A mobile edge computing trajectory design method and system based on networked drones

Abstract

The present application discloses a mobile edge computing trajectory design method and system based on a networked unmanned aerial vehicle, wherein the mobile edge computing trajectory design method based on a networked unmanned aerial vehicle specifically includes the following steps: initializing parameters; obtaining an optimal waypoint access sequence in response to initialization status information; obtaining an initial trajectory of a first unmanned aerial vehicle based on the optimal waypoint access sequence; determining an optimal transmission strategy based on the initial trajectory of the first unmanned aerial vehicle; by exploring the optimal transmission strategy between two consecutive cruise points, the present application can reasonably utilize limited computing resources, reduce task completion time and energy consumption, and can obtain processed data in a timely manner in order to formulate a response plan and output the optimal transmission strategy.

Description

Technical Field

[0001] The present application relates to the field of mobile communications, and specifically, to a mobile edge computing trajectory design method and system based on networked unmanned aerial vehicles. Background Art

[0002] With the rapid development of 5G technology and Internet of Things technology, traditional ground communication technology can no longer meet the growing demand for data throughput. As a tool with high mobility and scalability, drones play an increasingly important role in the communication field. They can expand the network to the air and achieve full coverage of the communication network. Drones can not only serve as air base stations, but also as relays, air users, etc. Drones as data information collectors are a promising application. The academic community has conducted a lot of research on them and formed relatively mature solutions. However, the use of drones for multi-location patrol inspection tasks has received little attention at present, and there is no effective trajectory initialization solution. In this scenario, drones need to perform data collection tasks at multiple fixed locations, which puts higher requirements on the order in which drones traverse fixed data collection points. For the problem of traversing several fixed points, the existing trajectory design solutions are the pickup-and-delivery problem and the traveling salesman problem. These two trajectory design solutions are considered from different perspectives. Specifically, the delivery problem requires that the drone must pass through fixed point B after passing through fixed point A. Under this strict causal constraint, the shortest path is found. However, this strictly causal solution is not applicable to most scenarios that require traversing fixed points, especially when the number of inspection points and the number of base stations are not equal. The traveling salesman problem only requires the shortest total distance. Although this solution has a certain effect on reducing the energy consumption of drone flight, it only considers the flight distance and completely ignores the impact of data transmission time on the energy consumption of the drone system. When the cruise point is far away from the base station, the drone will spend a lot of time transmitting data when unloading large amounts of data to the base station due to the long distance. Therefore, the traveling salesman problem solution has certain limitations and is not completely applicable to inspection scenarios, which will ultimately lead to less than ideal results.

[0003] Therefore, how to provide a minimum ratio traveling salesman problem solution, thereby making up for the limitations of the traveling salesman problem solution, is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] This application proposes a new trajectory design scheme for the fixed-point inspection scenario of drones. Considering that the networked drone needs to unload the information collected at the cruise point to the base station for processing, the traditional trajectory initialization scheme cannot effectively reduce the overall energy of the drone system, and when the drone needs to traverse K cruise points, there are K! trajectories, which is a non-deterministic polynomial problem. The trajectory initialization scheme proposed in this application shows excellent performance when facing large-scale data unloading, making up for the limitations and shortcomings of the traditional trajectory initialization scheme.

[0005] In order to solve the above problems, the present application provides a mobile edge computing trajectory design method based on networked drones, which specifically includes the following steps: initializing parameters; obtaining the best cruise point visit order in response to initialization status information; obtaining the initial trajectory of the first drone based on the best cruise point visit order; determining the optimal transmission strategy based on the initial trajectory of the first drone; and outputting the optimal transmission strategy.

[0006] As above, the parameter initialization is to assign values ​​to the parameters, specifically including: input cruise point Location K represents the number of cruise points, k represents a natural number, and the starting point q I With the end point q F Location and base stations Location The height of the base station is set to H G .

[0007] As above, the parameter initialization also includes setting the drone to be at a constant height H during flight. f Maintain a constant speed, that is, speed v = V max =50m / s; the starting point, end point and cruising point are collectively referred to as nodes, and the time T that the drone spends between any two nodes a and b is set a,b Expressed as g a and g b Represent the positions of the two-dimensional plane of node a and node b respectively.

