A safety rate maximization method, system, medium

Abstract

The application discloses a kind of safety rate maximization method, system, medium, comprising the following steps: initialization unmanned aerial vehicle flight parameter;According to the unmanned aerial vehicle flight parameter, system channel model, energy consumption model and system safety rate maximization model are established;Task allocation and scheduling factor, unmanned aerial vehicle transmission power, unmanned aerial vehicle flight trajectory are calculated by alternating iterative optimization algorithm, until based on unmanned aerial vehicle auxiliary MEC system safety rate convergence, and the safety rate maximization result after optimization is output.The application meets the demand of low latency and calculation for computationally intensive or delay-sensitive tasks, enhances the security of unmanned aerial vehicle ground communication system, effectively improves the performance of communication system.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment monitoring and control system technology, specifically to a method, system, and medium for maximizing safe speed. Background Technology

[0002] Unmanned aerial vehicles (UAVs), as highly mobile and flexible communication devices, have been widely used in fifth-generation (5G) wireless networks. UAVs, with their excellent line-of-sight transmission capabilities, can provide better channel conditions in communication, thereby improving communication quality. However, as application scenarios such as real-time video recognition, augmented reality, and virtual reality place higher demands on computing power and data processing latency, traditional UAVs, limited by computing resources and energy, face challenges to their computing performance.

[0003] The emergence of Mobile Edge Computing (MEC) technology provides a solution for 5G communication, allowing drones to offload computing tasks to MEC servers at the network edge via wireless links, thereby significantly improving the computing performance and communication efficiency of drones. However, due to the strong line-of-sight characteristics of drone communication links, the risk of eavesdropping increases, threatening the security of data transmission. Therefore, enhancing the security of drone-assisted communication systems has become an urgent problem to be solved.

[0004] Existing physical layer security (PLS) technologies have been applied in some scenarios, but they still suffer from problems such as complex algorithms and low optimization efficiency. In particular, in environments where multi-task computing offloading and eavesdropping threats coexist, how to maximize security speed under limited energy and computing resources remains a technical challenge. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and storage medium for maximizing security rate, which meets the low latency and computational requirements of computationally intensive or latency-sensitive tasks, enhances the security of UAV-to-ground communication systems, and effectively improves the performance of communication systems.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for maximizing safe rate includes the following steps:

[0008] Initialize the drone's flight parameters;

[0009] Based on the UAV flight parameters, establish a system channel model, an energy consumption model, and a system safe rate maximization model;

[0010] The task allocation, scheduling factor, UAV launch power, and UAV flight trajectory are solved by alternating iterative optimization algorithm until the safe rate of the UAV-assisted MEC system converges, and the optimized safe rate maximization result is output.

[0011] Establishing a system channel model includes:

[0012] Determine the channel power gain between the UAV and the ground AP, the channel power gain between the UAV and the ground eavesdropper, and the channel power gain between the ground AP and the ground eavesdropper;

[0013] Define the scheduling factor and determine its constraints;

[0014] Determine the rates between the UAV and the ground AP, the rates between the UAV and the ground eavesdropper, and the secure rate achieved by the UAV in the nth time slot and the kth ground AP, and construct a system channel model.

[0015] In the above technical solution, establishing a system energy consumption model includes:

[0016] Determine the mission energy consumption, which includes computational mission energy consumption, communication mission energy consumption, and flight propulsion energy consumption;

[0017] The constraints on the energy consumption of each task are determined based on the energy consumption of the task; and a system energy consumption model for the UAV in the process of transmission power, computational task allocation, and trajectory optimization is constructed based on the constraints.

[0018] In the above technical solution, establishing a safe rate maximization model includes:

[0019] Define scheduling factor, task allocation, UAV launch power, and UAV trajectory as block variables;

[0020] By combining the system energy consumption model and the system channel model, and under the constraints of the system energy consumption model, the maximum safe rate of the system is determined by optimizing the task offloading decision, the UAV's launch power, and the flight trajectory.

