KR102650604B1 - Pesticide spraying management system using multiple drones and operation method therof - Google Patents

Abstract

The present invention relates to a pesticide spraying management system using a plurality of drones and a method of operating the same. More specifically, a plurality of flight contour lines drawn according to the outline of the target area are assigned to a plurality of drones to each autonomous flight path. It relates to a pesticide spraying management system and method of operation using multiple drones that can fly in groups and automate customized pesticide spraying work for the target area. For this purpose, the pesticide spraying management system using a plurality of drones includes a collection unit that collects outline objects detected by photographing the target area using observation drones, and the outline objects from real-time location information transmitted from the observation drones. A selection unit that selects mapping location information corresponding to the mapping location information, a line setting unit that generates a target area map by setting a reference coordinate line for the target area based on the mapping location information, the reference coordinate line, and the target area. Based on the area and the preset contour separation distance, a drawing unit that draws a plurality of flight contour lines that do not overlap each other on the target area map, and each autonomous flight path of each of the plurality of flight contour lines to a plurality of spraying drones. It includes an integrated control unit that individually assigns and controls autonomous swarm flight.

Description

Pesticide spraying management system using multiple drones and its operation method {PESTICIDE SPRAYING MANAGEMENT SYSTEM USING MULTIPLE DRONES AND OPERATION METHOD THEROF}

The present invention relates to a pesticide spraying management system using a plurality of drones and a method of operating the same. More specifically, a plurality of flight contour lines drawn according to the outline of the target area are assigned to a plurality of drones to each autonomous flight path. It relates to a pesticide spraying management system and method of operation using multiple drones that can fly in groups and automate customized pesticide spraying work for the target area.

Generally, the method of spraying chemicals such as pesticides and fertilizers carried out in rural areas is carried out by diluting pesticides in water and putting them in a spray bottle and spraying them directly, or putting them in a long plastic bag and spraying them by holding the plastic bag at both ends. It is becoming.

This conventional spraying method requires a large number of manpower, has the problem that workers can become addicted to pesticides and harm their health, and has the problem of causing damage to crops because they have to step on and move crops in the process of spraying pesticides.

In addition, the method of spraying pesticides using a sprayer and hose requires the worker to enter the rice paddy or field and spray while holding the hose and spray nozzle connected to the sprayer, which causes problems such as a significant decrease in workability and inconvenience. there is.

In order to solve the above problems, a method of spraying chemicals such as pesticides using unmanned aerial vehicles such as unmanned helicopters is emerging. Unmanned aerial vehicles have superior cruising speed and payload compared to airplanes, and are practical because they can be operated at a relatively low price.

However, although unmanned aerial vehicles can stay in the air for a long period of time and spray chemicals over a large area, the chemicals cannot be sprayed evenly due to the operator's operating skill, work area, weather effects, and confusion in the work location, and are not sprayed in the same location multiple times. There is a problem that the drug may be wasted as it is applied.

The present invention is to solve the above problems, and the purpose of the present invention is to assign a plurality of flight contour lines drawn according to the outline of the target area to a plurality of drones to each autonomous flight path to perform autonomous swarm flight while flying in the target area. The purpose is to provide a pesticide spraying management system and operation method using multiple drones that can automate customized pesticide spraying operations.

The above and other objects and advantages of the present invention will become apparent from the following description of preferred embodiments.

To achieve the above object, the pesticide spraying management system using a plurality of drones according to an embodiment of the present invention includes a collection unit that collects outline objects detected by photographing the target area using an observation drone, A selection unit that selects mapping location information corresponding to the outline object from real-time location information transmitted from an observation drone, and creates a target area map by setting a reference coordinate line for the target area based on the mapping location information. A line setting unit that draws a plurality of flight contour lines that do not overlap each other on the target area map based on the reference coordinate line, the area of the target area, and a preset contour separation distance, and the plurality of flight contour lines It includes an integrated control unit that individually assigns each autonomous flight path to a plurality of spraying drones and controls them to fly in an autonomous group.

In an embodiment, the shape of the plurality of flight contour lines corresponds to the shape of the reference coordinate line and includes movement coordinate information for each distance according to autonomous flight.

In an embodiment, the integrated control unit selectively sends a spraying operation signal for controlling the pesticide spraying operation to each spraying drone based on test flight path information for each drone that is fed back according to autonomous swarm flight from the plurality of spraying drones. send to

In an embodiment, the integrated control unit transmits the spraying operation signal when the test flight route information of any one of the spraying drones among the test flight route information for each drone corresponds to the corresponding flight contour, thereby setting a preset spraying operation time. Based on this, a drone return signal is transmitted.