[0008] As above, parameter initialization also includes dividing the path from node a to node b into N line segments, and the drone position and flight time corresponding to the nth line segment are q n and τ n During this period, the time it takes for the drone to establish contact with different base stations in each time slot is expressed as

[0009] As above, obtaining the best cruise point visit sequence includes determining the data throughput of the UAV communicating with a base station at a distance less than a specified distance.

[0010] As above, where the data throughput Q a,b for:

[0011]

[0012] Where B is the bandwidth, P n is the transmission power of the UAV in the nth time slot, γ m It is related to the propagation environment and the base station antenna gain. α is the path loss component, where N means the path from node a to node b is divided into N line segments, and H G represents the height of the base station, w m represents the base station location, q n represents the position of the UAV corresponding to the nth line segment, H f Indicates that the drone is at a constant altitude during flight.

[0013] As above, the obtained optimal cruise point access sequence determines the initial trajectory of the first UAV, wherein the starting point, each cruise point and the end point are sequentially connected according to the optimal cruise point access sequence to obtain the initial trajectory of the first UAV.

[0014] As above, in the process of determining the optimal transmission strategy, it also includes determining the transmission trajectory of the third UAV.

[0015] A mobile edge computing trajectory design system based on a networked unmanned aerial vehicle, specifically comprising an initialization unit, an optimal cruise point access sequence acquisition unit, a first unmanned aerial vehicle initial trajectory acquisition unit, an optimal transmission strategy acquisition unit and an output unit; the initialization unit is used to perform parameter initialization; the optimal cruise point access sequence acquisition unit is used to acquire the optimal cruise point access sequence; the first unmanned aerial vehicle initial trajectory acquisition unit is used to acquire the first unmanned aerial vehicle initial trajectory according to the optimal cruise point access sequence; the optimal transmission strategy acquisition unit is used to determine the optimal transmission strategy according to the first unmanned aerial vehicle initial trajectory; the output unit is used to output the optimal transmission strategy.

[0016] As above, the optimal transmission strategy acquisition unit further includes a third UAV initial trajectory acquisition module, which is used to acquire the third UAV initial trajectory.

[0017] This application has the following beneficial effects:

[0018] (1) By exploring the optimal transmission strategy between two consecutive cruise points, this application can reasonably utilize limited computing resources, reduce task completion time and energy consumption, and obtain processed data in a timely manner to formulate response plans.

[0019] (2) This application can greatly improve system performance and work efficiency by formulating clear measurement standards. Compared with selecting any cruise point access sequence and TSP scheme, the scheme proposed by the present invention is more operational and practical when facing large-scale data unloading.

[0020] (3) This application can achieve the goal of minimizing the overall energy consumption of the drone system. When the amount of information collected is small, the traveling salesman problem solution is used to initialize the trajectory; when the task volume is large, the minimum ratio traveling salesman problem solution is used to achieve rapid data transmission and reduce data transmission time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0022] Figure 1 It is an internal structure diagram of a mobile edge computing trajectory design system based on a networked drone provided in an embodiment of the present application;

[0023] Figure 2 Another internal structure diagram of a mobile edge computing trajectory design system based on a networked drone provided in an embodiment of the present application;

[0024] Figure 3 It is a flow chart of a mobile edge computing trajectory design method based on a networked drone provided in an embodiment of the present application;

[0025] Figure 4 It is a flowchart of the sub-steps in the mobile edge computing trajectory design method based on a networked drone provided in an embodiment of the present application;

[0026] Figure 5 1 is a schematic diagram comparing a first initial trajectory and a second initial trajectory of a UAV provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0028] The present invention proposes a mobile edge computing trajectory design method and system based on a networked drone for a fixed-point inspection scenario of a drone. Specifically, the drone needs to start from a fixed starting point, go through K cruise points and finally fly to the end point. During the flight, it is necessary to unload part of the information collected at the cruise point to the base station for processing, and before flying to the next cruise point (the last stage is to fly to the end point), the information collected at the previous cruise point is processed. The purpose of this application is to minimize the total energy consumption of drone flight, drone communication energy consumption and drone computing energy consumption. Considering the delay sensitivity, the present invention transforms the original problem into a research problem of the optimal transmission strategy between two consecutive cruise points. The present invention designs a new metric to determine the optimal access order of the cruise points. Simply put, it is to determine the order of the cruise points when the ratio of the flight energy consumption of the drone to the throughput in this process is the smallest. At this time, the corresponding drone trajectory is the optimal initial trajectory under the new metric, which can not only meet the basic requirements of traversing each cruise point, but also take into account the influence of distance and base station. In general, the trajectory initialization scheme proposed by the present invention breaks through the limitations of traditional schemes and makes up for the shortcomings.

