A UAV collaborative low-altitude monitoring and processing method

Abstract

The present invention belongs to the technical field of unmanned aerial vehicle monitoring. The present invention provides a method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles, comprising: dividing an unmanned aerial vehicle cluster having n unmanned aerial vehicle nodes into m unmanned aerial vehicle hierarchical clusters, acquiring flight data of the unmanned aerial vehicle hierarchical clusters, obtaining monitoring benefit performance values ​​of the unmanned aerial vehicle hierarchical clusters based on the flight data of the unmanned aerial vehicle hierarchical clusters, acquiring configuration data of the unmanned aerial vehicles, processing and analyzing the configuration data and monitoring task data of the unmanned aerial vehicles, obtaining the ratio of the number of unmanned aerial vehicles required to be dispatched by the unmanned aerial vehicle hierarchical clusters, and obtaining a determination value for executing a task of the unmanned aerial vehicle hierarchical clusters based on the ratio of the number of unmanned aerial vehicle required to be dispatched by the unmanned aerial vehicle hierarchical clusters and the monitoring benefit performance value of the unmanned aerial vehicle hierarchical clusters through processing and output, and selecting the unmanned aerial vehicle hierarchical cluster to execute the monitoring task according to the determination value for executing the task of the unmanned aerial vehicle hierarchical clusters. The present invention improves the network optimization scheduling capability used by unmanned aerial vehicles.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned aerial vehicle monitoring, and in particular to a method for unmanned aerial vehicle coordinated low-altitude monitoring and processing. Background Art

[0002] With the rapid development of drone technology, drones are increasingly being used in environmental monitoring, target identification, geographic mapping and other fields. Most current drones use a single-machine action mode. Although drones have the advantages of low cost, high efficiency and strong maneuverability, they are limited by their small operating radius, short endurance and limited detection capabilities. A single drone cannot perform large-scale low-altitude monitoring tasks. At present, large-scale drone collaborative operations are in their infancy, and exploring multi-unmanned collaborative operations to improve their service capabilities is a powerful means.

[0003] Based on the above factors, the present invention provides a method for collaborative low-altitude monitoring and processing of drones. It automatically divides the operation area into optimal scheduling for distributed drones based on the task size of the monitoring operation area, the flight distance, the number of drones and the transmission network structure, and offloads tasks from the cloud control center to the edge subnetwork to jointly complete drone monitoring tasks in large areas. Summary of the invention

[0004] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a method for coordinating low-altitude monitoring and processing with unmanned aerial vehicles, comprising:

[0006] Step 1: Divide a drone cluster with n drone nodes into m drone hierarchical clusters, and each drone hierarchical cluster has a drone leader;

[0007] Step 2: Send monitoring mission data to any drone leader. When the drone leader receives the monitoring mission data, it sends the received monitoring mission data and the current location data of the drone hierarchical cluster to the cloud control center, and sends location data transmission instructions to other drone leaders. After receiving the location data transmission instructions, other drone leaders also send the current location data of the drone hierarchical cluster to the cloud control center.

[0008] Step 3: After receiving the monitoring task data and the current location data of the UAV hierarchical cluster, the cloud control center processes and analyzes the monitoring task data and the current location data of the UAV hierarchical cluster to obtain the flight data of the UAV hierarchical cluster. Based on the flight data of the UAV hierarchical cluster, the monitoring benefit performance value XFG of the UAV hierarchical cluster is obtained;

[0009] Step 4: Obtain the configuration data of the drone, process and analyze the configuration data and monitoring task data of the drone, and obtain the demand data, wherein the configuration data of the drone includes the diameter of the collection range of the drone, and the demand data includes the number of drones required to be dispatched by the drone hierarchical cluster XCF;

[0010] Step 5: Based on the ratio of the number of drones required to be dispatched by the drone hierarchical cluster XCF and the monitoring benefit performance value XFG of the drone hierarchical cluster, the output is processed to obtain the drone hierarchical cluster execution task determination value XB, and according to the drone hierarchical cluster execution task determination value XB, the drone hierarchical cluster is selected to perform the monitoring task;

[0011] Step 6: Based on the selected UAV hierarchical cluster, obtain the historical flight comparison data of the UAVs in the UAV hierarchical cluster, wherein the historical flight comparison data includes the representation value of the historical flight number ratio and the historical flight time ratio of the UAV, and obtain the UAV flight aging degree value DV based on the historical flight data. According to the UAV flight aging degree value DV, select a UAV in the monitoring UAV hierarchical cluster to perform the monitoring task.

