Intelligent monitoring method for unmanned aerial vehicle swarm scheduling

Abstract

The invention relates to an intelligent monitoring method for unmanned aerial vehicle swarm scheduling, and the method comprises the steps: dividing an unmanned aerial vehicle cluster into a master control unmanned aerial vehicle, a tracking unmanned aerial vehicle and a following unmanned aerial vehicle through the initialization of an unmanned aerial vehicle swarm; the unmanned aerial vehicles in the bee colony move in a preset region range, and the main control unmanned aerial vehicle sends positioning data information to the handheld base station of the user at regular time; when the positioning data information sent by the unmanned aerial vehicle and received by the handheld base station is less than a preset value, determining that the unmanned aerial vehicle has an escape event; the relatively independent functional unmanned aerial vehicles are arranged, the escaping unmanned aerial vehicles are monitored under the condition that the unmanned aerial vehicles meet the flight formation requirement, dynamic monitoring is carried out while the master control unmanned aerial vehicle does not affect bee colony control, and the motion mode of the unmanned aerial vehicle bee colony can be controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to an intelligent monitoring method for scheduling unmanned aerial vehicle swarms. Background Art

[0002] The swarm technology of drones involves the Internet of Things relationship between drones. With the development of drone technology, the human-machine control of drones, based on safety and operational needs, how to solve the problem of dynamic adjustment of operations in the event of a drone escape, requires judging whether the swarm should continue to navigate in order to determine the swarm's path. Summary of the invention

[0003] The purpose of the present invention is to provide a method for intelligent monitoring of drone swarm scheduling to solve the problems raised in the above background technology.

[0004] The technical solution of the present invention is: a method for intelligent monitoring of drone swarm scheduling, which divides the drone cluster into a master drone, a tracking drone and a follower drone by initializing the drone swarm; the drones in the swarm are active within a preset area, and the master drone sends positioning data information to the user's handheld base station at a fixed time; when the positioning data information sent by the drone received by the handheld base station is less than a preset value, it is determined that the drone has escaped; the handheld base station analyzes the final location area and the predicted escape travel area of ​​the drone before the escape event, forms a search instruction and sends it to the tracking drone to perform the search task; the tracking drone performs the search task in the search area according to the search instruction, and reports the search result; The main control drone is equipped with a main control system, and the tracking drone is equipped with an auxiliary control system; the main control system includes a main flight control device, a main data acquisition device, a main response device and a main analysis device; the main data acquisition device continuously associates and collects the flight parameters of the following drone, the main analysis device summarizes the above flight parameters and transmits them back to the handheld base station, and receives the signal of the handheld base station to the main flight control device through the main response device; The auxiliary control system includes an auxiliary flight control device, an auxiliary collection device, an auxiliary response device and an auxiliary analysis device. The auxiliary response device responds to the information of the handheld base station to track the escaping drone, adopts the escaping drone data through the auxiliary collection device, and judges the status of the escaping drone through the auxiliary analysis device to determine whether the tracking drone chooses to return to the swarm and continue tracking.

[0005] The main data acquisition unit includes an image acquisition module, a data processing module and a behavior module. The image acquisition module is bidirectionally electrically connected to the central control unit. The image acquisition module stores the acquired information to the main storage unit through the data processing module. The behavior module is bidirectionally electrically connected to the video storage module. The main data acquisition unit also includes a positioning monitoring device, which monitors the motion trajectory in real time, stores the motion trajectory in the main storage unit, and then sends it to the handheld base station; each follower drone is equipped with a distance sensor and a positioning sensor, and the main analysis device analyzes the distance and number of the follower drones to determine the abnormal situation of the follower drones; if the main analysis device analyzes that the distance or number of the follower drones is less than a predetermined value, it is determined to be an abnormal situation. At this time, the tracking drone closest to the main control drone responds, and the auxiliary acquisition device of the tracking drone performs spatial analysis to determine whether an escape has occurred.

[0006] According to the information transmission from the handheld base station, the main response device is used to respond to the command information to determine whether the swarm continues to navigate in order to determine the route of the drone swarm.