[0021] In the above technical solution, the task allocation, scheduling factor, UAV transmission power, and UAV flight trajectory are solved by an alternating iterative optimization algorithm, including:

[0022] The initial UAV flight parameters are iteratively optimized, the UAV trajectory and transmission power are preset, the first stage problem is solved, and the scheduling factor and task allocation for the first stage are determined.

[0023] The above technical solution, which uses an alternating iterative optimization algorithm to solve for task allocation and scheduling factors, UAV transmission power, and UAV flight trajectory, also includes:

[0024] Based on the scheduling factor, task allocation, and initial UAV trajectory of the first stage, the second stage problem is solved by the continuous convex approximation method to obtain the UAV transmission power of the second stage.

[0025] The above technical solution, which uses an alternating iterative optimization algorithm to solve for task allocation, scheduling factor, UAV transmission power, and UAV flight trajectory, also includes:

[0026] By incorporating the scheduling factor and task allocation in the first stage and the UAV launch power in the second stage, relaxation variables are introduced into the security rate and flight propulsion energy consumption achieved by the UAV in the nth time slot and the kth ground AP. The third stage problem is solved using the continuous convex approximation method to obtain the UAV trajectory and relaxation variables in the third stage.

[0027] The above technical solution, based on the safe rate convergence of the UAV-assisted MEC system, includes:

[0028] The difference between the maximum system safety rate of the current iteration and the maximum system safety rate of the previous iteration is less than the convergence value or the maximum number of iterations has been reached.

[0029] This invention provides a drone control system, including

[0030] Rotary-wing drones;

[0031] At least one ground access point;

[0032] Mobile edge computing server;

[0033] A control unit for implementing any of the above-described methods for maximizing the safe rate.

[0034] The present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the security rate maximization method as described in any of the above claims.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] This application provides a method, system, and medium for maximizing the security rate. It uses an alternating iterative optimization algorithm to solve for task allocation, scheduling factors, UAV transmission power, and UAV flight trajectory to make judgments until the security rate of the UAV-assisted MEC system converges. This maximizes the system's security rate under constraints such as UAV energy consumption and system security rate, effectively improving the performance and security of the communication system. Attached Figure Description

[0037] Figure 1 This is a system structure view provided in an embodiment of the present invention.

[0038] Figure 2This is a schematic diagram of a process provided in an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1:

[0041] This embodiment provides a method for maximizing security rate, which addresses the limitations of resource-constrained drones in meeting the application's requirements for low-latency communication and computing, and the need to meet computationally intensive and latency-sensitive task requirements while ensuring the security of the communication system in the presence of ground eavesdroppers, thereby completing the overall computing task.

[0042] Based on unmanned aerial vehicle-assisted MEC systems, such as Figure 1 As shown, one drone starts from a fixed starting point, passes through K ground access points (APs), and finally reaches its destination. The drone can offload computational tasks to ground APs equipped with MEC servers. The entire computational task must be completed within the drone's flight time T. The drone can compute the task itself or offload part of the computational task to the ground APs during flight. Furthermore, considering the potential for eavesdroppers on the ground, ground APs also apply interference noise to enhance the security of the communication system.

[0043] like Figure 2 As shown, this method includes the following steps:

[0044] Step S1: Initialize the UAV flight parameters, which include the UAV's start point, end point, flight time, flight speed, energy consumption parameters, and computational workload. Specifically, the UAV flight parameters include the input locations of various ground access points (APs). k ={w1,w2,…,w k}, k∈K={1,2,…,K}, the starting point of the drone is q I With the endpoint q F And the location of the ground eavesdropper w e .

