124 results about "Lyapunov optimization" patented technology

The invention discloses a cloud side-end cooperative task scheduling and efficiency optimization method and system for heterogeneous patrol resources, and relates to the technical field of intelligent scheduling and resource optimization. According to the method, accurate perception of a resource state is realized by constructing a digital twinborn and federated learning mechanism, resource contention conflicts are solved by adopting a space-time diagram attention network and multi-agent reinforcement learning, and multi-target optimization and trusted execution are realized in combination with a quantum genetic algorithm and a block chain smart contract. Finally, the stability of the system is verified through Lyapunov optimization, a complete scheduling system from resource perception and conflict resolution to steady state maintenance is formed, and the task scheduling efficiency and the system stability in the heterogeneous resource environment are remarkably improved.

The invention belongs to the technical field of smart city safety management, and discloses a smart park safety shunting control method, which comprises the steps of collecting data through a multi-modal sensor network to construct a space-time tensor, predicting people flow distribution through tensor decomposition and calculating a residual error, generating a path strategy by adopting a multi-agent game, and performing intelligent park safety shunting control. A shunting instruction is dynamically adjusted in combination with Lyapunov optimization, and closed-loop control is realized by cooperatively updating model parameters through residual feedback; the system comprises a multi-modal sensor network module, a spatio-temporal data fusion module, a tensor decomposition and prediction module, a multi-agent game planning module, a Lyapunov optimization control module and a parameter collaborative updating module. According to the method, the space-time tensor is constructed through multi-modal sensing and data fusion, people flow prediction is realized in combination with tensor decomposition, individual and system targets are balanced and coordinated by using a game, and based on a Lyapunov dynamic optimization shunting strategy, parameters are adaptively adjusted through a residual feedback closed loop, so that the stability of park safety management and control is improved.

The invention relates to the technical field of spraying control, and discloses a water turbine spraying process parameter real-time monitoring, regulating and controlling method and system.The water turbine spraying process parameter real-time monitoring, regulating and controlling method comprises the following steps that multi-modal data in the spraying process is collected, and a four-dimensional data structure is constructed; tensor decomposition is carried out, and a core feature tensor and a factor matrix are extracted; constructing an enhanced game strategy model, and outputting a process parameter control strategy; lyapunov optimization is introduced, and regulation and control input is generated; the control strategy and the control input are combined to generate a spraying execution action; and performing feedforward compensation based on the disturbance observer to form a final control instruction. According to the method, the multi-modal process data fusion and tensor decomposition technology is adopted, the four-dimensional data tensor is constructed, the core features of the spraying process are extracted, and by means of the method, the system can more comprehensively capture dynamic changes in the spraying process, and an accurate technological parameter control strategy is provided.

The invention relates to an intelligent water affair remote monitoring and control system based on the Internet of Things, in particular to the field of intelligent water affair remote monitoring and control, and the system comprises the steps: firstly, pre-training a long and short-term memory network model through a cloud, and generating a lightweight edge prediction model to reduce calculation and transmission loads; the data transmission module effectively reduces the occupation of transmission bandwidth based on a dynamic quantization threshold value of a residual error and a differential coding technology, meanwhile, the high precision of the data is kept, the verification feedback module monitors the accuracy of the transmission data in real time through confidence coefficient detection, and triggers model re-calibration when the confidence coefficient is reduced, so that the long-term stability of the system is ensured, and the reliability of the system is improved. The sampling optimization module dynamically adjusts the sampling frequency according to the confidence coefficient and the residual fluctuation characteristics, balance is achieved between bandwidth occupation and reconstruction precision by using the Lyapunov optimization theory, the real-time performance and accuracy of water affair monitoring are improved, and meanwhile bandwidth consumption and energy consumption are effectively reduced.

The invention discloses an industrial edge reasoning task-oriented model segmentation collaborative unloading method, which comprises the following steps of: obtaining a preferentially processed reasoning task, selecting a reasoning model, constructing a model segmentation candidate set, carrying out retraining and calibration on each segment, establishing a multi-edge collaborative unloading model, and according to a task scale, a segmentation position and a network state, carrying out multi-edge collaborative unloading on the multi-edge collaborative unloading model. Deciding an execution mode of each segment on a local node, an edge node or a cloud node; a Lyapunov optimization framework is introduced, a long-term precision constraint model is constructed, an initial resource allocation scheme is generated, collaborative optimization is carried out in combination with an improved multi-agent near-end strategy optimization algorithm, and an HCRA-AT-MAPPO fusion scheduling result is obtained; based on a Lyapunov optimization decision and an HCRA-AT-MAPPO fusion scheduling result, a model segmentation and unloading strategy is dynamically updated, and network fluctuation and task load change are adapted in real time. The assembling quality can be improved, and the production period can be shortened.

