Multi-unmanned aerial vehicle optimal edge service method and system for mobile vehicle

Abstract

The invention discloses a multi-unmanned aerial vehicle optimal edge service method and system for a mobile vehicle, and the method comprises the steps: in a research range, a plurality of unmanned aerial vehicles carrying mobile edge computing (MEC) provide edge computing service for the mobile vehicle, and a base station provides additional computing service for the unmanned aerial vehicles. Each moving vehicle only carries one task, and task types are judged through priorities: conventional and emergency. A certain conventional task completes task unloading and calculation within the coverage of the initial unmanned aerial vehicle, and if task calculation is not completed in the initial unmanned aerial vehicle, the task is secondarily unloaded among the unmanned aerial vehicles along with the change of a vehicle driving area. And when the vehicle carrying the emergency task appears, unloading all conventional tasks in the initial unmanned aerial vehicle to the base station BS for the second time to complete subsequent calculation, and returning the initial unmanned aerial vehicle to the original position after completing task calculation along with the vehicle carrying the emergency task.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a method and system for optimal edge services of multiple unmanned aerial vehicles of a mobile vehicle. Background Art

[0002] Due to the good deployment flexibility of drones, drone edge computing hopes to use drones as edge computing nodes to provide transmission, computing and data caching services for ground equipment, which can significantly improve the computing power in areas with weak infrastructure and be applied to scenarios such as disaster relief and military communications. Ground mobile devices can offload computing tasks to drone edge nodes on demand. After completing the computing tasks, the latter returns the results to ground equipment, thereby saving energy consumption of ground equipment and improving the timeliness of computing services. In order to improve service coverage and service quality, multiple drone edge nodes jointly provide services for specific mission areas, which has become a feasible new computing paradigm. In this scenario, how to efficiently utilize the limited computing resources and battery supply of drone platforms is one of the key issues to be considered in the field of drone edge computing.

[0003] At present, the industry has carried out some work in the optimization scheduling of the drone edge computing field. For example, the invention patent application with application number 202211168983.0, "A task offloading scheduling method for energy efficiency optimization in a drone edge computing network", provides a task offloading scheduling method for energy efficiency optimization in a drone edge computing network, which minimizes the energy consumption of IoT mobile devices by jointly optimizing offloading decisions, task scheduling order, transmission bit allocation and drone trajectory; the Chinese invention patent application with application number 202210268185.9, "A linearly dependent task offloading method for a drone edge computing network", can effectively obtain the optimal value of energy consumption by jointly optimizing offloading decisions, resource allocation and drone trajectory, thereby reducing equipment energy consumption; the Chinese invention patent with application number 20221106470.7, "A drone deployment and task offloading method in a multi-drone edge computing network", maximizes the number of tasks carried by the drone by jointly optimizing task offloading decisions, resource allocation and drone position and elevation angle;

[0004] However, the current work still has the following shortcomings: First, it does not jointly consider the problems of multi-UAV trajectory planning, MEC resource scheduling and mobility task offloading. Second, most of them do not consider that traditional optimization algorithms cannot adapt to dynamically changing environments and cannot achieve long-term optimization of the entire service cycle. Third, when facing a multi-UAV resource scheduling system, it is still a single agent deep learning algorithm, which has problems such as low efficiency and poor effect. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention proposes an optimal edge service solution for multiple drones of mobile vehicles, which takes into account the impact of task requests and mobile tasks on dynamic edge computing services, and takes the amount of repeated task offloading and the energy consumption cost of repeated offloading as indicators. By using multi-agent deep Q learning, the drone edge service is modeled as a multi-agent Markov dynamic decision-making process to achieve the optimal edge server resource allocation strategy.