In an embodiment, the integrated control unit determines the target separation between the reference coordinate line and the corresponding flight contour line when the test flight path information of any one of the spraying drones among the test flight path information for each drone does not correspond to the corresponding flight contour line. Based on the distance, a direction correction signal for correcting the flight separation distance between the reference coordinate line and the test flight path information is transmitted to one of the spraying drones.

In an embodiment, the drawing unit includes a counting unit that counts the number of the plurality of flight contour lines, a comparison unit that compares the number difference between the plurality of flight contour lines and the plurality of spraying drones, and based on the number difference, It includes a contour correction unit that overlaps and connects at least two flight contours with a short flight distance among the plurality of flight contour lines to correct them into a single flight contour line.

In an embodiment, based on a preset abnormal vibration signal detected through each vibration detection sensor provided in the observation drone and the plurality of spraying drones, the operation state of each drone is divided into one of a normal state and a failure state. A failure state diagnosis unit that diagnoses, a flight management unit that transmits an emergency return signal to one of the drones when any one of the observation drone and the plurality of spraying drones is diagnosed as being in a failure state, and the test flight It includes a spray cycle control unit that individually adjusts the spray cycle for each spraying module of each spraying drone for a certain period of time based on the flight time information detected from the path information.

In an embodiment, the flight management unit uses an artificial intelligence-based pest occurrence prediction model that is modeled by learning the previously collected flight contour images for each target area and pest occurrence points through machine learning, to a plurality of flight contours. Predicts the pest occurrence area, and the spraying cycle control unit controls to increase the number of spraying for at least one spraying drone that flies close to the pest occurrence area within a certain distance among the plurality of spraying drones.

According to an embodiment of the present invention, a plurality of flight contour lines drawn according to the outline of the target area are assigned to a plurality of drones on each autonomous flight path to fly in a group, while automating the customized pesticide spraying operation for the target area, thereby spraying pesticides. Makes work easier and more efficient.

Figure 1 is a diagram schematically showing a pesticide spraying management system 100 using a plurality of drones according to an embodiment of the present invention.
FIG. 2A is an example diagram showing a target area and an outline object, and FIG. 2B is an example of a target area map on which a plurality of flight contour lines are drawn.
FIG. 3A is a block diagram of the observation drone 10 of FIG. 1, and FIG. 3C is a block diagram of one of the spraying drones 20 of FIG. 1.
FIG. 4 is a block diagram for specifically explaining the drawing unit 140 of FIG. 1 .
FIG. 5 is a block diagram for specifically explaining the integrated control unit 150 of FIG. 1.

Hereinafter, the present invention will be described in detail with reference to embodiments of the present invention and drawings. These examples are merely presented as examples to explain the present invention in more detail, and it will be apparent to those skilled in the art that the scope of the present invention is not limited by these examples. .

Additionally, unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by a person skilled in the art to which the present invention pertains, and in case of conflict, this specification including definitions The description will take precedence.

In order to clearly explain the proposed invention in the drawings, parts unrelated to the description have been omitted, and similar reference numerals have been assigned to similar parts throughout the specification. And, when it is said that a part "includes" a certain component, this means that it does not exclude other components, but may further include other components, unless specifically stated to the contrary. Additionally, “unit” as used in the specification refers to a unit or block that performs a specific function.

Identification codes (first, second, etc.) for each step are used for convenience of explanation. The identification codes do not describe the order of each step, and each step does not clearly state a specific order in context. It may be carried out differently from the order specified above. That is, each step may be performed in the same order as specified, may be performed substantially simultaneously, or may be performed in the opposite order.

Figure 1 is a diagram schematically showing a pesticide spraying management system 100 using a plurality of drones according to an embodiment of the present invention, Figure 2a is an example diagram showing a target area and outline object, and Figure 2b is a plurality of flights. This is an embodiment of a target area map on which contour lines are drawn, FIG. 3A is a block diagram of the observation drone 10 of FIG. 1, and FIG. 3C is a block diagram of one of the spraying drones 20 of FIG. 1. am.

1 to 3C, the pesticide spraying management system 100 using a plurality of drones is connected to the observation drone 10 and a plurality of spraying drones 20_1 to 20_N through a network. It can be implemented in various types of devices, such as smartphones, TVs, PDAs, tablet PCs, PCs, laptop PCs, and other user terminal devices.