The invention discloses a power distribution network data high-frequency acquisition and low-delay interaction method and system, and the method comprises the steps: firstly, constructing a communication cooperative network model, taking the minimum end-to-end delay of power distribution service data and the weighted sum of network operation cost as a target, converting an optimization problem into a form suitable for real-time solving through employing a Lyapunov optimization method, and carrying out the real-time solving of the optimization problem; and further based on the optimization problem, constructing a Markov model, improving a loss function of a deep Q network (DQN) to adapt to the acquisition requirements of distribution network services, and designing a corresponding network learning training method to optimize terminal scheduling and data compression decisions. And finally, a transmission power and bandwidth distributed optimization method based on an alternating direction multiplier method (ADMM) is adopted, so that the efficiency of network resource allocation is further improved. According to the technical scheme, an optimal data acquisition strategy with low time delay, low cost and balanced scheduling is realized, and the method is suitable for high-frequency data acquisition scenes such as a smart power grid and an industrial internet of things.

The invention discloses a joint optimization method for task unloading and power control in a satellite edge computing network, and provides a time delay-energy consumption balance optimization framework oriented to dynamic task arrival aiming at the long-term random optimization problem in satellite edge computing task unloading. Through fusion of Lyapunov optimization and a virtual queue mechanism, the long-term delay constraint is successfully converted into a time slot level optimization target which can be executed online, and a self-adaptive decision process is realized. And aiming at the generated non-convex optimization problem, constructing a robust solution algorithm under a PDD framework, and integrating secondary transformation and an SCA technology to realize efficient solution. Simulation experiments show that the method can obviously reduce the energy consumption in a random task arrival scene, and strictly meets the delay constraint requirement at the same time.

The invention relates to the field of micro-services, and provides an elastic resource scheduling method and device for micro-services and electronic equipment.The method comprises the steps that a to-be-scheduled micro-service system is determined, and the micro-service system comprises a server and a micro-service instance deployed on the server; constructing a long-term scheduling optimization model by taking the minimization of the long-term average request time delay of the micro-service system as a target; converting the long-term scheduling optimization model into a time slot scheduling optimization model in each time slot based on a Lyapunov optimization method; and obtaining an initial solution of the optimization model based on a near-end optimization strategy, then optimizing the initial solution for a dynamically changing request through a pre-trained deep reinforcement learning model, and performing elastic resource scheduling of the micro-service based on an optimization result. According to the elastic resource scheduling method and device for the micro-service and the electronic equipment provided by the invention, scaling and migration of the micro-service instance are performed aiming at the dynamic request arrival rate, and multi-objective optimization of micro-service scaling and migration cost and user request time delay is realized.

The invention discloses a perception-assisted unmanned aerial vehicle track and power distribution joint optimization method and system, and the method comprises the steps: firstly, extracting environment related features from high-dimensional CSI data, constructing a weighted graph, and calculating a shortest path distance matrix; and the CSI is mapped to a two-dimensional space through a twin neural network to maintain distance consistency, and user distribution is obtained by combining with known position correction. On the basis, an optimization model taking the minimization of the energy consumption of the unmanned aerial vehicle as a target and the user rate demand as a constraint is established, the optimization model is decomposed into a power distribution and trajectory optimization sub-problem, and the Lyapunov optimization and successive convex approximation are used for alternate solution. According to the method, high-precision positioning is realized through the channel map under the scene that the user position is unknown, the energy consumption of the unmanned aerial vehicle system is reduced by 27% or above through joint optimization, and meanwhile, 98% of user communication requirements are guaranteed.

