A hybrid optimization method and system for drone-assisted edge computing

Abstract

The present invention discloses a hybrid optimization method and system for unmanned aerial vehicle assisted edge computing, which specifically includes the following steps: step one, edge load; step two, motion consumption statistics; step three, planning and optimization; step four, information storage, and the present invention relates to the field of unmanned aerial vehicle assisted computing technology. The hybrid optimization method and system for unmanned aerial vehicle assisted edge computing counts the energy consumed by the unmanned aerial vehicle when performing actions and performing the actions, thereby realizing the calculation of the energy consumption required for the general control end to issue action instructions, and by comparing with the energy surplus value of the unmanned aerial vehicle, it is ensured that no one can effectively complete the instructions, and the unmanned aerial vehicle that cannot complete the instructions issues an alarm, and uses the energy surplus value and the energy value required to complete the instructions as screening conditions to screen out the nearest surrounding unmanned aerial vehicles, so as to achieve the purpose of replacing the instructions with the operation instructions of the unmanned aerial vehicle with sufficient energy, thereby ensuring the effective operation of multiple unmanned aerial vehicles.

Description

Technical Field

[0001] The present invention relates to the field of drone-assisted computing technology, and in particular to a hybrid optimization method and system for drone-assisted edge computing. Background Art

[0002] As a highly flexible mobile platform, drones have been studied intensively in recent years both in military and life. In particular, the study of drone-assisted edge computing has become a hot topic. Edge computing is a distributed computing model that makes computer data storage closer to where it is needed. For drone-assisted edge computing, the operation process of a drone includes receiving instructions and completing instructions. In the process of completing instructions, the drone needs to be controlled to perform a variety of actions. In the process of using multiple drones to operate together, the different instructions completed by the drones lead to different power consumption levels. As a result, other drones are often working normally, but one or more drones lack energy and cannot work, which seriously affects the work quality of multiple drones. For this reason, a hybrid optimization method and system for drone-assisted edge computing are proposed to monitor the energy of the drone. By comparing the energy consumed by receiving instructions and performing actions, the purpose of replacing instructions with the running instructions of drones with sufficient energy is achieved, thereby ensuring the effective operation of multiple drones. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the deficiencies in the prior art, the present invention provides a hybrid optimization method and system for drone-assisted edge computing to solve the above-mentioned problems.

[0005] (II) Technical solution

[0006] To achieve the above object, the present invention provides the following technical solution: a hybrid optimization method for drone-assisted edge computing, specifically comprising the following steps:

[0007] Step 1: Edge load: Bind the edge processor to the drone, correspond to the drone number, and transmit information with the master control end to receive and send drone operation instructions, calculate the straight-line distance between several drones, and record the energy consumption of the edge processor when it is working;

[0008] Step 2: Movement consumption statistics: After receiving the control command, the drone performs the corresponding action. The energy consumption of the drone when performing different actions is calculated and counted based on time. After the energy consumption required for the corresponding action is counted, the energy consumption data of the corresponding action is sent to the master control end. After receiving the command, the drone counts the energy consumption required to complete the command and records the energy balance. When the energy balance is less than or equal to zero, the energy alarm value is reached. After reaching the alarm value, an alarm message is sent to the master control end, and the energy balance value is synchronized to the master control end.

[0009] Step 3: Planning and selection: The master control end sets the target drone operation task and sends the target drone operation task as a command to the edge processor in step 1. After receiving the alarm signal fed back by the corresponding target drone, the energy consumption required for the target drone operation task is used as the standard value, and compared with the energy surplus value synchronized by the surrounding drones, and the preliminary candidate drones corresponding to the energy surplus value exceeding the standard value are screened out. At the same time, the energy consumption required for the instructions being executed by the preliminary candidate drones is calculated, and the drones with the energy consumption required for executing the instructions of the preliminary candidate drones lower than the energy surplus of the target drone are further screened out as secondary candidate drones. Then, the straight-line distance between the target drone and the secondary candidate drone is calculated through the edge processor in step 1, and the nearest secondary candidate drone is screened out as a replacement drone for task replacement with the target drone.

