CN112817331A - Intelligent forestry information monitoring system based on multi-machine cooperation - Google Patents
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Abstract
The invention belongs to the field of forestry information monitoring, and relates to a forestry information intelligent monitoring system based on multi-machine cooperation. The method specifically comprises the following steps: the unmanned aerial vehicle cluster task allocation module allocates tasks to the unmanned aerial vehicle cluster based on forest area monitoring requirements; the multi-track collaborative planning module carries out multi-track collaborative planning on the unmanned aerial vehicle cluster based on the navigation task; the flight path online re-planning module carries out secondary dynamic planning on the surveying route of the unmanned aerial vehicle cluster according to the temporary emergency; the forest region information acquisition module dynamically acquires forest region information in a monitoring region based on sensing equipment carried by the unmanned aerial vehicle cluster; the forest resource analysis and visualization module analyzes forest images acquired by the unmanned aerial vehicle cluster to obtain forest resource information and visually display the forest resource information. The invention utilizes the cooperative working capacity of the unmanned aerial vehicle cluster system, reduces the forest resource surveying cost and improves the working efficiency and quality of forest resource survey.
Description
Technical Field
The invention belongs to the field of forestry information monitoring, and particularly relates to a forestry information intelligent monitoring system based on multi-machine cooperation.
Background
Forest management is a branch of forestry and is also an important ring of ecological management. The main tasks of forest management comprise afforestation, felling trees, forest growth improvement, fertilization, pest and disease monitoring, fire early warning and the like. The basis for executing the above operations needs to comprehensively and macroscopically grasp forest resource information.
The monitoring of forests, trees, animals and plants living in forest zones and their environmental conditions is called forest zone information monitoring. The information obtained by this means can be used to identify the type of tree, the height of the tree, the age of the tree, the health of the tree, the forest zone boundary and other relevant information for the trees in the forest zone. In addition, data obtained by forest region information monitoring can be used for identifying vegetation recovery conditions in forest regions, wildlife activity and other related information.
The traditional forest region information monitoring method mainly comprises the steps of carrying out periodic actual measurement on a ground setting sample plot, wherein the actual measurement is slow in action, and a large amount of manpower, material resources and financial resources are consumed; the monitoring by using the aerospace remote sensing technology has the defects of high cost, difficult processing of images and the like. The unmanned aerial vehicle-based aerial survey method has the advantages of small size, convenience, easy control, low cost and the like, and is gradually becoming an important tool for forest resource investigation in recent years.
When forest information monitoring is carried out based on an unmanned aerial vehicle, the problems that the area to be monitored is too large, the area to be monitored is too wide and the number of objects to be monitored is too large are often faced, the single unmanned aerial vehicle is heavy in task and limited in carried sensing equipment, and therefore the defects that the monitoring efficiency is low and the reliability of the monitoring result is not high exist.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-machine cooperation-based intelligent forestry information monitoring system, which provides a high-efficiency, reliable and cost-effective monitoring means for the field of forestry monitoring, reduces the cost of forest region information monitoring, and improves the accuracy of forest region information monitoring based on an unmanned aerial vehicle.
The purpose of the invention is realized by adopting the following technical scheme:
the invention provides a forestry information intelligent monitoring system based on multi-machine cooperation, which comprises:
an intelligent forestry information monitoring system based on multi-machine cooperation is composed of an unmanned aerial vehicle cluster task distribution module, a multi-track cooperative planning module, a track online re-planning module, a forest area information acquisition module and a forest area resource analysis and visualization module.
Preferably, the unmanned aerial vehicle cluster is formed by a plurality of single unmanned aerial vehicles which are isomorphic or heterogeneous, and a forestry monitoring task is expanded in a cooperative manner; the forestry monitoring task is to acquire a high-precision forest area image based on sensing equipment carried by an unmanned aerial vehicle cluster; the high-precision forest region image is mainly used for obtaining forest region resource information after processing and analysis; the forest resource information mainly comprises forest resource statistics, forest pest and disease damage analysis, forest fire smoke monitoring and the like; the forest resource statistics mainly comprise information such as forest quantity, height, density and boundary.
Specifically, the invention executes the monitoring tasks based on the unmanned aerial vehicle cluster system, and completes the execution of a plurality of tasks by the cooperation implementation of the minimum task cost, the most reasonable area distribution, the optimized flight line, the best online re-planning and the most accurate information acquisition, so that the unmanned aerial vehicle cluster can complete all tasks quickly, efficiently and autonomously on the premise of investing minimum resources.