[0012] As a further technical solution of the present invention, the flight data of the hierarchical cluster of drones includes the predicted flight distance ratio of the hierarchical cluster of drones, the flight angle deviation ratio of the hierarchical cluster of drones, and the proportion of the number of flight obstacles. According to the predicted flight distance ratio of the hierarchical cluster of drones, the flight angle deviation ratio of the hierarchical cluster of drones, and the proportion of the number of flight obstacles, the monitoring benefit performance value XFG of the hierarchical cluster of drones is processed and outputted.

[0013] As a further technical solution of the present invention, the method for obtaining the predicted flight distance ratio of the UAV hierarchical cluster is as follows:

[0014] Obtain the location of the monitoring area and the location of the hierarchical cluster of drones, mark them on the map, connect the center point of the location of the monitoring area with the center point of the location of the hierarchical cluster of drones with a straight line, and obtain the predicted flight route of the hierarchical cluster of drones;

[0015] Measure the distance lengths of the predicted flight routes of all drone hierarchical clusters and sum them up to obtain the predicted total flight distance of all drone hierarchical clusters, measure the distance lengths of the predicted flight routes of drone hierarchical clusters to obtain the predicted flight distance of drone hierarchical clusters, and perform ratio processing on the predicted flight distance of drone hierarchical clusters and the predicted total flight distance of all drone hierarchical clusters to obtain the predicted flight distance ratio of drone hierarchical clusters.

[0016] As a further technical solution of the present invention, the method for obtaining the flight angle deviation ratio of the UAV hierarchical cluster is as follows:

[0017] The center point of the monitoring location area is used as the origin to construct an XY two-dimensional coordinate system, and the center point of the location of the UAV hierarchical cluster is connected to the origin with a straight line to obtain the predicted flight path of the UAV hierarchical cluster;

[0018] The wind direction of the real-time environment is obtained by using sensors. Based on the wind direction of the real-time environment, a construction point is randomly selected on the predicted flight path of the UAV hierarchical cluster. A straight line is drawn based on the construction point and is consistent with the environmental wind direction angle. The straight line is marked as the wind direction line. The deviation angle between the wind direction line and the predicted flight path of the UAV hierarchical cluster is measured to obtain the flight wind direction angle deviation of the UAV hierarchical cluster. The flight angle deviation of the UAV hierarchical cluster is ratio-processed with the circular angle to obtain the flight angle deviation ratio of the UAV hierarchical cluster.

[0019] As a further technical solution of the present invention, the deviation angle is measured as follows: based on the construction point, a straight line is made perpendicular to the predicted flight path of the layered cluster of UAVs, and it is marked as a reference line. If the direction of the wind direction line and the predicted flight path of the layered cluster of UAVs are on the same side of the reference line, the measured deviation angle should be less than 90°. If the direction of the wind direction line and the predicted flight path of the layered cluster of UAVs are not on the same side of the reference line, the measured deviation angle should be greater than 90°.

[0020] As a further technical solution of the present invention, the method for obtaining the proportion of the number of flight obstacles is:

[0021] Based on the predicted flight routes of the hierarchical clusters of drones, the number of obstacles on the predicted flight routes of all hierarchical clusters of drones is counted to obtain the total number of obstacles. The number of obstacles on the predicted flight routes of the hierarchical clusters of drones is counted and the ratio is processed with the total number of obstacles to obtain the proportion of the number of flight obstacles.

