245 results about "Mobile edge computing" patented technology

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 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 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 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 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 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 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 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.

The present invention relates to the technical field of communications, and provides a 6G space-air-ground-sea integrated fusion architecture based on software-defined cloud-edge collaboration, comprising: three tiers of cloud nodes from low to high: an edge cloud, a space-based cloud, and a core cloud; the edge cloud comprises 6G-enabled ships based on software-defined networking and mobile edge computing technologies; the space-based cloud comprises low earth orbit satellites based on software-defined satellite technology and low earth orbit satellite constellations connected by means of an inter-satellite link; the core cloud comprises a space-based 6G core network; the edge cloud, the space-based cloud, and the core cloud follow a task offloading mechanism and an information feedback mechanism; the task offloading mechanism is configured to select, on the basis of characteristics of a task, a cloud node of a target tier for task processing; and the information feedback mechanism is configured such that the cloud node of the upper tier sends feedback information to a cloud node of the lower tier, so as to optimize task processing of the lower tier. The present invention achieves fast and flexible networking for a 6G space-air-ground-sea integrated fusion network, thereby realizing efficient utilization and collaborative management and control of resources.

The invention discloses an unmanned aerial vehicle auxiliary mobile edge computing performance optimization method based on a large language model and deep reinforcement learning, and the method comprises the steps: building an unmanned aerial vehicle auxiliary mobile edge computing system model which covers an unmanned aerial vehicle motion, communication and calculation unloading model; a target optimization problem containing a task unloading strategy is formalized into a partially observable Markov decision process; a deep reinforcement learning algorithm based on a Transform module is constructed, an Actor network generates actions according to states and observation, and a Critic network evaluates the actions; and an experience playback buffer area is adopted to store interaction experience and update network parameters. According to the method, priori knowledge is generated through LLM pre-training to guide strategy updating, the model generalization ability and the multi-target optimization ability are remarkably improved, the communication time delay, energy consumption control and resource allocation are optimized, and an intelligent unloading solution is provided for unmanned aerial vehicle edge calculation.

The application discloses a sensing-integrated waveform and unmanned aerial vehicle (UAV) trajectory design method for realizing secure mobile edge computing, and aims at the case that potential eavesdroppers exist in a mobile edge computing network architecture in which multiple ground users perform uplink information transmission to a UAV, optimizes and designs a sensing-integrated waveform and a UAV trajectory with the minimum total user energy consumption as the target. First, an optimization problem conforming to the scene is constructed, then the original optimization problem is decoupled into sub-problems about the sensing-integrated waveform and the UAV flight trajectory based on a Block Coordinate Descent (BCD) method, and a convex approximation fitting is performed on each non-convex sub-problem, finally, an iterative algorithm is adopted to solve the problem, so that a global suboptimal solution of the whole optimization problem and optimal sensing-integrated waveform and UAV flight trajectory are found.

The invention discloses a wireless energy supply mobile edge computing system-level energy efficiency optimization method based on distributed optimization. The method comprises the following steps: 1, system modeling and problem construction; 2, performing problem reconstruction and convex processing; 3, an ADMM (Alternating Direction Media Of Multipliers) distributed framework design is carried out, and the distributed framework design is carried out according to the ADMM (Alternating Direction Method of Multipliers); 4, coupling variable updating assisted by the dichotomy; and 5, iterative optimization and convergence guarantee. According to the method, a green WPMEC (Wireless Power Mobile Edge Computing) system which can still run efficiently under the constraint of nonlinear energy collection, multi-user task unloading and strict delay and is high in expandability and optimal in energy is realized through a closed-loop process of modeling, reconstruction, decomposition, solution and iteration. The method is suitable for a large-scale Internet of Things equipment access scene.

The invention relates to the technical field of unmanned aerial vehicle auxiliary edge computing task offloading, and particularly provides an edge computing task offloading strategy based on an improved quantum particle swarm optimization (IQP) SO (Inter Quantum Particle Swarm Optimization) algorithm, and relates to an edge computing task offloading method based on the IQP SO algorithm and an edge computing task offloading system based on the IQP SO algorithm based on the IQP SO algorithm based on the IQP SO algorithm based on the IQP SO algorithm based on the IQP SO algorithm. According to the method, an MEC unloading structure of multi-user mobile equipment (UE) supported by an unmanned aerial vehicle is established; under the structure, communication, time delay and energy consumption models are formulated to evaluate time delay and energy consumption required by the unloading task of the mobile equipment; according to the improved quantum particle swarm optimization, the unloading efficiency is improved, and the time delay and energy consumption problems of tasks are considered in the optimization process; the algorithm combines quantum characteristics, has excellent global search capability and rapid convergence characteristics, and effectively avoids the problem of global optimal solution omission caused by premature convergence when optimizing an edge unloading strategy; according to the method, the average time delay and the energy consumption of the mobile edge computing task can be remarkably reduced, and the optimization of the system is realized.

