604 results about "Mobile edge computing" patented technology

An intelligent reflecting surface assistance-based task unloading and resource allocation method for an unmanned aerial vehicle mobile edge computing network system, comprising: constructing an intelligent reflecting surface-assisted unmanned aerial vehicle mobile edge computing network system model; constructing an intelligent reflecting surface-assisted unmanned aerial vehicle mobile edge computing network communication model ; according to the communication model, obtaining a time delay model and an energy consumption model; establishing a multivariable joint optimization model so as to minimize the energy consumption of an unmanned aerial vehicle; and using a deep reinforcement learning method to solve the objective optimization model. By means of intelligent reflecting surface assistance, joint optimized unloading decision and resource allocation, the present application effectively reduces the energy consumption of the unmanned aerial vehicle and improves the performance of the unmanned aerial vehicle network system.

The invention discloses a CNN (Convolutional Neural Network)-oriented mobile edge computing dynamic unloading method and equipment medium, and the method comprises the steps: obtaining image task data, equipment state data and network state data in a communication, sensing and computing integrated system, and forming a data set; the method comprises the following steps of: constructing a general-inductance-calculation integrated system model, and converting the general-inductance-calculation integrated system model into a calculation task segmentation and resource allocation problem for a calculation task; a Markov decision model is constructed; a joint optimization strategy based on a multi-agent deep reinforcement learning algorithm is adopted, and a neural network parameter model meeting a termination condition is obtained through training image task data and network state data; deploying the trained neural network parameter model into the system; and generating a calculation task segmentation decision and a calculation resource allocation decision according to the real-time data, and transmitting decision result information to each node to realize unloading of the calculation task. According to the invention, smooth execution of the calculation task is realized under the condition of shortage of network environment and network resources.

The invention discloses a wireless spectrum intelligent allocation and edge computing cooperation method, which relates to the technical field of mobile edge computing and wireless communication, and comprises the following steps: acquiring spectrum occupation data and service load data; constructing a spectrum resource allocation matrix according to the spectrum occupation data, and constructing an edge computing resource allocation matrix based on the service load data; and inputting the spectrum resource allocation matrix and the edge computing resource allocation matrix into a trained deep reinforcement learning model, outputting a joint optimization strategy, and performing collaborative scheduling of spectrum resources and computing tasks through a bidirectional matching algorithm. According to the invention, a multi-dimensional resource data acquisition mechanism is established, so that real-time monitoring and evaluation of a network resource state are realized; quantitative expression and association mapping of heterogeneous resource characteristics are realized by constructing a mathematical model of a spectrum resource allocation matrix and an edge computing resource allocation matrix; a deep reinforcement learning model is adopted for strategy optimization, and the limitation of a traditional fixed rule algorithm is overcome.

The invention relates to a vehicle-mounted edge computing network scheduling method based on information age, and belongs to the field of mobile edge computing. The method is oriented to a vehicle-mounted edge computing network in which a fixed server and a vehicle-mounted mobile server coexist, and aims to minimize the long-term average information age of the system by selecting a device node combination scheduled by the server in each time slot. The position constraint, the link conflict constraint and the server computing resource constraint of a vehicle-mounted mobile server are considered, an optimization problem is modeled as a Markov decision process, and a deep breach Q network is improved by using a bifurcation structure and an action mask to obtain a scheduling strategy of an equipment node. According to the method, the bifurcation structure and the action mask are introduced into the deep breach Q network, so that the problem solving complexity is reduced, and the data delivery freshness of the vehicle-mounted edge computing network system is guaranteed.

