16 results about "Queue stability" patented technology

The invention provides an integral shopping mall order rolling processing optimization method and system based on big data, and the method comprises the steps: employing a dynamic priority adjustment mechanism for a conflict order list, and adjusting the order processing priority based on the user behavior track data, including historical exchange preference, and obtaining a resource distribution scheme; integrating the order sequence through a queue reordering mechanism, reordering based on the resource allocation scheme to obtain a final processing sequence, preferentially executing the high-priority request, and maintaining the queue stability at the same time; if residual conflicts exist in the final processing sequence, supplementary resources are extracted from a preset standby inventory through a standby resource set scheduling mechanism, a complete execution plan is obtained, and the plan covers all order processing paths; and on the basis of the complete execution plan, monitoring the order processing progress, and obtaining a stable operation order flow through progress tracking and anomaly detection, real-time state feedback and iterative adjustment.

The invention provides an on-line resource management system and method oriented to a requirement of integration of communication, inductance and calculation, and the method comprises the steps: carrying out the correlation scheduling constraint between a sensing target and edge intelligent equipment under the constraint of transmitting power resources, the constraint of calculation resources of a base station end, the constraint of long-term queue backlog and the constraint of average power; constructing a non-convex optimization problem of the common inductance calculation integrated system under a long-term optimization framework; and solving the non-convex optimization problem to obtain an optimal perception scheduling decision, an optimal transmitting beam forming vector of perception and communication, an optimal receiving beam forming variable of perception echoes and calculation resource allocation of a base station end. The online resource management strategy provided by the invention aims to maximize the long-term weighted average rate, meet the requirements of queue stability, average power constraint and quality of service (QoS) at the same time, and explore the cooperative gain of the communication, inductance and calculation integrated system from a long-term perspective.

The invention provides a service demand-oriented 5G network slice dynamic arrangement method, which belongs to the technical field of 5G communication, and comprises the following steps of: establishing a network optimization model comprising a differential transmission rate model, a system utility model and a queue delay model; in combination with the network optimization model, proposing a random optimization problem of meeting a queue stability condition, a bandwidth allocation scheme, a time delay maximum threshold value and a minimum total cost in a multi-service coexistence scene of the power internet of things; and solving a stochastic optimization problem, decomposing the randomness of the problem into joint optimization of a queue drift amount and a cost penalty term, and carrying out collaborative optimization on time delay and throughput based on a dual-time scale mechanism dynamic decision. The method has the advantages that efficient collaborative management of network resources in a multi-service scene is realized; through dynamic weight adjustment and real-time resource allocation, the time-varying characteristics of the service priority and the channel state are considered, and a solution which is high in adaptability and stable is provided for network slice management.

The application discloses a low-altitude unmanned aerial vehicle communication and perception integrated beamforming method based on queue stability, which comprises the following steps: establishing a system model under a low-altitude unmanned aerial vehicle communication and perception integrated scene; the system model comprises a communication and perception signal propagation model between an unmanned aerial vehicle and a ground user and a data transmission queue model at the unmanned aerial vehicle end; establishing a long time slot optimization problem of the low-altitude unmanned aerial vehicle communication and perception integrated beamforming, converting the long time slot optimization problem into a short time slot optimization problem based on a current time slot state; solving the short time slot optimization problem to obtain a beamforming weight vector meeting the communication performance, the perception performance and the queue stability requirements. The application can effectively balance the transmission power consumption and the data transmission stability of the unmanned aerial vehicle system while guaranteeing the communication and perception performances, and realizes minimization of long-term average power consumption.

This invention discloses a client selection method and system for multi-task federated learning. Addressing the shortcomings of client selection and insufficient consideration of task urgency in multi-task dynamic federated learning scenarios, this invention first constructs a multi-task federated learning system model, defining a utility function that includes learning quality and penalty terms. Second, it establishes fairness constraints and introduces a fairness queue to transform the problem into a queue stability problem. Then, based on Lyapunov optimization theory, it constructs a drift-plus-utility function, transforming a long-term stochastic optimization problem into a deterministic optimization problem for each round of communication by minimizing its upper bound. Finally, it constructs an auxiliary bipartite graph to transform client selection into a minimum-weight bipartite graph matching problem. This invention, by jointly optimizing fairness, learning quality, and task urgency through the Lyapunov framework, transforms long-term constraints into a solvable problem for each round, reducing computational complexity and achieving efficient and fair dynamic client selection.