Specifically, the method comprises the following steps:
a1, unmanned aerial vehicle cluster task allocation.
And A2, multi-track collaborative planning.
And A3, performing on-line flight path re-planning.
And A4, acquiring forest area information.
A5, forest resource analysis and visualization.
Preferably, the step a1 includes:
and the unmanned aerial vehicle cluster task allocation is mainly based on monitoring requirements to reasonably allocate tasks to the unmanned aerial vehicle cluster.
Preferably, the unmanned aerial vehicle cluster generally comprises conventional unmanned aerial vehicles such as helicopters, fixed wings and multi-rotor wings, and other special unmanned aerial vehicles; the monitoring demand refers to dividing a monitoring area range for a forest area to be monitored based on a forest area monitoring task;
specifically, the reasonable task allocation means that unmanned aerial vehicle clusters are determined to form based on the area range to be monitored and the object to be monitored, and then area allocation is performed on the single unmanned aerial vehicle based on the determined unmanned aerial vehicle clusters. Specifically, the unmanned aerial vehicle cluster comprises the types and the number of the unmanned aerial vehicles and the carried sensing equipment; the regional distribution means that a set of ordered targets are distributed to multiple unmanned aerial vehicles in the system according to the distribution of the takeoff and landing fields of the unmanned aerial vehicles on the basis of the requirements of monitoring targets based on known environmental information and forestry information, so that the unmanned aerial vehicle cluster system can finish an investigation task as efficiently as possible.
Specifically, the region allocation is calculated by adopting one or more of a differential evolution algorithm, an immune algorithm and a simulated annealing algorithm.
Specifically, single unmanned aerial vehicle comprises power management module, GPS module, action part, gesture analysis module, data acquisition module and reservation interface.
Specifically, the power management module dynamically monitors the electric quantity and power consumption of the unmanned aerial vehicle, and power supply and power consumption management are performed on the unmanned aerial vehicle through power management, so that the service cycle of the unmanned aerial vehicle is effectively prolonged; the GPS module is used for positioning the unmanned aerial vehicle in real time; the action part comprises a motor, a holder and the like and is used for realizing the tracking navigation control of the unmanned aerial vehicle; the attitude analysis module consists of an accelerometer gyroscope, an electronic compass and the like and is used for acquiring attitude information of the unmanned aerial vehicle; the data acquisition module consists of a situation perception sensor and is used for acquiring forest area environment information; the reserved interface mainly comprises a data transmission module and is used for transmitting information such as picture data.
Preferably, the unmanned aerial vehicle cluster control system is configured, and can carry out unified planning and management on all single unmanned aerial vehicles and monitor the state information of all single unmanned aerial vehicles; the state information comprises electric quantity state information, position and track information, attitude information and sensor real-time data information of all the single unmanned aerial vehicles.
Preferably, the step a2 includes:
the multi-track collaborative planning is mainly used for carrying out multi-track collaborative planning on the unmanned aerial vehicle cluster based on a navigation task.
Preferably, the multi-track collaborative planning satisfies a minimum mission cost and an optimized flight path.
Specifically, the minimum task cost means that the total range distance of the multiple machines is shortest, the total range electricity consumption of the multiple machines is least, and the total flight time of the multiple machines is shortest.
Specifically, the optimized flight route is based on reasonable area distribution, and a safe and flyable flight route is reasonably planned according to the self and environmental constraints of the unmanned aerial vehicle, so that the overall efficiency is ensured to be optimal.
Specifically, the calculation method of the minimum task cost includes: and calculating the task cost of multi-machine cooperation by using one or more methods based on a spatial vertical incisional plane method, an obstructed connected graph or a Voronoi graph.
Specifically, the unmanned aerial vehicle self-constraint refers to maximum range constraint, maximum flight time constraint, safety distance constraint among multiple machines and maximum rotation angle constraint of the unmanned aerial vehicle;
specifically, the unmanned aerial vehicle environmental constraints comprise static constraints and dynamic constraints, wherein the static constraints comprise constraints including multi-radar interference and no-fly zone and three-dimensional terrain constraints, and the static constraints are constraint conditions to be considered by the multi-track collaborative planning module; the dynamic constraints comprise air convection interference, natural bird flight interference and the like, and the dynamic constraints are constraint conditions to be considered by the flight path online re-planning module.