The invention relates to the technical field of wireless communication, and discloses an unmanned aerial vehicle mobile edge computing resource allocation method based on deep reinforcement learning, which constructs a mixed integer non-convex optimization problem, and the objective of the optimization problem is to collaboratively optimize the flight path of each unmanned aerial vehicle, a mobile user scheduling strategy and a task unloading proportion. Therefore, the time delay-energy consumption minimization is realized; and solving the optimization problem by adopting a multi-agent double-delay depth deterministic strategy gradient algorithm: converting the optimization problem into a Markov game model, and realizing the solution of the optimization problem through an experience playback pool and a double-evaluation network updating strategy. According to the method provided by the invention, the flight path and the unloading proportion of the unmanned aerial vehicle can be adaptively adjusted, so that calculation resource allocation for ground users is efficiently completed. Compared with a global unloading scheme, an average unloading scheme and other deep reinforcement learning methods, the method provided by the invention has excellent performance of lower energy consumption and shorter time delay while completing tasks.

The invention relates to a DDRL-based aircraft mission offloading method in a low earth orbit satellite network. The method comprises the following steps: (1) constructing an LEO low earth orbit satellite network system into an LEO satellite mobile edge computing network system; (2) modeling an LEO low earth orbit satellite network system utility maximization problem into a joint decision problem for optimizing task unloading and resource allocation; and (3) converting a joint decision problem into a Markov decision process MDP, learning an optimal task unloading decision by using a dual depth Q network algorithm DDQN under a dual depth reinforcement learning DDRL framework, and obtaining an optimal resource allocation strategy by using a time difference triple strategy gradient algorithm TD3PG on this basis. Simulation experiments show that compared with a reference algorithm, the scheme can effectively access and cooperatively compute concurrent tasks, and has better convergence and superiority under different environment variables.

The invention discloses a multi-unmanned aerial vehicle path planning method for power transmission line inspection, and belongs to the field of path planning. According to the method, mobile edge computing (MEC) and unmanned aerial vehicle airborne intelligent decision making are combined, and the method comprises the steps that firstly, an MEC server synchronizes a cluster state and issues a global demand matrix; the unmanned aerial vehicle dynamically updates the global risk matrix through real-time environment perception; and fusing the real-time state, the risk and the demand matrix of each unmanned aerial vehicle airborne unit, and autonomously making a decision to generate an optimal short-term planning path. Before the path is executed officially, the path is reported to an MEC server for cooperative conflict detection, and the unmanned aerial vehicle can execute flight only after it is confirmed that no conflict exists. According to the method, the unmanned aerial vehicle cluster is endowed with autonomous perception and rapid avoidance capability for dynamic unknown risks, path planning becomes a real-time evolution intelligent decision-making process, and the safety of cluster collaborative operation is ensured through centralized conflict detection, so that the efficiency, robustness and reliability of a power transmission line inspection task are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle assisted edge computing, and discloses a deep neural network task-oriented collaborative reasoning and unmanned aerial vehicle trajectory optimization algorithm, which comprises the following steps of S1, establishing a multi-unmanned aerial vehicle assisted mobile edge computing system model; s2, providing an optimal segmentation point selection algorithm, selecting an optimal segmentation point for each user task, and determining a task unloading proportion; s3, providing an unmanned aerial vehicle and ground user matching algorithm, and allocating computing resources of the unmanned aerial vehicle; and S4, modeling the unmanned aerial vehicle trajectory optimization and ground user transmitting power selection problem as a Markov decision process, providing a dynamic DNN task unloading and trajectory optimization algorithm based on deep reinforcement learning, and obtaining the optimal trajectory of the unmanned aerial vehicle and the transmitting power of the ground user in each time slot by training an intelligent agent. According to the algorithm, rewards can be continuously improved, and delay and energy consumption of DNN tasks in the MEC system are effectively reduced.