[0006] In a first aspect, the present invention provides a multi-UAV optimal edge service method for a mobile vehicle. Within the research scope, there are multiple UAVs equipped with mobile edge computing MEC to provide edge computing services for the mobile vehicle, and there is also a base station to provide additional computing services for the UAVs; wherein each of the mobile vehicles carries only one task, and the tasks are divided into two types: regular and emergency according to priority;

[0007] The method comprises:

[0008] For any mobile vehicle, first determine the type of mission it carries:

[0009] If it is a routine task, the mobile vehicle will unload the routine task it carries to the corresponding drone within the current drone service range for calculation, and when the mobile vehicle drives to the next drone service range, the current drone will unload the unfinished routine task to the corresponding drone within the next drone service range for continued calculation until the mobile vehicle completes all calculations of the routine tasks it carries;

[0010] If it is an emergency task, the corresponding drone within the drone service range of the mobile vehicle will unload all the routine tasks it undertakes to the base station, and the mobile vehicle will unload the emergency task it carries to the current drone for calculation. At the same time, the current drone will follow the mobile vehicle until the calculation of the emergency task is completed and then return to its original position.

[0011] Furthermore, if there is more than one mobile vehicle carrying an emergency mission within the service range of a certain drone, after the drone undertakes an emergency mission, it dispatches the remaining drones that are not currently undertaking emergency missions to provide edge computing services for the remaining emergency missions.

[0012] Furthermore, the tasks are divided into two types according to priority: routine and urgent, including:

[0013] When the priority of task a is Y a ≥Y th , task a is an urgent task, otherwise task a is a regular task; where a is the task number, k is the elasticity function coefficient, x a is the normalized importance index of task a, τ ais the completion time of task a.

[0014] Furthermore, the method further comprises:

[0015] Taking all the mobile vehicles within the service range of each drone as a cluster and minimizing the global secondary offloading task amount of each drone as the objective function, the intra-cluster resource allocation optimization problem of each cluster is constructed; wherein, the secondary offloading includes the drone offloading the tasks it undertakes to other drones, and the drone offloading the tasks it undertakes to the base station;

[0016] Drones are used as intelligent agents, and a multi-agent deep Q-learning algorithm is used to find the optimal solution to the intra-cluster resource allocation optimization problem of each cluster. The optimal solution is the optimal resource allocation strategy for each drone, achieving the best edge service for multiple drones.

[0017] Furthermore, the expression of the intra-cluster resource allocation optimization problem of each cluster is:

[0018]

[0019] stC1:d im ≤R

[0020] C2:ξ≤α

[0021]

[0022] in, is the global secondary offloading task volume of UAV m, m is the UAV number, R m s1 is the amount of routine tasks that cannot be completed by UAV m and unloaded to other UAVs, R m s2 is the amount of routine tasks that drone m unloads to the base station to complete the emergency task; in constraint condition C1, d im is the straight-line distance between the vertical projection point of UAV m on the ground and the mobile vehicle carrying routine task i, i is the routine task number, and R is the service range of UAV m; in constraint condition C2, ≤ is the minimum task completion rate, α is the task completion rate of UAV m; in constraint condition C3, R i,m is the amount of data transmitted from routine mission i to drone m, D m,max is the maximum amount of mission data transmission that UAV m can support; in constraint C4, is the computing power of drone m for routine task i, is the maximum computing capacity of the MEC carried by UAV m, and U represents the set of UAVs; in constraint C5, f i bs is the computing capacity of the base station BS for conventional task i, is the maximum computing capability of the base station BS for the routine task i, and A1 represents the routine task set.

[0023] Furthermore, the amount of unfinished tasks that drone m unloads to other drones is R m s1 The expression is as follows:

[0024]

[0025] (x) + =max{0,x}

[0026] In the formula, A1 is the set of regular tasks, T mov =R / V is the driving time of a mobile vehicle carrying a routine mission i within the service range of UAV m, V is the speed of a mobile vehicle carrying a routine mission i, B is the bandwidth of the transmission channel between the mobile vehicle and the UAV, is the time taken by the mobile vehicle to unload the routine task i to the drone m, is the power attenuation of the transmission channel between the mobile vehicle carrying the conventional mission i and the UAV m, L0 is the reference distance, Γ dB is the additional loss caused by the environment, is the distance between the mobile vehicle carrying the routine mission i and the UAV m, H is the flight altitude of the UAV, d im is the lateral distance between the mobile vehicle carrying the conventional mission i and the UAV m, p is the signal power, σ 2 is the noise power.

