CN121254865A - A farmland information collection system for communication between drones and ground IoT nodes - Google Patents
A farmland information collection system for communication between drones and ground IoT nodesInfo
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- CN121254865A CN121254865A CN202511399591.9A CN202511399591A CN121254865A CN 121254865 A CN121254865 A CN 121254865A CN 202511399591 A CN202511399591 A CN 202511399591A CN 121254865 A CN121254865 A CN 121254865A
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Abstract
The invention discloses a farmland information acquisition system for communication between an unmanned aerial vehicle and a ground Internet of things node, and relates to the technical field of agriculture. The fixed wing unmanned aerial vehicle and the multi-rotor unmanned aerial vehicle form an acquisition module, the fixed wing unmanned aerial vehicle and the multi-rotor unmanned aerial vehicle cooperate in a division manner, the task planning module can accurately screen out abnormal areas based on a farmland GIS map through grid division, calculation of normalized vegetation indexes and other operations, the fixed wing unmanned aerial vehicle and the multi-rotor unmanned aerial vehicle are reasonably distributed for different monitoring requirements and farmland area characteristics, the fixed wing unmanned aerial vehicle is used for preliminary macroscopic monitoring of large-area and land form flat areas, local fine monitoring areas formed by abnormal grid aggregation are arranged for targeted deep monitoring of the multi-rotor unmanned aerial vehicle, tasks can be reasonably distributed according to different farmland area characteristics and monitoring requirements, and the working efficiency is greatly improved.
Description
Technical Field
The invention relates to the technical field of agriculture, in particular to a farmland information acquisition system for communication between an unmanned aerial vehicle and a ground Internet of things node.
Background
The intelligent agriculture is intelligent economy in agriculture, along with the development of the age and the progress of technology, the intelligent agriculture represents the deep combination of modern technology and agricultural planting so as to realize unmanned, automatic and intelligent management of agricultural production, and farmers in the new age not only need to know the growth condition of crops but also need to timely detect and analyze the growth condition of the crops when planting the crops, and determine management strategies of irrigation, fertilization, pest and disease control, so as to realize automatic irrigation, fertilization and deinsectization, and the intelligent agriculture can realize the precision, the intellectualization and the high efficiency of agricultural production management through the wide application of advanced technologies such as the Internet of things, big data, artificial intelligence and the like.
In the prior art, when farmland information is acquired through communication between an unmanned aerial vehicle and nodes of the ground Internet of things, although monitoring data can be acquired efficiently, the unmanned aerial vehicle only adopts a single type (fixed wing or multiple rotors), the fixed wing unmanned aerial vehicle has the advantages of rapid navigational speed, long navigational distance, harsh landing conditions and poor adaptability to complex terrains, the multiple rotor unmanned aerial vehicle has the advantages of high flexibility and convenient landing, short endurance time and low monitoring efficiency, and tasks of the unmanned aerial vehicle of different types cannot be reasonably distributed according to the characteristics and monitoring requirements of different areas of a farmland, so that the operation efficiency is low.
Therefore, an agricultural monitoring system for communication between an unmanned aerial vehicle and a ground internet of things node is provided to solve the above problems.
Disclosure of Invention
The invention mainly aims to provide an agricultural monitoring system for communication between an unmanned aerial vehicle and a ground internet of things node, so as to solve the problems in the background.