[0010] Step 4, information storage: Receive the information of the drone's actions and the energy consumption data after the corresponding actions are performed, record them with the drone number as the directory, and the number of operations as the sub-directory, integrate the drone's action data and the energy consumption data of the corresponding actions, compare the energy consumption under the same action in units of times, mark the number of abnormal energy consumption increases, and use them to judge the battery loss process of the drone.

[0011] By adopting the above technical solution, the energy of the drone is monitored, and the energy consumed by the drone when performing an action and after performing the action is counted, thereby realizing the calculation of the energy consumption required for the general control end to issue an action command. By comparing with the energy remaining value of the drone, it is ensured that no one can effectively complete the command, and an alarm is issued for the drone that fails to complete the command. The energy remaining value and the energy value required to complete the command are used as screening conditions to screen out the nearest surrounding drones, so as to achieve the purpose of replacing the command with the running command of the drone with sufficient energy, thereby ensuring the effective operation of multiple drones.

[0012] The present invention also discloses a hybrid optimization system for drone-assisted edge computing, including an information processing system, wherein the information processing system includes an edge load unit, a motion consumption statistics unit, a planning optimization unit and an information storage unit, wherein the edge load unit is respectively connected to the motion consumption statistics unit and the information storage unit, the motion consumption statistics unit is connected to the planning optimization unit, and the planning optimization unit is connected to the information storage unit.

[0013] The present invention is further configured as follows: the edge load unit includes a load binding transmission module and an instruction recording module;

[0014] The load binding transmission module is used to bind the edge processor to the drone and correspond it to the drone number to realize information transmission with the master control end;

[0015] The instruction recording module is used to receive and send drone operation instructions, calculate the straight-line distance between several drones, and record the energy consumption of the edge processor when it is working.

[0016] By adopting the above technical solution, the edge processor is directly bound to the drone, and there is no need to transmit data to the master control end in real time. It will not be connected to the master control end in real time until the alarm signal is triggered. This ensures the effective operation of the drone while reducing the energy loss caused by data transmission during the operation of the drone.

[0017] The present invention is further configured as follows: the motion consumption statistics unit includes a drone operation control module, an energy consumption calculation module, an alarm trigger module and a surplus synchronization module;

[0018] The UAV operation control module is used to control the UAV to perform corresponding actions after the UAV receives the control command;

[0019] The energy consumption calculation module is used to calculate and count the energy consumption of the drone when performing different actions based on the time scale, and after calculating the energy consumption required for the corresponding action, send the energy consumption data of the corresponding action to the master control end;

[0020] The alarm trigger module is used to count the energy consumption required to complete the command after the drone receives the command, and record the energy surplus at the same time. When the energy surplus is less than or equal to zero, the energy alarm value is reached, and an alarm message is sent to the master control end after the alarm value is reached;

[0021] The surplus synchronization module is used to synchronize the energy surplus value to the master control terminal.

[0022] By adopting the above technical solution, the energy remaining values ​​of the drones are simultaneously calculated and directly compared with the energy values ​​required to complete the instructions, so that the work can be completed simply and quickly, the time required for data processing can be reduced, and the quality of collaborative work of multiple drones can be further guaranteed.

[0023] The present invention is further configured as follows: the planning optimization unit includes a task allocation module, a threshold screening module and a proximity screening module, the task allocation module is connected to the threshold screening module, and the threshold screening module is connected to the proximity screening module.

[0024] The present invention is further configured as follows: the task allocation module is used to set the target drone operation task, as a master control terminal, and sends the target drone operation task as an instruction to the edge processor bound to the drone;

[0025] The threshold screening module is used to, after receiving the alarm signal fed back by the corresponding target drone, use the energy consumption required for the target drone to run the task as the standard value, compare it with the energy surplus value synchronized by the surrounding drones, screen out the preliminary candidate drones corresponding to the energy surplus value exceeding the standard value, and at the same time calculate the energy consumption required for the instructions being executed by the preliminary candidate drones, and further screen out the drones whose energy consumption required for executing the instructions of the preliminary candidate drones is lower than the energy surplus of the target drone as the secondary candidate drones;

[0026] The proximity screening module is used to receive the straight-line distance between the target UAV and the secondary candidate UAV calculated by the edge processor, and screen out the nearest secondary candidate UAV as a replacement UAV for mission exchange with the target UAV.