[0027] Furthermore, the amount of routine tasks R that drone m unloads to the base station to complete the emergency task is m s2 The expression is as follows:

[0028]

[0029] (x) + =max{0,x}

[0030] Where A1 is the set of routine tasks, j is the routine task number, R j,m is the amount of data transmitted from routine task j to UAV m, T j,m c f is the time that drone m has provided computing for routine task j when the emergency task occurs, j,m uav is the computing capacity of UAV m for conventional task j.

[0031] Furthermore, drones are used as intelligent agents, and a multi-agent deep Q-learning algorithm is used to find the optimal solution to the intra-cluster resource allocation optimization problem of each cluster, including:

[0032] Taking drones as intelligent agents, the optimal resource allocation strategy for each intelligent agent is found through the multi-intelligence deep Q learning algorithm, where the state parameter s of the deep Q learning algorithm is {D i ,R i,m ,R m s}, the action parameter a is the resource V allocated by drone m to regular task i i ={ω i , l i 、e i}, reward function ε is the proportionality coefficient, D i ={x i ,τ i}, x i is the normalized importance index of regular task i, τ i is the completion time limit of regular task i, ω i is the number of CPU cycles per bit allocated by the MEC onboard UAV m for routine task i, l i The capacitance coefficient assigned by MEC for routine task i of drone m is, e i The local processing energy consumption allocated by MEC for routine task i of drone m.

[0033] In a second aspect, the present invention provides a multi-UAV optimal edge service system for a mobile vehicle, the system comprising a plurality of mobile vehicles carrying tasks, a plurality of UAVs equipped with MEC, and a base station;

[0034] Based on the multi-UAV optimal edge service method for mobile vehicles as described above, the UAV provides edge computing services for the mobile vehicle, and the base station provides additional computing services for the UAV.

[0035] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions that, when executed by a computing device, enable the computing device to execute the multi-UAV optimal edge service method for a mobile vehicle as described above.

[0036] In a fourth aspect, the present invention provides an electronic device comprising one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the multi-UAV optimal edge service method for a mobile vehicle as described above.

[0037] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0038] (1) This scheme models the resource scheduling of MEC edge servers when processing tasks for mobile vehicle users as an optimization problem of the transmission volume of secondary offloading tasks.

[0039] (2) This plan takes into account the priorities of different tasks and gives priority to emergency vehicle tasks.

[0040] (3) This scheme seeks to minimize the amount of secondary unloading tasks and obtain the minimum secondary unloading energy consumption while ensuring the task completion rate of each cluster.

[0041] (4) This scheme aims at the optimization problem and develops a multi-agent deep Q-learning algorithm based on deep learning, where each cluster of drones is regarded as an agent. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is the overall layout diagram of the system where the drone and user vehicle are located.

[0043] Figure 2 This is a diagram of the secondary offloading model for tasks between UAVs.

[0044] Figure 3 Model diagram for secondary unloading of drone tasks to base station.

[0045] Figure 4 This is a flowchart of the multi-agent deep learning algorithm. DETAILED DESCRIPTION

[0046] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0047] The present invention provides a multi-drone optimal edge service system for mobile vehicles, the system comprising several mobile vehicles carrying tasks, several drones equipped with MEC and a base station. Multiple drones equipped with MEC provide edge computing for the tasks carried by the mobile vehicles, each vehicle carries only one task, each task has a different priority, and there may be multiple emergency tasks among them. Each drone is modeled as a minimization problem of the task amount in the secondary unloading process for multiple tasks within its coverage area, and deep Q learning is designed to find the best resource allocation scheme in different MECs. When an emergency task occurs, all tasks of the drone are unloaded to the base station BS to provide the best MEC resources for the emergency task to complete the task. It is modeled as a global energy consumption minimization problem, and the global minimum energy consumption is found through the gradient fast descent algorithm.