Specifically, the pesticide spraying management system 100 using a plurality of drones may include a collection unit 110, a selection unit 120, a line setting unit 130, a drawing unit 140, and an integrated control unit 150. You can.

First, the collection unit 110 may collect the outline object (OL) detected by photographing the target area (R) using the observation drone 10.

Here, the outline object (OL) refers to an object located at the border of the target area (R), as shown in FIG. 2A, and that is easy to identify based on color or shape differences from the target area (R). For example, For example, if the target area (R) is a rice field, the outline object (OL) may be a passage or structure located at the boundary of the rice field.

At this time, the observation drone 10 may be equipped with a camera module 11, a GPS module 12, and a communication module 13, as shown in FIG. 3A.

Specifically, the observation drone 10 can photograph a target area through the camera module 110 and detect an outline object (OL) from the captured image through a preset OpenCV graphics program. Additionally, the observation drone 10 can collect real-time location information through the GPS module 12. In addition, the observation drone 10 is connected to the pesticide spraying management system 100 through the communication module 13, and can transmit the outline object (OL) and real-time location information to the collection unit 110 through the network. .

In other words, the observation drone 10 is flown manually according to a remote manual operation signal transmitted through the communication module 13 from the pesticide spraying management system 100, or is unmanned through a preset autonomous flight program according to an autonomous flight control signal. You can also fly.

Each of these observation drones (10) and a plurality of spraying drones (20_1 to 20_N) carries out unmanned flight and devices including propellers (not shown) and motors (not shown) for flying or changing the direction of the drone body. Additional control devices may be included, but are not limited thereto.

Next, the selection unit 120 may select mapping location information corresponding to the outline object OL from the real-time location information transmitted from the observation drone 10.

Specifically, the selection unit 120 may map the collected location information to the outline object OL each time an outline object OL is detected from the observation drone 10 to select the mapping position information.

Next, the line setting unit 130 may generate a target area map (M) by setting a reference coordinate line (L1) corresponding to the target area (R) based on the mapping position information.

Here, the reference coordinate line L1 may correspond to the outline of the target area R, and the target area map M may correspond to the area of the target area R, as shown in FIG. 2B.

At this time, the reference coordinate line L1 may include location information.

Next, the drawing unit 140 creates a plurality of flight contour lines (L2 to LN) that do not overlap each other, based on the reference coordinate line (L1), the area of the target area (R), and the preset contour separation distance (R). You can create and draw it on the target area map (M).

Here, the shape of the plurality of flight contour lines (L2 to LN) corresponds to the shape of the reference coordinate line (L1) and may include movement coordinate information for each distance according to autonomous flight.

At this time, the number of the plurality of flight contour lines (L2 to LN) may be inversely proportional to the preset contour separation distance (R).

Specifically, the drawing unit 140 uses an OpenCV contour generation algorithm that receives the reference coordinate line (L1), the area of the target area (R), and the preset contour separation distance and outputs a plurality of flight contour lines (L2 to LN). Available.

Next, the integrated control unit 150 can individually assign each of the plurality of flight contours (L2 to LN) to each autonomous flight path to the plurality of spraying drones (20_1 to 20_N) and control them to fly autonomously. there is.

For example, autonomous swarm flight may mean that a plurality of spraying drones (20_1 to 20_N) autonomously fly in a swarm along each flight contour line (L2 to LN).

Here, each of the plurality of spraying drones 20_1 to 20_N may include a GPS module 21, a communication module 22, and a spraying module 23.

Specifically, each of the plurality of spraying drones (20_1 to 20_N) collects real-time location information through the GPS module 21, is connected to the pesticide spraying management system 100 through the communication module 22, and sprays It can be placed at the bottom of the drone body to spray pesticides through the module 23.

The integrated control unit 150 according to the embodiment sends a spraying operation signal allowing pesticide spraying operation based on test flight path information for each drone that is fed back according to autonomous swarm flight from a plurality of spraying drones (20_1 to 20_N). Can be selectively transmitted to spraying drones (102_1~102_N).

Here, the test flight route information may include flight time information, flight speed information, and location information by time.

Specifically, as the integrated control unit 150 allocates each autonomous flight path, it can receive feedback on test flight path information for each drone from the plurality of spraying drones 20_1 to 20_N.