[0027] By adopting the above technical solution, the replacement drone is determined through triple screening. While the screening results are clear, the effective operation of other drones will not be interfered with, thereby ensuring the quality of collaborative work of multiple drones.

[0028] The present invention is further configured as follows: the information storage unit includes a consumption record module, a consumption comparison module and an abnormal marking module, wherein the consumption record module is connected to the consumption comparison module, and the consumption comparison module is connected to the abnormal marking module.

[0029] The present invention is further configured as follows: the consumption recording module is used to receive information about the actions taken by the drone and the energy consumption data after the corresponding actions are taken, and the drone number is used as a directory to record the information, and the number of operations is used as a subdirectory to integrate the drone action data and the energy consumption data for achieving the corresponding actions;

[0030] The consumption comparison module is used to compare the energy consumption of the same action in units of times;

[0031] The abnormal marking module is used to mark the number of abnormal energy consumption increases and to judge the battery loss process of the drone.

[0032] By adopting the above technical solution, the collected drone movements and the energy consumed in making the movements are summarized and sorted, so as to monitor the performance of the batteries equipped on the drone side, providing a clear numerical reference for the optimal use of the battery and the timely replacement of the battery.

[0033] (III) Beneficial effects

[0034] The present invention provides a hybrid optimization method and system for drone-assisted edge computing. It has the following beneficial effects:

[0035] (1) The hybrid optimization method and system for drone-assisted edge computing monitors the energy of drones and counts the energy consumed by drones when performing actions and after performing the actions, thereby realizing the calculation of the energy consumption required for the master control end to issue action instructions. By comparing with the energy remaining value of the drones, it is ensured that no one can effectively complete the instructions, and an alarm is issued when the drone fails to complete the instructions. The energy remaining value and the energy value required to complete the instructions are used as screening conditions to screen out the nearest surrounding drones, so as to achieve the purpose of replacing the instructions with the operating instructions of the drones with sufficient energy, thereby ensuring the effective operation of multiple drones.

[0036] (2) The hybrid optimization method and system for drone-assisted edge computing directly binds the edge processor to the drone, without transmitting data to the master control end in real time. It will not establish a real-time connection with the master control end until an alarm signal is triggered, thereby ensuring the effective operation of the drone and reducing the energy loss caused by data transmission during the operation of the drone.

[0037] (3) The hybrid optimization method and system for drone-assisted edge computing simultaneously calculates the energy reserve value of the drone and directly compares it with the energy value required to complete the instruction, thereby completing the work simply and quickly, reducing the time required for data processing, and further ensuring the quality of collaborative work of multiple drones.

[0038] (4) The hybrid optimization method and system for drone-assisted edge computing determines the replacement drone through triple screening. While the screening results are clear, they will not interfere with the effective operation of other drones, thereby ensuring the quality of collaborative work of multiple drones.

[0039] (5) The hybrid optimization method and system for drone-assisted edge computing monitors the performance of the battery equipped on the drone side by summarizing and organizing the collected drone actions and the energy consumed in making the actions, providing a clear numerical reference for the optimal use of the battery and the timely replacement of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a system principle block diagram of the present invention;

[0041] Figure 2 It is a system principle block diagram of the edge load unit of the present invention;

[0042] Figure 3 It is a system principle block diagram of the sports consumption statistics unit of the present invention;

[0043] Figure 4 A system principle block diagram of the planning and selection unit of the present invention;

[0044] Figure 5 This is a system principle block diagram of the information storage unit of the present invention.