[0048] The present invention provides a multi-UAV optimal edge service method for a mobile vehicle. The method seeks the optimal system edge server resource allocation scheme to ensure that the secondary offloading task transmission volume is minimized under the movable edge services of different task types, and obtains the minimum user task secondary offloading energy consumption.

[0049] like Figure 1 As shown in the figure, within the range area D*D, there are M unmanned aerial vehicles (UAVs) equipped with MEC to provide task edge services for ground user mobile vehicles, and there is a base station BS in the center of the range to provide additional computing services for the UAVs. The M UAVs are represented by the set U = {1,2,...M}, and the mobile vehicles have a total of tasks A = {A1,A2} to be performed: regular tasks A1 = {1,2,...,a1}, and emergency tasks A2 = {1,2,...,a2}.

[0050] Define task attributes D a ={x a ,τ a}, x a is the normalized importance index of task a, τ a is the completion time limit of task a. When a task appears, calculate the priority Y of each task a :

[0051]

[0052] Where k is the elasticity function coefficient, which is a fixed constant. Define the priority threshold Y th , Y a ≥Y th , task a is judged to be an urgent task.

[0053] like Figure 2 As shown, a mobile vehicle A carrying a conventional task i 1i After entering the service range of UAV m, it starts to transmit mission data with UAV m. 1i When the drone n reaches the service range boundary at a normal speed, the routine task i has not been completely processed by the drone m. At this time, the drone m unloads the remaining amount of the routine task i to the drone n for a second time and continues the calculation and processing.

[0054] Within the service range of UAV m, the routine task i is unloaded and calculated, including:

[0055] Step a) Define the signal power p and noise power σ 2 , and the subchannel bandwidth is B, which are fixed values. The amount of data transmitted between routine task i and drone m is R i,m :

[0056]

[0057] Define the UAV flight altitude as a fixed value H, and 1i The horizontal distance is d im , then the distance between the two is A 1i with U m Transmission channel power attenuation h i,m (t) are as follows:

[0058]

[0059] Where L0 is the reference distance, which is set to 1 meter here. dB It is the additional loss caused by the environment.

[0060] Step b) Define the computing power of drone m for routine task i as Therefore, the computation time of conventional task i on drone m is:

[0061]

[0062] And the drone coverage is R, so the mission vehicle A 1i The driving time within the service range of drone m is: T mov =R / V.

[0063] like Mission Vehicle A 1i The conventional task i carried by drone m completes all computing tasks.

[0064] like Mission Vehicle A 1i The conventional task i carried cannot complete all the computing tasks on UAV m. Task vehicle A 1i Driving at a constant speed from the service range of UAV m to the service range of UAV n, UAV m communicates with UAV n and completes the unloading of unfinished tasks, and the unfinished tasks are completed by UAV n until the task vehicle A 1i All the regular tasks carried out have been completed.

[0065] Mission Vehicle A 1i The calculation task volume of the conventional task i carried within the service range of UAV m is The remaining amount of data that has not been completed at this time is

[0066]

[0067] The unfinished tasks are unloaded to UAV m for subsequent calculations. Since this part of the task volume undergoes more than one unloading, the unloading process is called the secondary unloading process.

[0068] In summary, in the secondary unloading of “UAV to UAV”, the total remaining task volume of the global UAV is

[0069]

[0070] (x) + =max{0,x}.

[0071] like Figure 3 As shown, a mobile vehicle A carrying an emergency task j 2j , after entering the service range of UAV m, UAV m will interrupt the routine tasks it is currently processing and transfer all of them to the base station BS for subsequent task processing. This process is called secondary unloading of tasks from UAV to base station. 2j Unload the emergency mission j to drone m, which follows A. 2j Fly until all calculations of the emergency task j are completed and then fly back to the original position.