In order to achieve the aim, the technical scheme adopted by the invention is that a farmland information acquisition system for communication between an unmanned aerial vehicle and a ground Internet of things node comprises an acquisition module, a task planning module, a flight management and control module and a man-machine interaction module;
The acquisition module is formed by a fixed-wing unmanned aerial vehicle and a multi-rotor unmanned aerial vehicle, is respectively used for large-area macroscopic monitoring and local fine monitoring, and is used for arranging data relay nodes through a farmland GIS map;
The task planning module divides a farmland GIS map into a plurality of regular rectangular grids based on the farmland GIS map, calculates normalized vegetation indexes according to farmland GIS map monitoring data acquired by the fixed wing unmanned aerial vehicle, judges and screens abnormal grids based on a set threshold value, gathers adjacent abnormal grids into a local fine monitoring area by adopting a regular adjacent clustering algorithm for the abnormal grids, marks and displays the local fine monitoring area on the GIS map, and sends a take-off instruction to a ground control center;
The flight control module is used for receiving a take-off instruction, displaying a local fine monitoring area based on a mark on a GIS map, planning a main path by adopting an AI algorithm, and generating a flight path of the multi-rotor unmanned aerial vehicle by using RRT local re-planning when an obstacle is encountered;
The man-machine interaction module is used for transmitting the collected monitoring data to the ground control center through the data relay node, and the chart is drawn through Tableau.
Preferably, the acquisition module comprises an acquisition unit and a deployment unit;
The acquisition unit comprises a fixed wing unmanned aerial vehicle and a multi-rotor unmanned aerial vehicle, and the fixed wing unmanned aerial vehicle is matched with an Internet of things sensor and a wireless communication module;
The multi-rotor unmanned aerial vehicle is matched with various hyperspectral imaging devices and wireless communication modules, the Internet of things sensor comprises a temperature and humidity sensor, an illumination intensity sensor, a soil humidity sensor and a wind speed and wind direction sensor, the temperature and humidity sensor, the illumination intensity sensor, the soil humidity sensor and the wind speed and wind direction sensor are arranged on the ground, the temperature and humidity sensor, the illumination intensity sensor, the soil humidity sensor and the wind speed and wind direction sensor monitor the temperature, the humidity, the illumination intensity, the soil humidity and the wind speed and wind direction of the ground in real time, and data are transmitted into an unmanned aerial vehicle system in real time through the wireless communication modules;
the deployment unit deploys the data relay node based on the farmland GIS map, and the data relay node is matched with the wireless communication module.
Preferably, the task planning module comprises a segmentation unit, a macroscopic detection unit, an abnormality judgment unit, a clustering fine unit and a transmission unit.
Preferably, the dividing unit divides the farmland GIS map into a plurality of regular rectangular grids, and the grid size is set according to the farmland scale and the monitoring precision requirement;
The macro detection unit is used for carrying out large-area image spectrum data acquisition on a farmland GIS map area through flying of the fixed-wing unmanned aerial vehicle according to a preset route by means of a GPS and an inertial navigation system.
Preferably, the anomaly judgment unit collects farmland GIS map monitoring data based on the fixed wing unmanned aerial vehicle, and performs preprocessing on the monitoring data, wherein the preprocessing comprises radiation correction and geometric correction, and then calculates a normalized vegetation index, and the calculation formula is as follows:
;
Wherein, the Represents the index of vegetation,Representing the reflectivity of the near-infrared band,Represented as red band reflectivity;
Setting an index threshold range according to crop types and historical experience of growing stages, setting a maximum threshold A and a minimum threshold S, and indexing vegetation The mesh when the threshold value a is greater than and less than the threshold value S is determined as an abnormal mesh.
Preferably, the clustering fine unit is configured to aggregate adjacent abnormal grids into a local fine monitoring area by using a regular adjacent clustering algorithm, and includes the following steps:
Step 1, setting 2 times of the side length of a grid as a distance threshold T;
Step 2, for each non-clustered abnormal grid A, calculating a distance calculation formula of the abnormal grid A and the abnormal grid B as follows:
;
Wherein, the Representing the distance between anomaly mesh a and anomaly mesh B,AndRepresenting the abscissa and ordinate of the anomaly mesh a in the planar coordinate system,AndRepresenting the abscissa and ordinate, respectively, of the anomaly grid B in the planar coordinate system, ifNon-clustered grids less than or equal to a distance threshold T are merged into a region;
and 3, repeatedly searching the grids of the newly added area until no new grids can be added, and finally polymerizing the grids into a local fine monitoring area.