[0007] The main data acquisition unit also includes an early warning unit, a patrol unit, a standby unit and a main power detection unit. The alarm unit is connected to the infrared sensor and the control station respectively. The infrared sensor detects the number of livestock and compares it with the number of livestock in the current livestock activity area circled by the drone positioning signal of the control station to control the switch state of the alarm unit; the patrol unit is electrically connected to the main flight control device, the main power detection unit is respectively connected to the power supply of the driving drone and the main flight control device, and the main power detection unit monitors the remaining power of the driving drone in real time; the standby unit is connected to the control station and the hangar respectively, and the standby unit is started when the driving drone is located in the hangar, and the control station sets the standby time of the standby unit.

[0008] The statistical method used by the auxiliary analysis device to analyze the escaped drone is to use the mean square error for evaluation, and the formula is: ; m is the number of training samples; Use parameters and the predicted value of X, y is the original training sample value, i.e. the standard answer, and the superscript i is the i-th sample.

[0009] The present invention provides an intelligent monitoring method for drone swarm scheduling by improvement, which has the following improvements and advantages compared with the prior art: The present invention proposes an intelligent monitoring method for UAV swarm scheduling. By setting relatively independent functional UAVs, escaping UAVs are monitored when the UAVs meet the flight formation requirements. Dynamic monitoring is performed while the main control UAV does not affect the swarm control, thereby realizing the motion mode of the UAV swarm being controlled. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The present invention will be further explained below in conjunction with the accompanying drawings and embodiments: Figure 1 It is a signal transmission diagram of the present invention. DETAILED DESCRIPTION

[0011] The present invention is described in detail below, and the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] The present invention provides an intelligent monitoring method for UAV swarm scheduling through improvement. The technical solution of the present invention is: As shown in the figure, a method for intelligent monitoring of drone swarm scheduling is provided. By initializing a drone swarm, the drone cluster is divided into a master drone, a tracking drone and a follower drone. The drones in the swarm are active within a preset area, and the master drone periodically sends positioning data information to the user's handheld base station. When the handheld base station receives less positioning data information sent by the drone than a preset value, it is determined that the drone has escaped. The handheld base station analyzes the final location area and the predicted escape travel area of ​​the drone before the escape event, forms a search instruction and sends it to the tracking drone to perform the search task. The tracking drone performs the search task in the search area according to the search instruction and reports the search result. The main control drone is equipped with a main control system, and the tracking drone is equipped with an auxiliary control system; the main control system includes a main flight control device, a main data acquisition device, a main response device and a main analysis device; the main data acquisition device continuously associates and collects the flight parameters of the following drone, the main analysis device summarizes the above flight parameters and transmits them back to the handheld base station, and receives the signal of the handheld base station to the main flight control device through the main response device; The auxiliary control system includes an auxiliary flight control device, an auxiliary collection device, an auxiliary response device and an auxiliary analysis device. The auxiliary response device responds to the information of the handheld base station to track the escaping drone, adopts the escaping drone data through the auxiliary collection device, and judges the status of the escaping drone through the auxiliary analysis device to determine whether the tracking drone chooses to return to the swarm and continue tracking.

[0013] The main data acquisition unit includes an image acquisition module, a data processing module and a behavior module. The image acquisition module is bidirectionally electrically connected to the central control unit. The image acquisition module stores the acquired information to the main storage unit through the data processing module. The behavior module is bidirectionally electrically connected to the video storage module. The main data acquisition unit also includes a positioning monitoring device, which monitors the motion trajectory in real time, stores the motion trajectory in the main storage unit, and then sends it to the handheld base station; each follower drone is equipped with a distance sensor and a positioning sensor, and the main analysis device analyzes the distance and number of the follower drones to determine the abnormal situation of the follower drones; if the main analysis device analyzes that the distance or number of the follower drones is less than a predetermined value, it is determined to be an abnormal situation. At this time, the tracking drone closest to the main control drone responds, and the auxiliary acquisition device of the tracking drone performs spatial analysis to determine whether an escape has occurred.

[0014] According to the information transmission from the handheld base station, the main response device is used to respond to the command information to determine whether the swarm continues to navigate in order to determine the route of the drone swarm.