The invention provides a pelican optimization strategy-based mobile edge computing unloading method, and aims to solve the problems of low computing task unloading efficiency and poor system performance in application scenes such as Internet of Things and smart cities. By introducing chaotic mapping and a flight strategy, a traditional pelican optimization algorithm is improved, and the global search ability and the ability of jumping out of a local optimal solution of the algorithm are enhanced. A calculation unloading model of a multi-user multi-edge server is constructed, and delay and energy consumption in the data transmission and calculation process are comprehensively considered. Experimental results show that the method can effectively reduce the time delay and energy consumption of the system, optimize resource allocation and improve the overall performance of the system. Especially in a multi-user and multi-task complex scene, the method disclosed by the invention is excellent in performance, and a new solution is provided for the calculation unloading problem in the mobile edge calculation.

The invention is suitable for the technical field of mobile edge computing, and provides a hybrid energy WPT-MEC system-based task unloading and resource allocation method, which comprises the following steps of: establishing a system model; an optimization problem with the goal of minimizing the long-term average power grid energy consumption is drawn up and converted into a Markov decision process; implementing an action space reduction strategy, and executing data normalization processing; constructing an enhanced depth deterministic policy gradient (EDDPG) algorithm; and generating and outputting an optimal strategy according to a real-time system state by using the trained network. According to the method, the wireless power transmission (WPT) technology, the green energy harvesting technology and the mobile edge computing technology are combined, so that the energy use duration and the computing capacity of the wireless equipment are improved. The online decision-making mechanism based on the EDDPG algorithm can dynamically optimize task unloading and resource allocation strategies, adapts to a dynamic environment, reduces energy cost and environmental pollution, and provides reliable technical support for large-scale deployment of the Internet of Things.

Disclosed is an adaptive deep learning inference system that adapts to changing network latency and executes deep learning model inference to ensure end-to-end data processing service latency when providing a deep learning inference service in a mobile edge computing (MEC) environment. An apparatus and method for providing a deep learning inference service performed in an MEC environment including a terminal device, a wireless access network, and an edge computing server are provided. The apparatus and method provide deep learning inference data having deterministic latency, which is fixed service latency, by adjusting service latency required to provide a deep learning inference result according to a change in latency of the wireless access network when at least one terminal device senses data and requests a deep learning inference service.

The present application belongs to the technical field of wireless communications. Disclosed are a multi-user multi-task computation offloading method and apparatus with throughput prediction, and a medium. The method comprises: constructing a multi-user multi-task mobile edge computing system model; on the basis of a machine learning algorithm, constructing a first prediction model for RSRP and uplink throughput; on the basis of an ARIMA model, constructing a second prediction model for RSRP in a time sequence; on the basis of task offloading and constraint conditions, constructing a task offloading problem by taking the minimization of the minimum energy consumption of all user equipments as an objective; and on the basis of the constructed task offloading problem, solving an offloading decision for each task of each user equipment. In the present application, an ARIMA model is used to predict the current RSRP by integrating historical RSRP, and the predicted RSRP is used to predict uplink throughput, such that an accurate uplink throughput value is provided for a constraint condition of an optimization problem and the computation of the delay of a task offloading link, thereby realizing high-precision uplink throughput prediction and low-power-consumption edge computing.

The invention relates to an unmanned aerial vehicle multi-dimensional perception intelligent inspection early warning system and method based on edge calculation. The system comprises a multi-dimensional sensing data acquisition device, a mobile edge computing server and an intelligent computing monitoring and decision support platform. A multi-dimensional sensing data acquisition device sends acquired multi-dimensional sensing data to a mobile edge computing server, and the mobile edge computing server determines state data of an inspected range according to the multi-dimensional sensing data, generates corresponding early warning information and sends the information to an intelligent computing monitoring and decision support platform; and the intelligent calculation monitoring and decision support platform feeds the early warning information back to an operator. According to the system, the calculation process is transferred to the mobile edge calculation server, the dependence on the cloud is reduced, the data delay is reduced, the safety and the response speed are improved, the data leakage and the interference risk possibly suffered in the transmission process are also avoided, and the global perception of unmanned aerial vehicle inspection is realized.