[0029] Embodiment 1

[0030] like Figure 1 As shown, this application provides a mobile edge computing trajectory design system based on networked drones.

[0031] The system of the present application specifically includes: an initialization unit 110, an optimal cruise point visit sequence acquisition unit 120, a first drone initial trajectory acquisition unit 130, an optimal transmission strategy acquisition unit 140, and an output unit 150.

[0032] The initialization unit 110 is used to perform parameter initialization.

[0033] The optimal cruise point visiting sequence acquiring unit 120 is connected to the initializing unit 110 and is used to acquire the optimal cruise point visiting sequence.

[0034] The first UAV initial trajectory acquisition unit 130 is connected to the optimal cruise point visit sequence acquisition unit 120 and is used to acquire the first UAV initial trajectory according to the optimal cruise point visit sequence.

[0035] The optimal transmission strategy acquisition unit 140 is connected to the first UAV initial trajectory acquisition unit 130 and is used to determine the optimal transmission strategy according to the first UAV initial trajectory.

[0036] Specifically, among them Figure 2As shown, the optimal transmission strategy acquisition unit 140 specifically includes an initialization module 210, a third UAV initial trajectory acquisition module 220, a first optimization module 230, a second optimization module 240, a condition judgment module 250, an optimization output module 260, and an analysis module 270.

[0037] The initialization module 210 is used to initialize parameters;

[0038] The third UAV initial trajectory acquisition module 220 is connected to the initialization module 210 and is used to acquire the third UAV initial trajectory;

[0039] The first optimization module 230 is connected to the third UAV initial trajectory acquisition module 220, and is used to optimize the task completion time, communication scheduling, and computing resource allocation according to the acquired third initial trajectory.

[0040] The second optimization module 240 is connected to the first optimization module 230 and is used to optimize the third initial trajectory according to the optimized task completion time, communication scheduling, and computing resource allocation.

[0041] The condition judgment module 250 is connected to the first optimization module 230 and the second optimization module 240 respectively, and is used to judge the condition.

[0042] The optimization output module 260 is connected to the condition judgment module 250 and is used to output the optimized initial trajectory, task completion time, communication scheduling and computing resource allocation of the third UAV.

[0043] The analysis module 270 is connected to the optimization output module 260, and is used to analyze the output optimized initial trajectory of the third UAV, the task completion time, the communication scheduling and the computing resource allocation to obtain the best transmission strategy.

[0044] The output unit 150 is connected to the optimal transmission strategy acquisition unit 140 and is used to output the optimal transmission strategy.

[0045] Embodiment 2

[0046] like Figure 3 As shown, this application provides a mobile edge computing trajectory design method based on a networked drone, which specifically includes the following steps:

[0047] Step S310: Initialize parameters.

[0048] Parameter initialization is to assign values ​​to some parameters. The specific operations include:

[0049] Enter each cruise point Location K represents the number of cruise points, k represents a natural number, and the starting point q IWith the end point q F and base stations Location The height of the base station is uniformly set to H G .

[0050] Set the drone to fly at a constant altitude H f Maintain a constant speed, that is, v = V max =50m / s, other fixed speeds can also be set.

[0051] In this embodiment, the starting point, the end point, and the cruise point are collectively referred to as nodes. The starting point, the end point, and the cruise point are all subsets of the node, and the node = {starting point, end point, cruise point}.

[0052] The time T that the drone spends between any two nodes a,b Expressed as g a and g b They represent the two-dimensional plane positions of node a and node b respectively, and the drone only maintains communication with the nearest base station during the process of flying from node a to node b.

[0053] It is worth noting that any two nodes must be two different cruise points, and cannot represent the starting point and the end point at the same time. For example, the starting point and the end point cannot be called any two nodes, nor can the same cruise point s a To the cruise point a are called any two nodes.

[0054] The path discretization technology is used to divide the path from node a to node b into N segments. The position and flight time of the drone corresponding to the nth segment are q n and τ n During this period, the time it takes for the drone to establish contact with different base stations in each time slot can also be expressed as

[0055] Step S320: In response to the initialization state information, the optimal cruise point visiting sequence is obtained.

[0056] The parameters initialized in step S3100 are used for iterative optimization to determine the best cruise point visiting sequence.