[0045] The drone is assumed to maintain a constant altitude H during flight. The flight time T is uniformly divided into N (n∈N={1,2,…,N}) time slots, with each time slot lasting δ=T / N. The total task L to be completed by the drone, the channel power gain β0 at a reference distance d=1m, the communication bandwidth B, and the noise power σ at the receiver are all considered.2 The interference power P of the AP j The blade power P of the drone in hovering state o and induced power P i U tip v represents the tip velocity of the rotor blades. o d represents the average rotor induced velocity during forward flight. o ε and s represent the fuselage drag ratio and rotor rigidity, respectively, while ε and M represent air density and rotor disk area. Path loss index The drone calculates the number of loops C of the central processing unit (CPU) for each task. u The energy consumption of the drone per CPU cycle is expressed as P. u The CPU computing frequency f of the drone u The total energy of the drone is E u Peak power of the drone P max The maximum speed of the drone, v max Convergence value θ and maximum number of iterations R max The computational task of the UAV unloading to the k-th ground AP in the nth time slot is l. k [n], the flight speed of the UAV in the nth time slot is v[n], and the initial transmit power of the UAV unloaded to the kth ground AP in the nth time slot is P. k [n], initialize the unmanned trajectory corresponding to the nth time slot as q[n].

[0046] Step S2: Establish a system channel model, energy consumption model, and system safety rate maximization model based on the UAV flight parameters.

[0047] Furthermore, establishing a system channel model includes:

[0048] Determine the channel power gain between the UAV and the ground AP, the channel power gain between the UAV and the ground eavesdropper, and the channel power gain between the ground AP and the ground eavesdropper;

[0049] Define the scheduling factor and determine its constraints;

[0050] Determine the rates between the UAV and the ground AP, the rates between the UAV and the ground eavesdropper, and the secure rate achieved by the UAV in the nth time slot and the kth ground AP, and construct a system channel model.

[0051] The channel power gains for the UAV and ground AP, the UAV and ground eavesdropper, and the ground AP and ground eavesdropper are respectively:

[0052]

[0053] ξ is the Rayleigh fading coefficient that follows a unit mean exponential distribution.

[0054] Consider using a Time Division Multiple Access (TDMA) protocol to offload computation from the UAV, meaning the UAV can associate with at most one ground access point (AP) per time slot. Define a scheduling factor a. k [n] represents when a k When [n] = 1, the UAV has a connection with the k-th ground AP in the nth time slot; otherwise, a k [n] = 0. Therefore, a k [n] must satisfy the following constraints:

[0055]

[0056] The rate of the UAV in the nth time slot and the rate of the ground AP in the kth time slot are expressed as:

[0057]

[0058] Because of the presence of eavesdroppers on the ground, the speed difference between the drone and the ground eavesdroppers is:

[0059]

[0060] Among them, Ε ε [·] represents the mathematical expectation of the random variable ξ, and the inequality in equation (6) is due to Jensen's inequality and the fact that the function is concave with respect to ξ. Equation (6) shows R ue The upper bound of [n]. Assume Eve can reach this upper bound to consider the worst-case security rate performance. Based on the above discussion, the security rate achievable by the UAV in the nth time slot and the kth ground AP is given by the following equation:

[0061]

[0062] in

[0063] Establishing a system energy consumption model includes:

[0064] Determine the mission energy consumption, which includes computational mission energy consumption, communication mission energy consumption, and flight propulsion energy consumption;

[0065] The constraints on the energy consumption of each task are determined based on the energy consumption of the task; and a system energy consumption model for the UAV in the process of transmission power, computational task allocation, and trajectory optimization is constructed based on the constraints.

[0066] In the process of calculating energy consumption for computational tasks, to reduce the pressure on local computing, UAVs can offload some computing tasks to ground-based APs equipped with MEC servers for computation. k [n] represents the computational task of the UAV unloading to the k-th ground AP in the nth time slot, and satisfies:

[0067]

[0068] The security rate of the UAV in the nth time slot and the kth ground AP must not be lower than the offloading rate to ensure that the offloaded data bits are secure, that is:

[0069]

[0070] The drone communicates with the ground access point (AP) sequentially. The computing power consumption and communication power consumption of the drone are as follows:

[0071]

[0072] P k [n] represents the transmit power of the UAV unloaded to the k-th ground AP in the nth time slot. Due to the influence of battery life, it is affected by the peak power P. max The limitations are:

[0073]

[0074] The drone's calculation time is:

[0075]

