## 1 Introduction
Second, traditional multiplexing algorithms usually consider only a single parameter for channel multiplexing. As channel characteristics become more complex, problems such as delay and frame residue caused by incomplete algorithm parameter design become increasingly apparent. Finally, traditional algorithms lack effective time slot management strategies, with fixed allocation of scheduling time slots, making it difficult to achieve high multiplexing efficiency when the characteristics of the source traffic are unknown. In [18], a new AOS virtual channel scheduling algorithm based on frame urgency was proposed, considering service priority, scheduling delay urgency, and frame residue urgency as key factors affecting the scheduling order of virtual channels, and comprehensively considering the priority of asynchronous data, the isochronous nature of synchronous data, and the ur gency of VIP data. In [19], a cross-layer optimization polling weight scheduling method for AOS multiplexing was proposed, establishing a cross-layer optimization model to comprehensively consider channel state information at the physical layer and queue state information at the link layer, optimizing the polling weight allocation of virtual channels to achieve more effective resource allocation and scheduling. In recent years, researchers have also conducted studies on virtual channel scheduling algorithms based on intelligent algorithms such as ant colony algorithms [20], genetic algorithms [21; 22], and machine learning [23; 24]. In [22], the authors established an AOS hybrid scheduling model and proposed an asynchronous virtual channel algorithm based on genetic-particle swarm sorting. This algorithm combines the evolutionary operators of genetic algorithms and the search capabilities of particle swarm algorithms, establishing a fitness function model considering factors such as service priority, scheduling delay urgency, and frame residue urgency to optimize the scheduling order of asynchronous virtual channels. Reference [24] combines AOS multiplexing technology with the demands of Industry 5.0, proposing an AOS adaptive framing algorithm based on optimized thresholds. This algorithm can adaptively adjust the frame waiting time according to packet arrival and use differential evolution algorithms to optimize frame waiting time thresholds. It also proposes an AOS virtual channel scheduling algorithm based on a deep Q-network (DQN), considering service priority, scheduling delay, and frame residue to find the optimal virtual channel scheduling order. Intelligent algorithms can optimize AOS scheduling performance to some extent, but due to their tendency to fall into local optimal solutions, and issues such as high algorithm complexity, high resource consumption, and poor real-time performance, practical application and engineering implementation face difficulties. As network traffic complexity increases, the use of traffic prediction to optimize the AOS algorithm also has great advantages in view of the complexity of AOS network traffic. In [25], an intelligent optimization threshold algorithm based on traffic prediction is proposed. The wavelet neural network is used to predict self-similar traffic, and the comprehensive evaluation function is optimized by the artificial fish swarm algorithm to dynamically determine the optimal threshold value to improve system performance. In [26], an adaptive frame generation algorithm based on wavelet neural network traffic prediction is proposed. The network parameters are optimized by the genetic algorithm, and the framing time is adjusted according to the prediction results to optimize the frame multiplexing efficiency. It can be seen from the literature that the use of the wavelet neural network for traffic prediction can achieve high prediction accuracy, and it can be effectively applied in the AOS algorithm. However, the above research only optimizes the frame generation algorithm and does not consider the joint optimization problem with the virtual channel scheduling algorithm.