452 results about "Mobile edge computing" patented technology

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.

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 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 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 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 belongs to the technical field of mobile edge computing, and discloses a task scheduling method based on dynamic cloud edge collaboration. According to the invention, through link-level modeling and graph neural network feature extraction, adaptive expansion of different numbers of terminals and servers is realized; according to the method, the problems of instability and over-estimation in training are effectively avoided by combining cutting updating of a near-end strategy optimization algorithm and a generalized advantage estimation method. According to the method, the elastic computing power of the cloud and the edge low-delay characteristic can be fully utilized, the dynamically changing network scale and terminal requirements can be adapted, flexible unloading of tasks and dynamic resource allocation can be realized, task processing delay can be effectively reduced in a dynamic environment, the resource utilization rate is improved, and the resource utilization rate is increased. And good expansibility and stability are maintained under the condition that the number of terminals and the scale of the server fluctuate, the problems of computing resource bottleneck and task scheduling stiffness existing in the virtual reality video service of the existing mobile edge computing are solved, and the interaction experience of the user is remarkably improved.

The invention discloses a multi-unmanned aerial vehicle cooperative inspection trajectory control method for optimizing medical area coverage and service efficiency. The method comprises the following steps: firstly, constructing a medical multi-unmanned aerial vehicle auxiliary inspection mobile edge computing system model, defining an unmanned aerial vehicle and mobile user set, and establishing a communication model containing A2G and A2A links, an unmanned aerial vehicle mobile model and an energy consumption model; then taking a joint function of a coverage score, a system throughput and an emergency task completion rate as an optimization target, under energy and communication connectivity constraints, proposing an LT-MADDPG algorithm, adopting a CTDE framework, processing a time sequence state by an actor network integrated with LSTM, fusing global information by a commentator network embedded with Transform through multi-head attention, and finally obtaining an emergency task. And modeling a cooperative relationship between the unmanned aerial vehicles and a medical task priority. Experiments show that the method is superior to a traditional algorithm in the aspects of coverage, service fairness and system throughput, and the medical inspection efficiency and reliability are effectively improved.

The invention belongs to the technical field of wireless optical communication, and particularly relates to a mobile edge computing task unloading and resource allocation method in wireless optical communication assisted by an unmanned aerial vehicle. According to the method, the data model, the queue model and the communication model are constructed, a task unloading strategy is optimized, and efficient and low-power-consumption edge computing resource allocation is achieved; an optimization technology is adopted to decompose a long-term optimization problem into sub-problems of each time slot, and the sub-problems are solved through a continuous convex approximation algorithm and a BFGS algorithm; finally, joint optimization of task unloading, computing resources, transmitting power, task return transmitting power, time slot scheduling and UAV trajectory is realized, and an optimization framework with more fine granularity in multi-dimensional allocation is formed. The characteristics of flexibility of the unmanned aerial vehicle and high bandwidth of wireless optical communication are utilized, the long-term throughput of the system is improved, the long-term power consumption of Internet of Things equipment is reduced, the high real-time performance of task processing is ensured, and the method is suitable for outdoor mobile Internet of Things application scenes with strict requirements for low power consumption and high real-time performance.

The invention belongs to the technical field of mobile communication. The invention provides a resource optimization and trajectory planning method for an online NOMA enhanced unmanned aerial vehicle assisted mobile edge computing network. According to the embodiment of the invention, a dynamic NOMA user pairing strategy is adopted, the channel gain difference between users is optimized, the spectrum efficiency of the system is enhanced, the Markov decision process is combined to predict the user state, and meanwhile, a low-complexity online optimization algorithm is designed, so that the user experience is improved. Sub-problems such as task allocation, computing resource optimization and unmanned aerial vehicle service point selection are decomposed and solved step by step through a block coordinate descent method, convex optimization methods such as SCA and a Lagrange duality algorithm are adopted for solving, and through alternate iteration, the unmanned aerial vehicle service point selection algorithm is obtained. And a mobile user computing resource allocation decision, an unmanned aerial vehicle computing resource allocation decision, a transmission power control strategy and an unmanned aerial vehicle service point selection decision are obtained.