[0076] The computation time of the drone should be less than the flight time of the drone, and the following conditions should be met:

[0077] T u ≤T (14)

[0078] In this system, the drone flies at a constant altitude and travels from the starting point to the destination within time T. The drone's trajectory will then be subject to the following constraints:

[0079] ||q[n+1]-q[n]||=v[n]δ,n∈N (15)

[0080]

[0081] q[1]=q I ,q[N+1]=q F (17)

[0082] The drone flies at a fixed altitude, and only kinetic energy is considered in the flight energy consumption. For a rotary-wing drone with a flight speed of v[n], the flight propulsion energy consumption of the drone at the nth time slot is expressed as:

[0083]

[0084] The total energy of the drone is finite. During the flight time T, the energy consumed by the drone must meet the following constraints:

[0085] E ulo +E utr +E fly ≤E u (20)

[0086] Given the two models above, establish a safe rate maximization model, including:

[0087] Define scheduling factor, UAV trajectory, task allocation, and launch power as block variables;

[0088] By combining the system energy consumption model and the system channel model, and under the constraints of the system energy consumption model, the maximum safe rate of the system is determined by optimizing the UAV's launch power, mission offloading decision, and flight trajectory.

[0089] Define block variables Let represent the scheduling factor, the UAV's trajectory, task allocation, and transmission power, respectively. Therefore, the system's safe rate maximization model can be expressed as:

[0090]

[0091] st(4),(8),(9),(12),(14),(16),(17),(20) (21a)

[0092] Step S3: Solve the task allocation, scheduling factor, UAV transmission power and UAV flight trajectory through an alternating iterative optimization algorithm until the safe rate of the UAV-assisted MEC system converges, and output the optimized safe rate maximization result.

[0093] Specifically, the initial UAV flight parameters are iteratively optimized, the UAV trajectory and transmission power are preset, the first-stage problem is solved, and the scheduling factor and task allocation for the first stage are determined.

[0094] Based on the scheduling factor and task allocation of the first stage and the initialized UAV trajectory, the second stage problem is solved by the continuous convex approximation method to obtain the UAV transmission power of the second stage.

[0095] By incorporating the scheduling factor and task allocation in the first stage and the UAV launch power in the second stage, relaxation variables are introduced into the security rate and flight propulsion energy consumption achieved by the UAV in the nth time slot and the kth ground AP. The third stage problem is solved using the continuous convex approximation method to obtain the UAV trajectory and relaxation variables in the third stage.

[0096] The algorithm iteratively optimizes the UAV flight parameters obtained in step S1 to find the optimal result, i.e., the result that maximizes the security rate. By jointly optimizing the scheduling factor, calculating task allocation, UAV transmission power, and flight trajectory, the system security rate is maximized under constraints such as UAV energy consumption and system security rate. This alternating iterative optimization algorithm divides the original problem into three sub-problems. Each stage yields a suboptimal solution to the sub-problem, and finally, it iterates back to the initial stage until the convergence condition is met to obtain the optimal solution, effectively improving the performance and security of the communication system.

[0097] In the first stage, when the UAV trajectory Q and the transmission power P are preset, the original problem (P1) has been transformed into a convex problem, and the constraints (21a) have been transformed into convex constraints. The scheduling factor A and task allocation L can be solved directly using the CVX tool in Matlab. The local optimal solution of the convex optimization problem will also be the global optimal solution.

[0098] In the second stage, firstly, given the optimized scheduling factor A, task assignment L, and initial UAV trajectory Q, then (21) is combined with the non-convex constraint (9) in (21a). The expression is converted to a convex expression using the SCA (Continuous Convex Approximation) method and then solved using the CVX tool in Matlab.

[0099] The SCA method (continuous convex approximation) includes:

[0100] If f(x) is a concave function, first determine an initial point x0, and then perform a first-order Taylor expansion at the initial point x0 to obtain the global upper bound of f(x), expressed as:

[0101]

[0102] After a first-order Taylor expansion, the function It is a linear function of x, so the function It is a convex function, and can be used in subsequent processing. It is used to approximate the original function f(x).

