CN117502196A - Afforestation intelligent irrigation decision-making system and method based on unmanned aerial vehicle - Google Patents
Afforestation intelligent irrigation decision-making system and method based on unmanned aerial vehicle Download PDFInfo
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- CN117502196A CN117502196A CN202311362880.2A CN202311362880A CN117502196A CN 117502196 A CN117502196 A CN 117502196A CN 202311362880 A CN202311362880 A CN 202311362880A CN 117502196 A CN117502196 A CN 117502196A
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- 230000002262 irrigation Effects 0.000 title claims abstract description 61
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- 238000005507 spraying Methods 0.000 claims abstract description 17
- 239000000575 pesticide Substances 0.000 claims abstract description 13
- 238000001514 detection method Methods 0.000 claims description 149
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 67
- 239000007921 spray Substances 0.000 claims description 38
- 239000003337 fertilizer Substances 0.000 claims description 37
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- 239000007788 liquid Substances 0.000 claims description 5
- 210000003608 fece Anatomy 0.000 claims description 4
- 230000036541 health Effects 0.000 claims description 4
- 239000003621 irrigation water Substances 0.000 claims description 4
- 239000010871 livestock manure Substances 0.000 claims description 4
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 230000004720 fertilization Effects 0.000 abstract description 10
- 238000004458 analytical method Methods 0.000 abstract description 4
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D1/00—Dropping, ejecting, releasing, or receiving articles, liquids, or the like, in flight
- B64D1/16—Dropping or releasing powdered, liquid, or gaseous matter, e.g. for fire-fighting
- B64D1/18—Dropping or releasing powdered, liquid, or gaseous matter, e.g. for fire-fighting by spraying, e.g. insecticides
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01C—PLANTING; SOWING; FERTILISING
- A01C23/00—Distributing devices specially adapted for liquid manure or other fertilising liquid, including ammonia, e.g. transport tanks or sprinkling wagons
- A01C23/007—Metering or regulating systems
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01C—PLANTING; SOWING; FERTILISING
- A01C23/00—Distributing devices specially adapted for liquid manure or other fertilising liquid, including ammonia, e.g. transport tanks or sprinkling wagons
- A01C23/04—Distributing under pressure; Distributing mud; Adaptation of watering systems for fertilising-liquids
- A01C23/042—Adding fertiliser to watering systems
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G25/00—Watering gardens, fields, sports grounds or the like
- A01G25/16—Control of watering
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G25/00—Watering gardens, fields, sports grounds or the like
- A01G25/16—Control of watering
- A01G25/167—Control by humidity of the soil itself or of devices simulating soil or of the atmosphere; Soil humidity sensors
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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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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U20/00—Constructional aspects of UAVs
- B64U20/80—Arrangement of on-board electronics, e.g. avionics systems or wiring
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U20/00—Constructional aspects of UAVs
- B64U20/80—Arrangement of on-board electronics, e.g. avionics systems or wiring
- B64U20/87—Mounting of imaging devices, e.g. mounting of gimbals
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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
- B64U2101/30—UAVs specially adapted for particular uses or applications for imaging, photography or videography
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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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Environmental Sciences (AREA)
- Water Supply & Treatment (AREA)
- Soil Sciences (AREA)
- Aviation & Aerospace Engineering (AREA)
- Mechanical Engineering (AREA)
- Remote Sensing (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Pest Control & Pesticides (AREA)
- Insects & Arthropods (AREA)
- Wood Science & Technology (AREA)
- Zoology (AREA)
- Catching Or Destruction (AREA)
Abstract
The invention belongs to the technical fields of computers, data processing and intelligent control, and particularly relates to an intelligent landscaping irrigation decision-making system and method based on an unmanned aerial vehicle. The intelligent irrigation decision system for landscaping is realized based on an unmanned aerial vehicle platform, a feedback control mechanism is combined through the data acquisition and analysis of each device, the device is orderly matched and started, pesticide proportion, spraying quantity, flight track control and correction of the unmanned aerial vehicle are realized, the accurate control of spraying angles is aimed at making a more intelligent irrigation and spraying decision scheme, the labor intensity of workers is reduced, the irrigation efficiency of large-area gardens is improved, accurate fertilization and irrigation are carried out for plants of different types, the uniformity and accuracy of fertilization and irrigation are guaranteed, greening trees in gardens are guaranteed to be better cared and managed, future trends of garden management are represented, and the greening quality and the sustainability are hopefully improved.
Description
Technical Field
The invention belongs to the technical fields of computers, data processing and intelligent control, and particularly relates to an intelligent landscaping irrigation decision-making system and method based on an unmanned aerial vehicle.
Background
In the existing garden management technology, fertilization and irrigation of greening trees in gardens are a crucial task. However, the conventional manual control of the spray truck has a series of significant drawbacks and challenges, which limit its efficiency and practicality, and seriously affect the maintenance and beautification of gardens.
Inefficiency and manpower resource intensive: the manual mode of operation typically requires a significant amount of human resources because the worker must hold the spray gun or equipment to apply fertilizer and spray, either plant by plant or on a per-plant basis. This is not only time consuming and laborious, but also inefficient, especially in large-area gardens.
Difficult plant type adaptation: different plant types and varieties typically require different types and proportions of fertilization and irrigation. The manual mode is difficult to adjust to the needs of a particular plant because operators may lack accurate information and tools to ensure accurate fertilization and irrigation.