[0057] Specifically, referring to the fixed-point inspection method in the prior art, the UAV flight energy consumption E of the K+1 segment is minimized by ensuring that the UAV meets the condition that it starts from the starting point and passes through K patrol points without repetition to reach the end point. a,b The sum of K+1 segments of the drone and the nearest base station g m The data throughput of the communication is Q a,b to determine the best cruise point visiting order.

[0058] Specifically, in the segment from node a to node b, the drone only maintains communication with the nearest base station. m The data throughput Q of the communication between the nearest base station, i.e. the base station whose distance between the drone and the base station is less than the specified threshold a,b Specifically expressed as:

[0059]

[0060] Where B is the bandwidth, P n is the transmission power of the UAV in the nth time slot, γ m It is related to the propagation environment and the base station antenna gain, α is the path loss component, and N means that the path from node a to node b is divided into N line segments.

[0061] In the section from node a to node b, the corresponding UAV flight energy consumption is:

[0062]

[0063] Where P 0 ,U tip ,P i ,v 0 ,d 0 ,ρ,s,A are the relevant parameters of the rotary wing UAV, specifically, P 0 Indicates the blade profile power in the hovering state; U tip represents the tip speed of the rotor blade; P i Indicates the induced power in the hovering state; v 0 represents the average rotor induced speed during hovering; d 0 represents the fuselage drag ratio; ρ represents the air density; s represents the rotor stability; A represents the rotor disk area; T a,b represents the time the drone spends between any two nodes, V max It means that the drone is at a constant altitude H during flight. f flight speed.

[0064] Specifically, assuming that the drone traversal order is π is the traversal order. We only know that the first one is the starting point and the last one is the end point. The order of the specific cruise points is uncertain, which is also the problem that this application needs to solve. π(0) here represents the starting point, that is, the place where the drone departs, π(1),...,π(K) respectively represent the first cruise point, the second cruise point, and the Kth cruise point that the drone passes through, and π(K+1) represents the end point, where the drone finally flies to. The flight energy consumption generated when the drone flies from the starting point to the first cruise point is E π(0),π(1), corresponding to the data throughput generated by communicating with the nearest base station during flight is Q π(0),π(1) ; The flight energy consumption and data throughput generated from the first cruise point to the second cruise point are E π(1),π(2) , Q π(1),π(2) The rest of the process is analogous to this, until the UAV flies from the Kth cruising point to the end point. The flight energy consumption and data throughput generated in this process are E π(K),π(K+1) , Q π(K),π(K+1) By minimizing To determine the order in which the drone traverses the cruise points, that is, when W takes the smallest value, the corresponding π is the result we are looking for.

[0065] Preferably, the solution to the optimal cruise point visiting sequence is similar to the traditional traveling salesman problem solution, for example, simulated annealing algorithm, ant colony algorithm and genetic algorithm can be applied.

[0066] Step S330: Obtain the initial trajectory of the first UAV according to the optimal cruise point visit sequence.

[0067] Specifically, the optimal cruise point access sequence obtained in step S320 is used to determine the initial trajectory of the first UAV, wherein the starting point, each cruise point and the end point are sequentially connected according to the optimal cruise point access sequence to obtain the initial trajectory of the first UAV.

[0068] like Figure 5 As shown in the figure, 6 cruise points and 5 base stations are designed. The black solid line with arrows and the black dotted line with arrows indicate the order of cruise points under the new measurement standard proposed in this application. At this time, the order of cruise points is starting point, s 1 、s 2 、s 3 、s 4 、s 5 、s 6 , the end point, and connect them to form the initial trajectory of the first UAV.

[0069] Step S340: Determine the optimal transmission strategy according to the initial trajectory of the first UAV.

[0070] In response to determining the optimal cruise point access sequence in step S330, the optimal transmission strategy is specifically the optimal transmission strategy between π(1) and π(2), the optimal transmission strategy between π(2) and π(3), and finally the optimal transmission strategy between π(K) and π(K+1). The following are the steps performed for any two cruise points (including the end point).

[0071] Among them Figure 4 As shown, step S340 specifically includes the following sub-steps:

[0072] Step S3401: Initialize parameters.

[0073] Specifically, initializing the parameters includes setting the starting point position, the end point position and the cruise point position to be consistent with the parameters in step S310.