At this time, the integrated control unit 150 selectively sends a spraying operation signal based on the correspondence between the test flight path information of one spraying drone (e.g., 20_1) and the corresponding flight contour (e.g., L2). It can be transmitted to a drone (e.g., 20_1).

According to one embodiment, when the test flight path information of any one spraying drone (e.g., 20_1) among the test flight path information for each drone and the corresponding flight contour line correspond, the integrated control unit 150 transmits a spraying operation signal. Accordingly, a drone return signal can be automatically transmitted based on the preset spraying operation time.

According to another embodiment, when the difference between the test flight path information of one spraying drone (e.g., 20_1) and the corresponding flight contour line (e.g., L2) is more than a certain level, the integrated control unit 150 controls the reference coordinate line (L1) Based on the target separation distance between and the corresponding flight contour line (e.g., L2), a direction correction signal for correcting the flight separation distance between the reference coordinate line (L1) and the test flight path information is sent to any spraying drone (e.g. , 20_1).

According to another embodiment, the integrated control unit 150 sends a registration request signal corresponding to the takeoff and landing point to the plurality of spraying drones (20_1 to 20_N) before allocating the plurality of flight contours (L2 to LN). Based on the registration confirmation signal received in response to transmission, a drone corresponding to a communication failure (eg, 20_1) can be selected and excluded from the plurality of spraying drones 20_1 to 20_N.

FIG. 4 is a block diagram for specifically explaining the drawing unit 140 of FIG. 1 .

1 to 4 , the drawing unit 140 may include a counting unit 141, a comparison unit 142, and a contour correction unit 143.

First, the counting unit 141 can count the number of flight contour lines (L2 to LN).

Next, the comparison unit 142 may compare the number difference between the plurality of flight contours (L2 to LN) and the plurality of spraying drones (20_1 to 20_N).

Next, based on the number difference, the contour correction unit 143 may overlap and connect at least two flight contours with short flight distances among the plurality of flight contour lines (L2 to LN) and modify them into one flight contour line.

FIG. 5 is a block diagram for specifically explaining the integrated control unit 150 of FIG. 1.

When described with reference to FIGS. 1 to 5 , the integrated control unit 150 may include a drone status diagnosis unit 151, an operation status diagnosis unit 152, and a flight management unit 153.

First, the failure state diagnosis unit 151 is based on a preset abnormal vibration signal detected through each vibration detection sensor (not shown) provided in the observation drone 10 and the plurality of spraying drones 20_1 to 20_N. Thus, the operation state of each drone can be diagnosed as either a normal state or a failure state.

Next, the flight management unit 152 sends an emergency return signal when any one drone (e.g., 20_1) among the observation drone 10 and the plurality of spraying drones 20_1 to 20_N is diagnosed as being in a disabled state. It can be transmitted to a drone (eg, 20_1).

Next, the injection cycle control unit 153 can adjust the injection cycle for each spraying module 23 of each spraying drone (220_1 to 220_N) for a certain time based on the flight time information detected from the test flight path information. .

According to one embodiment, the flight management unit 152 controls the observation drone 10 to fly fixedly at the corresponding location based on the event signal transmitted from the observation drone 10 and simultaneously performs manual manipulation through the output screen. You can request a mode.

Here, the event signal may be a signal transmitted as the observation drone 10 detects an obstacle such as a bird or telephone pole.

According to another embodiment, the flight management unit 152 configures a plurality of spraying drones to fly at the lowest altitude within the altitude restriction section prescribed by law, based on wind speed information confirmed from weather information transmitted from a weather control center (not shown). By controlling the fields (20_1 to 20_N), it is possible to limit the spread of pesticides to places other than the target area.

According to another embodiment, the flight management unit 152 uses an artificial intelligence-based pest occurrence prediction model that is modeled by learning the collected flight contour images for each spray area and pest occurrence points for each spray area through machine learning. , the pest occurrence area for the target area map (M) can be predicted.

Here, the artificial intelligence-based pest occurrence prediction model is an artificial neural network algorithm that receives the target area map (M) on which multiple flight contour lines (L2 ~ LN) are drawn and predicts the pest occurrence area for the target area map (M). It can be.

This artificial intelligence-based pest outbreak prediction model may be one of the following algorithms: Artificial Neural Network, SVM (Support Vector Machine), Decision Tree, and Random Forest. For example, artificial neural networks are mainly used in deep learning, are statistical learning algorithms inspired by machine learning and neural networks in biology, and can be convolutional neural networks that include feature extraction neural networks and classification neural networks.