Preferably, the path planning includes an initial path planning and a path trajectory optimization.
Specifically, the initial flight path planning is to complete unmanned aerial vehicle cluster multi-flight path planning by using one or more of ant colony algorithm, particle swarm algorithm, genetic algorithm and other intelligent evolutionary algorithms under the condition that the unmanned aerial vehicle distribution region result is known.
Specifically, the track trajectory optimization refers to smoothing the initial track by using one or more path fitting methods such as a B-spline, a cubic spline, a bezier curve and the like to meet the maximum turning angle constraint of the unmanned aerial vehicle.
Preferably, the step a3 includes:
and the on-line path re-planning is mainly used for carrying out secondary dynamic planning on the survey route of the unmanned aerial vehicle cluster according to temporary emergencies.
Specifically, the flight path online re-planning module is used for re-planning the invalid part in the global flight path by acquiring the change of the updated environment information and the current running state of the unmanned aerial vehicle, adjusting the existing flight path and ensuring that the unmanned aerial vehicle cluster can safely and efficiently complete the task.
Specifically, the change of the environmental information comprises the movement of a radar area, air convection interference and natural bird flight interference; the current running state of the unmanned aerial vehicle refers to the condition that a single unmanned aerial vehicle fails to execute a task.
Specifically, the on-line flight path re-planning is completed by one or more of CFCM, PGA and POMDP algorithms.
Preferably, the step a4 includes:
forest zone information acquisition is mainly based on sensing equipment carried by an unmanned aerial vehicle cluster to dynamically acquire forest zone information in a monitored area.
Specifically, the sensing device comprises at least one of a radar system, a single-lens reflex camera, an infrared camera, a multispectral camera and a hyperspectral camera. The method mainly aims to obtain the unmanned aerial vehicle remote sensing image with high resolution and large scale.
Preferably, forest zone information acquisition module can also integrate the forest zone information acquisition equipment of waiting to monitor forest zone ground configuration except that the perception module that carries through the unmanned aerial vehicle cluster carries out image acquisition, like the forest robot who carries environmental information perception equipment such as illumination intensity sensor, temperature sensor, humidity transducer, infrared trigger camera to constitute the forest zone information monitoring network of a ground-air integration, acquire more comprehensive abundant forest zone initial state information, provide more reliable concrete data basis for forest zone resource analysis and visual module.
Preferably, the step a5 includes:
the forest resource analysis and visualization module is mainly used for processing and analyzing forest images acquired by the unmanned aerial vehicle cluster, so that forest resource information is obtained and visualized display is carried out.
Specifically, based on the unmanned aerial vehicle remote sensing image, a digital image processing system is utilized to carry out splicing reduction on the shot image, and then relevant forest resource information is obtained through analysis.
Specifically, the forest resource information includes information of forest resources such as tree height, tree density, forest boundary, plant diseases and insect pests.
The invention has the beneficial effects that:
1. the method and the system realize the resource acquisition of the forest area information based on the unmanned aerial vehicle cluster, and have the characteristics of low operation and maintenance cost, ecological environment protection and convenient control compared with the existing monitoring means.
2. The invention is based on unmanned aerial vehicle cluster cooperative work, has the advantages of high monitoring efficiency, wide monitoring range and capability of acquiring real-time data, and can efficiently finish forest resource investigation tasks through cooperative control of an integrated system.
3. According to the invention, through reasonable task allocation of the unmanned aerial vehicle cluster, the high-efficiency safe operation of the system can be ensured by combining multi-track collaborative planning and optimization and a track online re-planning mechanism, and meanwhile, the automation and intelligence level of the forestry information monitoring system is greatly improved.
4. According to the invention, through the high-precision sensing equipment configured by the unmanned aerial vehicle cluster, the unmanned aerial vehicle remote sensing image with high resolution and large scale can be obtained, meanwhile, forest land ground information acquisition equipment can be integrated, a ground-air integrated monitoring network is built, and a reliable data base can be provided for forest land resource analysis.
Drawings
FIG. 1 is a schematic structural diagram of an intelligent forestry information monitoring system based on multi-machine cooperation according to the present invention;
fig. 2 is a schematic diagram of an unmanned aerial vehicle cluster control system of the present invention;
FIG. 3 is a schematic flow chart of the present invention;
FIG. 4 is a forest information monitoring route map of the unmanned aerial vehicle cluster under a safe and non-threat situation;
fig. 5 is a forest region information monitoring route map when an unmanned aerial vehicle cluster of the invention encounters an unexpected fault of an unmanned aerial vehicle of a certain number of times;
fig. 6 is an online re-planning forest area information monitoring route map of the unmanned aerial vehicle cluster of the present invention under a new threat.