[0022] As a further technical solution of the present invention, the number of drones required to be dispatched by the drone hierarchical cluster is obtained in the following manner:

[0023] The number of drones required to be dispatched in each drone hierarchical cluster is obtained respectively, and the sum is calculated to obtain the total number of drones required to be dispatched. The number of drones required to be dispatched in the drone hierarchical cluster is ratioed to the total number of drones required to be dispatched to obtain the ratio XCF of the number of drones required to be dispatched in the drone hierarchical cluster.

[0024] As a further technical solution of the present invention, the number of drones required to be dispatched in the drone hierarchical cluster is obtained as follows:

[0025] Based on the monitoring area position included in the monitoring task data, the outline of the monitoring area is outlined on the map, the predicted flight path of the hierarchical cluster of drones is extended, and the predicted flight path of the hierarchical cluster of drones after extension is marked as a flight extension line, the outline of the monitoring area is segmented by the flight extension line to obtain a first area outline and a second area outline, and a contour point is selected on the first area outline and the second area outline respectively, and marked as a first contour point and a second contour point;

[0026] Calculate the vertical distances from the first contour point and the second contour point to the predicted flight path of the UAV hierarchical cluster, and sum them to obtain the distance of the range to be monitored;

[0027] The distance of the monitored range is ratioed to the diameter of the drone's collection range to obtain the number of drones required for the drone hierarchical clustering.

[0028] As a further technical solution of the present invention, the first contour point and the second contour point are selected in the following manner: on the first area contour, the distance from the first contour point to the flight extension line is the longest; on the second area contour, the distance from the second contour point to the flight extension line is the longest.

[0029] As a further technical solution of the present invention, the method for obtaining the historical flight frequency ratio representation value and the historical flight time ratio representation value of the UAV is as follows:

[0030] Obtain the flight times of each drone in the drone hierarchical cluster and sum them up to get the total flight times of all drones. Ratio the flight times of the drone to the total flight times of all drones to get the representation value of the historical flight times of the drone.

[0031] The total flight time of each drone in the drone hierarchical cluster is obtained and summed up to obtain the total flight time of all drones. The total flight time of the drone is ratioed to the total flight time of all drones to obtain a representation value of the drone's historical flight time ratio.

[0032] The beneficial effects of the present invention are as follows:

[0033] 1. After receiving the monitoring task data and the current position data of the hierarchical cluster of unmanned aerial vehicles through the cloud control center, the present invention processes and analyzes the monitoring task data and the current position data of the hierarchical cluster of unmanned aerial vehicles to obtain the flight data of the hierarchical cluster of unmanned aerial vehicles, wherein the flight data of the hierarchical cluster of unmanned aerial vehicles includes the predicted flight distance ratio of the hierarchical cluster of unmanned aerial vehicles, the flight angle deviation ratio of the hierarchical cluster of unmanned aerial vehicles and the proportion of the number of flight obstacles. According to the flight data of the hierarchical cluster of unmanned aerial vehicles, the monitoring benefit performance value XFG of the hierarchical cluster of unmanned aerial vehicles is processed and outputted. By analyzing the flight distance of the hierarchical cluster of unmanned aerial vehicles, the angle deviation between the flight route and the ambient wind direction, and the number of obstacles in the flight route, the monitoring benefit of the unmanned aerial vehicles is analyzed. When the unmanned aerial vehicles are assigned to perform tasks, the waste of resources used by the unmanned aerial vehicles can be reduced.

[0034] 2. The present invention obtains the configuration data of the drone, processes and analyzes the configuration data and monitoring task data of the drone, and obtains the demand data, wherein the configuration data of the drone includes the diameter of the drone's collection range, and the demand data includes the ratio XCF of the number of drones required to be dispatched by the drone layered cluster, and according to the ratio XCF of the number of drones required to be dispatched by the drone layered cluster and the monitoring benefit performance value XFG of the drone layered cluster, the processing output is obtained to obtain the drone layered cluster execution task judgment value XB, and according to the drone layered cluster execution task judgment value XB, its drone layered cluster is selected to perform the monitoring task, and the number of drones required to be dispatched by the drone layered cluster is analyzed and judged by the scope of the monitoring area and the flight route of the drone layered cluster, and it is combined with the monitoring benefit of the drone layered cluster for analysis, so as to select a suitable drone layered cluster to perform the monitoring task, and further reduce the waste of resources used by drones.

