Traffic self-learning method and device of TSN switch and electronic equipment

By implementing the traffic self-learning method on the TSN switch and using hardware timestamps to create and update the flow table, the problem of the current TSN configuration solution lacking dynamic traffic perception and self-learning capabilities is solved, and it realizes automatic adaptation to network traffic changes and real-time optimization of traffic configuration, improving network efficiency and reliability.

CN119676175APending Publication Date: 2025-03-21KYLAND TECH CO LTD
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Patent Information

Application Number
CN202411881956.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-19
Publication Date
2025-03-21

AI Technical Summary

Technical Problem

The current way of generating TSN configuration schemes lacks dynamic traffic perception and self-learning capabilities, which leads to the inability of the network to automatically optimize the configuration according to actual traffic changes and cannot fully adapt to complex and changeable network environments.

Method used

By implementing a traffic self-learning method on a TSN switch, creating and updating a flow table using hardware timestamps, determining the periodicity of the traffic, and sending the flow table to a centralized network configuration to generate and return the TSN configuration scheme.

Benefits of technology

This enables no manual intervention. The TSN switch can automatically adapt to network traffic changes, automatically generate TSN configuration solutions, optimize traffic configuration in real time, and improve network efficiency and reliability.

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Abstract

The invention relates to a traffic self-learning method and device of a TSN switch and electronic equipment. The method comprises the steps of creating a first flow table of first learning according to a first rule and first message information and a first hardware timestamp of a received first flow message in a first learning period; at the end of the first learning time, determining the periodicity of the first flow according to a first table item corresponding to the first flow in the first flow table learned for the first time, and inserting the periodicity of the first flow into the first table item to obtain a second flow table learned for the first time; the second flow table learned for the first time is sent to the centralized network configuration, so that the centralized network configuration generates and returns a TSN configuration scheme learned for the first time according to the second flow table learned for the first time; and updating the configuration processing flow according to the TSN configuration scheme learned for the first time. The technical problem that the current mode for generating the TSN configuration scheme lacks dynamic flow sensing and self-learning capabilities is solved.
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