The application provides a big data-based integral mall order rolling processing optimization method and system, which comprises the following steps: for the conflict order list, a dynamic priority adjustment mechanism is adopted, the order processing priority is adjusted based on the user behavior trajectory data including historical exchange preferences, and a resource allocation scheme is obtained; the order sequence is integrated through a queue reordering mechanism, the order sequence is rearranged based on the resource allocation scheme, a final processing sequence is obtained, the high-priority request is preferentially executed, and the queue stability is maintained; if there is a remaining conflict in the final processing sequence, a standby resource set scheduling mechanism is used to extract supplementary resources from a pre-device inventory, a complete execution plan is obtained, and the plan covers all order processing paths; based on the complete execution plan, the order processing progress is monitored, the real-time state is fed back through progress tracking and abnormality detection, iterative adjustment is performed, and a stable running order flow is obtained.

The invention discloses a traffic signal control system, method and device considering stability of average queuing length, and belongs to the technical field of intelligent traffic. The system comprises a vehicle-mounted unit, a roadside unit and a roadside signal machine, and the vehicle-mounted unit is connected with the roadside unit and used for collecting vehicle information and sending the vehicle information to the roadside unit; the roadside unit is connected with the roadside signal machine, analyzes the queue stability through a stability index calculation method based on a quadratic function based on vehicle information, and constructs a multi-objective optimization strategy of traffic signal control; and the roadside signal machine is used for receiving the multi-target optimization strategy to control the signal lamp. The signal lamp is controlled based on the multi-objective optimization strategy provided by the invention, and the traffic capacity of the intersection can be effectively improved.

The invention discloses a CACC controller parameter optimization method considering communication delay and a related device, and the method comprises the steps: building a CACC model, and determining the stability constraint and communication delay boundary of the CACC model; constructing an optimization objective function according to the CACC model, and constructing a Gaussian process model based on the optimization objective function; and based on the Gaussian process model, solving optimal parameters in the stable region by using a Bayesian optimization algorithm. On the basis of a stability optimization framework of the CACC model, the Bayesian optimization algorithm is introduced innovatively, accurate optimization of parameters of the CACC controller under communication delay constraints is realized, and queue stability and disturbance suppression capability are improved.

The application relates to a wireless network congestion control optimization method based on a heavy ball method, which comprises the following steps: initialization, selection of system parameters and iteration step length; emptying all queues at the initial state; initial value assignment of weights; iteration, start of calculation of the difference of the weight parameters, determination of a service rate for each link by a scheduling module to obtain a service rate vector; congestion control, determination of an integer random variable for each data flow, wherein the random variable needs to meet the constraint of queue stability; update of the queue length and update of the weight parameters by using the heavy ball method; and finally, return to the iteration start point until the algorithm converges. The application realizes the maximization of network utility under the constraint of queue stability by introducing the heavy ball method based on momentum, effectively reduces the queue delay, and achieves the effect of improving the communication quality by reliable resource allocation and routing scheduling when the devices accessing the data link network are continuously increased.

A switch dynamic ECN configuration method and system based on reinforcement learning and a switch belong to the field of network congestion control and AI optimization, and the method comprises the following steps: training a reinforcement learning model by using historical data, deploying the model, collecting multi-dimensional state data S of a switch port in real time, and outputting an ECN configuration action value A; and calculating a reward value R, updating an experience pool in combination with state transition data {S, A, R, S '}, and optimizing a model by adopting a double-inference mechanism, a gradual change epsilon-greedy strategy and historical optimal action backtracking. By dynamically optimizing the ECN threshold, balancing the throughput rate and the queue stability, reducing the packet loss rate and delay jitter and improving the network performance and the model reliability, the method is suitable for a real-time congestion control scene of a large-scale data center switch.