[0103] In the third stage, given the scheduling factor A optimized in the first stage, the task allocation L, and the UAV launch power P in the second stage, (21) is then combined with (21a) the non-convex expression in the non-convex constraint (9). By introducing slack variables And satisfy

[0104]

[0105] The expression is converted to a convex expression using the SCA method, and then calculated using the CVX tool in Matlab.

[0106] The task allocation, scheduling factor, UAV transmit power, and UAV flight trajectory are determined by an alternating iterative optimization algorithm until the safe rate of the UAV-assisted MEC system converges. This convergence is achieved when the difference between the maximum safe rate of the current iteration and the maximum safe rate of the previous iteration is less than the convergence value, or when the maximum number of iterations is reached. Let... And calculate η (r+1) Find the optimal value and reinitialize the variables. When |η (r+1) -η (r) |<θ or r>R max Then exit the loop iteration.

[0107] If the output system safety rate converges, then output the optimized safety rate maximization result. Otherwise, continue iterative optimization until convergence is achieved and the system safety rate is maximized.

[0108] The implementation method of the safe rate maximization method based on UAV-assisted MEC system described in this invention is attached. Figure 2 As shown. The software environment configuration used in this invention includes Matlab and the CVX toolbox. Numerical results are given in the following examples to illustrate the performance of the proposed algorithm. (In a 1×1km...) 2 K = 4 APs are distributed within the geographical area, and the flight altitude of the UAV is set at H = 50m.

[0109] Table 1 Parameter Settings

[0110]

[0111] The method for maximizing the safe rate of UAV-assisted MEC systems provided in this application meets the requirements of low latency and computation for computationally intensive or latency-sensitive tasks, enhances the security of UAV-to-ground communication systems, and effectively improves the performance of communication systems.

[0112] Based on the same inventive concept, this application provides a drone control system, including...

[0113] Rotary-wing drones;

[0114] At least one ground access point (AP);

[0115] Mobile edge computing (MEC) server;

[0116] A control unit for implementing any of the above-described methods for maximizing the safe rate.

[0117] Based on the same inventive concept, the present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned method.

[0118] In this application, the secure rate maximization method emphasizes optimizing communication confidentiality under eavesdropping threats. When mobile energy storage products and outdoor energy storage devices are connected to the Internet of Things (IoT) or require remote management, this method can enhance data transmission security, preventing sensitive data from being eavesdropped on or tampered with in wireless networks, and ensuring the reliability and privacy of device communication. Outdoor energy storage devices can introduce edge computing to process and optimize power distribution tasks in real time, meeting complex power demand scenarios. The task offloading and scheduling factors in this method can be applied to energy storage systems to achieve more flexible power management and further optimize the efficiency of power distribution and utilization.

[0119] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in computer-readable media can be appropriately added to or subtracted according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0120] This invention can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0121] This application also provides an electronic device, including a memory, a plurality of processors, and a program stored in the memory, the program being configured to be executed by the processors, wherein the plurality of processors, when executing the program, implement the steps of the above-described method.

[0122] Furthermore, the present invention also provides a storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned method. The present invention can be used in numerous general-purpose or special-purpose computing system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0123] The device in this embodiment and the method in the previous embodiment are two aspects based on the same inventive concept. The implementation process of the method has been described in detail above, so those skilled in the art can clearly understand the structure and implementation process of the system in this embodiment based on the foregoing description. For the sake of brevity, it will not be described again here.