The invention relates to the field of communication, in particular to a method for delivering cache content of a mobile edge computing network for energy consumption optimization. For a cache-based MEC cellular network, the method reasonably utilizes user movement information, and calculates corresponding path fading, channel gain and shadow fading by utilizing future position information of a user. Determining the minimum transmission power by using the calculated channel gain and path fading, then setting a time slot, carrying out content delivery decision on data packets requested by different users, and allocating wireless communication resources; and with minimization of the total emission energy consumption of the base station as a target, modeling a cache content delivery problem into a mixed integer programming problem to carry out optimization solution so as to obtain an energy consumption minimization decision for the base station to deliver all data packets.

The invention relates to a mobile edge computing task unloading optimization method based on an unmanned aerial vehicle and RIS assistance. The method comprises the following steps: constructing a mobile edge computing system model based on unmanned aerial vehicle and RIS assistance and a communication channel model of the system; constructing a time delay model and an energy consumption model of a system processing task, and constructing an energy consumption model of the unmanned aerial vehicle; according to the time delay model and the energy consumption model of the mobile edge computing system processing task and the energy consumption model of the unmanned aerial vehicle, calculating the maximum time delay and the system energy consumption of the user equipment processing task; based on the unmanned aerial vehicle task scheduling constraint, the user equipment, the RIS and unmanned aerial vehicle movement range constraint, the task unloading proportion constraint, the RIS phase range constraint, the unmanned aerial vehicle resource constraint and the system minimum task constraint, a target optimization function P after task processing time delay and energy consumption weighting of the system are minimized; and solving the target optimization function P by using a deep reinforcement learning method to obtain an optimal joint optimization scheme. According to the invention, the task unloading performance of the mobile edge computing system can be improved.

The invention provides a DRL-based edge computing service deployment and resource optimization method, which relates to the technical field of mobile edge computing, and comprises the following steps of: considering an edge computing network environment with dynamic and diversified user requests, limited edge resource dispersion and limited service deployment cost; the method comprises the following steps of: remodeling a problem into a limited Markov decision process problem by aiming at constraints such as a specific service satisfaction delay demand of a user, edge service deployment budget and edge server resources in an edge computing environment and aiming at problem characteristics, and respectively defining a state, an action, a reward function and a constraint function; according to the security deep reinforcement learning method based on enhanced near-end strategy optimization, the problem is solved, and the coupling of decision variables in the deep reinforcement learning problem is processed by adopting a multi-deep neural network strategy; the invention aims to maximize the overall user satisfaction of the system under multiple conditions of deployment budget and edge server resource constraint.

The invention discloses a BIM-based electromechanical installation resource management and control system, and the system comprises a data sensing layer which integrates a laser radar, an RFID, a UWB and an environment sensor, collects multi-modal data, and forms a total-factor digital twin base data stream after the multi-modal data is preprocessed by a mobile edge computing node; the model fusion layer is used for binding the construction progress with BIM component attributes, dynamically deducing a component-level progress path through an AI algorithm, identifying key path conflicts, integrating ERP resource data to generate a thermodynamic diagram and a cost deviation curve, and automatically detecting pipeline collision, progress lag and cost hyper-branched risks through a rule engine machine learning dual-mode engine; triggering multi-stage early warning and dynamically adjusting a threshold value; the intelligent decision-making layer is used for predicting the progress deviation probability of the future seven days by adopting LSTM, generating a non-tampering deviation correction scheme in combination with a block chain, and realizing hierarchical response through a yellow-orange-red three-level alarm system; and the user interaction layer is used for realizing multi-terminal collaboration and executing verification data feedback through AR glasses, a mobile terminal and a large-screen monitoring center.