Detailed Description
For the purpose of better explaining the present invention to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
The invention provides a forestry information intelligent monitoring system based on multi-machine cooperation, which comprises an unmanned aerial vehicle cluster task allocation module, a multi-track cooperative planning module, a track online re-planning module, a forest area information acquisition module and a forest area resource analysis and visualization module, as shown in figure 1. The unmanned aerial vehicle cluster task allocation module is mainly used for reasonably allocating tasks to an unmanned aerial vehicle cluster based on monitoring requirements; the multi-track collaborative planning module is mainly used for planning tracks of the unmanned aerial vehicle cluster based on navigation tasks; the flight path online re-planning module is mainly used for carrying out secondary dynamic planning on the surveying route of the unmanned aerial vehicle cluster according to temporary emergency; the forest region information acquisition module is mainly used for dynamically acquiring forest region information in a monitored region based on sensing equipment carried by the unmanned aerial vehicle cluster; the forest resource analysis and visualization module is mainly used for processing and analyzing forest images acquired by the unmanned aerial vehicle cluster, so that forest resource information is obtained and visualized display is carried out. The invention fully utilizes the flexible control characteristic of the unmanned aerial vehicle cluster, greatly reduces the forest resource surveying cost, and improves the working efficiency and quality of forest resource survey.
The method specifically comprises the following steps: the unmanned aerial vehicle cluster task allocation module is used for reasonably allocating tasks to the unmanned aerial vehicle cluster based on monitoring requirements;
the multi-track collaborative planning module is used for carrying out multi-track planning on the unmanned aerial vehicle cluster based on a navigation task;
the flight path online re-planning module is used for carrying out secondary dynamic planning on the surveying route of the unmanned aerial vehicle cluster according to the temporary emergency;
the forest zone information acquisition module is used for dynamically acquiring forest zone information in a monitored area based on sensing equipment carried by the unmanned aerial vehicle cluster; the sensing equipment is used for acquiring an unmanned aerial vehicle remote sensing image with high resolution and large scale; the system comprises at least one of a radar system, a single-lens reflex camera, an infrared camera, a multispectral camera and a hyperspectral camera.
The forest resource analysis and visualization module is used for processing and analyzing forest images acquired by the unmanned aerial vehicle cluster, so that forest resource information is obtained and visualized and displayed.
In addition, the forestry information intelligent monitoring system also comprises an unmanned aerial vehicle cluster control system, wherein the unmanned aerial vehicle cluster control system is used for carrying out unified planning and management on all single unmanned aerial vehicles and monitoring the state information of all the single unmanned aerial vehicles;
the state information comprises electric quantity state information, position and track information, attitude information and sensor real-time data information of all the single unmanned aerial vehicles.
Furthermore, the unmanned aerial vehicle cluster related in the multi-track collaborative planning module is a cluster formed by a plurality of single unmanned aerial vehicles; the single unmanned aerial vehicle comprises a power management module, a GPS module, an action part, an attitude analysis module, a data acquisition module and a reserved interface.
Further, the multi-track planning satisfies a predefined minimum mission cost and an optimized flight path.
The multi-track planning comprises initial track planning and track trajectory optimization;
and the track optimization refers to smoothing the initial track so as to meet the maximum corner constraint of the unmanned aerial vehicle.
Further, the track online re-planning module comprises:
the acquisition unit is used for acquiring and updating the change of the environmental information and the current running state of the unmanned aerial vehicle;
and the adjusting unit is used for replanning the invalid part in the global flight path through the change of the environmental information acquired by the acquisition unit and the current running state of the unmanned aerial vehicle, and adjusting the existing flight path.
Furthermore, the forest region information acquisition module is also used for integrating forest region information acquisition equipment configured on the ground of the forest region to be monitored;
the forest region information acquisition equipment is a ground-air integrated forest region information monitoring network formed by forest robots of the environment information sensing equipment, and is used for acquiring forest region original state information and providing a specific data basis for a forest region resource analysis and visualization module;
wherein the environmental information perceiving device includes: the portable illumination intensity sensor, the temperature sensor, the humidity sensor and the infrared trigger camera.