The invention discloses an online secondary frequency modulation method considering regulation capacity aggregation of a hydrogen fuel cell vehicle. The method comprises the following steps: 1) constructing a power system operation model of the hydrogen fuel cell vehicle HFCV based on a hydrogen fuel cell vehicle cluster; 2) constructing a Minkowski sum-based hydrogen fuel cell vehicle frequency modulation capacity aggregation model; 3) solving the frequency modulation capacity aggregation model of the hydrogen fuel cell vehicle to obtain a frequency modulation capacity feasible region of an HFCV cluster; 4) on the basis of the power system operation model, constructing an online secondary frequency modulation model of the hydrogen fuel cell vehicle; 5) based on the frequency modulation capacity feasible region, the system scheduling mechanism adopts a Lyapunov optimization method to solve the online secondary frequency modulation model of the hydrogen fuel cell vehicle to obtain an AGC instruction signal, and the AGC instruction signal is issued to an aggregator; and 6) the aggregator distributes the AGC instruction to each hydrogen fuel cell vehicle in real time to participate in the secondary frequency modulation of the power system. According to the method, AGC signal prediction information is not needed, and the frequency response and frequency modulation operation cost can be better balanced.

The invention relates to the technical field of pressure-bearing equipment safety, and discloses a pressure-bearing special equipment safety risk analysis method. The method comprises the following steps: acquiring equipment operation state data through a multi-source sensing device, and constructing a multi-dimensional feature tensor comprising temperature field distribution, stress-strain features and material microstructure parameters; performing residual stress field reconstruction to generate an equipment state evolution graph with space-time continuity; predicting a damage critical value based on the map, and determining risk probability distribution of a plurality of potential failure modes; according to the risk probability distribution, generating dynamic safety threshold constraint conditions including maximum allowable deformation, a fatigue crack growth rate limit value, a material creep critical parameter and the like; and a risk mitigation strategy sequence is generated by adopting a Lyapunov optimization algorithm, and then an execution mechanism is controlled to adjust equipment operation parameters. According to the method, multi-source data are integrated, space-time relevance is considered, the comprehensiveness and accuracy of security risk analysis can be improved, and dynamic management and control of equipment risks are achieved.

The invention belongs to the field of metallurgical industry automation, and relates to an iron and steel joint enterprise energy management system scheduling method based on Lyapunov optimization and deep reinforcement learning. In order to solve the problems that an existing system depends on static rules or experience, is difficult to adapt to dynamic changes, is low in energy scheduling efficiency and the like, a multi-dimensional state space covering energy storage, a virtual queue and environment parameters is constructed, and a scheduling action space including capacity adjustment, multi-energy medium charging and discharging control and the like is defined; a Lyapunov drift function is introduced as a stability constraint, a reinforcement learning agent of an Actor-Critic framework is constructed, and global energy collaborative optimization is realized in combination with an energy sub-optimization problem solving and parallel sampling mechanism. The method can give consideration to scheduling economy and system stability, has dynamic adaptive ability, ensures project feasibility of the scheme, can improve energy utilization efficiency, reduces cost and carbon emission, and is suitable for iron and steel enterprises of different scales.

The invention relates to a deep neural network segmentation and task unloading joint scheduling method and device, and the method comprises the steps: constructing a dynamic optimization model based on a Lyapunov function, and constructing a Lyapunov optimization framework containing adjustable parameters according to the dynamic optimization model; determining a deep neural network segmentation strategy meeting a preset optimal condition, and dynamically selecting a mixed mode of all-local execution, all-server execution or partial local and partial server execution of the task by using the computing power, server load and network state of any terminal device; and constructing an optimization objective function containing real-time energy consumption and a queue state, and jointly scheduling a deep neural network segmentation and task unloading strategy under the condition that the optimization objective function meets the calculation capability constraint of any server, so as to determine a final deep neural network segmentation and task unloading joint scheduling result. Therefore, the problems of high task processing energy consumption, large delay and the like in an edge computing system are solved.

The invention relates to the technical field of electric power, in particular to a data center electric power and computing power collaborative optimization method and a related device, and the method comprises the steps: obtaining data processing pressure data of a data center and UPS operation data of the data center, and solving a pre-constructed electric power and computing power collaborative optimization problem model; and obtaining an optimal scheduling scheme with the goal of maximizing the weighted difference between the data processing amount and the energy consumption cost of the data center, and completing the collaborative optimization of the power and the computing power of the data center. According to the method, the dynamic service processing pressure of different servers of the data center and the influence of the UPS stability on the power and computing power collaborative optimization of the data center are fully considered, so that the data center meets the service requirements, and meanwhile, the energy consumption is reduced to the maximum extent. According to the method, the Lyapunov optimization theory and the genetic algorithm are combined for optimization, the optimization complexity is reduced, rapid convergence can be achieved, and the problems that an existing data center is low in electric power and computing power collaborative optimization accuracy, high in complexity and low in convergence speed are solved.

