Research on evaluation of optical transport network health model based on data augmentation technology

Wang Yu; Tang Du; Li Fang; Xu Yunbin

doi:10.11959/j.issn.1000-0801.DXKX250673

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Research on evaluation of optical transport network health model based on data augmentation technology

Research and Development | 更新时间：2026-07-21

- Research on evaluation of optical transport network health model based on data augmentation technology
- Telecommunications Science Vol. 42, Issue 6, Pages: 158-173(2026)
- 作者机构：
  
  中国信息通信研究院，北京 100091
- 作者简介：
- 基金信息：
  
  MIIT Manufacturing Industry Special Program-2024 Optical Network New Technology Verification Project(20240248)
- DOI：10.11959/j.issn.1000-0801.DXKX250673
  CLC： TN929.11
- Received：20 November 2025，
  
  Revised：2025-12-22，
  
  Accepted：29 December 2025，
  
  Online First：21 July 2026，
  
  Published：20 June 2026
- 稿件说明：
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王郁,唐都,李芳等.基于数据增强技术的光传送网健康度模型测评研究[J].电信科学,2026,42(06):158-173.

Wang Yu,Tang Du,Li Fang,et al.Research on evaluation of optical transport network health model based on data augmentation technology[J].Telecommunications Science,2026,42(06):158-173.
王郁,唐都,李芳等.基于数据增强技术的光传送网健康度模型测评研究[J].电信科学,2026,42(06):158-173. DOI： 10.11959/j.issn.1000-0801.DXKX250673.

Wang Yu,Tang Du,Li Fang,et al.Research on evaluation of optical transport network health model based on data augmentation technology[J].Telecommunications Science,2026,42(06):158-173. DOI： 10.11959/j.issn.1000-0801.DXKX250673.

摘要

随着光传送网（optical transport network，OTN）网络向高阶自智生态演进，OTN健康度模型的预测准确度直接关系到网络优化决策的可靠性。为突破传统通用时间序列增强方法缺乏物理约束的局限，提出一种基于数据增强技术与OTN物理传输特征相结合的健康度模型准确度测评方法。该方法基于OTN关键性能参数的物理变化规律设计基底函数，结合函数发生器与光网络仿真器，构建“物理引导—数学变换—光域验证”的数据增强机制，生成高保真合成数据集，模拟渐变、突变等多种性能变化场景，形成用于模型训练与验证的基准数据集。此外，详细设计了包含数据加载、增强、处理、模型训练与测评的完整流程，并通过实验验证了该方法的有效性。

Abstract

As optical transport network (OTN) evolve toward a higher-level autonomous and intelligent ecosystem

the prediction accuracy of OTN health models was directly related to the reliability of network optimization decisions. To overcome the limitation of conventional general time-series augmentation methods lacking physical constraints

an evaluation method for health model accuracy was proposed by integrating data augmentation technology with OTN physical transmission characteristics. In this method

basis functions were designed based on the physical variation patterns of key OTN performance parameters

and a data augmentation mechanism of “physics-guided mathematical transformation followed by optical-domain verification” was constructed by combining a function generator with an optical network simulator. High-fidelity synthetic datasets were generated to simulate various performance variation scenarios such as gradual changes and abrupt changes

thereby forming benchmark datasets for model training and validation. Furthermore

a complete workflow encompassing data loading

augmentation

processing

model training

and evaluation was detailed

and the effectiveness of the method was validated through experiments.

关键词

Keywords

references

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