The invention discloses a vehicle task unloading system and method based on a deep Q network, and relates to the technical field of Internet of Vehicles mobile edge computing, and the method comprises the steps that a client vehicle receives a locally generated computing task, obtains basic parameters of the task according to the current hardware configuration and task requirements, carries out task unloading decision making through a deep Q network model, and carries out task unloading; an optimal task unloading strategy is obtained; when the task is unloaded to the edge node, the edge node dynamically adjusts the allocation of computing resources according to the computing resource allocation condition of the existing task, and executes concurrent task scheduling; then, the client vehicle updates the weight of the deep Q network model according to the task completion condition, and trains and updates the global model; therefore, by the adoption of the vehicle task unloading system and method based on the deep Q network, the task unloading strategy can be dynamically adjusted and the task unloading decision can be optimized according to the actual conditions of the client vehicle and the edge computing node in the face of the complex task unloading environment.

The invention provides a distributed reasoning task allocation method for an edge computing large model, which belongs to the technical field of mobile edge computing and comprises the following steps of: considering a large language model distributed deployment scene, constructing a workflow for cooperatively finishing a reasoning task by a plurality of edge servers, and adopting accurate secondary reconstruction by considering the generation type characteristic of the reasoning task; the uncertainty of reasoning task resource occupancy is solved, mathematical model expression is constructed for the edge reasoning task allocation problem in the scene, the aim is to maximize the service provider income, the constructed mathematical model is reconstructed into a combination selection problem, an approximate solution with theoretical guarantee is given by adopting a dual method, and the problem of edge reasoning task allocation is solved. And the reasoning task distribution decision of the service provider is optimized, so that the income of the service provider is maximized.

The invention relates to the technical field of mobile edge computing, in particular to an ocean communication processing method and system based on mobile edge computing, and the method comprises the following steps: obtaining submarine topography information and node capability parameters, computing node deployment adaptation indexes, extracting signal intensity and angle changes, obtaining a link offset trend, and identifying power and packet loss abnormal regions. Generating path reconstruction parameters, adjusting a network structure in combination with tide level changes, and obtaining a communication task scheduling result according to entity behavior characteristics. According to the method, the adaptability of the node site selection environment is evaluated by analyzing the topographic features and the node capacity, the deviation trend is jointly recognized through signals and angle data, the pre-judgment capacity of link abnormity is improved, the network structure is dynamically adjusted in combination with the tide level change, and the stability of the communication network structure is optimized; and task scheduling grading is driven by adopting communication entity behavior characteristics, so that the accuracy of resource allocation and the regional regulation and control efficiency are improved, and the self-adaptive regulation and control capability in a dynamic environment is realized.

A task unloading method based on unmanned aerial vehicle cooperation, relating to the field of unmanned aerial vehicles. The method comprises: constructing an unmanned aerial vehicle cooperation task unloading system; according to the number of CPU cycles which can be executed by a ground user in unit time and the number of CPU cycles of a task, creating a local task unloading model of the ground user; according to the transmitting power of the ground user, the number of CPU cycles which can be executed by MEC servers on an original unmanned aerial vehicle and a cooperative unmanned aerial vehicle in unit time and the number of CPU cycles of the task, creating an unmanned aerial vehicle task unloading model of the ground user; according to the hovering power and the hovering delays of the unmanned aerial vehicles, creating a hovering model of the unmanned aerial vehicles; and, according to the local task unloading model of the ground user, the unmanned aerial vehicle task unloading model of the ground user and the hovering model of the unmanned aerial vehicles, computing by using a second-price sealed-bid auction algorithm to obtain an optimal task unloading allocation solution so as to unload the task. The present invention uses unmanned aerial vehicle assisted mobile edge computing for performing task unloading, thereby improving quality of service and experience of service for users.