The invention discloses an unmanned aerial vehicle moving edge calculation trajectory planning method based on multi-target reinforcement learning. Aiming at the problems of limited energy and uneven task distribution of unmanned aerial vehicles in a multi-unmanned aerial vehicle assisted mobile edge computing system, the method comprises the following steps: collecting real-time state data of the unmanned aerial vehicles, evaluating system target preferences (minimizing service interruption time or maximizing computing task load), constructing a distributed partially observable Markov decision process model, and calculating the target preferences of the unmanned aerial vehicles in the distributed partially observable Markov decision process model. And iteratively updating model parameters in combination with a crowding distance optimization experience playback mechanism so as to dynamically generate an optimal flight path. Experiments show that the method can adaptively balance and calculate the task load and the service continuity, effectively improves the system stability and the resource utilization rate, and is suitable for dynamic scenes such as disaster rescue and intelligent traffic.

Disclosed is a blockchain apparatus and method for mobile edge computing, the blockchain method comprising: receiving a user transaction from clients associated with a first edge chain, determining an execution order for transactions, processing the transactions according to the transaction processing order, reading and updating data on the first edge chain or reading and updating the data on a main chain of the main network depending on presence or absence of locality in the user transaction, creating an edge chain block by collecting the results of a batch of processed transaction, propagating the edge chain block to all local BSP auditor nodes associated with the first edge chain and all BSP servers in the main network.

The invention discloses a low-altitude edge network anti-fault task migration and DNN adaptive segmentation method, which comprises the steps of constructing a multi-unmanned aerial vehicle auxiliary MEC system, and performing DNN task division through a DNN adaptive division strategy; constructing a fault migration model, and migrating an uncompleted DNN task on the failed unmanned aerial vehicle to a normal unmanned aerial vehicle; the weighted energy consumption minimization problem of the multi-unmanned aerial vehicle assisted MEC system is reconstructed into a Markov decision process, and an optimal strategy is learned through interaction with the environment; a DKSAC-PER joint optimization framework is constructed, transmission power is optimized through the DKSAC-PER, calculation resource allocation optimization is performed through a Lagrange multiplier method, a reward value of a current step is calculated through an SAC-PER algorithm, network parameters are updated, and a complex mixed decision space is solved. According to the invention, the flight paths of the remaining unmanned aerial vehicles can be automatically adjusted, the calculation load is redistributed, and the survivability of the system in a severe environment is greatly improved.

The invention discloses an unmanned aerial vehicle mobile edge computing security authentication method and system based on identity-based encryption and a physical unclonable function, and belongs to the field of mobile edge computing and unmanned aerial vehicle communication security. A physical response is generated in combination with PUF hardware of the unmanned aerial vehicle node, the physical response and the ID are bound as a credible voucher, and meanwhile, a derived private key for IBE is generated for the unmanned aerial vehicle node based on the ID; after the unmanned aerial vehicle is networked, the KGC periodically initiates challenge response verification, the unmanned aerial vehicle generates a current response by using PUF hardware and returns the current response, the KGC performs comparison, and if an abnormality occurs, the KGC broadcasts to a full text to isolate an abnormal node; iBE is directly carried out on the basis of the ID of the receiver in communication between the unmanned aerial vehicles, and certificate exchange overhead is avoided. According to the method, lightweight identity authentication, physical attack resistance and efficient key management under dynamic topology are realized, and the method is suitable for unmanned aerial vehicle edge computing scenes with limited resources.

The invention discloses a dual-scale resource optimization method for a multi-unmanned aerial vehicle auxiliary edge computing system. The method comprises the following steps: establishing a dynamic mobile edge computing system model comprising multiple unmanned aerial vehicles and ground mobile users; establishing a joint optimization problem of task unloading, computing resource allocation and unmanned aerial vehicle trajectory planning by taking minimization of total energy consumption of an unmanned aerial vehicle system as an optimization target; based on a designed double-time-scale layered optimization framework, aiming at a joint optimization problem, on a small time scale, an improved clustering algorithm and a closed solution method are adopted, and a real-time optimal task unloading decision and a computing resource allocation strategy are efficiently obtained; and on a large time scale, a near-end strategy optimization algorithm in deep reinforcement learning is used to carry out autonomous learning optimization, and an optimized unmanned aerial vehicle flight path is obtained. The invention provides a resource optimization method which can give consideration to dynamic adaptability, multi-time scale collaboration, global energy efficiency optimality and low calculation complexity.