Cannot cover difficult-to-access areas: in gardens, there are areas that are difficult to access, such as narrow small diameters, overhead crowns, or obstacles, which are not covered by conventional spray vehicles, resulting in insufficient attention to some plants.
Not fine and accurate enough: manual control has artificial factors, and uniformity and accuracy of fertilization and irrigation are difficult to ensure. This can lead to excessive or insufficient nutrient input, affecting plant growth and health.
The above information disclosed in the above background section is only for enhancement of understanding of the background art for the technology described herein and therefore it may contain some information that does not form the prior art that is already known in the country to a person of ordinary skill in the art.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent landscaping intelligent irrigation decision-making system and method based on an intelligent unmanned aerial vehicle, which can make a more intelligent irrigation and sprinkling scheme, improve efficiency, accuracy and adaptability and ensure that greening trees in gardens are better cared and managed. Can adjust fertilization and irrigation according to plant type, water and fertilizer demand degree and garden environment's requirement, provide more accurate service, intelligent unmanned aerial vehicle can cover the area that is difficult to get into simultaneously. These new technologies represent a future trend in garden management, and are expected to improve greening quality and sustainability.
The technical scheme adopted by the invention is as follows:
an intelligent landscaping decision-making method based on unmanned aerial vehicle comprises the following steps:
step 1: acquiring weather information (weather forecast information and real-time weather information) of a current time node, comparing the weather information with weather type data which can be operated and are prestored in a database, and if the judging result does not accord with the weather type which can be operated, stopping operation, otherwise, executing the next step;
step 2: the unmanned aerial vehicle executes commands to fly to the position right above a first target point in a planning operation area, and starts to execute a preparation program before irrigation operation, and sequentially executes the following instructions:
detecting whether personnel activities exist in the area to be operated, if so, driving away, otherwise, executing the next step
Acquiring the operation target of the current operation area and the image information of the environment, comparing the operation target with the pre-stored data in a database, adjusting the pesticide ratio and the spraying amount of the water fertilizer, and determining the flight track of the unmanned aerial vehicle;
step 3: after the front preparation work is finished, the water and fertilizer irrigation operation on the target is started, and in the flight process of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and the target, the wind speed and wind direction information of the obstacle and the current time node are collected in real time, and the flight track and the spraying angle of the spray gun are adjusted in real time.
The intelligent landscaping irrigation decision-making method is realized based on an intelligent control system, and the system comprises:
the device comprises a main control device, a weather information acquisition module, an infrared detection device, an alarm device, an image acquisition device, a water and fertilizer configuration device, an illumination detection device, a temperature and humidity detection device, a water yield control device, a track preset module, a distance detection device, a track correction module, an obstacle recognition device, a wing control module, a wind speed and direction detection device, a spray gun angle control device and a timing device, wherein the weather information acquisition module, the infrared detection device, the alarm device, the image acquisition device, the water and fertilizer configuration device, the illumination detection device, the temperature and humidity detection device, the water yield control device, the track preset module, the distance detection device, the track correction module, the obstacle recognition device, the wing control module, the wind speed and direction detection device and the spray gun angle control device are respectively electrically connected with the main control device;
the weather information acquisition module is used for acquiring weather information of the current time node;
the infrared detection device is used for detecting whether personnel activities exist in the area to be operated or not;
the alarm device is used for reminding a person to leave the area to be worked;
the image acquisition device is used for acquiring image information of a target plant to be operated;
the water and fertilizer preparation device is used for regulating and controlling the pesticide ratio in the water and fertilizer;
the illumination detection device is used for detecting illumination intensity in a current operation area;
the temperature and humidity detection device is used for detecting air humidity, soil temperature and soil humidity in the current operation area;
the water yield control device is used for adjusting the water yield of the spray gun in unit time;
the track presetting module is used for generating an unmanned aerial vehicle flight track according to the shape characteristics of the plants;
the distance detection device is used for acquiring distance data between the unmanned aerial vehicle and a target in real time;
the track correction module is used for correcting the flight track in real time according to the real-time distance data;
the obstacle recognition device is used for recognizing obstacle information of the unmanned aerial vehicle in the current flight track direction;
the wing control module is used for adjusting the flight direction of the unmanned aerial vehicle so as to enable the unmanned aerial vehicle to avoid obstacles in an emergency;
the wind speed and direction detection device is used for detecting the wind speed and direction received by the unmanned aerial vehicle at the current time node;
the spray gun angle control device is used for controlling the spray angle of the water and fertilizer;
the timing device is used for acquiring time sequence information of the current time node.
In the step 1 and/or 2, the main control device controls the weather information acquisition module and the timing device to be normally opened, and the infrared detection device, the alarm device, the image acquisition device, the liquid manure configuration device, the illumination detection device, the temperature and humidity detection device, the water yield control device and the track preset module to be normally closed;
the weather information acquisition module acquires weather information of the current time node to meet operation conditions, and the main control device controls the unmanned aerial vehicle to execute command flight to the position right above a first target point in a planning operation area and controls the infrared detection device to be started;
the infrared detection device detects whether personnel move in the to-be-operated area, if so, the alarm device is controlled to give an alarm to dispel personnel in the to-be-operated area, otherwise, the main control device controls the image acquisition device, the illumination detection device and the temperature and humidity detection device to be started;
the image acquisition device acquires image information of a target to be operated, controls the water and fertilizer configuration device to be started according to the type and the health degree of the target, and adjusts the pesticide ratio (formula and concentration) of the water and fertilizer;
the illumination detection device and the temperature and humidity detection device respectively acquire illumination intensity, air humidity, soil temperature and soil humidity in a current operation area, and the main control device controls the water yield control device to be started according to the parameters so as to adjust the irrigation water consumption required by a current target;
after the water and fertilizer proportioning and water consumption are determined, the main control device controls the image acquisition device to acquire three-dimensional information of the target, and inputs the three-dimensional information into an airplane track generation model preset in the track preset module to acquire the flight track of the current target.