At this time, the spray cycle control unit 153 controls the number of sprays for at least one spray drone (e.g., 20_1, 20_2) that flies close to the pest outbreak area within a certain distance among the plurality of spray drones (20_1 to 20_N). can be controlled to increase.

In this specification, only a few examples of various embodiments performed by the present inventors are described, but the technical idea of the present invention is not limited or limited thereto, and of course, it can be modified and implemented in various ways by those skilled in the art.

10: Observation drone
20_1~20_N: Multiple spraying drones
100: Pesticide spraying management system using various drones
110: Collection department
120: Sorting unit
130: Line setting unit
140: Drawing section
150: Integrated control unit

Claims (8)

A collection unit that collects outline objects detected by photographing the target area using an observation drone;
a selection unit that selects mapping location information corresponding to the outline object from real-time location information transmitted from the observation drone;
a line setting unit that generates a target area map by setting a reference coordinate line for the target area based on the mapping position information;
a drawing unit that draws a plurality of flight contour lines that do not overlap each other on the target area map based on the reference coordinate line, the area of the target area, and a preset contour line separation distance; and
An integrated control unit that individually assigns each of the plurality of flight contours to each autonomous flight path to a plurality of spraying drones and controls autonomous swarm flight,
The autonomous swarm flight means that the plurality of spraying drones autonomously fly in a swarm along each flight contour,
The outline object refers to an object located at the border of the target area and easy to identify based on color or shape differences from the target area,
The reference coordinate line corresponds to the outline of the target area, and the target area map corresponds to the area of the target area,
The shape of the plurality of flight contour lines corresponds to the shape of the reference coordinate line and includes movement coordinate information for each distance according to autonomous flight,
The number of the plurality of flight contour lines is inversely proportional to the preset contour line separation distance,
The integrated control unit selectively transmits a spraying operation signal for controlling pesticide spraying to each spraying drone based on test flight path information for each drone that is fed back from the plurality of spraying drones according to the autonomous swarm flight,
When the test flight path information of any one of the test flight path information for each drone corresponds to the corresponding flight contour line, the integrated control unit transmits the spraying operation signal, and based on the preset spraying operation time, the drone transmit a return signal,
If the test flight path information of any one of the test flight path information for each drone and the corresponding flight contour line do not correspond to the test flight path information for each spraying drone, the integrated control unit is based on the target separation distance between the reference coordinate line and the corresponding flight contour line, Transmitting a direction correction signal to correct the flight separation distance between the reference coordinate line and the test flight path information to one of the spraying drones,
The drawing unit includes a counting unit that counts the number of the plurality of flight contour lines;
A comparison unit that compares the number difference between the plurality of flight contour lines and the plurality of spraying drones; and
Based on the number difference, it includes a contour correction unit that connects at least two flight contours with a short flight distance among the plurality of flight contour lines to overlap and correct them into a single flight contour line,
The integrated control unit diagnoses the operation state of each drone as either a normal state or a failure state based on a preset abnormal vibration signal detected through each vibration detection sensor provided in the observation drone and the plurality of spraying drones. Diagnosis of disability status;
A flight management unit that transmits an emergency return signal to one of the observation drones and the plurality of spraying drones when one of the observation drones and the plurality of spraying drones is diagnosed as being in a failure state; and
Based on the flight time information detected from the test flight path information, it includes a spray cycle control unit that individually adjusts the spray cycle for each predetermined time for the spray module of each spray drone,
The flight management department uses an artificial intelligence-based pest occurrence prediction model that is modeled by learning the collected flight contour images and pest occurrence points for each target area through machine learning to determine the pest occurrence area for a plurality of flight contour lines. predict,
The spraying cycle controller controls to increase the number of sprays for at least one spraying drone that flies close to a pest infestation area within a certain distance among the plurality of spraying drones,
The artificial intelligence-based pest occurrence prediction model is an artificial neural network algorithm that receives the target area map on which the plurality of flight contour lines are drawn and predicts the pest occurrence area for the target area map. It is an artificial neural network (Artificial Neural Network) , any one of SVM (Support Vector Machine), Decision Tree, and Random Forest,
The flight management unit controls a plurality of spraying drones to fly at the lowest altitude within the height limit section stipulated by law, based on wind speed information confirmed from weather information transmitted from the weather control center. Pesticide spraying using multiple drones. Management system.



delete delete delete delete delete delete delete