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 invention discloses a dynamic state updating scheduling method in a multi-terminal wireless communication system. The method comprises the following steps of: aiming at the problem that uplink resources are limited, constructing a virtual queue to represent the historical resource use condition of a terminal; dynamic updating indexes are designed to quantify urgency of state updating; high-value terminal screening and an optimal scheduling decision are executed in each time slot; and a Lyapunov optimization framework is creatively combined with a dynamic threshold mechanism. By adopting the method provided by the invention, the average information urgency can be minimized, and the information transmission performance of the system is effectively improved.

The invention discloses a test task scheduling and management system based on a 5G power virtual private network, and relates to the technical field of 5G communication and power system automation. A two-layer distributed architecture based on core and region edge nodes is adopted, and task domain division management and resource synchronization are realized through a lightweight Raft protocol; calculating a task key index TCI by using a four-dimensional label and a key dimension index amplification method, and dynamically scheduling resources in combination with a Lyapunov optimization model; and according to the health degree index of the access equipment, adaptively correcting resource reservation through the prediction model. According to the method, the problems of high time delay, extensive resource allocation and the like of a traditional centralized architecture are solved, distributed collaborative scheduling and resource dynamic optimization of the power test tasks are realized, and the system reliability is improved.

The invention discloses a video transmission method and device based on Lyapunov optimization, electronic equipment and a storage medium. The method is applied to satellite node ends. The method comprises the following steps: acquiring initial state information of a current transmission time slot and video data to be transmitted; the initial state information comprises queue state information representing a coding environment and transmission state information representing a transmission environment; inputting the initial state information into a preset strategy action model to obtain action control parameters corresponding to the initial state information; and coding and transmitting the video data to be transmitted according to the action control parameters. Therefore, compared with the prior art that the video to be transmitted is coded and transmitted by adopting the preset fixed configuration parameters, the action control parameters output by the strategy action model dynamically adapt to the coding environment and the transmission environment of the satellite node end; the satellite node end encodes and transmits the video data to be transmitted according to the action control parameters, so that the video quality is better, and the transmission effect is better.

The invention belongs to the technical field of communication, and discloses a physical layer security-oriented single-user photoelectric fusion edge computing system resource allocation method, which comprises the following steps of: establishing a queue model of each step of task unloading in a photoelectric fusion system, and deducing a queue length dynamic expression; performing problem modeling by taking maximization of an average processing rate on all frames as a target; a physical layer security resource transmission allocation strategy based on a photoelectric dual-channel channel is provided, and under the condition of considering the security of a physical layer, the optimization problem is solved by using a Lyapunov optimization theory by taking the throughput of a system as a target. The method has a good effect on reasonable resource allocation of single-user communication, the eavesdropping effect of the eavesdropping end is reduced, and the resource utilization rate and the communication quality of the whole communication network architecture are improved.

The invention provides an unmanned aerial vehicle task unloading method and device based on deep reinforcement learning, and relates to the technical field of Internet of Vehicles. The method comprises the following steps: judging that a task is processed by a vehicle or an edge server according to the computing power of the vehicle; if the vehicle is processed by the edge server, a Gaussian-Markov mobility model of the vehicle is constructed to obtain the position of the vehicle, and the unmanned aerial vehicle or the unmanned aerial vehicle and the intelligent roadside facility are judged to execute according to the computing resources of the unmanned aerial vehicle and the distance between the vehicle and the intelligent roadside facility; the unmanned aerial vehicle outputs a path planning result of the unmanned aerial vehicle according to the DGBCO model; the intelligent roadside facility optimizes task allocation according to a computing reuse technology; an unmanned aerial vehicle energy constraint problem based on Lyapunov optimization is constructed; and solving by adopting an MADDPG algorithm to obtain a task unloading strategy. The UAV trajectory is optimized, a joint optimization method is designed in combination with the mobility of the vehicle and the dynamic change of the computing resources, and the comprehensive performance of the system is improved.