[0035] 3. The present invention obtains historical flight comparison data of the drones in the drone hierarchical cluster based on the selected drone hierarchical cluster, wherein the historical flight comparison data includes a representation value of the proportion of the drone's historical flight times and a representation value of the proportion of historical flight time. The drone flight aging degree value DV is obtained based on the historical flight data. According to the drone flight aging degree value DV, a drone is selected from the monitoring drone hierarchical cluster to perform the monitoring task. The present invention analyzes the aging degree of the drone and selects a drone with a smaller aging degree to perform the monitoring task, thereby ensuring the monitoring quality of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] Figure 1 is a flowchart of the steps of Embodiment 1 of the present invention;

[0038] Figure 2 is a hierarchical cluster diagram of drones according to Embodiment 1 of the present invention;

[0039] Figure 3 This is a flow chart for obtaining the flight angle deviation ratio of the hierarchical cluster of drones in Example 1 of the present invention. DETAILED DESCRIPTION

[0040] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0041] Example 1

[0042] like Figure 1 As shown, a method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles according to an embodiment of the present invention includes:

[0043] Step 1: Divide a drone cluster with n drone nodes into m drone hierarchical clusters according to the shooting range of the drones, and each drone hierarchical cluster has a drone leader;

[0044] It should be noted that the method of dividing drone hierarchical clusters according to the shooting range area of ​​the drone is: drones with the same shooting range area are divided into one drone hierarchical cluster;

[0045] For example, Figure 2 As shown, the drones are divided into three drone hierarchical clusters, among which 1, 5, and 8 are drone leaders;

[0046] It should be noted that each drone node represents a drone;

[0047] Step 2: Send monitoring mission data to any drone leader. When the drone leader receives the monitoring mission data, it sends the received monitoring mission data and the current location data of the drone hierarchical cluster to the cloud control center, and sends location data transmission instructions to other drone leaders. After receiving the location data transmission instructions, other drone leaders also send the current location data of the drone hierarchical cluster to the cloud control center.

[0048] Among them, the monitoring task data includes the location of the monitoring area;

[0049] The current position data of the drone hierarchical cluster includes the current position of the drone hierarchical cluster;

[0050] Step 3: After receiving the monitoring task data and the current position data of the UAV hierarchical cluster, the cloud control center processes and analyzes the monitoring task data and the current position data of the UAV hierarchical cluster to obtain the flight data of the UAV hierarchical cluster, wherein the flight data of the UAV hierarchical cluster includes the predicted flight distance ratio of the UAV hierarchical cluster, the flight angle deviation ratio of the UAV hierarchical cluster and the proportion of the number of flight obstacles. According to the flight data of the UAV hierarchical cluster, the monitoring benefit performance value XFG of the UAV hierarchical cluster is obtained by processing and outputting;

[0051] Specifically, the predicted flight distance ratio of the UAV hierarchical cluster, the flight angle deviation ratio of the UAV hierarchical cluster, and the proportion of the number of flight obstacles are obtained, and they are marked as SD, SF, and SG respectively;

[0052] The obtained UAV hierarchical cluster predicted flight distance ratio SD, UAV hierarchical cluster flight angle deviation ratio SF and flight obstacle quantity ratio SG are processed by the formula: The monitoring benefit performance value XFG of the UAV hierarchical cluster is obtained, where a1, a2 and a3 are all preset proportional coefficients;

[0053] Among them, the method of obtaining the flight distance ratio of the drone hierarchical cluster prediction is as follows:

[0054] Obtain the location of the monitoring area and the location of the hierarchical cluster of drones, mark them on the map, connect the center point of the location of the monitoring area with the center point of the location of the hierarchical cluster of drones with a straight line, and obtain the predicted flight route of the hierarchical cluster of drones;