Preferably, the transmission unit is used for acquiring a grid coordinate set of the local fine monitoring area from the clustering fine unit, creating a vector image layer in the farmland GIS map through QGIS for labeling the local fine monitoring area, connecting the vector image layer into a polygon or other proper geometric figures according to the grid coordinate set, drawing the polygon or other proper geometric figures on the newly-built labeling image layer, and sending a take-off instruction to the ground control center.
Preferably, the flight control module includes a receiving unit and an airline unit.
Preferably, the receiving unit comprises information management, land parcel management, route management, task scheduling and job management;
the information management is used for login and management of personnel and equipment information;
the land management is used for maintaining land information and wheat field growth conditions;
The route management is used for realizing route planning and management functions;
The task scheduling utilizes an intelligent patrol task scheduling algorithm to realize live video broadcast of the unmanned aerial vehicle and an airport and viewing and management of aerial photographing data;
the job management comprises the processing and recording of the job and the alarm information;
The method comprises the steps that a line unit receives unit signals in real time and generates a main path of the multi-rotor unmanned aerial vehicle through an AI algorithm based on local fine monitoring area identification, the multi-rotor unmanned aerial vehicle detects obstacles in a certain range in front in real time through a vision sensor carried by the multi-rotor unmanned aerial vehicle in the flight process along the main path, if the obstacles are detected and located on the main path, a local re-planning mechanism is triggered, the current position of the multi-rotor unmanned aerial vehicle is traced back to a target node through a fast expansion random algorithm, a local re-planning path is generated, the part influenced by the obstacles in the original main path is replaced, a new flight main path is formed, and the new flight main path is transmitted to a multi-rotor unmanned aerial vehicle control system.
Preferably, the man-machine interaction module comprises a control unit and a chart unit;
The control unit is used for transmitting the collected monitoring data to the ground control center through the data relay node and transmitting control signals of the ground control center to the unmanned aerial vehicle control system through the data relay node;
the chart unit draws a chart based on the monitoring data and Tableau, the chart including a histogram, a line graph, a scatter graph, and a thermodynamic diagram.
The invention has the following beneficial effects:
1. According to the invention, the fixed wing unmanned aerial vehicle and the multi-rotor unmanned aerial vehicle form an acquisition module, the fixed wing unmanned aerial vehicle and the multi-rotor unmanned aerial vehicle cooperate in a division manner, the task planning module can accurately screen out abnormal areas based on a farmland GIS map through grid division, normalized vegetation index calculation and other operations, tasks of the fixed wing unmanned aerial vehicle and the multi-rotor unmanned aerial vehicle are reasonably distributed for different monitoring requirements and farmland area characteristics, the fixed wing unmanned aerial vehicle is used for preliminary macroscopic monitoring of a large area and a land flat area, local fine monitoring areas formed by abnormal grid aggregation are arranged for targeted deep monitoring of the multi-rotor unmanned aerial vehicle, tasks can be reasonably distributed according to the different farmland area characteristics and monitoring requirements, and the operation efficiency is greatly improved.
2. According to the method, a task planning module builds a space analysis framework based on a farmland GIS map, accurately locks a region needing fine monitoring through three-stage processing flows of grid division, NDVI calculation and an adjacent clustering algorithm, divides the farmland into regular grids, calculates the NDVI by utilizing multispectral data collected by a fixed wing unmanned aerial vehicle, combines a crop growth stage dynamic adjustment threshold value to screen out grids with abnormal vegetation growth, combines adjacent abnormal grids through an Euclidean distance clustering algorithm to form a continuous monitoring region, reduces scattered point interference, and enables the operation target of the multi-rotor unmanned aerial vehicle to be clear.