[0015] The main data acquisition unit also includes an early warning unit, a patrol unit, a standby unit and a main power detection unit. The alarm unit is connected to the infrared sensor and the control station respectively. The infrared sensor detects the number of livestock and compares it with the number of livestock in the current livestock activity area circled by the drone positioning signal of the control station to control the switch state of the alarm unit; the patrol unit is electrically connected to the main flight control device, the main power detection unit is respectively connected to the power supply of the driving drone and the main flight control device, and the main power detection unit monitors the remaining power of the driving drone in real time; the standby unit is connected to the control station and the hangar respectively, and the standby unit is started when the driving drone is located in the hangar, and the control station sets the standby time of the standby unit.

[0016] The statistical method used by the auxiliary analysis device to analyze the escaped drone is to use the mean square error for evaluation, and the formula is: ; m is the number of training samples; Use parameters and the predicted value of X, y is the original training sample value, i.e. the standard answer, and the superscript i is the i-th sample.

[0017] The above description enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent monitoring method for dispatching drone swarms, which initializes the drone swarm and divides the drone cluster into a master drone, a tracking drone and a follower drone; the drones in the swarm move within a preset area, and the master drone sends positioning data information to the user's handheld base station at regular intervals; When the handheld base station receives less positioning data from the drone than the preset value, it is considered that the drone has escaped; The handheld base station analyzes the final location area and predicted escape travel area of ​​the drone before the escape event, forms a search instruction and sends it to the tracking drone to perform the search task; The tracking drone performs the search task in the search area according to the search instruction and reports the search results; The main control drone is equipped with a main control system, and the tracking drone is equipped with an auxiliary control system; the main control system includes a main flight control device, a main data acquisition device, a main response device and a main analysis device; the main data acquisition device continuously associates and collects the flight parameters of the following drone, the main analysis device summarizes the above flight parameters and transmits them back to the handheld base station, and receives the signal of the handheld base station to the main flight control device through the main response device; The auxiliary control system includes an auxiliary flight control device, an auxiliary collection device, an auxiliary response device and an auxiliary analysis device. The auxiliary response device responds to the information of the handheld base station to track the escaped drone, uses the escaped drone data through the auxiliary collection device, and judges the state of the escaped drone through the auxiliary analysis device to determine whether the tracking drone chooses to return to the swarm or continue tracking; The main data acquisition unit includes an image acquisition module, a data processing module and a behavior module. The image acquisition module is bidirectionally electrically connected to the central control unit. The image acquisition module stores the acquired information to the main storage unit through the data processing module. The behavior module is bidirectionally electrically connected to the video storage module. The main data acquisition unit also includes a positioning monitoring device, which monitors the motion trajectory in real time, stores the motion trajectory in the main storage unit, and then sends it to the handheld base station; each follower drone is equipped with a distance sensor and a positioning sensor, and the main analysis device analyzes the distance and number of the follower drones to determine the abnormal situation of the follower drones; if the main analysis device analyzes that the distance or number of the follower drones is less than a predetermined value, it is determined to be an abnormal situation. At this time, the tracking drone closest to the main control drone responds, and the auxiliary acquisition device of the tracking drone performs spatial analysis to determine whether an escape has occurred.

2. The method for intelligent monitoring of drone swarm scheduling according to claim 1, characterized in that: According to the information transmission from the handheld base station, the main response device is used to respond to the command information to determine whether the swarm continues to navigate in order to determine the route of the drone swarm.

3. The method for intelligent monitoring of drone swarm scheduling according to claim 1 is characterized by: The main data acquisition unit also includes an early warning unit, a patrol unit, a standby unit and a main power detection unit. The alarm unit is connected to the infrared sensor and the control station respectively. The infrared sensor detects the number of livestock and compares it with the number of livestock in the current livestock activity area circled by the drone positioning signal of the control station to control the switch state of the alarm unit; the patrol unit is electrically connected to the main flight control device, the main power detection unit is respectively connected to the power supply of the driving drone and the main flight control device, and the main power detection unit monitors the remaining power of the driving drone in real time; the standby unit is connected to the control station and the hangar respectively, and the standby unit is started when the driving drone is located in the hangar, and the control station sets the standby time of the standby unit.

4. The method for intelligent monitoring of drone swarm scheduling according to claim 1, characterized in that: The statistical method used by the auxiliary analysis device to analyze the escaped drone is to use the mean square error for evaluation, and the formula is: ； m is the number of training samples; Use parameters and the predicted value of X, y is the original training sample value, i.e. the standard answer, and the superscript i is the i-th sample.

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