[0055] Measure the distance lengths of the predicted flight routes of all drone hierarchical clusters and sum them up to obtain the predicted total flight distances of all drone hierarchical clusters; measure the distance lengths of the predicted flight routes of drone hierarchical clusters to obtain the predicted flight distances of drone hierarchical clusters; perform ratio processing on the predicted flight distances of drone hierarchical clusters and the predicted total flight distances of all drone hierarchical clusters to obtain the predicted flight distance ratio of drone hierarchical clusters;

[0056] like Figure 3 As shown in the figure, the method for obtaining the flight angle deviation ratio of the UAV hierarchical cluster is:

[0057] The center point of the monitoring location area is used as the origin to construct an XY two-dimensional coordinate system, and the center point of the location of the UAV hierarchical cluster is connected to the origin with a straight line to obtain the predicted flight path of the UAV hierarchical cluster;

[0058] The wind direction of the real-time environment is obtained by using sensors. Based on the wind direction of the real-time environment, a construction point is randomly selected on the predicted flight path of the UAV hierarchical cluster. A straight line is drawn based on the construction point in accordance with the angle of the environmental wind direction and marked as the wind direction line. The deviation angle between the wind direction line and the predicted flight path of the UAV hierarchical cluster is measured to obtain the flight wind direction angle deviation of the UAV hierarchical cluster. The flight angle deviation of the UAV hierarchical cluster is processed by ratio with the circumferential angle to obtain the flight angle deviation ratio of the UAV hierarchical cluster.

[0059] The deviation angle is measured as follows: a straight line perpendicular to the predicted flight path of the UAV hierarchical cluster is drawn based on the construction point, and marked as the reference line. If the direction of the wind direction line and the predicted flight path of the UAV hierarchical cluster are on the same side of the reference line, the measured deviation angle should be less than 90°. If the direction of the wind direction line and the predicted flight path of the UAV hierarchical cluster are not on the same side of the reference line, the measured deviation angle should be greater than 90°.

[0060] The method for obtaining the percentage of flight obstacles is as follows:

[0061] Based on the predicted flight routes of the hierarchical clusters of drones, the number of obstacles on the predicted flight routes of all the hierarchical clusters of drones is counted to obtain the total number of obstacles. The number of obstacles on the predicted flight routes of the hierarchical clusters of drones is counted and the ratio is processed with the total number of obstacles to obtain the proportion of the number of flight obstacles.

[0062] It should be noted that obstacles include, but are not limited to, buildings and trees whose height is higher than the low-altitude flight height of the drone;

[0063] Step 4: Obtain the configuration data of the drone, process and analyze the configuration data and monitoring task data of the drone, and obtain the demand data, wherein the configuration data of the drone includes the diameter of the collection range of the drone, and the demand data includes the number of drones required to be dispatched by the drone hierarchical cluster XCF;

[0064] Specifically, based on the location of the monitoring area included in the monitoring task data, the outline of the monitoring area is outlined on the map, the predicted flight path of the hierarchical cluster of drones is extended, and the predicted flight path of the hierarchical cluster of drones after extension is marked as a flight extension line, the outline of the monitoring area is segmented by the flight extension line to obtain a first area outline and a second area outline, and a contour point is selected on the first area outline and the second area outline respectively, and marked as a first contour point and a second contour point;

[0065] The first contour point and the second contour point are selected in the following manner: on the first area contour, the distance from the first contour point to the flight extension line is the longest; on the second area contour, the distance from the second contour point to the flight extension line is the longest;

[0066] Calculate the vertical distances from the first contour point and the second contour point to the predicted flight path of the UAV hierarchical cluster, and sum them to obtain the distance of the range to be monitored;

[0067] The distance of the monitored range is processed by the ratio of the diameter of the drone's collection range to obtain the number of drones required for the drone hierarchical cluster;

[0068] The number of drones required to be dispatched in each drone hierarchical cluster is obtained respectively, and the sum is calculated to obtain the total number of drones required to be dispatched. The number of drones required to be dispatched in the drone hierarchical cluster is processed by ratio with the total number of drones required to be dispatched to obtain the ratio XCF of the number of drones required to be dispatched in the drone hierarchical cluster.