Drawings
FIG. 1 is a flow chart of a farmland information acquisition system for communication between an unmanned aerial vehicle and a ground Internet of things node;
FIG. 2 is a flow chart of an abnormality judging unit of the farmland information acquisition system for communication between an unmanned aerial vehicle and a ground Internet of things node;
FIG. 3 is a flow chart of a route unit of the farmland information acquisition system in which the unmanned aerial vehicle communicates with the nodes of the ground Internet of things;
fig. 4 is a schematic diagram of a receiving unit of a farmland information acquisition system in which an unmanned aerial vehicle communicates with a node of the internet of things on the ground.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, the invention provides a technical scheme that a farmland information acquisition system for communication between an unmanned aerial vehicle and a ground internet of things node comprises an acquisition module, a task planning module, a flight management and control module and a man-machine interaction module;
The acquisition module is formed by a fixed-wing unmanned aerial vehicle and a multi-rotor unmanned aerial vehicle, is respectively used for large-area macroscopic monitoring and local fine monitoring, and is used for arranging data relay nodes through a farmland GIS map;
The task planning module divides the farmland GIS map into a plurality of regular rectangular grids based on the farmland GIS map, calculates normalized vegetation indexes according to farmland GIS map monitoring data acquired by the fixed wing unmanned aerial vehicle, judges and screens abnormal grids based on a set threshold value, gathers adjacent abnormal grids into a local fine monitoring area by adopting a regular adjacent clustering algorithm for the abnormal grids, marks and displays the local fine monitoring area on the GIS map, and sends a take-off instruction to a ground control center;
The flight control module is used for receiving a take-off instruction, displaying a local fine monitoring area based on a mark on a GIS map, planning a main path by adopting an AI algorithm, and generating a flight path of the multi-rotor unmanned aerial vehicle by using RRT local re-planning when an obstacle is encountered;
The man-machine interaction module is used for transmitting the collected monitoring data to the ground control center through the data relay node, and the chart is drawn through Tableau.
The acquisition module comprises an acquisition unit and a deployment unit;
the acquisition unit comprises a fixed wing unmanned aerial vehicle and a multi-rotor unmanned aerial vehicle, wherein the fixed wing unmanned aerial vehicle is matched with an Internet of things sensor and a wireless communication module;
the multi-rotor unmanned aerial vehicle is matched with various hyperspectral imaging devices and wireless communication modules, wherein the hyperspectral imaging devices comprise hyperspectral imagers, laser radars or oblique photographic cameras;
The deployment unit deploys the data relay node based on the farmland GIS map, and the data relay node is matched with the wireless communication module.
Specifically, fixed wing unmanned aerial vehicle carries out the spectral scan in a large scale to farmland through thing networking sensor, thing networking sensor includes temperature and humidity sensor, illumination intensity sensor, soil humidity sensor and wind speed wind direction sensor, obtain farmland information, farmland information includes meteorological parameter such as soil temperature, humidity, illumination intensity, wind speed, combine wireless communication module to return data to ground control center in real time, many rotor unmanned aerial vehicle start after fixed wing unmanned aerial vehicle discernment unusual region, utilize the hyperspectral imager of carrying, laser radar or oblique photographic camera, carry out the fine measurement to parameters such as soil humidity, temperature, wind speed, and send data to adjacent relay node through wireless communication module, deployment unit arranges data relay node according to topography fluctuation and barrier distribution condition in the farmland GIS map, for example set up the node in the position of every certain distance in the meshing mode, form redundant communication network.
The task planning module comprises a segmentation unit, a macroscopic detection unit, an abnormality judgment unit, a clustering fine unit and a transmission unit.
Dividing the farmland GIS map into a plurality of regular rectangular grids by a dividing unit, wherein the grid size is set according to the farmland scale and the monitoring precision requirement;
The macroscopic detection unit is used for carrying out large-area image spectrum data acquisition on the farmland GIS map area through the fixed wing unmanned aerial vehicle flying according to a preset route by means of the GPS and the inertial navigation system.