The temperature and humidity detection device comprises an air humidity detection device and a soil temperature and humidity detection device, wherein the air humidity detection device and the soil temperature and humidity detection device are preset in each operation area of gardens in advance, the air humidity detection device is fixed on the ground, and the soil temperature and humidity detection device is embedded in the soil.
The main control device controls the distance detection device, the track correction module, the obstacle recognition device, the wing control module, the wind speed and direction detection device and the spray gun angle control device to be normally closed;
after the front preparation work of the irrigation operation in the step 2 is finished;
the main control device controls the distance detection device to be started, and the distance detection device obtains the unmanned current time node
Machine for making food
The real-time distance between the wing control module and the target is fed back to the main control device, the main control device inputs the real-time distance to the track correction module, controls the wing control module to be started, and corrects the flight track in real time;
in the operation process of the unmanned aerial vehicle according to the corrected flight track, the main control device simultaneously controls the obstacle detection device and the wind speed and wind direction detection device to be started;
the obstacle detection device recognizes that the unmanned aerial vehicle has an obstacle in the current flight track direction, the main control device controls the wing control module to be opened, and adjusts the flight direction of the unmanned aerial vehicle to enable the unmanned aerial vehicle to avoid the obstacle in an emergency manner
The wind speed and direction detection device detects the wind speed and direction received by the unmanned aerial vehicle at the current time node, the main control device receives the wind speed and direction information, analyzes and processes the data by combining the distance information between the current time node and the target, and sends an instruction to control the spray gun angle control device to be started so as to adjust the spray direction of the spray gun.
The system also comprises a flame detection device, wherein the main control device controls the flame detection device to be normally closed, the main control device controls the unmanned aerial vehicle to execute a command to fly to the position right above a first target point in the planning operation area and controls the flame detection device to be started, the flame detection device detects whether a fire disaster occurs in the operation area or not, and if the fire disaster occurs, the alarm device is controlled to give an alarm to park staff and send fire disaster alarm information to the alarm platform.
The system still includes dose detection device and electric quantity detection device, and main control device control dose detection device and electric quantity detection device normally open, carries out real-time supervision to unmanned aerial vehicle's dose and electric quantity in the execution irrigation operation in-process, and current dose and electric quantity are less than alarm value, when being insufficient for accomplishing this operation quantity, control unmanned aerial vehicle returns.
An intelligent landscaping decision-making system based on unmanned aerial vehicle is used for realizing the intelligent landscaping decision-making method.
A storage medium, on which a computer program is stored, which when executed performs the landscaping intelligent irrigation decision-making method
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
compared with the prior art, the intelligent landscaping irrigation decision system is designed, the intelligent landscaping irrigation decision system is realized based on an unmanned aerial vehicle platform and is used for providing intelligent decision scheme guidance for the unmanned aerial vehicle in landscaping irrigation operation, a plurality of data acquisition and analysis and feedback control mechanisms are combined through each device, the devices are orderly matched and started to cooperate, the pesticide proportion, the spraying amount, the flight track control and the correction of the unmanned aerial vehicle are realized, the precise control of the spraying angle is formulated, the intelligent irrigation spray decision scheme is designed, the irrigation efficiency of large-area landscaping can be improved while the labor intensity is reduced, the precise irrigation is performed for different types of plants, the uniformity and the accuracy of fertilization and irrigation are ensured, the better landscaping and management of trees in the landscaping are ensured, the future greening quality is expected to be improved, and the future greening trend is expected to be improved.
Drawings
The invention will now be described by way of example and with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of an intelligent landscaping decision-making method based on an unmanned aerial vehicle in the invention;
fig. 2-4 are schematic structural diagrams of an intelligent landscaping decision-making system based on an unmanned aerial vehicle in the invention;
FIG. 5 is a schematic diagram of an image recognition module according to the present invention;
fig. 6 is a schematic diagram of a hardware structure of the main control device in the present invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
In the description of the embodiments of the present application, it should be noted that, directions or positional relationships indicated by terms such as "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or those that are conventionally put in use of the inventive product, are merely for convenience of description and simplicity of description, and are not indicative or implying that the apparatus or element to be referred to must have a specific direction, be configured and operated in a specific direction, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Example 1
The invention provides an intelligent landscaping irrigation decision-making method based on an unmanned aerial vehicle,
referring to fig. 1, comprising:
step 1: acquiring weather information of a current time node, wherein the weather information comprises weather forecast information and real-time weather information, comparing the weather information with weather type data which can be subjected to operation and is prestored in a database, and stopping operation if a judging result does not accord with the weather type which can be subjected to operation, otherwise, executing the next step;
step 2: the unmanned aerial vehicle executes commands to fly to the position right above a first target point in a planning operation area, and starts to execute a preparation program before irrigation operation, and sequentially executes the following instructions:
detecting whether personnel activities exist in the area to be operated, if so, driving away, otherwise, executing the next step
Acquiring image information of an operation target and an environment of a current operation area, wherein the image information comprises, but is not limited to, information data such as a high-definition image, a remote sensing image, an infrared thermal imaging and a high-definition video, comparing the information data with prestored data in a database, adjusting the pesticide ratio and the spraying amount of a liquid fertilizer, and determining the flight track of an unmanned aerial vehicle;
step 3: after the front preparation work is finished, the water and fertilizer irrigation operation on the target is started, and in the flight process of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and the target, the wind speed and wind direction information of the obstacle and the current time node are collected in real time, and the flight track and the spraying angle of the spray gun are adjusted in real time.
