5 results about "Queue stability" patented technology

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 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.