Specifically, the segmentation unit firstly acquires boundary coordinates and terrain data of a farmland GIS map, calculates grid dimensions based on the total farmland area and monitoring precision parameters set by a user, for example, when the farmland area is 1000 mu and the monitoring precision requirement is 10 meters, generates rectangular grids with the side length of 50 meters, then the macro detection unit calls a navigation system of the fixed wing unmanned aerial vehicle, sequentially flies through the center point of each grid according to a preset route, continuously shoots image data through a multispectral camera, and in the flight process, the inertial navigation system provides continuous pose estimation when GPS signals are temporarily lost, ensures the route tracking precision, automatically correlates and stores the acquired image data with the grid coordinates, and provides a basis for the subsequent vegetation index calculation.
The anomaly judgment unit collects farmland GIS map monitoring data based on the fixed wing unmanned aerial vehicle, performs pretreatment on the monitoring data, wherein the pretreatment comprises radiation correction and geometric correction, and then calculates a normalized vegetation index, and the calculation formula is as follows:
;
Wherein, the Represents the index of vegetation,Representing the reflectivity of the near-infrared band,Represented as red band reflectivity;
Setting an index threshold range according to crop types and historical experience of growing stages, setting a maximum threshold A and a minimum threshold S, and indexing vegetation The mesh when the threshold value a is greater than and less than the threshold value S is determined as an abnormal mesh.
Specifically, the pretreatment stage firstly carries out radiation correction on the multispectral image collected by the fixed wing unmanned aerial vehicle to eliminate the influence of atmospheric scattering and illumination intensity change on reflectivity, then uses geometric correction to carry out space alignment on the image and a farmland GIS map to ensure that each pixel coordinate is consistent with the actual farmland position, calculates normalized vegetation indexes according to near infrared and red wave band reflectivities after pretreatment is completed, for example, when the reflectivity of the red wave band is 0.1 and the reflectivity of the near infrared wave band is 0.5, the calculated vegetation index value is 0.67, and then, according to the type and the growth stage of the current monitoring crop, a corresponding threshold range is called from a preset threshold library, for example, if the current monitoring region is used for planting winter wheat and is in a jointing stage, the maximum threshold A can be set to be 0.75, the minimum threshold S can be set to be 0.5, and when the vegetation index of a certain grid exceeds the threshold range, the abnormal grid can be determined to be caused by diseases and insect pests, water shortage or fertilizer shortage.
The clustering fine unit is used for gathering adjacent abnormal grids into a local fine monitoring area by adopting a regular adjacent clustering algorithm, and comprises the following steps:
Step 1, setting 2 times of the side length of a grid as a distance threshold T;
Step 2, for each non-clustered abnormal grid A, calculating a distance calculation formula of the abnormal grid A and the abnormal grid B as follows:
;
Wherein, the Representing the distance between anomaly mesh a and anomaly mesh B,AndRepresenting the abscissa and ordinate of the anomaly mesh a in the planar coordinate system,AndRepresenting the abscissa and ordinate, respectively, of the anomaly grid B in the planar coordinate system, ifNon-clustered grids less than or equal to a distance threshold T are merged into a region;
and 3, repeatedly searching the grids of the newly added area until no new grids can be added, and finally polymerizing the grids into a local fine monitoring area.
Specifically, the distance threshold is set to be twice the grid side length, for example, when the grid size is 10 meters, the threshold can be set to be 20 meters, the center coordinates of each abnormal grid are obtained through the plane coordinate system of the GIS map, the Euclidean distance formula is adopted to calculate the inter-grid distance, when the abnormal grid is detected, other abnormal grids are automatically traversed and the distance is calculated, if adjacent grids meeting the threshold condition exist, the adjacent grids are combined into the same monitoring area, the combined area boundary is dynamically expanded through recursively traversing the adjacent grids until all the adjacent grids in the area do not meet the combination condition, and finally the complete local fine monitoring area is formed.
The transmission unit is used for acquiring a grid coordinate set of the local fine monitoring area from the clustering fine unit, creating a vector image layer in the farmland GIS map through QGIS for labeling the local fine monitoring area, connecting the local fine monitoring area into a polygon or other proper geometric figures according to the grid coordinate set, drawing the polygon or other proper geometric figures on the newly-built labeling image layer, and sending a take-off instruction to the ground control center.