Further, the forest resource analysis and visualization module comprises an image processing unit;
and the image processing unit is used for splicing and restoring the shot images by using a digital image processing system based on the unmanned aerial vehicle remote sensing images, and further analyzing to obtain related forest resource information.
In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 2 is a schematic diagram of the unmanned aerial vehicle cluster control system of the present invention, which specifically includes the following steps:
the unmanned aerial vehicle firstly determines the flight height according to the visibility of the field of vision and the point cloud precision requirement on the day;
then, the forest area information monitoring system allocates corresponding target areas for the unmanned aerial vehicles according to the number of the unmanned aerial vehicles, the area of the investigation area and the distribution condition of the ground stations of the unmanned aerial vehicles;
then the system acquires the threat situation of the investigated area through sensors such as an all-sky imager and the like, and gives instructions for each unmanned aerial vehicle to fly according to a bow-shaped route;
if an emergency situation or a new threat occurs in the process of executing task investigation, the information monitoring system carries out online re-planning on the unmanned aerial vehicle, and a monitoring route is redesigned;
the sensor system is used for storing and transmitting a forest resource image acquired by the unmanned aerial vehicle, and carrying one or more of a single-lens reflex camera, an infrared camera, a multispectral camera, a hyperspectral camera and a radar according to actual needs.
Meanwhile, the embodiment provides a specific implementation flow of the system, and a flowchart is shown in fig. 3, which is specifically as follows:
step 1, distributing an unmanned aerial vehicle investigation region.
Specifically, before the investigation region is redistributed, the extreme conditions of multiple radar threats, no-fly zones, complex three-dimensional topography and the like possibly existing in the monitored region are avoided.
Specifically, on the basis of meeting the minimum task cost, one or more of a differential evolution algorithm, an immune algorithm and a simulated annealing algorithm are adopted to distribute corresponding investigation regions for the multiple unmanned aerial vehicles in the system according to the distribution of the ground stations.
And 2, determining the flying height of the unmanned aerial vehicle.
Specifically, the monitoring flight height of the unmanned aerial vehicle is determined according to the weather condition and the point cloud precision requirement.
And 3, surveying according to the dynamic route.
Specifically, on the basis of optimal regional allocation, aiming at self and cooperative constraints of multiple unmanned aerial vehicles, such as multiple radar threats, no-fly zone constraints, three-dimensional terrain constraints, maximum range constraints, maximum flight time constraints, safety distance constraints among multiple machines and the like, multi-track cooperative planning is completed by using one or more of intelligent evolutionary algorithms such as ant colony algorithm, particle swarm algorithm, genetic algorithm and the like, and meanwhile, one or more of curve fitting methods such as B splines, cubic splines and the like are used for smoothing a generated route so as to meet the maximum turning angle constraints of the unmanned aerial vehicles, so that the overall efficiency of the system is guaranteed to be optimal.
Fig. 4 is a forest information monitoring route map of the unmanned aerial vehicle cluster under the safe and non-threat condition.
And 4, judging whether the electric quantity of the unmanned aerial vehicle cluster is enough, if so, continuing to execute the step 5, and otherwise, ending the task.
Specifically, the power management system determines in real time whether the power is sufficient to continue the task, and if not, the power management system lands on the nearest ground station, and if so, continues the survey task.
And 5, recording information.
Specifically, the sensing equipment based on unmanned aerial vehicle cluster configuration records forest resource information.
And 6, judging whether a new threat appears, if so, executing the step 7, otherwise, returning to continuously executing the step 3.
Specifically, in the process of executing tasks by the unmanned aerial vehicle cluster, whether a new threat appears on a navigation route is judged in real time.
And 7, path on-line re-planning.
Specifically, in the unmanned aerial vehicle cluster navigation process, if the situations such as movement of a radar area, air convection interference, natural bird flight interference, single unmanned aerial vehicle accidental faults and the like are met, one or more of CFCM (computational fluid dynamics), PGA (programmable logic array) and POMDP (point-of-sale) algorithms are used for replanning and adjusting the failed part in the global flight path to obtain the existing flight path, and the multi-unmanned aerial vehicle system can safely and efficiently complete tasks.
Fig. 5 is a forest region information monitoring route map when an unmanned aerial vehicle cluster encounters an unexpected fault of an unmanned aerial vehicle of a certain number of times; fig. 6 is an online re-planning forest area information monitoring route map of the unmanned aerial vehicle cluster under a new threat situation.