The invention discloses a service migration method and system based on user trajectory prediction, and relates to the field of service migration in a mobile edge environment, and the method comprises the steps: collecting urban road network data, user historical long-term trajectory data and user short-term trajectory data, and predicting a user future trajectory through an STG-Informer model; judging whether service migration is needed or not based on the distance between the future trajectory of the user and the current edge server; if migration is needed, a target migration node with the optimal comprehensive reputation value is selected according to the future trajectory of the user, the optimal migration path is planned through a graph search algorithm, and real-time migration of the service is achieved; otherwise, merging the trajectory points into the short-term trajectory data of the user along with the movement of the user, re-predicting the future trajectory of the user, and judging whether service migration is needed or not. The method can effectively reduce the service delay and improve the resource utilization rate, is suitable for scenes such as intelligent traffic and mobile edge computing, and provides seamless service experience for users.

The invention discloses an authentication method for the Internet of Vehicles, and the method comprises the steps: completing the authentication and key negotiation process of a vehicle and a road side unit through a trusted authority center TA under the condition that the vehicle and the road side unit are detected to be authenticated for the first time; under the condition that authentication is not carried out for the first time, the authentication of the vehicle is rapidly completed through the current road side unit, and a session key between the vehicle and the road side unit is updated; when it is detected that the authenticated vehicle moves and is switched to an unauthenticated area, data exchange and inspection between the road side unit of the current area and the road side unit authenticated last time are utilized; reliable central server auxiliary authentication is still needed when the vehicle accesses the Internet of Vehicles for the first time, then the authenticated vehicle accesses the Internet of Vehicles again, rapid re-authentication and switching authentication based on mobile edge computing are adopted, verification of the central server is not needed, the pressure of central authentication is relieved, and meanwhile the authentication efficiency is improved. The calculation and communication overhead of vehicle authentication is reduced, and the authentication efficiency is effectively improved.

The invention discloses a mobile edge computing unloading and service caching method and device based on deep reinforcement learning. The method comprises the following steps: constructing a mobile edge computing system model; constructing a service cache model and a decision model; constructing a cost calculation model; establishing a task unloading and service cache optimization problem according to the mobile edge computing system model, the service cache model and the decision model; and solving a task unloading and service cache optimization problem based on a deep reinforcement learning method by taking the minimum cost as an optimization objective function to obtain an unloading decision and a service cache decision. According to the method and the device, the calculation complexity in the MEC calculation unloading solving process can be reduced.

To provide a method of optimizing coupling of computation offloading and resource allocation in a mobile edge computing network based on a hierarchical game.SOLUTION: A method includes: establishing a mobile edge computing computation offloading and a resource allocation model in a multi-user multi-task scenario; performing computation offloading and resource allocation by a multilayer game algorithm; and obtaining an optimal computation offloading plan and resource allocation plan while minimizing the weighted sum of latency and energy consumption. The method includes: determining whether the current task needs computation offloading on the basis of computation resource size necessary for the task and computation capability on the user; determining whether the task is to be divided on the basis of task attribute; making a decision as to whether to perform complete computation offloading or partial computation offloading; and allocating a mobile edge server to offloaded task or subdivided tasks on the basis of the decision on the offloading.SELECTED DRAWING: Figure 1

The invention discloses a cloud edge collaborative task scheduling method based on a directed acyclic graph (DAG) and a near-end policy optimization (PPO) algorithm, and belongs to the technical field of deep reinforcement learning and mobile edge computing. The method comprises the following steps of: initializing a cloud edge environment containing mobile equipment, an edge server and a cloud server, and constructing a task dependency graph containing task calculation amount, dependency relationship and data transmission amount by using a DAG (Directed Acyclic Graph); establishing a function by taking minimization of the total response time of the application program as a target, and converting a scheduling problem into a Markov decision process; and inputting the current state to the trained intelligent agent based on the PPO algorithm, obtaining final action probability distribution through task dependence screening and legal action mask filtering, and sampling and executing scheduling. According to the method, the intelligent agent adopts a PPO algorithm and is trained based on DAG, the adaptive capacity to cloud edge collaborative scene resources and network changes is achieved, task accumulation delay is remarkably reduced, the resource utilization rate is remarkably increased, and the method has wide applicability.