[0069] Step 5: Based on the ratio of the number of drones required to be dispatched by the drone hierarchical cluster XCF and the monitoring benefit performance value XFG of the drone hierarchical cluster, the output is processed to obtain the drone hierarchical cluster execution task determination value XB, and according to the drone hierarchical cluster execution task determination value XB, the drone hierarchical cluster is selected to perform the monitoring task;

[0070] Specifically, the monitoring benefit performance value XFG of the drone hierarchical cluster is compared with the number of drones required to be dispatched by the drone hierarchical cluster XCF to obtain the drone hierarchical cluster task execution judgment value XB, and the drone hierarchical cluster corresponding to the largest drone hierarchical cluster task execution judgment value XB is selected to perform the monitoring task, and the drone hierarchical cluster that performs the monitoring task is marked as the monitoring drone hierarchical cluster.

[0071] Step 6: Based on the selected drone hierarchical cluster, obtain the historical flight comparison data of the drones in the drone hierarchical cluster, wherein the historical flight comparison data includes the representation value of the proportion of the historical flight times and the representation value of the historical flight time of the drone, obtain the drone flight aging degree value DV based on the historical flight data, and select a drone from the monitoring drone hierarchical cluster to perform the monitoring task according to the drone flight aging degree value DV;

[0072] Specifically, the UAV flight aging degree value DV is obtained by multiplying the historical flight number representation value and the historical flight time representation value;

[0073] Among them, the method of obtaining the representation value of the historical flight frequency ratio of the drone is as follows:

[0074] Obtain the flight times of each drone in the drone hierarchical cluster and sum them up to get the total flight times of all drones. Ratio the flight times of the drone to the total flight times of all drones to get the representation value of the historical flight times of the drone.

[0075] The method for obtaining the historical flight time ratio representation value of the drone is as follows:

[0076] Obtain the total flight time of each drone in the drone hierarchical cluster, and sum them up to get the total flight time of all drones, and perform ratio processing on the total flight time of the drone to the total flight time of all drones to obtain the representation value of the drone's historical flight time ratio;

[0077] All drones in the monitoring drone hierarchical cluster are sorted according to the drone flight aging value DV, and a drone sorting table is generated. Drones are selected in order from the drone sorting table according to the number of drones required to be dispatched for the monitoring drone hierarchical cluster, and the selected drones are used as drones to perform monitoring tasks.

[0078] The working principle of the present invention is as follows: after the cloud control center receives the monitoring task data and the current position data of the drone hierarchical cluster, the cloud control center processes and analyzes the monitoring task data and the current position data of the drone hierarchical cluster to obtain the drone hierarchical cluster flight data, wherein the drone hierarchical cluster flight data includes the drone hierarchical cluster predicted flight distance ratio, the drone hierarchical cluster flight angle deviation ratio and the proportion of the number of flight obstacles; according to the drone hierarchical cluster flight data, the monitoring benefit performance value XFG of the drone hierarchical cluster is obtained by processing and outputting, the configuration data of the drone is obtained, the configuration data of the drone and the monitoring task data are processed and analyzed, and the demand data is obtained, wherein the drone configuration data includes the collection of the drone Range diameter, demand data includes the ratio XCF of the number of drones required for the drone hierarchical cluster, and according to the ratio XCF of the number of drones required for the drone hierarchical cluster and the monitoring benefit performance value XFG of the drone hierarchical cluster, the processing output is obtained to obtain the drone hierarchical cluster execution task determination value XB, and according to the drone hierarchical cluster execution task determination value XB, the drone hierarchical cluster sub-network is selected to perform the monitoring task, and the number of drones required for the drone hierarchical cluster is analyzed and determined by the range of the monitoring area and the flight route of the drone hierarchical cluster, and it is combined with the monitoring benefit of the drone hierarchical cluster for analysis, so as to select the appropriate drone hierarchical cluster to perform the monitoring task, and further reduce the waste of resources used by drones;

[0079] The historical flight comparison data of the UAVs in the UAV hierarchical cluster is obtained, wherein the historical flight comparison data includes the representation value of the historical flight number ratio and the historical flight time ratio of the UAV. The UAV flight aging degree value DV is obtained based on the historical flight data. According to the UAV flight aging degree value DV, UAVs are selected in the monitoring UAV hierarchical cluster to perform monitoring tasks. The aging degree of the UAVs is analyzed, and UAVs with a smaller aging degree are selected to perform monitoring tasks, thereby ensuring the monitoring quality of the UAVs.