[0074] It also includes setting the flight speed of the drone, the amount of information collected at each cruise point, the maximum computing frequency of the CPU on the base station and the CPU on the drone, and the drone parameter P involved in the drone flight energy consumption formula. 0 ,U tip ,P i ,v 0 ,d 0 ,ρ,s,A and the parameters involved in calculating data throughput, bandwidth B, and the transmission power P of the drone in the nth time slot n , γ m , path loss component α, UAV flight altitude and base station altitude.

[0075] Step S3402: In response to initializing the parameters, an initial trajectory of the third UAV is obtained.

[0076] Before obtaining the initial trajectory of the third UAV, the method further includes obtaining the initial trajectory of the second UAV.

[0077] Specifically, in the process of acquiring the initial trajectory of the second UAV, the TSP solution obtained according to the TSP method in the prior art is the cruise point arrangement order when the total distance is the shortest, and the initial trajectory of the second UAV is obtained according to the cruise point arrangement order when the total distance is the shortest.

[0078] Among them Figure 5 As shown in the figure, the arrows of the black solid arrow and the black dashed arrow point to the order of cruise points obtained by using the shortest distance as the criterion, that is, the order obtained by using the TSP scheme. In this case, the visit order is the starting point, s 1 、s 2 、s 6 、s 3 、s 4 、s 5 , the end point, and connect them to form the initial trajectory of the second UAV.

[0079] The third UAV initial trajectory is the first UAV initial trajectory and the second UAV initial trajectory obtained in step S230.

[0080] The following steps S2403-S2406 are respectively performed using the first UAV initial trajectory and the second UAV initial trajectory as the third UAV initial trajectory. Specifically, the first UAV initial trajectory is used as the third UAV initial trajectory to perform steps S2403-S2406, and the second UAV initial trajectory is used as the third UAV initial trajectory to perform steps S2403-S2406.

[0081] Step S3403: Optimize task completion time, communication scheduling, and computing resource allocation according to the acquired third initial trajectory.

[0082] Specifically, through the initial trajectory of the third UAV in step S3402, the alternating optimization algorithm and the convex optimization tool are used to optimize the task completion time, communication scheduling and computing resource allocation.

[0083] Step S3404: Optimize the third initial trajectory according to the optimized task completion time, communication scheduling, and computing resource allocation.

[0084] Specifically, the optimized task completion time, communication scheduling, and computing resource allocation values ​​obtained in step S3403 are applied to optimize the third initial trajectory.

[0085] The alternating optimization algorithm and convex optimization tools are still used to optimize the third initial trajectory.

[0086] The execution of steps S3403 and S3404 is an iterative optimization. After the first iterative optimization, a second iterative optimization is performed again. After the second iterative optimization is completed, step S3405 is executed.

[0087] Step S3405: ​​Perform conditional judgment in response to optimizing task completion time, communication scheduling, and computing resource allocation.

[0088] Specifically, by analyzing the objective function value, that is, analyzing the change in the objective function value after two consecutive iterative optimizations, it is determined whether the objective function value has reached the convergence condition. If the convergence condition is reached, the optimized task completion time, communication scheduling, computing resource allocation and the trajectory of the third UAV are output.

[0089] If the convergence condition is not met, the optimized third initial trajectory is applied in step S3403, and step S3403 is re-executed to continue the next round of iterative optimization.

[0090] Specifically, for example, the objective function value achieved after setting the optimized task completion time, communication scheduling, and computing resource allocation is A, and the objective function value achieved after optimizing the third initial trajectory is B. Both objective function values ​​refer to the sum of the achieved UAV flight energy consumption and communication energy consumption and the energy consumption generated by the communication between the UAV and the base station. And set a very small value C for judgment. The smaller C is, the higher the accuracy is. After optimizing the third initial trajectory, the objective function value is B when performing the rth round of optimization. r , in the r+1th round of optimization, the objective function value is B r+1 , when |B r+1 -B r When |<C, the convergence condition is met.

[0091] Step S3406: Output the optimized initial trajectory, task completion time, communication scheduling, and computing resource allocation of the third UAV.

[0092] Specifically, since the first UAV initial trajectory and the second UAV initial trajectory are respectively used as the third UAV initial trajectory to execute steps S3403-S3406, two optimized third UAV initial trajectories are obtained, as well as two task completion times, communication scheduling and computing resource allocation results. The two optimized third UAV initial trajectories are output, as well as two task completion times, communication scheduling and computing resource allocation results.