The application provides a virtual power plant dynamic feasible region solving method and system based on coupling constraint decoupling, belongs to the technical field of power plant resource allocation management, establishes a virtual power plant model considering distributed energy and flexible load resources, and determines a virtual power plant feasible region solving model based on a vertex search method; through Lyapunov optimization, virtual queues of energy storage, heating ventilation air conditioning and electric vehicles and other flexible loads are respectively established, and the time coupling constraint of the flexible load is decoupled into a virtual queue stability problem; a virtual time queue is constructed to integrate the decoupled optimization problem into the virtual power plant dynamic feasible region solving of day-ahead and day-ahead, so that the day-ahead and day-ahead feasible region solving algorithm is obtained. Based on the Lyapunov optimization theory, the time coupling constraint is decoupled, a weight coefficient is introduced, and a mathematical model taking the vertex search and queue stability as targets is provided, and finally the virtual power plant dynamic feasible region solving of day-ahead and day-ahead is realized.

The present application relates to a kind of static-mobile energy storage collaborative space-time flexible regulation method, it relates to a kind of energy storage optimization scheduling method, the method includes the following steps: (1) the distribution of regional integrated energy system new energy, load access characteristic and consumption demand are analyzed, and surplus model of renewable energy is constructed;(2) according to the source and load characteristics of regional integrated energy system, static energy storage charging and discharging scheduling model is constructed;(3) according to surplus model of renewable energy, the space-time scheduling strategy of mobile energy storage is proposed;(4) according to static energy storage charging and discharging scheduling model and the space-time scheduling strategy of mobile energy storage, the queuing model of static-mobile energy storage charging and discharging strategy is constructed;(5) according to the queuing model of static-mobile energy storage charging and discharging strategy, based on improved Lyapunov optimization, static-mobile energy storage collaborative dynamic scheduling is realized.The method of the present application provides support for the collaborative consumption of distributed renewable energy between regional integrated energy system.

The invention discloses an edge computing unloading method and device, electronic equipment and a storage medium, and is used for solving the problems that an existing edge computing unloading method is low in safety and poor in task unloading strategy. The method comprises the following steps: according to the privacy level of the mobile equipment in the t time slot and a constructed cost function for processing subtasks of executable tasks of the mobile equipment in the t time slot, constructing a task unloading problem with minimum cost and maximum privacy level; a Lyapunov optimization theory is adopted to convert a task unloading problem into a single time slot task unloading strategy optimization problem, and an optimization objective function is obtained; and modeling the optimization objective function as a Markov decision process, and determining an optimal task unloading strategy according to a preset deep reinforcement learning algorithm.

The application discloses a server energy efficiency dynamic optimization method based on workload prediction and a deep learning model, relates to the cross field of server energy efficiency optimization and deep learning technology, and comprises the following steps: collecting server multi-source data, performing space-time alignment on the server multi-source data, extracting causal characteristics, constructing a standardized data set, constructing a time series graph convolution network, combining a multi-head attention mechanism, outputting a probabilistic load prediction interval, constructing an energy efficiency objective function based on a Lyapunov optimization framework, quantifying performance loss and temperature drift, and outputting multi-agent optimization constraints, defining a multi-reinforcement learning agent, outputting a collaborative adjustment instruction through a counterfactual baseline algorithm, constructing a digital twin shadow model to deduce energy efficiency, combining a meta-learning fine-tuning model, forming a closed-loop optimization of physical and digital double verification, realizing dynamic and accurate optimization of server energy efficiency, and adapting to multiple computing power scenes.

The invention provides a space-air-ground integrated network resource management method and system and a storage medium, and belongs to the technical field of wireless communication and network resource dispatch. The method comprises the steps that a space-air-ground integrated network is constructed, a GU is provided with an energy collection module and a backscatter circuit, and the backscatter circuit is connected with the GU; low-power-consumption data transmission and energy collection are realized by reflecting a radio frequency carrier signal emitted by the UAV; the UAV carries a mobile edge computing server to process a task, and the task is unloaded to the LEO when the task is overloaded; a central controller collects network state and channel state information in real time, a random optimization function is constructed by taking minimization of long-term average energy consumption of a system as a target and combining constraints such as task queue stability and energy sustainability, a Lyapunov optimization framework is adopted to convert the problem into a single time slot problem, and the problem is solved through alternate optimization and concave-convex process technologies. According to the method, the stability of the task queue is guaranteed, the system energy consumption is remarkably reduced, the resource utilization rate is improved, and reliable support is provided for remote area power emergency communication.