[0080] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles, characterized in that: include: Step 1: Divide a drone cluster with n drone nodes into m drone hierarchical clusters according to the shooting range of the drones, and each drone hierarchical cluster has a drone leader; Step 2: Send monitoring mission data to any drone leader. When the drone leader receives the monitoring mission data, it sends the received monitoring mission data and the current location data of the drone hierarchical cluster to the cloud control center, and sends location data transmission instructions to other drone leaders. After receiving the location data transmission instructions, other drone leaders also send the current location data of the drone hierarchical cluster to the cloud control center. Step 3: After receiving the monitoring task data and the current location data of the UAV hierarchical cluster, the cloud control center processes and analyzes the monitoring task data and the current location data of the UAV hierarchical cluster to obtain the flight data of the UAV hierarchical cluster. Based on the flight data of the UAV hierarchical cluster, the monitoring benefit performance value XFG of the UAV hierarchical cluster is obtained; Step 4: Obtain the configuration data of the drone, process and analyze the configuration data and monitoring task data of the drone, and obtain the demand data, wherein the configuration data of the drone includes the diameter of the collection range of the drone, and the demand data includes the number of drones required to be dispatched by the drone hierarchical cluster XCF; Step 5: Based on the ratio of the number of drones required to be dispatched by the drone hierarchical cluster XCF and the monitoring benefit performance value XFG of the drone hierarchical cluster, the output is processed to obtain the drone hierarchical cluster execution task determination value XB, and according to the drone hierarchical cluster execution task determination value XB, the drone hierarchical cluster is selected to perform the monitoring task; Step 6: Based on the selected UAV hierarchical cluster, obtain the historical flight comparison data of the UAVs in the UAV hierarchical cluster, wherein the historical flight comparison data includes the representation value of the historical flight number ratio and the historical flight time ratio of the UAV, and obtain the UAV flight aging degree value DV based on the historical flight data. According to the UAV flight aging degree value DV, select a UAV in the monitoring UAV hierarchical cluster to perform the monitoring task.

2. The method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles according to claim 1 is characterized in that: The UAV hierarchical cluster flight data includes the UAV hierarchical cluster predicted flight distance ratio, the UAV hierarchical cluster flight angle deviation ratio and the proportion of the number of flight obstacles. According to the UAV hierarchical cluster predicted flight distance ratio, the UAV hierarchical cluster flight angle deviation ratio and the proportion of the number of flight obstacles, the monitoring benefit performance value XFG of the UAV hierarchical cluster is processed and outputted.

3. The method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles according to claim 2 is characterized in that: The method for obtaining the predicted flight distance ratio of the UAV hierarchical cluster is as follows: Obtain the location of the monitoring area and the location of the hierarchical cluster of drones, mark them on the map, connect the center point of the location of the monitoring area with the center point of the location of the hierarchical cluster of drones with a straight line, and obtain the predicted flight route of the hierarchical cluster of drones; Measure the distance lengths of the predicted flight routes of all drone hierarchical clusters and sum them up to obtain the predicted total flight distance of all drone hierarchical clusters, measure the distance lengths of the predicted flight routes of drone hierarchical clusters to obtain the predicted flight distance of drone hierarchical clusters, and perform ratio processing on the predicted flight distance of drone hierarchical clusters and the predicted total flight distance of all drone hierarchical clusters to obtain the predicted flight distance ratio of drone hierarchical clusters.