An unmanned aerial vehicle (UAV) may detect a risk of collision with one or more objects in an airspace serviced by a mobile edge computing (MEC) node. The MEC node may provide an edge detect and avoid (edge-DAA) function for use in the airspace. The UAV may determine a first resolution advisory (RA) to be acted on in order to avoid the collision with the one or more objects based on a local DAA function within the UAV. The UAV may receive, from the MEC node, a second RA to be acted on in order to avoid the collision with the one or more objects based on the edge-DAA function. If the second RA can be acted onto avoid the collision with the one or more objects, the UAV may act on the second RA and may send a message to the MEC node with an acknowledgement.

The invention relates to the technical field of mobile edge computing, and discloses a method for service migration, and the method comprises the steps: predicting the traffic flow of a set road section in a preset time period; planning a moving path of at least one target user terminal according to the traffic flow of the set road section; at least one target user terminal belongs to the same interest alliance; determining a target server of a to-be-migrated service corresponding to the interest alliance according to the moving path of each target user terminal; and migrating the to-be-migrated service to the corresponding target server. According to the method, the moving path of the user terminal is planned through the predicted traffic flow in the future time period, a congested road section can be avoided for the user terminal, and the to-be-migrated service is shunted in advance, so that the probability that the load of the target server is too large can be reduced, the probability of service interruption is reduced, and the user experience is improved. The invention further discloses a service migration device, electronic equipment and a storage medium.

A policy learning method with privacy protection in mobile edge computing for an intelligent agent is provided, relating to the technical field of mobile communication. The method includes: establishing an edge-collaborative computing offloading model, where the edge-collaborative computing offloading model includes a service caching model, a task offloading model, and a system cost model; establishing an optimization problem for task offloading, service caching, computing resource allocation and transmission power control based on the edge-collaborative computing offloading model for minimizing task processing costs; abstracting the optimization problem to a partially observable Markov decision process; and autonomously learning a task offloading strategy, a service caching strategy, a computing resource allocation strategy, and a transmission power control strategy by using a federated learning-based multi-agent deep reinforcement learning algorithm based on the Markov decision process.

The application relates to a multi-unmanned aerial vehicle (UAV) assisted edge computing task offloading method and system, and belongs to the technical field of mobile edge computing task offloading. The method comprises the following steps: constructing a multi-UAV assisted edge computing task offloading system model and an optimization problem model; based on the above model, a Markov decision process under a multi-agent environment is constructed, a reward function taking queue stability and information age minimization as targets is defined, a deep deterministic policy gradient algorithm is adopted, the optimization problem is solved through interaction between the multi-agent and the environment, and an optimal offloading strategy of a ground communication device computing task is obtained. The system comprises a ground communication device and a UAV, the UAV is in communication connection with the ground communication device, and an edge computing server arranged on the UAV is used for realizing the above method. The application not only guarantees the stability of a long-term data queue, but also obtains an optimal information age in an online mode, and solves problems such as communication congestion and poor user experience quality.

The application discloses a kind of air-ground integrated mobile core network deployment method, device, terminal and medium, by constructing air-ground integrated network architecture of mobile edge computing, deployment mobile core network, with minimum service delay as target, the objective function of the mobile core network is established, the optimal solution of the objective function is obtained, and the deployment set of the mobile core network is obtained.Therefore, the embodiment of the present application can realize the fusion of the data layer of the air platform Mesh network and the ground Mesh network;Avoid the problem of unstable network performance caused by the switching of air platform node movement;While guaranteeing network delay, it can effectively reduce the average data flow establishment delay, improve the practicability of air-ground integrated network.

The application discloses a scalable multi-unmanned aerial vehicle mobile edge computing cooperative scheduling method based on a Transformer, which comprises the following steps: acquiring local observation information of each unmanned aerial vehicle, and encoding initial observation and time slot observation into fixed-length word units respectively; utilizing a Transformer self-attention mechanism to perform cooperative feature fusion on word unit sequences of multiple unmanned aerial vehicles; generating multi-dimensional control actions meeting unique correlation, bandwidth and calculation frequency constraints through a structured action module according to the fused cooperative features; and utilizing a delay strategy update mechanism to improve learning stability, so that the obtained strategy can be directly deployed in different unmanned aerial vehicle scenarios, and cooperative service and energy consumption optimization are realized. The method significantly improves the scheduling efficiency, resource utilization rate and expansion capability of an edge computing network assisted by multiple unmanned aerial vehicles, and provides an efficient and reliable intelligent solution for large-scale unmanned aerial vehicle cooperative computing.