The invention discloses an automatic scaling method and system based on a mobile edge computing server cluster, and the method comprises the following steps: carrying out the initialization of scaling cooling time and scaling period T, and verifying whether the condition of scaling cooling time is satisfied at present; if the condition of the telescopic cooling time is met at present, obtaining information of all service cluster edge nodes participating in telescopic in the server cluster; obtaining a load index according to all service cluster edge node information requests; inputting the obtained load index into a model for load prediction to obtain a prediction result; determining an automatic scaling strategy according to a prediction result; and decision making and dynamic resource adjustment calculation are carried out according to an automatic scaling strategy. According to the application, the system resource utilization efficiency of the server cluster can be improved, the SLA requirement of the application service can be better met, and the system resource utilization efficiency is improved.

The invention discloses a multi-unmanned aerial vehicle cooperative auxiliary mobile edge computing task scheduling method and system, and belongs to the technical field of edge computing and intelligent scheduling, and the mobile edge computing task scheduling method comprises the following steps: obtaining the position information and computing task characteristics of a ground mobile terminal in a current time slot, performing preliminary task shunting according to the task data size and a preset threshold value; constructing a joint optimization model, and performing joint optimization on a two-dimensional track, a computing resource allocation strategy and a task migration decision of the unmanned aerial vehicle by taking minimization of total processing time delay of system tasks as a target; the optimization problem is solved by adopting a mixed action multi-agent deep reinforcement learning algorithm, the problem of complexity of multi-unmanned aerial vehicle cooperative scheduling in a dynamic environment is effectively solved, the task processing time delay is remarkably reduced, and the edge resource utilization rate and the system service continuity are improved.

The invention belongs to the field of mobile edge computing, and particularly relates to a resource pricing problem based on a Stackelberg game in mobile edge computing. The method comprises the following steps: constructing a mobile edge computing model, wherein the mobile edge computing model comprises a server base station, task equipment and cooperative equipment; in a task execution period, for a server base station profit maximization problem of resource pricing based on a Stackelberg game in mobile edge computing, delay brought by task unloading is fully considered in problem modeling, and the delay requirement of a task is met to the maximum extent; analyzing and establishing a dynamic punishment and redistribution mechanism for task unloading failure of the cooperative equipment, wherein the mechanism comprises a punishment cost calculation module, a task value reevaluation module and a game strategy updating module; and obtaining an unloading strategy of the task equipment, a task allocation strategy of the server base station and a resource pricing strategy after the server base station and the cooperative equipment carry out the Stackelberg game by utilizing a greedy strategy, thereby obtaining a final task allocation scheme for maximizing the profit of the server base station. According to the method, the optimal task execution and resource allocation strategy of the server base station is formulated, and the relationship among the task value, the resource cost and the additional resource purchase cost is balanced as required, so that more flexible and efficient task scheduling service can be provided.

The invention discloses an RIS-assisted UAV-MEC task unloading system and method based on a heterogeneous agent, and belongs to the field of mobile edge computing, Internet of Vehicles and deep reinforcement learning. The method comprises the following steps: firstly, constructing a three-layer multi-UAV cooperative unloading architecture, and solving the problems of channel fading and instability in the Internet of Vehicles through combination of an intelligent reflecting surface and a multiple-input-multiple-output technology; secondly, designing a UAV energy model powered by solar energy; then, a heterogeneous proxy near-end strategy optimization algorithm (HAPPO) is adopted, the central controller serves as a global coordinator to be responsible for task unloading decision making and resource allocation, and the RIS assists the UAV to serve as an autonomous proxy to dynamically adjust the flight path, RIS phase configuration and transmission power allocation; and finally, through layered decision architecture and trust domain constraint strategy optimization, converting an original mixed integer nonlinear programming problem into a layered multi-agent reinforcement learning problem. According to the method, system energy consumption and delay can be effectively reduced, the task completion success rate is improved, and efficient processing of compute-intensive applications in the Internet of Vehicles is realized.

