11-14 | 山东孟子居生态农业股份有限公司 | Plant protection unmanned aerial vehicle operation effect evaluation system |
| CN117929215A (en) * | 2024-03-21 | 2024-04-26 | 北京市农林科学院智能装备技术研究中心 | Aviation pesticide application liquid deposition distribution monitoring method and device and electronic equipment |
| CN117929215B (en) * | 2024-03-21 | 2024-07-02 | 北京市农林科学院智能装备技术研究中心 | Aviation pesticide application liquid deposition distribution monitoring method and device and electronic equipment |
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