[0072] When an emergency task occurs within the service range of UAV m, UAV m will offload all the original tasks to the base station BS, including:

[0073] Assume that when emergency task k in the emergency task set A2 appears in the service range of drone m, drone m is unloading and calculating routine task j, and its calculated time is Then the remaining task volume of conventional task j in UAV m is

[0074]

[0075] In summary, in the secondary unloading of “drone to base station”, the total remaining task volume of the global drone is

[0076]

[0077] It should be noted here that if more than one emergency task in the emergency task set A2 appears in the service range of drone m at the same time, the additional emergency tasks are communicated and calculated by dispatching other drones around drone m to fly to the mobile vehicle carrying the additional emergency tasks. The flight energy consumption of the drones in this scheduling process is negligible.

[0078] In the secondary unloading of "UAV to UAV" and "UAV to base station" defined in the present invention, the secondary unloading energy consumption ρ is defined for UAV m:

[0079]

[0080] in is the secondary unloading energy consumption required for UAV m to unload routine tasks to UAV n, is the secondary offloading energy consumption required for UAV m to offload routine tasks to base station BS.

[0081] Formulas for each part of secondary unloading energy consumption are explained:

[0082]

[0083] where p m,n is the information transmission power from UAV m to UAV n, p m,bs is the information transmission power from UAV m to base station BS, is the time required for drone m to offload routine tasks to drone n, It is the time required for UAV m to offload routine tasks to base station BS.

[0084] It should be noted here that the secondary unloading energy consumption defined in the present invention only considers the energy consumption generated by the drone secondary unloading unfinished tasks to other drones and base station BS, and the rest of the energy consumption in this process is not within the scope of consideration of the present invention.

[0085] In the present invention, all mobile vehicles within the service range of the drone are called a cluster, and local resource allocation modeling of the cluster is implemented. The resource allocation within the cluster is modeled as a problem of minimizing the global secondary unloading task volume while ensuring a certain task completion rate. The specific modeling is as follows:

[0086]

[0087] stC1:d im ≤R

[0088] C2:ξ≤α

[0089]

[0090] in, is the global secondary offloading task volume of UAV m, m is the UAV number, R m s1 is the amount of routine tasks that cannot be completed by UAV m and unloaded to other UAVs, R m s2 is the amount of routine tasks that drone m unloads to the base station to complete the emergency task. In constraint C1, d imis the straight-line distance between the vertical projection point of UAV m on the ground and the mobile vehicle carrying routine task i, i is the routine task number, and R is the service range of UAV m; in constraint condition C2, ξ is the minimum task completion rate, α is the task completion rate of UAV m; in constraint condition C3, R i,m is the amount of data transmitted from routine mission i to drone m, D m,max is the maximum mission data transmission volume that UAV m can support; in constraint C4, is the computing power of drone m for routine task i, is the maximum computing capacity of the MEC carried by UAV m, and U represents the set of UAVs; in constraint C5, f i bs is the computing capacity of the base station BS for conventional task i, is the maximum computing capability of the base station BS for the routine task i, and A1 represents the routine task set.

[0091] For UAV m, constraint C1 ensures that the distance between the UAV and the ground mission allows normal communication, constraint C2 ensures the mission completion rate, constraint C3 ensures that the communication capacity is within the maximum communication capacity of the UAV, and constraints C4 and C5 constrain the computing power of the UAV and the base station respectively.

[0092] like Figure 4 As shown, in the present invention, drones are used as intelligent agents, and a multi-agent deep Q learning algorithm is used to find the optimal solution to the intra-cluster resource allocation optimization problem of each cluster. The obtained optimal solution is the optimal resource allocation strategy for each drone, thereby achieving the best edge service for multiple drones.

[0093] Furthermore, the state parameter s of the multi-agent deep Q-learning algorithm is s={D i ,R i,m ,R m s}, the action parameter a is the resource V allocated by drone m to regular task i i ={ω i , l i 、e i}, reward function ε is the proportionality coefficient, D i ={x i ,τ i}, x i is the normalized importance index of regular task i, τ i is the completion time limit of regular task i, ω i is the number of CPU cycles per bit allocated by the MEC onboard UAV m for routine task i, l i The capacitance coefficient assigned by MEC for routine task i of drone m is, ei The local processing energy consumption allocated by MEC for routine task i of drone m.