A traceable method for task replication and resource allocation of a large-scale machine type communication network belongs to the technical field of wireless communication and mobile edge computing, and comprises the following steps: based on a Lyapunov optimization theory, establishing physical and virtual queue quantization constraints, and converting long-term optimization into a drift and penalty minimization problem of each time slot; decomposing into task replication, resource block allocation and edge server queue management sub-problems, and sequentially solving; determining an optimal task replication scheme, and storing the optimal task replication scheme as a first decision-making voucher; determining a resource block allocation scheme, and storing the resource block allocation scheme as a second decision-making voucher; determining an optimal edge server queue management scheme which comprises an edge server task processing rate and a task discarding amount, and storing the optimal edge server queue management scheme as a third decision-making voucher; updating a queue state, packaging the decision-making voucher and the queue state into a periodic transaction, writing the periodic transaction into a block chain, and forming an operation auditing log; according to the method, the optimal balance of long-term operation cost is realized, and the decision-making process has traceability.

The present application belongs to the technical field of mobile edge computing microservice, and particularly relates to an edge computing microservice deployment and task scheduling method based on microcell architecture in a 5G and Internet of Vehicles scenario.The method comprises the following steps: firstly, describing an edge computing network structure based on microcells in a 5G scenario; on this basis, systematically modeling the microservice deployment and task scheduling problem, with the optimization target being to minimize the delay of all users, which is mainly manifested as processing data upload delay; then, decomposing and completely solving the sub-problems to transform the original problem into a linear integer programming problem; and using an L2Box-ADMM algorithm to solve the linear integer programming problem.Test results show that, compared with other microservice deployment and task scheduling algorithms, the algorithm of the present application can find a microservice deployment and task scheduling strategy that satisfies the resource constraint condition and has smaller total delay, and can reduce the gap with the optimal solution by 35%.

The invention discloses a trust evaluation method and system for a mobile edge computing terminal node, and relates to the field of edge computing, network security and trust management.The evaluation method comprises the following steps that the terminal node generates local data and trains a local model according to a model updating instruction issued by a central server and the edge node; uploading the local update parameter to the edge node; the edge node collects behavior information of the terminal node, interacts with other edge nodes and shares recommendation information for the terminal node; calculating current trust and historical trust of the node, and acquiring direct trust by integrating the current trust and the historical trust; calculating recommendation trust of the nodes according to behavior factors of transmission time delay, interaction time difference, data integrity, node reliability, communication success rate and interaction times, and filtering malicious recommendation according to indexes of recommendation timeliness, recommendation distance and recommendation external trust; and calculating the comprehensive trust of the terminal by combining the final recommendation trust and the direct trust.

The application designs a self-adaptive mobile edge computing unloading and resource allocation method, mainly including the following steps: a user sends a task unloading request; a mobile edge computing system calculates the best unloading strategy of the task according to the data size of the task, the wireless channel information where the user is located and the available computing resources of each edge server in the mobile edge computing system and notifies the user; the user connects a base station according to the notified unloading strategy and sends a task; after the base station receives the task, the base station sends the task to an edge server connected to the base station, the edge server allocates resources to process the task; the edge server sends the processed result to the base station, and the base station returns the processed result to the user. The application adopts a deep reinforcement learning method, obtains the best task unloading scheme at the current time according to historical system information, and reasonably allocates resources to process the task under the limitation of task delay, so that the consumption of resources is reduced and the utilization rate of the edge server is improved.

The invention discloses an RSMA-MEC system resource allocation method based on data compression. The method specifically comprises the following steps: step 1, constructing a system model comprising a plurality of user equipment and a plurality of mobile edge computing MEC servers; step 2, establishing an end-to-end time delay model, and comprehensively considering local data compression time delay of a user, task transmission time delay through an RSMA link, and data decompression time delay and task calculation time delay of an MEC server side; 3, constructing a joint optimization problem for minimizing the maximum time delay; 4, modeling a joint optimization problem into a Markov decision process; 5, solving a Markov decision process by adopting a near-end strategy optimization algorithm, and outputting an optimal resource allocation strategy and a joint optimization decision; and step 6, carrying out resource allocation on the user equipment and the MEC server in the system. According to the invention, the time delay performance and the resource utilization efficiency of the multi-user multi-server MEC system are effectively improved.

The application relates to a multi-source multi-target fusion mobile edge computing method and system, which comprises the following steps: acquiring residual energy of each MEC site, setting an opening and closing energy threshold, determining the MEC sites participating in calculation according to the size relationship between the opening and closing energy threshold and the residual energy of each MEC site; grouping all the MEC sites participating in calculation, after all the MEC sites participating in calculation receive the calculation tasks transmitted by surrounding vehicles, for each group of MEC sites, allocating tasks to each MEC site according to the energy state of each MEC site and the transmission time between each MEC site; each MEC site respectively executes the allocated task, and returns to the vehicle after execution. Compared with the prior art, the application has the advantages of low time delay and applicability to various scenes.