[0093] Step S3407: Analyze the optimized initial trajectory of the third UAV, the task completion time, the communication scheduling and the computing resource allocation to obtain the best transmission strategy.

[0094] Specifically, the two optimized initial trajectories of the third UAV, as well as the two mission completion times, communication scheduling, and computing resource allocation results are analyzed to obtain the optimal transmission strategy.

[0095] Two optimized initial trajectories of the third UAV, as well as two mission completion times, communication scheduling and computing resource allocation results were used for simulation analysis. The final simulation results showed that the best transmission strategy was the optimized initial trajectory of the first UAV, as well as the optimized mission completion time, communication scheduling and computing resource allocation obtained based on the optimized initial trajectory of the first UAV.

[0096] Step S350: Output the optimal transmission strategy.

[0097] By executing the above steps, it can be proved that the trajectory initialization scheme proposed in this application can show superior performance when the amount of data information collected at the cruise point is large, and is better than the TSP scheme. The performance of the TSP scheme is better when the amount of information collected at the cruise point is small. Providing two schemes to comprehensively improve system performance is of great significance in real scenarios.

[0098] Among them through Figure 5 It can also be clearly seen that the TSP trajectory initialization scheme only considers the flight distance and completely ignores the influence of the base station. Although the trajectory initialization scheme proposed in the application does not have the shortest total flight distance, it comprehensively considers the dual influence of the total distance and the base station location. This can be seen from the s 2 -s 3 、s 5 -s 6 、s 6 As can be seen from the end segment, the corresponding trajectory is very close to the base station. When the drone approaches the base station, it will obtain better channel conditions, which will greatly reduce the time it takes for the drone to unload data to the base station and further reduce the drone's energy consumption.

[0099] This application has the following beneficial effects:

[0100] (1) By exploring the optimal transmission strategy between two consecutive cruise points, this application can reasonably utilize limited computing resources, reduce task completion time and energy consumption, and obtain processed data in a timely manner to formulate response plans.

[0101] (2) This application can greatly improve system performance and work efficiency by formulating clear measurement standards. Compared with selecting any cruise point access sequence and TSP scheme, the scheme proposed by the present invention is more operational and practical when facing large-scale data unloading.

[0102] (3) This application can achieve the goal of minimizing the overall energy consumption of the drone system. When the amount of information collected is small, the traveling salesman problem solution is used to initialize the trajectory; when the task volume is large, the minimum ratio traveling salesman problem solution is used to achieve rapid data transmission and reduce data transmission time.

[0103] Although the present application is described with reference to examples, this is for illustrative purposes only and is not intended to limit the present application, and changes, additions and / or deletions to the embodiments may be made without departing from the scope of the present application.

[0104] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A mobile edge computing trajectory design method based on networked drones, characterized in that: The specific steps include: Initialize parameters; In response to the initialization state information, obtaining an optimal cruise point visiting sequence; According to the optimal cruise point visit sequence, the initial trajectory of the first UAV is obtained; Determine the best transmission strategy based on the initial trajectory of the first UAV; Output the best transmission strategy; Obtaining the optimal cruise point visit sequence involves minimizing Determine the order in which the drone traverses the cruise points, where E π(i),π(i+1) represents the flight energy consumption from the i-th cruising point to the i+1-th cruising point, Q π(i),π(i+1) represents the data throughput generated by flying from the i-th cruise point to the i+1-th cruise point, K represents the number of cruise points, π(1),...,π(K) represent the first cruise point, the second cruise point, and finally the K-th cruise point passed by the UAV respectively; According to the initial trajectory of the first UAV, determining the optimal transmission strategy includes the following sub-steps: Initialize parameters; After completing the parameter initialization, the initial trajectory of the third UAV is obtained; before obtaining the initial trajectory of the third UAV, the method further includes obtaining the initial trajectory of the second UAV; the initial trajectory of the third UAV is respectively the initial trajectory of the first UAV and the initial trajectory of the second UAV; According to the obtained initial trajectory of the third UAV, the task completion time, communication scheduling, and computing resource allocation are optimized; Optimize the initial trajectory of the third UAV based on the optimized mission completion time, communication scheduling, and computing resource allocation; Determine whether the objective function value meets the convergence condition; If the convergence condition is met, the optimized task completion time, communication scheduling, computing resource allocation and the initial trajectory of the third UAV are output; The optimal transmission strategy is obtained by analyzing the initial trajectory of the third UAV, task completion time, communication scheduling, and computing resource allocation after output optimization.