[0094] Agent m observes the environment Then make an action And through the reward and punishment value Affects the next environmental observation value By analogy, we can obtain the experience trajectory of agent m:

[0095]

[0096] According to the experience trajectory of agent m, after z iterations, the resource V allocated to task i by the agent is i ={ω i , l i 、e i}, i∈A1 will tend to be stable, and this resource will be the optimal resource allocation result for task i under agent m.

[0097] The present invention also designs a multi-UAV optimal edge service system for mobile vehicles, comprising:

[0098] Several mobile vehicles carrying tasks, several drones equipped with MEC, and a base station;

[0099] The drone provides edge computing services for the mobile vehicle, and the base station provides additional computing services for the drone.

[0100] The technical solution of the above edge service system is similar to the above method and will not be repeated here.

[0101] Based on the same technical solution, the present invention also discloses a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the above-mentioned multi-UAV optimal edge service method for a mobile vehicle.

[0102] Based on the same technical solution, the present invention also discloses a computing device, including one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the above-mentioned multi-UAV optimal edge service method for mobile vehicles.

[0103] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0107] The above embodiments are only for illustrating the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A multi-drone optimal edge service method for mobile vehicles, It is characterized in that In the research scope, there are multiple drones equipped with mobile edge computing MEC to provide edge computing services for mobile vehicles, and there is also a base station to provide additional computing services for drones; Each of the mobile vehicles carries only one mission, and the missions are divided into two types: routine and emergency according to priority; The method comprises: For any mobile vehicle, first determine the type of mission it carries: If it is a routine task, the mobile vehicle will unload the routine task it carries to the corresponding drone within the current drone service range for calculation, and when the mobile vehicle drives to the next drone service range, the current drone will unload the unfinished routine task to the corresponding drone within the next drone service range for continued calculation until the mobile vehicle completes all calculations of the routine tasks it carries; If it is an emergency task, the corresponding drone within the drone service range of the mobile vehicle will unload all the routine tasks it undertakes to the base station, and the mobile vehicle will unload the emergency task it carries to the current drone for calculation. At the same time, the current drone will follow the mobile vehicle until the calculation of the emergency task is completed and then return to its original position.

2. The multi-drone optimal edge service method for a mobile vehicle according to claim 1, It is characterized in that If there is more than one mobile vehicle carrying an emergency mission within the service range of a drone, after the drone undertakes an emergency mission, it dispatches the remaining drones that are not currently undertaking emergency missions to provide edge computing services for the remaining emergency missions.

3. The multi-drone optimal edge service method for a mobile vehicle according to claim 1, It is characterized in that The tasks are divided into two types according to priority: routine and urgent, including: When the priority of task a is Y a ≥Y th , task a is an urgent task, otherwise task a is a regular task; where a is the task number, x a ∈[0,1], k>0, k is the elasticity function coefficient, x a is the normalized importance index of task a, τ a is the completion time of task a.

4. The multi-drone optimal edge service method for a mobile vehicle according to claim 1, It is characterized in that The method further comprises: Taking all the mobile vehicles within the service range of each drone as a cluster and minimizing the global secondary offloading task amount of each drone as the objective function, the intra-cluster resource allocation optimization problem of each cluster is constructed; wherein, the secondary offloading includes the drone offloading the tasks it undertakes to other drones, and the drone offloading the tasks it undertakes to the base station; Drones are used as intelligent agents, and a multi-agent deep Q-learning algorithm is used to find the optimal solution to the intra-cluster resource allocation optimization problem of each cluster. The optimal solution is the optimal resource allocation strategy for each drone, achieving the best edge service for multiple drones.