[0045] In the figure, 1. Information processing system; 2. Edge load unit; 3. Motion consumption statistics unit; 4. Planning and optimization unit; 5. Information storage unit; 6. Load binding transmission module; 7. Command recording module; 8. UAV operation control module; 9. Energy consumption calculation module; 10. Alarm trigger module; 11. Remaining energy synchronization module; 12. Task allocation module; 13. Threshold screening module; 14. Nearby screening module; 15. Consumption recording module; 16. Consumption comparison module; 17. Abnormal marking module. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0047] See also Figure 1-5 , an embodiment of the present invention provides a technical solution: a hybrid optimization method for drone-assisted edge computing, specifically comprising the following steps:

[0048] Step 1: Edge load: Bind the edge processor to the drone, correspond to the drone number, and transmit information with the master control end to receive and send drone operation instructions, calculate the straight-line distance between several drones, and record the energy consumption of the edge processor when it is working;

[0049] Step 2: Movement consumption statistics: After receiving the control command, the drone performs the corresponding action. The energy consumption of the drone when performing different actions is calculated and counted based on time. After the energy consumption required for the corresponding action is counted, the energy consumption data of the corresponding action is sent to the master control end. After receiving the command, the drone counts the energy consumption required to complete the command and records the energy balance. When the energy balance is less than or equal to zero, the energy alarm value is reached. After reaching the alarm value, an alarm message is sent to the master control end, and the energy balance value is synchronized to the master control end.

[0050] Step 3: Planning and selection: The master control end sets the target drone operation task and sends the target drone operation task as a command to the edge processor in step 1. After receiving the alarm signal fed back by the corresponding target drone, the energy consumption required for the target drone operation task is used as the standard value, and compared with the energy surplus value synchronized by the surrounding drones, and the preliminary candidate drones corresponding to the energy surplus value exceeding the standard value are screened out. At the same time, the energy consumption required for the instructions being executed by the preliminary candidate drones is calculated, and the drones with the energy consumption required for executing the instructions of the preliminary candidate drones lower than the energy surplus of the target drone are further screened out as secondary candidate drones. Then, the straight-line distance between the target drone and the secondary candidate drone is calculated through the edge processor in step 1, and the nearest secondary candidate drone is screened out as a replacement drone for task replacement with the target drone.

[0051] Step 4, information storage: Receive the information of the drone's actions and the energy consumption data after the corresponding actions are performed, record them with the drone number as the directory, and the number of operations as the sub-directory, integrate the drone's action data and the energy consumption data of the corresponding actions, compare the energy consumption under the same action in units of times, mark the number of abnormal energy consumption increases, and use them to judge the battery loss process of the drone.

[0052] The hybrid optimization system for drone-assisted edge computing includes an information processing system 1, which includes an edge load unit 2, a motion consumption statistics unit 3, a planning optimization unit 4 and an information storage unit 5. Specifically, as shown in the attached Figure 2 As shown, the edge load unit 2 includes a load binding transmission module 6 and an instruction recording module 7;

[0053] The load binding transmission module 6 is used to bind the edge processor to the drone and correspond to the drone number to realize information transmission with the master control end;

[0054] The instruction recording module 7 is used to receive and send UAV operation instructions, calculate the straight-line distance between several UAVs, and record the energy consumption of the edge processor when it is working.

[0055] As a preferred solution, the edge load unit 2 is connected to the motion consumption statistics unit 3 and the information storage unit 5 respectively. Specifically, as shown in the attached Figure 3 As shown, the motion consumption statistics unit 3 includes a UAV operation control module 8, an energy consumption calculation module 9, an alarm triggering module 10 and a surplus synchronization module 11;

[0056] The UAV operation control module 8 is used to control the UAV to perform corresponding actions after the UAV receives the control command;

[0057] The energy consumption calculation module 9 is used to calculate and count the energy consumption of the drone when performing different actions based on the time scale according to the actions performed by the drone, and after calculating the energy consumption required for the corresponding actions, the energy consumption data of the corresponding actions are sent to the master control end;

[0058] The alarm trigger module 10 is used to count the energy consumption required to complete the command after the drone receives the command, and record the energy surplus at the same time. When the energy surplus is less than or equal to zero, the energy alarm value is reached, and an alarm message is sent to the master control end after the alarm value is reached;

[0059] The energy balance synchronization module 11 is used to synchronize the energy balance value to the master control terminal.