In the step 2, the system performs the following processing steps for the acquired image information:
s1: collecting a target plant image a of an operation area, and comparing the target plant image a with plant images prestored in a database I to determine a target type;
s2: collecting a target plant image b of the operation area, and comparing the target plant image b with a pest image prestored in a database II to determine the type of the pest, wherein the pest degree comprises at least one of no pest, slight pest, medium pest and serious pest;
s3: determining the types and proportions of pesticides in the sprayed water and fertilizer according to the target types and insect pest types;
s4: determining the severity of the crop pest, and preparing a proper concentration for spraying according to the pesticide type and the severity of the crop pest, wherein the concentration configuration proportion comprises at least one of low concentration, medium concentration and high concentration.
The step 2 further includes:
s5: collecting environmental information of a current time node of an operation area, including illumination intensity, air humidity, soil temperature and soil humidity, inputting the environmental information into a water consumption model for training, and controlling a water outlet control device to be opened according to the parameters to regulate the current irrigation water consumption required by a target, wherein the water outlet control device can be an electromagnetic valve on a water pipe, and can control the water outlet flow and the total water outlet quantity;
example 2:
the invention also provides an unmanned aerial vehicle-based intelligent landscaping irrigation decision system for realizing the intelligent landscaping irrigation decision method, referring to fig. 2, the system comprises:
the device comprises a main control device, a weather information acquisition module, an infrared detection device, an alarm device, an image acquisition device, a water and fertilizer configuration device, an illumination detection device, a temperature and humidity detection device, a water yield control device, a track preset module, a distance detection device, a track correction module, an obstacle recognition device, a wing control module, a wind speed and direction detection device, a spray gun angle control device and a timing device, wherein the weather information acquisition module, the infrared detection device, the alarm device, the image acquisition device, the water and fertilizer configuration device, the illumination detection device, the temperature and humidity detection device, the water yield control device, the track preset module, the distance detection device, the track correction module, the obstacle recognition device, the wing control module, the wind speed and direction detection device and the spray gun angle control device are respectively electrically connected with the main control device;
and a main control device: and the central control unit of the system is used for coordinating and controlling the operation of each subsystem.
Weather information acquisition module: the weather information acquisition module is used for acquiring the weather information of the current time node, wherein the weather information comprises current weather forecast information and real-time weather information, so one implementation mode of the weather information acquisition module is a system built-in module, real-time data interaction is carried out with a weather forecast platform through a wireless communication module, the weather information is used for acquiring the weather information of the current time node, and in another real-time mode, the weather information acquisition module is a weather monitoring device fixedly arranged on an unmanned aerial vehicle, and comprises a plurality of environment sensors and a weather algorithm model, and the weather information acquisition module is used for acquiring the real-time weather information of the current time node;
infrared detection device: is used for detecting whether personnel activities exist in the area to be worked. Optionally, an infrared detector installed on the unmanned aerial vehicle is adopted;
an alarm device: if personnel activities are detected, the alarm device can remind the personnel to leave the area to be operated so as to ensure the safety of the personnel, and alternatively, the alarm device can adopt a voice alarm arranged on the unmanned aerial vehicle to inform the personnel in the area to leave by playing audio recorded in advance, and can also inform the personnel to evacuate on site by being connected with a control terminal of a main control room and a management personnel or being electrically connected with the control terminal of the management personnel;
an image acquisition device: the method comprises the steps of acquiring image information of a target plant to be operated, wherein the image information comprises, but is not limited to, information data such as a high-definition image, a remote sensing image, infrared thermal imaging and high-definition video;
liquid manure configuration device: the water and fertilizer configuration device comprises electromagnetic valves arranged at the water tank of the unmanned aerial vehicle and at the outlet of the medicine tank, and the ratio of different medicines can be controlled through the electromagnetic valves, so that the aim is fulfilled;
illumination detection device: the illumination detection device is used for detecting illumination intensity in a current working area, and the illumination detection device is an ultraviolet intensity sensor arranged on the illumination detection device;
temperature and humidity detection device: for detecting air humidity, soil temperature and soil humidity within the current work area to assist in determining irrigation and fertilization needs. Optionally, the temperature and humidity detecting device comprises an air humidity detecting device and a soil temperature and humidity detecting device, wherein the air humidity detecting device and the soil temperature and humidity detecting device are preset in each garden working area in advance, the air humidity detecting device is fixed on the ground, and the soil temperature and humidity detecting device is pre-buried in the soil;
water yield control device: the water outlet control device is an electromagnetic valve arranged on a water spraying pipe of the unmanned aerial vehicle, and is electrically connected with the main control device and used for controlling the water outlet flow and total amount of the spray gun;
track presetting module: the track preset module is a system built-in module. The unmanned aerial vehicle flight path is generated according to the shape characteristics of the plants;
distance detection device: the distance detection device is used for acquiring distance data between the unmanned aerial vehicle and a target in real time, and is an optional distance detection sensor fixedly arranged on the unmanned aerial vehicle;
the track correction module: the track correction module is a system built-in module and corrects the flight track in real time according to the real-time distance data;
obstacle recognition device: obstacle information used for identifying the unmanned aerial vehicle in the current flight track direction;
and the wing control module is used for: the unmanned aerial vehicle is used for adjusting the flight direction of the unmanned aerial vehicle so as to enable the unmanned aerial vehicle to avoid obstacles in an emergency;
wind speed and direction detection device: the wind speed and wind direction detection device is used for detecting the wind speed and the wind direction received by the unmanned aerial vehicle at the current time node, and optionally comprises a wind speed sensor and a wind direction sensor;
spray gun angle control device: the spray angle is used for controlling the spray angle of the water fertilizer;
a timing device: for obtaining timing information for the current time node to assist the system in time scheduling and planning.
