Optical channel fault diagnosis method and system based on transfer learning

Abstract

The invention discloses an optical channel fault diagnosis method based on transfer learning. The method comprises the following steps: acquiring optical network performance, alarm, log and topological data of a certain training area, and constructing a data sample set required by model training; extracting training state samples, training state features and training state relationships from the data sample set; selecting a transfer learning mode, and inputting the extracted training state samples, training state features and training state relationships for model training to obtain an optical channel state diagnosis training state model; selecting optical network performance, alarm, log and topological data of a reasoning area, and performing health state labeling on optical channel data as a reasoning state sample; loading the reasoning state sample into an optical channel state diagnosis training state model, and training an optical channel state diagnosis reasoning state model; and calling the newly generated optical channel state diagnosis reasoning state model to obtain the health state of the analysis object optical channel. The invention also provides a corresponding optical channel fault diagnosis system based on transfer learning.

Description

technical field [0001] The present invention relates to the technical field of OTN equipment management, and more specifically, to a method and system for diagnosing optical channel faults based on migration learning. Background technique [0002] With the rise of cloud computing and 5G interconnection, the demand for network capacity is increasing, and the traditional 10G network is gradually being replaced by 100G. With the rise of data centers, large-scale deployment of 100G backbone networks, more and more switches and routers with 100G Optical Transport Network (OTN) ports, due to the high cost, large size and power consumption of 100G backbone network equipment High, maintenance is difficult, and link failure may directly cause service interruption and affect user experience. Therefore, it is particularly important to predict the sub-health of the optical channel and trace the source of the fault point. [0003] Under the background that different operators have diffe...

AI Technical Summary

Problems solved by technology

There are problems in the existing technology: a single machine learning model is difficult to apply to the OTN network with frequent changes in reality, and transfer learning needs to be introduced to enhance the versatility and universality of traditional machine learning models

Images