Specifically, after abnormal grid clustering is completed, a transmission unit receives aggregate data containing all abnormal grid coordinates from a clustering fine unit, QGIS software is started and loads an original farmland GIS map as a base map, a vector image layer is newly built for storing marking information, grid coordinate aggregate is analyzed into point data, adjacent grid coordinate points are generated into polygonal boundary lines according to a preset rule through a space connection algorithm to form a closed monitoring area outline, the outline image is drawn into the newly built vector image layer and is overlapped on the base map to form a visual mark, after the mark is completed, a take-off instruction containing target area position information is packaged into a JSON format data packet, and the JSON format data packet is transmitted to a task queue of a ground control center through a wireless communication module.
The flight control module comprises a receiving unit and an air line unit.
The receiving unit comprises information management, land parcel management, route management, task scheduling and job management (the receiving unit is used for receiving collected data of farmland information when the unmanned aerial vehicle is communicated with a ground Internet of things node);
the information management is used for logging and managing personnel and equipment information (high-efficiency inspection of farmland information acquisition areas is realized, and the field condition can be fed back rapidly and accurately by acquiring monitoring information and analysis data in real time);
the land parcel management is used for maintaining land parcel information and wheat field growth conditions (real-time monitoring of land parcel information and land parcel monitoring is realized on a farmland according to node communication of an unmanned plane and the ground Internet of things);
the route management is used for realizing route planning and management functions (here, unmanned plane route planning operation and route management operation in the farmland information acquisition process);
the task scheduling utilizes an intelligent patrol task scheduling algorithm to realize live video broadcast of the unmanned aerial vehicle and an airport and view and manage aerial photo data (the unmanned aerial vehicle is used for finding abnormality during aerial photo patrol in the farmland information acquisition process, the intelligent patrol task scheduling algorithm can be utilized in real time to perform task scheduling in real time, and timeliness of processing after farmland information acquisition is improved);
the job management comprises the processing and recording of the job and the alarm information;
The method comprises the steps that a line unit receives unit signals in real time and generates a main path of the multi-rotor unmanned aerial vehicle through an AI algorithm based on local fine monitoring area identification, the multi-rotor unmanned aerial vehicle detects obstacles in a certain range in front in real time through a vision sensor carried by the multi-rotor unmanned aerial vehicle in the flight process along the main path, if the obstacles are detected and located on the main path, a local re-planning mechanism is triggered, the current position of the multi-rotor unmanned aerial vehicle is traced back to a target node through a fast expanding random algorithm, a local re-planning path is generated, the part influenced by the obstacles in the original main path is replaced, a new flight main path is formed, and the new flight main path is transmitted to a multi-rotor unmanned aerial vehicle control system.
Specifically, after receiving a take-off instruction sent by a ground control center through a wireless communication link, a receiving unit converts target area coordinate information in the instruction into an internal identifiable identification parameter, a route unit calls an AI algorithm according to the identification parameter, generates an optimal main path for connecting a take-off point and a target area based on terrain data in a farmland GIS map, and when the multi-rotor unmanned aerial vehicle flies along the path, an onboard vision sensor continuously scans a front environment, and if an obstacle invaded flight path is detected, a path re-planning mechanism is immediately triggered. At the moment, the fast random tree expansion algorithm takes the current position of the unmanned aerial vehicle as a starting point, takes a target waypoint as an end point to carry out random tree expansion, generates a local path segment bypassing the obstacle, carries out seamless connection on the segment and an unaffected part in the main path, finally forms a complete updating path, and sends the updated path to the unmanned aerial vehicle flight control system through a data transmission link to realize dynamic optimization of the flight track.
The man-machine interaction module comprises a control unit and a chart unit;
the control unit is used for transmitting the collected monitoring data to the ground control center through the data relay node and transmitting control signals of the ground control center to the unmanned plane control system through the data relay node;
the chart unit draws a chart including a histogram, a line graph, a scatter graph, and a thermodynamic diagram based on the monitoring data and Tableau.