[0124] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for maximizing safe rate, characterized in that, Includes the following steps: Initialize the drone's flight parameters; Based on the UAV flight parameters, establish a system channel model, an energy consumption model, and a system safe rate maximization model; The task allocation, scheduling factor, UAV launch power and UAV flight trajectory are solved by alternating iterative optimization algorithm until the safe rate of the UAV-assisted MEC system converges, and the optimized safe rate maximization result is output. Establishing a safe rate maximization model includes: Define scheduling factor, UAV trajectory, task allocation, and launch power as block variables; By combining the system energy consumption model and the system channel model, and under the constraints of the system energy consumption model, the maximum safe rate of the system is determined by optimizing the UAV's launch power, mission offloading decision, and flight trajectory. Define block variables Let these represent the scheduling factor, the UAV's trajectory, task allocation, and transmission power, respectively; the system's safe rate maximization model can be expressed as: q[1]=q I ,q[N+1]=q F AND ulo +E utr +E fly ≤E u Among them, the location w of each ground AP is input. k ={w1,w2,…,w k }, k∈K={1,2,…,K}, the starting point of the drone is q I With the endpoint q F And the location of the ground eavesdropper w e Assume the UAV maintains a constant speed at a constant altitude H during flight. The flight time T is evenly divided into N (n∈N={1,2,…,N}) time slots, with each time slot lasting δ=T / N. The total workload L to be completed by the UAV, the channel power gain β0 at a reference distance d=1m, the communication bandwidth B, and the noise power σ at the receiver are all given. 2 The interference power P of the AP j The blade power P of the drone in hovering state o and induced power P i U tip v represents the tip velocity of the rotor blades. o d represents the average rotor induced velocity during forward flight. o ε and s represent the fuselage drag ratio and rotor rigidity, respectively; ε and M represent air density and rotor disk area, respectively; and path loss index. The drone calculates the number of loops C of the central processing unit (CPU) for each task. u The energy consumption of the drone per CPU cycle is expressed as P. u The CPU computing frequency f of the drone u The total energy of the drone is E u Peak power of the drone P max The maximum speed of the drone, v max The computational task of the UAV unloading to the k-th ground AP in the nth time slot is l k [n], the flight speed of the UAV in the nth time slot is v[n], and the initial transmit power of the UAV unloaded to the kth ground AP in the nth time slot is P. k [n], initialize the unmanned trajectory corresponding to the nth time slot as q[n]; The algorithm calculates task allocation and scheduling factors, UAV launch power, and UAV flight trajectory using an alternating iterative optimization algorithm, and also includes: The initial UAV flight parameters are iteratively optimized, the UAV trajectory and transmission power are preset, the first-stage problem is solved, and the scheduling factor and task allocation for the first stage are determined. Based on the scheduling factor, task allocation, and initialized UAV trajectory from the first stage, the second stage problem is solved using a continuous convex approximation method to obtain the UAV transmit power for the second stage. By incorporating the scheduling factor and task allocation in the first stage and the UAV transmission power in the second stage, relaxation variables are introduced into the security rate and flight propulsion energy consumption achieved by the UAV in the nth time slot and the kth ground AP. The third stage problem is solved using a continuous convex approximation method to obtain the UAV trajectory and relaxation variables in the third stage. The system channel model is established, including: Determine the channel power gain between the UAV and the ground AP, the channel power gain between the UAV and the ground eavesdropper, and the channel power gain between the ground AP and the ground eavesdropper; Define the scheduling factor and determine its constraints; Determine the rates between the UAV and the ground AP, the rates between the UAV and the ground eavesdropper, and the secure rate achieved by the UAV in the nth time slot and the kth ground AP, and construct a system channel model; Establishing a system energy consumption model includes: Determine the mission energy consumption, which includes computational mission energy consumption, communication mission energy consumption, and flight propulsion energy consumption; The constraints on the energy consumption of each task are determined based on the energy consumption of the task; a system energy consumption model required by the UAV in the process of launch power, computational task allocation, and trajectory optimization is constructed based on the constraints. Based on the safe rate convergence of the UAV-assisted MEC system, including: The difference between the maximum system safety rate of the current iteration and the maximum system safety rate of the previous iteration is less than the convergence value or the maximum number of iterations has been reached.

2. A drone control system, characterized in that, include Rotary-wing drones; At least one ground access point; Mobile edge computing server; A control unit for implementing the safe rate maximization method as described in claim 1.

3. A storage medium, characterized in that, It stores a computer program that, when executed, implements the security rate maximization method of claim 1.

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