The application discloses an edge intelligent body flexible scheduling method based on Lyapunov optimization and Stackelberg game. The application constructs a cloud-edge-end three-layer edge intelligent body federated learning network architecture to complete distributed collaborative training under the premise of protecting data privacy. In view of the problems of heterogeneous edge intelligent body network nodes, non-independent and identically distributed data and limited communication resources, a long-term joint optimization model of client selection and bandwidth allocation is established, and model accuracy, time delay and energy consumption are considered. Lyapunov optimization is used to decouple long-term random optimization into real-time decision-making per time slot, and to convert constraints into virtual queue stability control. Dynamic client selection is realized based on Stackelberg game, and high-quality nodes are adaptively selected. Adaptive bandwidth allocation is completed through an evolutionary algorithm, and communication requirements of key clients are preferentially guaranteed. The application can significantly improve model accuracy, reduce time delay and energy consumption, guarantee long-term stability of the system, and is suitable for high-dynamic edge intelligent body federated learning scenes.

The invention provides a digital twin proxy pool system based on Lyapunov optimization and a resource optimization method, and belongs to the technical field of mobile edge computing. According to the technical scheme, the method comprises the following steps: S1, a digital twin proxy mechanism; s2, a pooling mechanism of the digital twin proxy; s3, a Lyapunov optimization mechanism is carried out; and S4, cloud edge collaborative digital twin storage and synchronization architecture. The method has the beneficial effects that the agent mode, the resource pooling and the Lyapunov optimization are introduced;

The present application relates to the field of distributed large model inference, and particularly relates to a distributed large model inference method based on token perception and Lyapunov optimization. The scheme comprises: after receiving a user input prompt, predicting the output token length of the corresponding task through a length perception semantic module, calculating the corresponding total amount of work, based on the token length prediction result, the real-time state of the device and the long-term virtual queue state, generating a final unloading decision through the Lyapunov guided unloading optimization module iterative unloading algorithm; the client unloads the task to the corresponding cloud edge device according to the final unloading decision, and the device executes the large model inference; the scheduler updates the virtual queue length according to the actual task execution time and the device computing power utilization rate, and provides input for the next time slot decision. The present application is suitable for inference systems utilizing distributed large models.

This invention discloses a federated learning method for intelligent IoT, comprising: dividing the global model into continuous fragments on the server side, configuring lightweight auxiliary head and tail segments to simulate contextual features; introducing an event-triggered resource monitoring and Lyapunov optimization-driven dynamic fragment adjustment mechanism; employing fragment-level knowledge distillation to achieve cross-fragment knowledge transfer, combined with split gradient descent optimization. This invention solves the problems of heavy communication and computational burdens and poor adaptability in resource-constrained scenarios of existing federated learning and fragmented federated learning technologies. This invention can reduce device storage, computation, and communication overhead, adapt to dynamic resource changes, improve model convergence speed and consistency, and performs excellently under both IID and Non-IID data distributions, making it suitable for distributed training scenarios of heterogeneous AIoT devices.

The present invention discloses an electric vehicle guiding method and system applicable to urban rapid charging stations, belonging to the field of urban electric vehicle charging guidance. Based on establishing a queue model of rapid charging stations in the area to describe the spatial distribution of electric vehicles, the present invention evaluates the stability degree of the charging station queue through the Lyapunov function; then, based on the Lyapunov optimization theory, the user dissatisfaction degree is introduced as a penalty function into the optimization process, and by constructing the form of a drift-penalty term function, an electric vehicle guiding decision model is established. The proposed model fully considers the compliance willingness of electric vehicle owners. By guiding the electric vehicles, the charging congestion situation of the rapid charging stations is effectively alleviated, the number of electric vehicles waiting inside the rapid charging stations is reduced, the time-consuming of the electric vehicle charging process is reduced, and the charging satisfaction of the vehicle owners is improved.