TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and medium

By deploying lightweight traffic detection modules and combining deep reinforcement learning (DRL) with the enhanced queue scheduling mechanism Pro-CQF in switches or routers, the heterogeneity problem of traffic management and scheduling in TSN local area scenarios is solved, achieving efficient scheduling of TT flow and burst flow, and improving network resource management and system stability.

CN120956672APending Publication Date: 2025-11-14NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
0 Cites 8 Cited by

Patent Information

Application Number
CN202511166929.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-20
Publication Date
2025-11-14

AI Technical Summary

Technical Problem

In TSN local scenarios, the heterogeneity of end devices leads to traffic management challenges, scheduling algorithms lack dynamic adaptability, and the CQF model experiences performance conflicts in mixed-stream scenarios, making it difficult to achieve a balance between high reliability and low latency.

Method used

By deploying lightweight traffic detection modules in switches or routers, and combining deep reinforcement learning (DRL) and the enhanced queue scheduling mechanism Pro-CQF, accurate identification and dynamic scheduling of time-triggered flows (TT flows) and burst flows can be achieved, intelligent queue scheduling tasks can be constructed, and priority labels and queue structures can be dynamically adjusted.

Benefits of technology

It improves the network's adaptability to diverse service scenarios, reduces frame drop rates and end-to-end latency in bursty flows, enhances network resource management and system stability, and achieves efficient traffic processing capabilities.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.
Need to check novelty before this filing date? Find Prior Art