The system has the main functions of acquiring current weather information, detecting personnel activities, providing alarms, collecting plant image information, regulating and controlling water and fertilizer ratio, monitoring illumination, temperature and humidity and ground humidity, controlling water spraying quantity, presetting flight tracks, measuring distances in real time, correcting tracks, identifying obstacles, adjusting flight directions, monitoring wind speed and wind direction and controlling spray gun angles. These functions work cooperatively to ensure that unmanned aerial vehicle can intelligently carry out the irrigation decision-making of afforestation, provide appropriate amount of liquid manure, irrigate, and fertilize, guarantee personnel's safety and the high-efficient operation of system simultaneously. The system can be realized in different ways according to the actual situation, for example by means of built-in modules or sensors and devices fixed on the unmanned aerial vehicle. The whole system aims to improve landscaping management in a highly automated and intelligent manner to meet plant demands, improve efficiency and ensure system reliability
Referring to fig. 3, in a further embodiment:
the main control device controls the weather information acquisition module and the timing device to be normally opened so as to keep the system running, and controls the infrared detection device, the alarm device, the image acquisition device, the water and fertilizer configuration device, the illumination detection device, the temperature and humidity detection device, the water yield control device and the track preset module to be normally closed;
the weather information acquisition module acquires weather information of the current time node to meet operation conditions, and the main control device controls the unmanned aerial vehicle to execute command flight to the position right above a first target point in a planning operation area and controls the infrared detection device to be started;
the infrared detection device detects whether personnel move in the to-be-operated area, if so, the alarm device is controlled to give an alarm, personnel in the to-be-operated area are dispersed, the infrared detection device detects whether the personnel move in the to-be-operated area again at intervals of ten minutes, the final detection result is that the personnel do not exist in the to-be-operated area, and the main control device controls the image acquisition device, the illumination detection device and the temperature and humidity detection device to be started;
the image acquisition device acquires image information of a target to be operated, and controls the water and fertilizer preparation device to be started according to the type and the health degree of the target, so as to adjust the pesticide ratio of the water and fertilizer;
the illumination detection device and the temperature and humidity detection device respectively acquire parameters such as illumination intensity, air humidity, soil temperature and soil humidity in a current operation area. The main control device controls the water yield control device to be started according to the parameters so as to adjust the current irrigation water required by the target and ensure proper irrigation;
once the water and fertilizer ratio and the water consumption are determined, the main control device controls the image acquisition device to acquire three-dimensional information of the target, and the information is input into the track preset module. The preset module comprises an airplane track generation model which generates a flight track of a current target so as to ensure that the unmanned aerial vehicle flies according to a correct path to finish irrigation and fertilization tasks.
In a further embodiment:
the main control device controls the distance detection device, the track correction module, the obstacle recognition device, the wing control module, the wind speed and direction detection device and the spray gun angle control device to be normally closed;
referring to fig. 4, after the pre-preparation work of the irrigation operation in step 2 is completed, the pre-preparation work ensures that the system has completed the pre-preparation work, such as checking weather conditions, determining target positions and preparing water and fertilizer ratios, before performing the irrigation operation;
the main control device controls the distance detection device to be started, and the distance detection device obtains the unmanned current time node
Machine for making food
The real-time distance between the wing control module and the target is fed back to the main control device, the main control device inputs the real-time distance to the track correction module, controls the wing control module to be started, and corrects the flight track in real time;
in the operation process of the unmanned aerial vehicle according to the corrected flight track, the main control device simultaneously controls the obstacle detection device and the wind speed and wind direction detection device to be started;
the obstacle detection device recognizes that the unmanned aerial vehicle has an obstacle in the current flight track direction, the main control device controls the wing control module to be opened, and adjusts the flight direction of the unmanned aerial vehicle to enable the unmanned aerial vehicle to avoid the obstacle in an emergency manner
The wind speed and direction detection device detects the wind speed and direction received by the unmanned aerial vehicle at the current time node, the main control device receives the wind speed and direction information, analyzes and processes the data by combining the distance information between the current time node and the target, and sends an instruction to control the spray gun angle control device to be started so as to adjust the spray direction of the spray gun.