2. The mobile edge computing trajectory design method based on networked drones according to claim 1, characterized in that: Parameter initialization is to assign values ​​to parameters, including inputting cruise points Location K represents the number of cruise points, k represents a natural number, and the starting point q I With the end point q F Location and base stations Location M represents the number of base stations, and the height of each base station is set to H G .

3. The mobile edge computing trajectory design method based on networked drones according to claim 2, characterized in that: Parameter initialization also includes setting the drone to fly at a constant altitude H f Maintain a constant speed, that is, speed v = V max =50m / s; The starting point, end point, and cruising point are collectively referred to as nodes, and the time T that the drone spends between any two nodes a and b is set a,b Expressed as g a and g b Represent the positions of the two-dimensional plane of node a and node b respectively.

4. The mobile edge computing trajectory design method based on networked drones according to claim 3, characterized in that: Parameter initialization also includes dividing the path from node a to node b into N segments, and the drone position and flight time corresponding to the nth segment are q n and τ n During this period, the time it takes for the drone to establish contact with different base stations in each time slot is expressed as 5. The mobile edge computing trajectory design method based on networked drones according to claim 4, characterized in that: Obtaining the optimal cruise point visit sequence includes determining the data throughput of the UAV communicating with a base station at a distance less than a specified distance.

6. The mobile edge computing trajectory design method based on networked drones according to claim 5, characterized in that: Data throughput Q a,b for: Where B is the bandwidth, P n is the transmission power of the UAV in the nth time slot, γ m It is related to the propagation environment and the base station antenna gain. α is the path loss component, where N means the path from node a to node b is divided into N line segments, and H G represents the height of the base station, w m represents the base station location, q n represents the position of the UAV corresponding to the nth line segment, H f Indicates that the drone is at a constant altitude during flight.

7. The mobile edge computing trajectory design method based on networked drones according to claim 1, characterized in that: The initial trajectory of the first UAV is determined according to the obtained optimal cruise point access sequence, wherein the starting point, each cruise point and the end point are sequentially connected according to the optimal cruise point access sequence to obtain the initial trajectory of the first UAV.

8. A mobile edge computing trajectory design system based on networked drones, characterized in that: Specifically, it includes an initialization unit, an optimal cruise point visit sequence acquisition unit, a first UAV initial trajectory acquisition unit, an optimal transmission strategy acquisition unit, and an output unit; An initialization unit, used for parameter initialization; An optimal cruise point visit sequence acquisition unit, used for acquiring an optimal cruise point visit sequence; A first UAV initial trajectory acquisition unit, used to acquire the first UAV initial trajectory according to the optimal cruise point visit sequence; An optimal transmission strategy acquisition unit, used to determine an optimal transmission strategy according to the initial trajectory of the first UAV; An output unit, used for outputting an optimal transmission strategy; The optimal cruise point visit sequence acquisition unit acquires the optimal cruise point visit sequence by minimizing Determine the order in which the drone traverses the cruise points, where E π(i),π(i+1) represents the flight energy consumption from the i-th cruising point to the i+1-th cruising point, Q π(i),π(i+1) represents the data throughput generated by flying from the i-th cruise point to the i+1-th cruise point, K represents the number of cruise points, π(1),...,π(K) represent the first cruise point, the second cruise point, and finally the K-th cruise point passed by the UAV respectively; The optimal transmission strategy acquisition unit determines the optimal transmission strategy according to the initial trajectory of the first UAV, including the following sub-steps: Initialize parameters; After completing the parameter initialization, the initial trajectory of the third UAV is obtained; before obtaining the initial trajectory of the third UAV, the method further includes obtaining the initial trajectory of the second UAV; the initial trajectory of the third UAV is respectively the initial trajectory of the first UAV and the initial trajectory of the second UAV; According to the obtained initial trajectory of the third UAV, the task completion time, communication scheduling, and computing resource allocation are optimized; Optimize the initial trajectory of the third UAV based on the optimized mission completion time, communication scheduling, and computing resource allocation; Determine whether the objective function value meets the convergence condition; If the convergence condition is met, the optimized task completion time, communication scheduling, computing resource allocation and the initial trajectory of the third UAV are output; The optimal transmission strategy is obtained by analyzing the initial trajectory of the third UAV, task completion time, communication scheduling, and computing resource allocation after output optimization.

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