[0060] As a preferred solution, the exercise consumption statistics unit 3 is connected to the planning optimization unit 4. Specifically, as shown in the attached Figure 4 As shown, the planning and optimization unit 4 includes a task allocation module 12, a threshold screening module 13 and a nearby screening module 14. The task allocation module 12 is connected to the threshold screening module 13, and the threshold screening module 13 is connected to the nearby screening module 14. The task allocation module 12 is used to set the target drone operation task as the master control end, and send the target drone operation task as a command to the edge processor bound to the drone;

[0061] The threshold screening module 13 is used to, after receiving the alarm signal fed back by the corresponding target drone, take the energy consumption required for the target drone to run the task as the standard value, compare it with the energy surplus value synchronized by the surrounding drones, screen out the preliminary candidate drones corresponding to the energy surplus value exceeding the standard value, and at the same time calculate the energy consumption required for the instructions being executed by the preliminary candidate drones, and further screen out the drones whose energy consumption required for executing the instructions of the preliminary candidate drones is lower than the energy surplus of the target drone as the secondary candidate drones;

[0062] The proximity screening module 14 is used to receive the straight-line distance between the target UAV and the secondary candidate UAV calculated by the edge processor, and screen out the nearest secondary candidate UAV as a replacement UAV for mission exchange with the target UAV.

[0063] As a preferred solution, the planning and selecting unit 4 is connected to the information storage unit 5. Specifically, as shown in the attached Figure 5 As shown, the information storage unit 5 includes a consumption recording module 15, a consumption comparison module 16 and an abnormal marking module 17, wherein the consumption recording module 15 is connected to the consumption comparison module 16, and the consumption comparison module 16 is connected to the abnormal marking module 17. The consumption recording module 15 is used to receive the information of the action taken by the drone and the energy consumption data after the corresponding action is taken, and the drone number is used as a directory to record, and the number of operations is used as a sub-directory to integrate the action data of the drone and the energy consumption data of the corresponding action.

[0064] The consumption comparison module 16 is used to compare the energy consumption of the same action in units of times;

[0065] The abnormal marking module 17 is used to mark the number of abnormal energy consumption increases, and is used to determine the battery consumption process of the drone.

[0066] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A hybrid optimization method for drone-assisted edge computing, characterized in that: The specific steps include: Step 1: Edge load: Bind the edge processor to the drone, correspond to the drone number, and transmit information with the master control end to receive and send drone operation instructions, calculate the straight-line distance between several drones, and record the energy consumption of the edge processor when it is working; Step 2: Movement consumption statistics: After receiving the control command, the drone performs the corresponding action. The energy consumption of the drone when performing different actions is calculated and counted based on time. After the energy consumption required for the corresponding action is counted, the energy consumption data of the corresponding action is sent to the master control end. After receiving the command, the drone counts the energy consumption required to complete the command and records the energy balance. When the energy balance is less than or equal to zero, the energy alarm value is reached. After reaching the alarm value, an alarm message is sent to the master control end, and the energy balance value is synchronized to the master control end. Step 3: Planning and selection: The master control end sets the target drone operation task and sends the target drone operation task as a command to the edge processor in step 1. After receiving the alarm signal fed back by the corresponding target drone, the energy consumption required for the target drone operation task is used as the standard value, and compared with the energy surplus value synchronized by the surrounding drones, and the preliminary candidate drones corresponding to the energy surplus value exceeding the standard value are screened out. At the same time, the energy consumption required for the instructions being executed by the preliminary candidate drones is calculated, and the drones with the energy consumption required for executing the instructions of the preliminary candidate drones lower than the energy surplus of the target drone are further screened out as secondary candidate drones. Then, the straight-line distance between the target drone and the secondary candidate drone is calculated through the edge processor in step 1, and the nearest secondary candidate drone is screened out as a replacement drone for task replacement with the target drone. Step 4, information storage: Receive the information of the drone's actions and the energy consumption data after the corresponding actions are performed, record them with the drone number as the directory, and the number of operations as the sub-directory, integrate the drone's action data and the energy consumption data of the corresponding actions, compare the energy consumption under the same action in units of times, mark the number of abnormal energy consumption increases, and use them to judge the battery loss process of the drone.