Specifically, the receiving unit receives monitoring data such as temperature and humidity and illumination intensity collected by the unmanned aerial vehicle through the data relay node, forwards the monitoring data to the ground control center, and simultaneously reversely transmits a flight path adjustment instruction or a sensor control signal generated by the ground control center to the unmanned aerial vehicle, the chart unit calls Tableau chart generating engines based on the monitoring data stored by the ground control center, automatically matches chart types according to requirements of different monitoring indexes, for example, draws soil humidity changes in continuous time into a linear chart, draws vegetation index differences in different areas into a thermodynamic diagram, and therefore a user can visually check various charts through an interactive interface of the ground control center and quickly make farmland management decisions according to graphical analysis results.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.
Claims (10)
1. The farmland information acquisition system for the communication between the unmanned aerial vehicle and the ground Internet of things node is characterized by comprising an acquisition module, a task planning module, a flight management and control module and a man-machine interaction module;
The acquisition module is formed by a fixed-wing unmanned aerial vehicle and a multi-rotor unmanned aerial vehicle, is respectively used for large-area macroscopic monitoring and local fine monitoring, and is used for arranging data relay nodes through a farmland GIS map;
The task planning module divides a farmland GIS map into a plurality of regular rectangular grids based on the farmland GIS map, calculates normalized vegetation indexes according to farmland GIS map monitoring data acquired by the fixed wing unmanned aerial vehicle, judges and screens abnormal grids based on a set threshold value, gathers adjacent abnormal grids into a local fine monitoring area by adopting a regular adjacent clustering algorithm for the abnormal grids, marks and displays the local fine monitoring area on the GIS map, and sends a take-off instruction to a ground control center;
The flight control module is used for receiving a take-off instruction, displaying a local fine monitoring area based on a mark on a GIS map, planning a main path by adopting an AI algorithm, and generating a flight path of the multi-rotor unmanned aerial vehicle by using RRT local re-planning when an obstacle is encountered;
The man-machine interaction module is used for transmitting the collected monitoring data to the ground control center through the data relay node, and the chart is drawn through Tableau.
2. The system for collecting farmland information by communication of unmanned aerial vehicle and nodes of the internet of things on the ground according to claim 1, wherein the collecting module comprises a collecting unit and a deployment unit;
The acquisition unit comprises a fixed wing unmanned aerial vehicle and a multi-rotor unmanned aerial vehicle, and the fixed wing unmanned aerial vehicle is matched with an Internet of things sensor and a wireless communication module;
the multi-rotor unmanned aerial vehicle is matched with various hyperspectral imaging devices and wireless communication modules, and the Internet of things sensor comprises a temperature and humidity sensor, an illumination intensity sensor, a soil humidity sensor and a wind speed and wind direction sensor;
the deployment unit deploys the data relay node based on the farmland GIS map, and the data relay node is matched with the wireless communication module.
3. The farmland information acquisition system for communication between the unmanned aerial vehicle and the ground internet of things node according to claim 1, wherein the task planning module comprises a segmentation unit, a macro detection unit, an anomaly judgment unit, a clustering fine unit and a transmission unit.
4. The farmland information acquisition system for communication between the unmanned aerial vehicle and the ground internet of things node according to claim 3, wherein the dividing unit divides the farmland GIS map into a plurality of regular rectangular grids, and the size of the grids is set according to the farmland scale and the monitoring precision requirement;
The macro detection unit is used for carrying out large-area image spectrum data acquisition on a farmland GIS map area through flying of the fixed-wing unmanned aerial vehicle according to a preset route by means of a GPS and an inertial navigation system.