The invention relates to the technical field of industrial internet of things and mobile edge computing crossing, in particular to a computing task unloading and service caching collaborative optimization method, which comprises the following steps of: acquiring an IIOT equipment state, channel quality and edge server resources in real time by constructing a dynamic network awareness architecture, and analyzing a task DAG topological structure; designing a service cache delay compensation mechanism, parallelizing a cache process and task execution, predicting future required services based on a task dependency relationship, and calculating a delay compensation factor; a deep reinforcement learning algorithm based on Transform is adopted, and an unloading decision is generated in combination with a hierarchical attention mechanism and an improved DDQN network; tasks are dynamically allocated to the local, the edge or the cloud through a three-level unloading decision tree, task completion time is minimized, a resource constraint modeling and task preemption mechanism is further included, and the system response capability and the resource utilization rate are improved.

The present disclosure provides a method and apparatus for task offloading and communication management in mobile edge computing, and a medium. The method comprises: acquiring first state information that indicates a current state of a network environment and a current state of a mobile device; on the basis of the first state information, determining a plurality of first decision actions from a first Q table that is pre-obtained by means of reinforcement learning, and gain information and probability distribution information of each first decision action, and determining a plurality of second decision actions from a second Q table that is pre-obtained by means of reinforcement learning, and gain information and probability distribution information of each second decision action; and determining a first target action from among the plurality of first decision actions that indicate an action decision result of task offloading and computing resource allocation, and determining a second target action from among the plurality of second decision actions that indicate an action decision result of communication resource allocation, speed control and a communication mode. In this way, a task offloading decision and a communication management decision are optimized, and the task success rate in an Internet of Things system is improved.

The invention discloses a service migration method and system based on vehicle track and road network matching, and relates to the field of mobile edge computing service migration. Using a road network matching algorithm to match the predicted vehicle future driving track to an actual urban road to obtain a future vehicle road driving track; obtaining a minimum cost migration path based on a future vehicle road driving track; and synchronously migrating the service according to the actual movement of the vehicle based on the predicted future driving track of the vehicle. According to the method, the accuracy and efficiency of service migration can be effectively improved, the migration cost is reduced, and the service continuity and the user experience in an edge computing environment are improved.

The invention discloses an efficient multi-target unloading and scheduling optimization method for a large-scale directed acyclic graph (DAG) task, and belongs to the technical field of mobile edge computing (MEC) and industrial internet of things (IIOT). The method comprises the following steps: constructing a system model containing DAG task topology, delay energy consumption dual-objective optimization and resource competition constraint; a comprehensive multi-objective optimization solution scheme is adopted for solving, a dynamic probability coding mechanism is introduced to replace traditional individual representation, and the high-dimensional decision space search efficiency is improved; designing a cooperative target domain-based decomposition strategy to enhance convergence and diversity; and in combination with a serial active scheduling mechanism based on random weight, generating a task sequence meeting priority constraints. According to the method, the collaborative optimization problem of large-scale dependent tasks under delay, energy consumption and resource competition in an industrial Internet of Things environment is effectively solved, and the task execution efficiency and energy efficiency are remarkably improved.

The invention discloses an Internet of Vehicles cooperative unloading method based on hybrid action deep reinforcement learning, and relates to the technical field of Internet of Vehicles and mobile edge computing. In order to solve the technical problem of joint optimization of unloading target selection and resource allocation, the problem is modeled as a Markov decision process, and a mixed action soft actor-commentator (HA-SAC) algorithm is provided for solving; the core is that a multi-head strategy network outputs discrete actions and continuous actions at the same time in a decision period. A centralized training and decentralized execution architecture is adopted, and the value network receiving the global state guides the intelligent agent which only depends on the local state to make decisions to learn; compared with the prior art, the method has the advantages that the precision loss caused by action space discretization is avoided, the task completion time delay is obviously reduced, and the robustness and the adaptive decision-making capability of the system in a highly dynamic network environment are enhanced.