The main control device further includes an image recognition module, referring to fig. 5, where the image recognition module includes:
pretreatment submodule: this sub-module is used for preprocessing the target image acquired from the image acquisition device. Preprocessing may include image denoising, image enhancement, size normalization, etc. operations to ensure that image quality meets analysis and alignment requirements;
and (3) an extraction submodule: the extraction submodule is used for extracting important characteristic values from the target image. These characteristic values may include color, shape, texture, etc., as desired for a particular application. The extracted characteristic value is used for subsequent image comparison;
and a judging sub-module: the judging submodule is used for comparing the extracted characteristic values of the target image with images in the database to judge the similarity of the characteristic values and the images. In the comparison process, a threshold condition set in advance can be used for judging whether the similarity requirement is met. If the similarity of the target image and the images in the database exceeds a preset threshold, the system can make corresponding decisions, such as performing specific tasks or providing relevant information.
The image recognition module is used in the system to recognize objects within the work area, such as plant species, disease conditions, growth stages, and shape and size information of the objects. The image recognition function can further improve the automation level of the system, ensure proper water and fertilizer ratio and irrigation requirements, and meet specific requirements of landscaping.
In another preferred embodiment:
the system also comprises a flame detection device, wherein the main control device controls the flame detection device to be normally closed so as to reduce energy consumption, and when the main control device gives a command to require the unmanned aerial vehicle to fly to the position right above a first target point in a planned working area, the main control device controls the flame detection device to be started. The flame detection device is responsible for monitoring whether a fire event occurs in the area to be operated. If a fire is detected, the main control device will trigger an alarm device to send fire alarm information to the campus staff to ensure timely coping with the fire situation.
The system also comprises a medicine quantity detection device and an electric quantity detection device, wherein the main control device controls the medicine quantity detection device and the electric quantity detection device to be normally open;
the dosage detection device is kept in an open state in the whole irrigation operation process, and the dosage of the medicine carried by the unmanned aerial vehicle is monitored in the whole process. As the irrigation task is performed, it monitors the remaining amount of medication in real time. If the current medicine dosage is lower than a preset alarm value, indicating that the current task is insufficient to be completed, the main control device triggers an instruction to request the unmanned aerial vehicle to return to a base or a designated replenishment station;
the electric quantity detection device also keeps an on state all the time, and monitors the battery electric quantity of the unmanned aerial vehicle in real time. As the task is performed, it constantly monitors the remaining charge of the battery. If the current battery level is lower than the set alarm value, the main control device gives an instruction to require the unmanned aerial vehicle to return to a base or a designated charging station for charging or replacing the battery, so as to ensure that the unmanned aerial vehicle can continue to execute tasks or safely return.
The integrated use of the above devices improves the ability of the system to address potential risks and problems, including fires, insufficient medication to accomplish tasks, and insufficient electrical power. This helps to ensure the operational safety and reliability of the system, reducing potential risks and losses.
Example 3:
in this embodiment, the main control device includes a processor and a memory electrically connected to the processor, where the memory is used to store a computer program, and the processor is used to call the computer program to execute the intelligent irrigation decision method based on the unmanned aerial vehicle described in any one of the embodiments.
Example 4:
the invention further provides a storage medium, wherein a computer program is stored on the storage medium, and the computer program is executed when being run to execute the unmanned aerial vehicle-based intelligent landscaping decision-making method.
Those skilled in the art will appreciate that implementing all or part of the processes of the methods of the embodiments described above may be accomplished by computer programs characterized by computer instructions that, when executed, cause the associated hardware to perform the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory.
The non-volatile memory may include read-only memory, magnetic tape, floppy disk, flash memory, optical memory, etc. Volatile memory can include random access memory or external cache memory. By way of illustration, and not limitation, RAM can take many forms, such as static random access memory or dynamic random access memory.
In summary, compared with the prior art, the intelligent landscaping irrigation decision system and method based on the unmanned aerial vehicle provided by the invention are characterized in that a set of intelligent landscaping irrigation decision system integrating a weather information acquisition module, an infrared detection device, an alarm device, an image acquisition device, a water and fertilizer configuration device, an illumination detection device, a temperature and humidity detection device, a water yield control device, a track preset module, a distance detection device, a track correction module, an obstacle recognition device, a wing control module, a wind speed and wind direction detection device, a spray gun angle control device and a timing device is designed, the intelligent landscaping irrigation decision system based on an unmanned aerial vehicle platform is used for providing intelligent decision scheme guidance for the unmanned aerial vehicle in landscaping irrigation operation in scenic spots, and sequentially combining feedback control mechanisms for a plurality of data acquisition analysis through each device, realizing pesticide proportioning, spraying amount, flight track control and correction of the unmanned aerial vehicle, and precise control of spraying angle, aiming at making a more intelligent irrigation and spraying decision scheme, reducing the labor intensity, simultaneously improving the irrigation efficiency of large-area landscaping, accurately fertilizing irrigation aiming at different types of plants, ensuring uniformity and management trend, and future greening quality and continuous greening management.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; 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 (9)
1. An intelligent landscaping decision-making method based on unmanned aerial vehicle is characterized by comprising the following steps:
step 1: acquiring weather information of a current time node, comparing the weather information with weather type data which can be operated in advance in a database, stopping operation if the judging result does not accord with the weather type which can be operated, and otherwise, executing the next step;
step 2: the unmanned aerial vehicle executes commands to fly to the position right above a first target point in a planning operation area, and starts to execute a preparation program before irrigation operation, and sequentially executes the following instructions:
detecting whether personnel activities exist in the area to be operated, if so, driving away, otherwise, executing the next step
Acquiring the operation target of the current operation area and the image information of the environment, comparing the operation target with the pre-stored data in a database, adjusting the pesticide ratio and the spraying amount of the water fertilizer, and determining the flight track of the unmanned aerial vehicle;
step 3: after the front preparation work is finished, the water and fertilizer irrigation operation on the target is started, and in the flight process of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and the target, the wind speed and wind direction information of the obstacle and the current time node are collected in real time, and the flight track and the spraying angle of the spray gun are adjusted in real time.