2. A hybrid optimization system for drone-assisted edge computing, which executes the hybrid optimization method of claim 1, comprising an information processing system (1), characterized in that: The information processing system (1) comprises an edge load unit (2), a motion consumption statistics unit (3), a planning optimization unit (4) and an information storage unit (5), wherein the edge load unit (2) is connected to the motion consumption statistics unit (3) and the information storage unit (5), respectively, the motion consumption statistics unit (3) is connected to the planning optimization unit (4), and the planning optimization unit (4) is connected to the information storage unit (5).

3. A hybrid optimization system for drone-assisted edge computing according to claim 2, characterized in that: The edge load unit (2) comprises a load binding transmission module (6) and an instruction recording module (7); The load binding transmission module (6) is used to bind the edge processor to the drone and correspond it to the drone number to realize information transmission with the master control end; The instruction recording module (7) is used to receive and send drone operation instructions, calculate the straight-line distance between a number of drones, and record the energy consumption of the edge processor when it is working.

4. The hybrid optimization system for drone-assisted edge computing according to claim 2, characterized in that: The motion consumption statistics unit (3) comprises a drone operation control module (8), an energy consumption calculation module (9), an alarm trigger module (10) and a surplus synchronization module (11); The UAV operation control module (8) is used to control the UAV to perform corresponding actions after the UAV receives the control command; The energy consumption calculation module (9) is used to calculate and count the energy consumption of the drone when performing different actions based on the time scale, and after calculating the energy consumption required for the corresponding action, send the energy consumption data of the corresponding action to the master control end; The alarm trigger module (10) is used to count the energy consumption required to complete the command after the drone receives the command, and record the remaining energy at the same time. When the remaining energy is less than or equal to zero, the energy alarm value is reached, and an alarm message is sent to the master control end after the alarm value is reached; The surplus synchronization module (11) is used to synchronize the energy surplus value to the master control end.

5. The hybrid optimization system for drone-assisted edge computing according to claim 2, characterized in that: The planning optimization unit (4) comprises a task allocation module (12), a threshold screening module (13) and a proximity screening module (14); the task allocation module (12) is connected to the threshold screening module (13), and the threshold screening module (13) is connected to the proximity screening module (14).

6. A hybrid optimization system for drone-assisted edge computing according to claim 5, characterized in that: The task allocation module (12) is used to set the target drone operation task, serving as a master control terminal, and sends the target drone operation task as a command to an edge processor bound to the drone; The threshold screening module (13) is used to, after receiving the alarm signal fed back by the corresponding target drone, use the energy consumption required for the target drone to run the task as a standard value, compare it with the energy surplus values ​​synchronized by the surrounding drones, screen out the preliminary candidate drones corresponding to the energy surplus values ​​exceeding the standard value, and at the same time calculate the energy consumption required for the instructions being executed by the preliminary candidate drones, and further screen out the drones whose energy consumption required for executing the instructions by the preliminary candidate drones is lower than the energy surplus of the target drone as secondary candidate drones; The proximity screening module (14) is used to receive the straight-line distance between the target UAV and the secondary candidate UAV calculated by the edge processor, and screen out the closest secondary candidate UAV as a replacement UAV for performing mission replacement with the target UAV.

7. The hybrid optimization system for drone-assisted edge computing according to claim 2, characterized in that: The information storage unit (5) comprises a consumption recording module (15), a consumption comparison module (16) and an abnormal marking module (17), wherein the consumption recording module (15) is connected to the consumption comparison module (16), and the consumption comparison module (16) is connected to the abnormal marking module (17).

8. A hybrid optimization system for drone-assisted edge computing according to claim 7, characterized in that: The consumption recording module (15) is used to receive information about the actions performed by the drone and the energy consumption data after the corresponding actions are performed, record the information using the drone number as a directory, use the number of operations as a subdirectory, and integrate the drone action data and the energy consumption data for achieving the corresponding actions; The consumption comparison module (16) is used to compare the energy consumption of the same action in units of times; The abnormal marking module (17) is used to mark the number of abnormal energy consumption increases, and is used to judge the battery consumption process of the drone.

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