5. The farmland information acquisition system for communication between unmanned aerial vehicle and ground internet of things node according to claim 3, wherein the anomaly determination unit acquires farmland GIS map monitoring data based on the fixed wing unmanned aerial vehicle, performs preprocessing on the monitoring data, the preprocessing comprises radiation correction and geometric correction, and then calculates a normalized vegetation index, and the calculation formula is as follows:
;
Wherein, the Represents the index of vegetation,Representing the reflectivity of the near-infrared band,Represented as red band reflectivity;
Setting an index threshold range according to crop types and historical experience of growing stages, setting a maximum threshold A and a minimum threshold S, and indexing vegetation The mesh when the threshold value a is greater than and less than the threshold value S is determined as an abnormal mesh.
6. A farmland information collection system according to claim 3, wherein said clustering fine unit is configured to aggregate neighboring abnormal grids into a local fine monitoring area by using a regular proximity clustering algorithm, and comprises the following steps:
Step 1, setting 2 times of the side length of a grid as a distance threshold T;
Step 2, for each non-clustered abnormal grid A, calculating a distance calculation formula of the abnormal grid A and the abnormal grid B as follows:
;
Wherein, the Representing the distance between anomaly mesh a and anomaly mesh B,AndRepresenting the abscissa and ordinate of the anomaly mesh a in the planar coordinate system,AndRepresenting the abscissa and ordinate, respectively, of the anomaly grid B in the planar coordinate system, ifNon-clustered grids less than or equal to a distance threshold T are merged into a region;
and 3, repeatedly searching the grids of the newly added area until no new grids can be added, and finally polymerizing the grids into a local fine monitoring area.
7. The farmland information acquisition system for communication between the unmanned aerial vehicle and the ground internet of things node according to claim 3, wherein the transmission unit is used for acquiring a grid coordinate set of the local fine monitoring area from the clustering fine unit, creating a vector image layer in a farmland GIS map through QGIS for labeling the local fine monitoring area, connecting the vector image layer into a polygon or other proper geometric figures according to the grid coordinate set, drawing the polygon or other proper geometric figures on the newly-built labeling image layer, and sending a take-off instruction to a ground control center.
8. The farmland information acquisition system for communication between the unmanned aerial vehicle and the ground internet of things node according to claim 1, wherein the flight management and control module comprises a receiving unit and a route unit;
the receiving unit comprises information management, land block management, route management, task scheduling and job management;
the information management is used for login and management of personnel and equipment information;
the land management is used for maintaining land information and wheat field growth conditions;
The route management is used for realizing route planning and management functions;
The task scheduling utilizes an intelligent patrol task scheduling algorithm to realize live video broadcast of the unmanned aerial vehicle and an airport and viewing and management of aerial photographing data;
the job management includes processing and recording of jobs and alert information.
9. The farmland information acquisition system for communication between the unmanned aerial vehicle and the ground internet of things node according to claim 8, wherein the line unit receives the unit signal in real time and generates a main path of the multi-rotor unmanned aerial vehicle through an AI algorithm based on the local fine monitoring area identification, the multi-rotor unmanned aerial vehicle detects an obstacle in a certain range in front in real time through a vision sensor carried by the multi-rotor unmanned aerial vehicle in the process of flying along the main path, if the obstacle is detected and is positioned on the main path, a local re-planning mechanism is triggered, the current position of the multi-rotor unmanned aerial vehicle is traced back to a target node through a fast expansion random algorithm, a local re-planning path is generated, the part influenced by the obstacle in the original main path is replaced, a new main flying path is formed, and the new main flying path is transmitted to the multi-rotor unmanned aerial vehicle control system.
10. The farmland information acquisition system for communication between the unmanned aerial vehicle and the ground internet of things node according to claim 1, wherein the man-machine interaction module comprises a control unit and a chart unit;
The control unit is used for transmitting the collected monitoring data to the ground control center through the data relay node and transmitting control signals of the ground control center to the unmanned aerial vehicle control system through the data relay node;
the chart unit draws a chart based on the monitoring data and Tableau, the chart including a histogram, a line graph, a scatter graph, and a thermodynamic diagram.
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