2. The unmanned aerial vehicle-based landscaping intelligent irrigation decision-making method according to claim 1, wherein the landscaping intelligent irrigation decision-making method is implemented based on an intelligent control system, the system comprising: the device comprises a main control device, a weather information acquisition module, an infrared detection device, an alarm device, an image acquisition device, a water and fertilizer configuration device, an illumination detection device, a temperature and humidity detection device, a water yield control device, a track preset module, a distance detection device, a track correction module, an obstacle recognition device, a wing control module, a wind speed and direction detection device, a spray gun angle control device and a timing device, wherein the weather information acquisition module, the infrared detection device, the alarm device, the image acquisition device, the water and fertilizer configuration device, the illumination detection device, the temperature and humidity detection device, the water yield control device, the track preset module, the distance detection device, the track correction module, the obstacle recognition device, the wing control module, the wind speed and direction detection device and the spray gun angle control device are respectively electrically connected with the main control device;
the weather information acquisition module is used for acquiring weather information of the current time node;
the infrared detection device is used for detecting whether personnel activities exist in the area to be operated or not;
the alarm device is used for reminding a person to leave the area to be worked;
the image acquisition device is used for acquiring image information of a target plant to be operated;
the water and fertilizer preparation device is used for regulating and controlling the pesticide ratio in the water and fertilizer;
the illumination detection device is used for detecting illumination intensity in a current operation area;
the temperature and humidity detection device is used for detecting air humidity, soil temperature and soil humidity in the current operation area;
the water yield control device is used for adjusting the water yield of the spray gun in unit time;
the track presetting module is used for generating an unmanned aerial vehicle flight track according to the shape characteristics of the plants;
the distance detection device is used for acquiring distance data between the unmanned aerial vehicle and a target in real time;
the track correction module is used for correcting the flight track in real time according to the real-time distance data;
the obstacle recognition device is used for recognizing obstacle information of the unmanned aerial vehicle in the current flight track direction;
the wing control module is used for adjusting the flight direction of the unmanned aerial vehicle so as to enable the unmanned aerial vehicle to avoid obstacles in an emergency;
the wind speed and direction detection device is used for detecting the wind speed and direction received by the unmanned aerial vehicle at the current time node;
the spray gun angle control device is used for controlling the spray angle of the water and fertilizer;
the timing device is used for acquiring time sequence information of the current time node.
3. The intelligent irrigation decision-making method for landscaping based on the unmanned aerial vehicle according to claim 2, wherein in the step 1 and/or 2, the main control device controls the weather information acquisition module and the timing device to be normally opened, and the infrared detection device, the alarm device, the image acquisition device, the liquid manure configuration device, the illumination detection device, the temperature and humidity detection device, the water yield control device and the track preset module to be normally closed;
the weather information acquisition module acquires weather information of the current time node to meet operation conditions, and the main control device controls the unmanned aerial vehicle to execute command flight to the position right above a first target point in a planning operation area and controls the infrared detection device to be started;
the infrared detection device detects whether personnel move in the to-be-operated area, if so, the alarm device is controlled to give an alarm to dispel personnel in the to-be-operated area, otherwise, the main control device controls the image acquisition device, the illumination detection device and the temperature and humidity detection device to be started;
the image acquisition device acquires image information of a target to be operated, and controls the water and fertilizer preparation device to be started according to the type and the health degree of the target, so as to adjust the pesticide ratio of the water and fertilizer;
the illumination detection device and the temperature and humidity detection device respectively acquire illumination intensity, air humidity, soil temperature and soil humidity in a current operation area, and the main control device controls the water yield control device to be started according to the parameters so as to adjust the irrigation water consumption required by a current target;
after the water and fertilizer proportioning and water consumption are determined, the main control device controls the image acquisition device to acquire three-dimensional information of the target, and inputs the three-dimensional information into an airplane track generation model preset in the track preset module to acquire the flight track of the current target.
4. The intelligent landscaping decision-making method based on the unmanned aerial vehicle according to claim 3, wherein the temperature and humidity detection device comprises an air humidity detection device and a soil temperature and humidity detection device, the air humidity detection device and the soil temperature and humidity detection device are preset in each operation area of the garden in advance, the air humidity detection device is fixed on the ground, and the soil temperature and humidity detection device is embedded in the soil.