5. The multi-drone optimal edge service method for a mobile vehicle according to claim 4, It is characterized in that The expression of the resource allocation optimization problem within each cluster is: s.t.C1:d im ≤R C2:ξ≤α in, is the global secondary offloading task volume of UAV m, m is the UAV number, R m s1 is the amount of routine tasks that cannot be completed by UAV m and unloaded to other UAVs, R m s2 is the amount of routine tasks that drone m unloads to the base station to complete the emergency task; in constraint condition C1, d im is the straight-line distance between the vertical projection point of UAV m on the ground and the mobile vehicle carrying routine task i, i is the routine task number, and R is the service range of UAV m; in constraint condition C2, ξ is the minimum task completion rate, α is the task completion rate of UAV m; in constraint condition C3, R i,m is the amount of data transmitted from routine mission i to drone m, D m,max is the maximum amount of mission data transmission that UAV m can support; in constraint C4, is the computing power of drone m for routine task i, is the maximum computing capacity of the MEC carried by UAV m, and U represents the set of UAVs; in constraint C5, f i bs is the computing capacity of the base station BS for conventional task i, is the maximum computing capacity of the base station BS for routine task i, A 1 Represents a collection of general tasks.

6. The multi-drone optimal edge service method for a mobile vehicle according to claim 5, It is characterized in that The amount of unfinished tasks that drone m offloads to other drones R m s1 The expression is as follows: (x) + =max{0,x} In the formula, A 1 is a set of regular tasks, T mov =R / V is the driving time of a mobile vehicle carrying a routine mission i within the service range of UAV m, V is the speed of a mobile vehicle carrying a routine mission i, B is the bandwidth of the transmission channel between the mobile vehicle and the UAV, is the time taken by the mobile vehicle to unload the routine task i to the drone m, is the power attenuation of the transmission channel between the mobile vehicle carrying the conventional mission i and the UAV m, L 0 is the reference distance, Γ dB is the additional loss caused by the environment, is the distance between the mobile vehicle carrying the routine mission i and the UAV m, H is the flight altitude of the UAV, d im is the lateral distance between the mobile vehicle carrying the conventional mission i and the UAV m, p is the signal power, σ 2 is the noise power.

7. The multi-drone optimal edge service method for a mobile vehicle according to claim 5, It is characterized in that The amount of routine tasks R that drone m unloads to the base station to complete the emergency task m s2 The expression is as follows: (x) + =max{0,x} In the formula, A 1 is the set of regular tasks, j is the regular task number, R j,m is the amount of data transmitted from routine task j to UAV m, T j,m c f is the time that drone m has provided computing for routine task j when the emergency task occurs, j,m uav is the computing capacity of UAV m for conventional task j.

8. The multi-drone optimal edge service method for a mobile vehicle according to claim 5, It is characterized in that Using drones as intelligent agents, a multi-agent deep Q-learning algorithm is used to find the optimal solution to the intra-cluster resource allocation optimization problem of each cluster, including: Taking drones as intelligent agents, the optimal resource allocation strategy for each intelligent agent is found through the multi-intelligence deep Q learning algorithm, where the state parameter s of the deep Q learning algorithm is {D i ,R i,m ,R m s }, the action parameter a is the resource V allocated by drone m to regular task i i ={ω i , l i 、e i }, reward function ε is the proportionality coefficient, D i ={x i ,τ i }, x i is the normalized importance index of regular task i, τ i is the completion time limit of regular task i, ω i is the number of CPU cycles per bit allocated by the MEC onboard UAV m for routine task i, l i The capacitance coefficient assigned by MEC for routine task i of drone m is, e i The local processing energy consumption allocated by MEC for routine task i of drone m.

9. A multi-drone optimal edge service system for mobile vehicles, It is characterized in that The system includes several mobile vehicles carrying tasks, several drones carrying MEC, and a base station; Based on the method according to any one of claims 1 to 7, the drone provides edge computing services for the mobile vehicle, and the base station provides additional computing services for the drone.

10. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, It is characterized in that When the instructions are executed by a computing device, the computing device is caused to perform the method according to any one of claims 1 to 8.

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