And 8, judging whether the paths are overlapped, if so, returning to execute the step 7, and otherwise, returning to execute the step 3.
Specifically, it is determined in step 7 whether the re-planned route overlaps the monitored portion, if so, the design is re-calibrated, otherwise, the task is executed according to the new plan.
Preferably, the final information monitoring task is ended under the condition that all surveys in the monitored area are completed or the unmanned aerial vehicle is insufficient in electric quantity and automatically lands on the nearest ground station.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third and the like are for convenience only and do not denote any order. These words are to be understood as part of the name of the component.
Furthermore, it should be noted that in the description of the present specification, the description of the term "one embodiment", "some embodiments", "examples", "specific examples" or "some examples", etc., means that a specific feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, the claims should be construed to include preferred embodiments and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include such modifications and variations.
Claims (9)
1. The utility model provides a forestry information intelligent monitoring system based on multimachine is in coordination which characterized in that, forestry information intelligent monitoring system includes: the system comprises an unmanned aerial vehicle cluster task allocation module, a multi-track collaborative planning module, a track online re-planning module, a forest region information acquisition module and a forest region resource analysis and visualization module; wherein the content of the first and second substances,
the unmanned aerial vehicle cluster task allocation module is used for reasonably allocating tasks to the unmanned aerial vehicle cluster based on monitoring requirements;
the multi-track collaborative planning module is used for carrying out multi-track planning on the unmanned aerial vehicle cluster based on a navigation task;
the flight path online re-planning module is used for carrying out secondary dynamic planning on the surveying route of the unmanned aerial vehicle cluster according to the temporary emergency;
the forest zone information acquisition module is used for dynamically acquiring forest zone information in a monitored area based on sensing equipment carried by the unmanned aerial vehicle cluster;
the forest resource analysis and visualization module is used for processing and analyzing forest images acquired by the unmanned aerial vehicle cluster, so that forest resource information is obtained and visualized and displayed.
2. The system of claim 1, wherein the cluster of drones comprises a cluster of a plurality of single drones, the single drone comprising a power management module, a GPS module, an action component, an attitude resolution module, a data acquisition module, and a reserved interface.
3. The method of claim 1, wherein the forestry information intelligent monitoring system further comprises an unmanned aerial vehicle cluster control system, and the unmanned aerial vehicle cluster control system is used for performing unified planning management on all single unmanned aerial vehicles and monitoring the state information of all single unmanned aerial vehicles;
the state information comprises electric quantity state information, position and track information, attitude information and sensor real-time data information of all the single unmanned aerial vehicles.
4. The system of claim 1, wherein the multi-track plan satisfies a predefined minimum mission cost and an optimized flight path.
5. The system of claim 4, wherein the multi-track planning comprises an initial track planning and a track trajectory optimization;
and the track optimization refers to smoothing the initial track so as to meet the maximum corner constraint of the unmanned aerial vehicle.
6. The system of claim 1, wherein the track on-line re-planning module comprises:
the acquisition unit is used for acquiring and updating the change of the environmental information and the current running state of the unmanned aerial vehicle;
and the adjusting unit is used for replanning the invalid part in the global flight path through the change of the environmental information acquired by the acquisition unit and the current running state of the unmanned aerial vehicle, and adjusting the existing flight path.
7. The system of claim 1, wherein the sensing device is configured to obtain a high-resolution, large-scale drone remote sensing image; the system comprises at least one of a radar system, a single-lens reflex camera, an infrared camera, a multispectral camera and a hyperspectral camera.
8. The system of claim 1, wherein the forest zone information acquisition module is further configured to integrate forest zone information acquisition equipment configured on the ground of the forest zone to be monitored;
the forest region information acquisition equipment is a ground-air integrated forest region information monitoring network formed by forest robots of the environment information sensing equipment, and is used for acquiring forest region original state information and providing a specific data basis for a forest region resource analysis and visualization module;
wherein the environmental information perceiving device includes: illumination intensity sensor, temperature sensor, humidity transducer, infrared trigger camera.
9. The system of claim 1, wherein the forest resource analysis and visualization module comprises an image processing unit;
and the image processing unit is used for splicing and restoring the shot images by using a digital image processing system based on the unmanned aerial vehicle remote sensing images, and further analyzing to obtain related forest resource information.
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