5. The intelligent landscaping decision-making method based on the unmanned aerial vehicle according to claim 4, wherein the main control device controls the distance detection device, the track correction module, the obstacle recognition device, the wing control module, the wind speed and direction detection device and the spray gun angle control device to be normally closed;
after the front preparation work of the irrigation operation in the step 2 is finished;
the main control device controls the distance detection device to be started, and the distance detection device acquires the unmanned aerial vehicle with the current time node
The real-time distance between the wing control module and the target is fed back to the main control device, the main control device inputs the real-time distance to the track correction module, controls the wing control module to be started, and corrects the flight track in real time;
in the operation process of the unmanned aerial vehicle according to the corrected flight track, the main control device simultaneously controls the obstacle detection device and the wind speed and wind direction detection device to be started;
the obstacle detection device recognizes that an obstacle exists in the current flight track direction of the unmanned aerial vehicle, the main control device controls the wing control module to be started, and the flight direction of the unmanned aerial vehicle is adjusted to enable the unmanned aerial vehicle to avoid the obstacle in an emergency manner;
the wind speed and direction detection device detects the wind speed and direction received by the unmanned aerial vehicle at the current time node, the main control device receives the wind speed and direction information, analyzes and processes the data by combining the distance information between the current time node and the target, and sends an instruction to control the spray gun angle control device to be started so as to adjust the spray direction of the spray gun.
6. The intelligent landscaping decision-making method based on the unmanned aerial vehicle according to claim 5, wherein the system further comprises a flame detection device, the main control device controls the flame detection device to be normally closed, the main control device controls the unmanned aerial vehicle to execute a command to fly to the position right above a first target point in a planning operation area and controls the flame detection device to be started, the flame detection device detects whether a fire disaster occurs in the area to be operated, and if the fire disaster occurs, the alarm device is controlled to give an alarm to a park worker and send fire disaster alarm information to the alarm platform.
7. The intelligent irrigation decision-making method for landscaping based on the unmanned aerial vehicle according to claim 6, wherein the system further comprises a drug quantity detection device and an electric quantity detection device, the main control device controls the drug quantity detection device and the electric quantity detection device to be normally open, the drug quantity and the electric quantity of the unmanned aerial vehicle are monitored in real time in the whole process of executing irrigation operation, and when the current drug quantity and the electric quantity are lower than alarm values and insufficient to finish the current operation, the unmanned aerial vehicle is controlled to return.
8. An intelligent landscaping irrigation decision-making system based on an unmanned aerial vehicle, which is used for realizing the intelligent landscaping irrigation decision-making method according to any one of claims 1 to 7.
9. A storage medium having stored thereon a computer program which when executed performs the intelligent landscaping decision method of any of claims 1-7.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106962147A (en) * | 2017-03-15 | 2017-07-21 | 南京信息工程大学 | UAV Intelligent water-saving irrigation system and construction method based on Beidou satellite system |
| CN107743854A (en) * | 2017-11-29 | 2018-03-02 | 旷丽军 | A kind of crops System for detecting soil and automatic irrigation |
| CN109032172A (en) * | 2018-07-05 | 2018-12-18 | 深圳数翔地理科技有限公司 | A kind of automatic pesticide spraying system of unmanned plane and method |
| CN110333737A (en) * | 2019-07-05 | 2019-10-15 | 湖北理工学院 | A kind of high unmanned aerial vehicle control system of spraying efficiency for pesticide spraying and method |
| CN110488873A (en) * | 2019-09-20 | 2019-11-22 | 怀化学院 | A kind of plant protection drone operation flight course planning method and system |
| CN111665864A (en) * | 2020-06-12 | 2020-09-15 | 张玉红 | Farming plant protection operation wisdom management system based on big data thing networking |
| CN113408961A (en) * | 2021-07-24 | 2021-09-17 | 榆林学院 | Agricultural supervision platform based on agricultural Internet of things |
| CN215074963U (en) * | 2021-05-10 | 2021-12-10 | 宜宾绿恒生态农业有限公司 | Monitoring system is planted to prickly ash |
| CN116210570A (en) * | 2023-01-16 | 2023-06-06 | 江苏经贸职业技术学院 | Intelligent agricultural planting management method based on computational mathematics |
-
2023
- 2023-10-19 CN CN202311362880.2A patent/CN117502196A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106962147A (en) * | 2017-03-15 | 2017-07-21 | 南京信息工程大学 | UAV Intelligent water-saving irrigation system and construction method based on Beidou satellite system |
| CN107743854A (en) * | 2017-11-29 | 2018-03-02 | 旷丽军 | A kind of crops System for detecting soil and automatic irrigation |
| CN109032172A (en) * | 2018-07-05 | 2018-12-18 | 深圳数翔地理科技有限公司 | A kind of automatic pesticide spraying system of unmanned plane and method |
| CN110333737A (en) * | 2019-07-05 | 2019-10-15 | 湖北理工学院 | A kind of high unmanned aerial vehicle control system of spraying efficiency for pesticide spraying and method |
| CN110488873A (en) * | 2019-09-20 | 2019-11-22 | 怀化学院 | A kind of plant protection drone operation flight course planning method and system |
| CN111665864A (en) * | 2020-06-12 | 2020-09-15 | 张玉红 | Farming plant protection operation wisdom management system based on big data thing networking |
| CN215074963U (en) * | 2021-05-10 | 2021-12-10 | 宜宾绿恒生态农业有限公司 | Monitoring system is planted to prickly ash |
| CN113408961A (en) * | 2021-07-24 | 2021-09-17 | 榆林学院 | Agricultural supervision platform based on agricultural Internet of things |
| CN116210570A (en) * | 2023-01-16 | 2023-06-06 | 江苏经贸职业技术学院 | Intelligent agricultural planting management method based on computational mathematics |
Non-Patent Citations (1)
| Title |
|---|
| 钟静等: "《农业信息技术实战案例》", vol. 1, 31 August 2022, 北京邮电大学出版社, pages: 253 - 257 * |
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