The application relates to a micro-service scheduling method and system based on double-layer queue feedback and topology sensing, and the application introduces a shared graph attention network (GAT), deeply fuses and encodes residual computing power of a heterogeneous computing node and time-varying link quality, and gives the scheduling system a structure sensing capability of a global perspective; by establishing a dynamic feasibility action mask and a hierarchical degradation mechanism, the generation of illegal scheduling instructions is cut off from the root, and the physical executability of each decision is ensured. A virtual queue tracking mechanism is introduced to realize nanosecond-level real-time cumulative evaluation of scheduling actions in the same time slot on node load, and fine load sharing is realized in a concurrent storm; a Lyapunov drift and punishment principle is introduced to construct a composite reward function, guiding the DSAC agent to complete joint optimization of delay, energy consumption and queue stability in adaptive exploration.

The application provides a connected vehicle elastic distributed control method based on a limited time privacy protection mechanism, and relates to the technical field of intelligent transportation systems and vehicle control. The application realizes flexible privacy protection by introducing a mask function that decays over time, and realizes queue stability under hybrid attacks by combining a radial basis function neural network and a backstepping method to design an elastic controller.

The application belongs to the technical field of unmanned aerial vehicle cluster control, and particularly relates to an unmanned aerial vehicle cooperative reasoning and time delay control optimization method based on Lyapunov theory, which converts long-term average constraints into queue stability problems, designs suitable reward functions and algorithm architectures, and realizes effective balance among delay, energy consumption and system stability. First, an instant cost function is defined, and long-term average delay and energy consumption constraints are converted into instant delay and energy consumption of each time slot. Then, three queue models are constructed to describe the operation characteristics of the system: an actual task queue, a virtual delay queue and a virtual energy consumption queue. Finally, online optimization is carried out based on the Lyapunov algorithm, so as to minimize instant delay and energy consumption while ensuring system stability.

This invention discloses an energy-saving task scheduling method, apparatus, and equipment for campuses and data centers. The method includes: acquiring the directed acyclic graph structure, deadlines, and wireless link status of terminal jobs, and collecting electricity price information that changes dynamically over time; constructing a task scheduling model with energy consumption and electricity cost as optimization objectives; constructing an objective function and constraint terms reflecting multi-task resource competition and queue stability based on Lyapunov drift with penalty; constructing a heterogeneous graph containing task nodes and resource nodes, and using a graph neural network to jointly output task offloading decisions and task delay processing times. This invention achieves timed processing and collaborative offloading of campus and data center jobs under dynamic electricity price environments. While ensuring jobs do not exceed deadlines, it schedules executable tasks to periods with lower electricity prices as much as possible, improving resource utilization and reducing system energy consumption and electricity costs by sacrificing some communication costs.

A task-dependent long-slot general computing resource allocation method belongs to the field of communication, and comprises the following steps: calculating a data uploading rate by a CPU (Central Processing Unit); the CPU calculates task delay consumption of each user; the CPU constructs a long-time-slot task scheduling optimization model, decouples a long-time-slot task scheduling optimization problem into a single-time-slot optimization sub-problem by adopting a Lyapunov optimization method, and solves and obtains an optimal long-time-slot task scheduling scheme by utilizing a reinforcement learning method; and the CPU sends the optimal long time slot task scheduling scheme to the requesting user and each access point for service. According to the method, long-term queue stability, task dependence management and time delay optimization are converted into a real-time decision problem, a virtual queue mechanism is adopted to perceive a network state, and power distribution, a task unloading proportion and access point computing resource scheduling are optimized in combination with reinforcement learning. Through task dependency analysis, the execution sequence of the sub-tasks can be optimized, the waiting time is shortened, and the parallel processing capacity is improved under the condition that computing resources are limited.

The invention discloses a low-altitude unmanned aerial vehicle communication and sensing integrated beam forming method based on queue stability. The method comprises the following steps: establishing a system model in a low-altitude unmanned aerial vehicle communication and sensing integrated scene; the system model comprises a communication and sensing signal propagation model between the unmanned aerial vehicle and a ground user, and a data transmission queue model at an unmanned aerial vehicle end; establishing a long-time-slot optimization problem of low-altitude unmanned aerial vehicle flux-sensing integrated beam forming, and converting the long-time-slot optimization problem into a short-time-slot optimization problem based on a current time slot state; and solving a short-time slot optimization problem to obtain a beam forming weight vector meeting the requirements of communication performance, sensing performance and queue stability. According to the invention, while the communication and sensing performance is ensured, the emission power consumption and the data transmission stability of the unmanned aerial vehicle system are effectively balanced, and the minimization of long-term average power consumption is realized.