The invention belongs to the technical field of mobile edge computing, and particularly relates to a space-air-ground integrated network-oriented unloading task resource scheduling method, which comprises the following steps of: constructing a joint optimization problem of task unloading, channel allocation, unmanned aerial vehicle track allocation and computing resource allocation by taking a weighted sum of minimum time delay and energy consumption cost as a target; aiming at a joint optimization problem, performing normalization processing on computing resources allocated to each user task by the unmanned aerial vehicle and the low-orbit satellite respectively to obtain computing resource allocation sub-problems of the unmanned aerial vehicle and the low-orbit satellite respectively, and solving the computing resource allocation sub-problems by adopting a Newton interior point method; and substituting the solution of the resource allocation sub-problem into a joint optimization problem, and solving a task unloading, trajectory planning and channel allocation strategy by using a multi-agent depth deterministic strategy gradient algorithm. The problems that in the prior art, SAGIN edge computing resource allocation focuses on single resource optimization, and wireless channel dynamic change, edge node movement and time delay and energy consumption joint optimization strategies are not considered are solved.

The invention discloses a deep reinforcement learning resource allocation method for green mobile edge computing, which comprises the following steps of: establishing a communication network model, and initializing a communication environment, the number of base stations, the number of users and the number of subcarriers; determining an optimization target and a constraint condition; the optimization problem is converted into a Markov decision process, intelligent agents, a state space, an action space and a reward function are determined, a deep reinforcement learning algorithm is used for training, and an optimal strategy is distributed for each intelligent agent; the intelligent agent continuously interacts with the environment through PPO and D3QN algorithms, and network parameters are optimized and updated; an optimal resource allocation scheme is obtained; through a distributed multi-agent deep reinforcement learning-based resource allocation algorithm, the maximization of long-term average energy efficiency is realized, and strategy coordination among multiple agents is promoted, so that resource allocation is optimized, and an efficient and sustainable resource management solution is provided for the development of edge computing and communication networks in the future.

The invention discloses a two-stage task cooperation unloading method in the Internet of Vehicles, and designs a task cooperation unloading framework fusing an LTE-V direct connection mode and an LTE-V cellular mode, and deploys a mobile edge computing server beside a roadside unit through wired connection. The mobile edge computing server at the roadside unit side cooperates with the idle service vehicle to provide computing service; meanwhile, a two-stage unloading decision scheme is provided, and the link quality is evaluated by a link quality evaluation model so as to improve the stability of data transmission between a prediction calculation node and a request vehicle. The response time is predicted through the unloading decision model, the unloading decision is made according to the predicted response time, and compared with an existing scheme, the proposed unloading decision scheme is more effective, the response time can be shortened, and energy consumption can be reduced.

The invention discloses a secure air implementation method integrating mobile edge computing and block chain technology, and the method constructs a secure edge computing framework integrating block chain assisted unmanned aerial vehicles, in an unmanned aerial vehicle assisted edge computing system, a plurality of unmanned aerial vehicles carrying micro servers serve as air edge servers, and the unmanned aerial vehicles carry micro servers. And a calculation unloading service is provided for the Internet of Things equipment. Meanwhile, block chain tasks are deployed on the edge servers, so that the edge servers also serve as consensus nodes of a block chain model; the system uses a practical Byzantine consensus mechanism to increase fault tolerance of blockchain nodes, an unmanned aerial vehicle edge server needs to mount received calculation tasks and corresponding calculation results on a blockchain platform as transactions, and blocks are packaged into the blockchain system through verification of the consensus nodes, so that privacy of users is protected. According to the method, a more rational and efficient unloading strategy is realized, so that the energy consumption and the time delay of the system are reduced, and the effectiveness of the system is improved.