面向低语料条件的网络配置转换方法研究

石鸿伟; 史精文; 黄韬

doi:10.11959/j.issn.1000-0801.DXKX260165

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面向低语料条件的网络配置转换方法研究

更新时间：2026-07-08

- 面向低语料条件的网络配置转换方法研究
- Network configuration translation methods under low-resource scenarios
- 电信科学 2026年
- 作者机构：
  
  1.东南大学网络空间安全学院，江苏南京 211189
  2.紫金山实验室未来网络研究中心，江苏南京 211111
  3.北京邮电大学网络与交换技术国家重点实验室，北京 100876
- 作者简介：
  
  石鸿伟（1982- ），男，东南大学网络空间安全学院博士生，紫金山实验室课题负责人，高级工程师，主要研究方向为未来网络体系架构、软件定义网络、网络智能控制等。
  史精文（1990- ），男，硕士，紫金山实验室科研人员，主要研究方向为网络编排系统、网络运维智能体、PaaS平台技术等。
  黄韬（1980- ），男，北京邮电大学网络与交换技术国家重点实验室教授、博士生导师，主要研究方向为路由与交换、软件定义网络、内容分发网络、确定性网络、算力网络等。
- 基金信息：
  
  工信部工业互联网创新发展工程基金资助项目(TC240AAKM-137);中国联合网络通信有限公司下一代互联网宽带业务应用国家工程研究中心项目(2025-STHZ-ZJSSYS-001)
- DOI：10.11959/j.issn.1000-0801.DXKX260165
  中图分类号： TP393
- 收稿：2026-03-11，
  
  修回：2026-06-02，
  
  录用：2026-07-08，
- 稿件说明：
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石鸿伟, 史精文, 黄韬. 面向低语料条件的网络配置转换方法研究[J/OL]. 电信科学, 2026.

SHI Hongwei, SHI Jingwen, HUANG Tao. Network configuration translation methods under low-resource scenarios[J/OL]. Telecommunications Science, 2026.
石鸿伟, 史精文, 黄韬. 面向低语料条件的网络配置转换方法研究[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260165.

SHI Hongwei, SHI Jingwen, HUANG Tao. Network configuration translation methods under low-resource scenarios[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260165.

摘要

针对跨厂商网络设备配置转换场景中低语料条件下转换准确性与稳定性不足的问题，提出了一种基于Transformer架构的网络配置转换模型（network configuration translation model，NetConfigTM）。该方法融合网络配置领域特性，构建面向配置语义的训练机制，通过对有限配置语料进行扩展与建模，在低标注条件下实现有效特征学习。进一步引入掩码语言建模、去噪自编码与有监督微调相结合的多阶段训练策略，增强模型对跨厂商配置语法结构与语义映射关系的建模能力。基于脱敏网络配置数据开展实验验证，结果表明，该方法在多类典型配置场景下能够保持稳定、可靠的转换准确性，并可将人工方式下天级的配置转换过程缩短至分钟级，验证了其在跨厂商网络环境中开展自动化配置转换的工程可行性。

Abstract

To address the insufficient accuracy and stability of network configuration translation under low-resource scenarios in cross-vendor environments

a Transformer-based network configuration translation model (NetConfigTM) was proposed. The model integrated domain characteristics of network configurations and constructed a configuration-semantic-oriented training mechanism. By expanding and modeling limited configuration corpora

effective feature learning was realized under low-label conditions. Furthermore

a multi-stage training strategy combining masked language modeling

denoising autoencoding

and supervised fine-tuning was introduced to enhance the modeling capability of cross-vendor configuration syntax structures and semantic mapping relationships. Experiments conducted on desensitized network configuration data demonstrated that the proposed method maintained stable and reliable translation accuracy in multiple typical configuration scenarios and reduced the configuration translation time from days of manual operation to minutes

verifying its engineering feasibility for automated configuration translation in cross-vendor network environments.

关键词

Keywords

references

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