4. The method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles according to claim 2 is characterized in that: The method for obtaining the flight angle deviation ratio of the UAV hierarchical cluster is as follows: The center point of the monitoring location area is used as the origin to construct an XY two-dimensional coordinate system, and the center point of the location of the UAV hierarchical cluster is connected to the origin with a straight line to obtain the predicted flight path of the UAV hierarchical cluster; The wind direction of the real-time environment is obtained by using sensors. Based on the wind direction of the real-time environment, a construction point is randomly selected on the predicted flight path of the UAV hierarchical cluster. A straight line is drawn based on the construction point and is consistent with the environmental wind direction angle. The straight line is marked as the wind direction line. The deviation angle between the wind direction line and the predicted flight path of the UAV hierarchical cluster is measured to obtain the flight wind direction angle deviation of the UAV hierarchical cluster. The flight angle deviation of the UAV hierarchical cluster is ratio-processed with the circular angle to obtain the flight angle deviation ratio of the UAV hierarchical cluster.

5. The method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles according to claim 4 is characterized in that: The deviation angle is measured as follows: based on the construction point, a straight line is made perpendicular to the predicted flight path of the UAV layered cluster, and it is marked as a reference line. If the direction of the wind direction line and the predicted flight path of the UAV layered cluster are on the same side of the reference line, the measured deviation angle should be less than 90°. If the direction of the wind direction line and the predicted flight path of the UAV layered cluster are not on the same side of the reference line, the measured deviation angle should be greater than 90°.

6. The method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles according to claim 2 is characterized in that: The method for obtaining the proportion of the number of flight obstacles is as follows: Based on the predicted flight routes of the hierarchical clusters of drones, the number of obstacles on the predicted flight routes of all hierarchical clusters of drones is counted to obtain the total number of obstacles. The number of obstacles on the predicted flight routes of the hierarchical clusters of drones is counted and the ratio is processed with the total number of obstacles to obtain the proportion of the number of flight obstacles.

7. The method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles according to claim 1 is characterized in that: The number of drones required to be dispatched by the drone hierarchical cluster is obtained by: The number of drones required to be dispatched in each drone hierarchical cluster is obtained respectively, and the sum is calculated to obtain the total number of drones required to be dispatched. The number of drones required to be dispatched in the drone hierarchical cluster is ratioed to the total number of drones required to be dispatched to obtain the ratio XCF of the number of drones required to be dispatched in the drone hierarchical cluster.

8. The method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles according to claim 7 is characterized in that: The method for obtaining the number of drones required to be dispatched in the drone hierarchical cluster is as follows: Based on the monitoring area position included in the monitoring task data, the outline of the monitoring area is outlined on the map, the predicted flight path of the hierarchical cluster of drones is extended, and the predicted flight path of the hierarchical cluster of drones after extension is marked as a flight extension line, the outline of the monitoring area is segmented by the flight extension line to obtain a first area outline and a second area outline, and a contour point is selected on the first area outline and the second area outline respectively, and marked as a first contour point and a second contour point; Calculate the vertical distances from the first contour point and the second contour point to the predicted flight path of the UAV hierarchical cluster, and sum them to obtain the distance of the range to be monitored; The distance of the monitored range is ratioed to the diameter of the drone's collection range to obtain the number of drones required for the drone hierarchical clustering.

9. The method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles according to claim 8 is characterized in that: The first contour point and the second contour point are selected in the following manner: on the first area contour, the distance from the first contour point to the flight extension line is the longest; on the second area contour, the distance from the second contour point to the flight extension line is the longest.

10. The method for cooperative low-altitude monitoring and processing of unmanned aerial vehicles according to claim 1, characterized in that: The method for obtaining the historical flight frequency ratio representation value and the historical flight time ratio representation value of the drone is as follows: Obtain the flight times of each drone in the drone hierarchical cluster and sum them up to get the total flight times of all drones. Ratio the flight times of the drone to the total flight times of all drones to get the representation value of the historical flight times of the drone. The total flight time of each drone in the drone hierarchical cluster is obtained and summed up to obtain the total flight time of all drones. The total flight time of the drone is ratioed to the total flight time of all drones to obtain a representation value of the drone's historical flight time ratio.

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