多维度量化的光网络智能化能力评测方法

孟海军

doi:10.13756/j.gtxyj.2026.250285

您当前的位置：

首页 >

文章列表页 >

多维度量化的光网络智能化能力评测方法

光通信系统与网络技术 | 更新时间：2026-08-27

- 多维度量化的光网络智能化能力评测方法
- Multidimensional Quantitative Evaluation Method for Intelligent Capability of Optical Networks
- 光通信研究 2026年第4期页码：115-122
- 作者机构：
  
  中国信息通信研究院　技术与标准研究所，北京　100191
- 作者简介：
  
  孟海军，工程师。E-mail：menghaijun@caict.ac.cn
- 基金信息：
  
  工信部制造业专项资助项目(125)
- DOI：10.13756/j.gtxyj.2026.250285
  中图分类号： TN929
- 收稿：2025-08-25，
  
  修回：2025-09-28，
  
  纸质出版：2026-08-10
- 稿件说明：
移动端阅览
孟海军. 多维度量化的光网络智能化能力评测方法[J]. 光通信研究，2026(4): 115-122.

Meng H J. Multidimensional Quantitative Evaluation Method for Intelligent Capability of Optical Networks[J]. Study on Optical Communications, 2026(4): 115-122.
孟海军. 多维度量化的光网络智能化能力评测方法[J]. 光通信研究，2026(4): 115-122. DOI： 10.13756/j.gtxyj.2026.250285.

Meng H J. Multidimensional Quantitative Evaluation Method for Intelligent Capability of Optical Networks[J]. Study on Optical Communications, 2026(4): 115-122. DOI： 10.13756/j.gtxyj.2026.250285.

摘要

目的

针对光网络智能化演进中传统评测方法难以量化评估人工智能（AI）驱动自治能力的问题，文章提出了一种多维度智能化能力评测方法，以解决动态业务场景下静态评测机制适应性不足和跨厂商兼容性差等核心问题。

方法

文章通过构建“基础设施-算法-应用”3级指标体系，设计了光网络能力成熟度指数（CMI），并融合动态权重调整机制实现了精准量化。首先，建立包含光层可编程性和故障自愈率等9项核心指标的评估模型，创新引入知识泛化能力维度；其次，提出了基于长短期记忆网络（LSTM）的在线权重调整算法，结合滑动窗口机制实现环境自适应；最后，研发了分布式评测系统，通过仿真平台（OMNeT++模拟12节点拓扑）与实验室多厂商组网联合验证，并基于注入光纤断裂和波长冲突等典型故障进行了对比分析。

结果

实验结果表明，动态权重机制使评测结果波动性降低了32%，故障定位时间（2.73±0.41 s）较传统方法缩短了44.5%；资源调配时延优化了51%，达到了38.6 ms；跨厂商环境下故障漏检率仅0.8%，优于静态权重法（5.7%）和传统电信管理网（TMN）方法（23.6%）。CMI在突发流量场景下稳定性达±0.11，满足±0.15的阈值要求。

结论

文章所提方法通过分层量化与动态调权机制，显著提升了光网络智能化评测的准确性与环境适应性，为设备选型和技术演进提供了标准化评估工具。未来需进一步融合数字孪生技术以增强物理层扰动模拟能力。

Abstract

Objective

To address the issue that traditional evaluation methods struggle to quantitatively assess Artificial Intelligence (AI)-driven autonomous capabilities in the intelligent evolution of optical networks

this paper proposes a multidimensional intelligent capability evaluation method. This method aims to resolve core problems such as the insufficient adaptability of static evaluation mechanisms in dynamic service scenarios and poor cross-vendor compatibility.

Methods

The paper designs an optical network Capability Maturity Index (CMI) by constructing a three-level indicator system of 'infrastructure–algorithm–application'

and integrates a dynamic weight adjustment mechanism to achieve precise quantification. Firstly

we establish an evaluation model that includes 9 core indicators such as optical layer programmability and fault self-healing rate

and innovatively introduce the dimension of knowledge generalization ability. Then

an online weight adjustment algorithm based on Long Short Term Momery (LSTM) is proposed. It is combined with a sliding window mechanism to achieve environment adaptation. Finally

a distributed evaluation system was developed and validated through a simulation platform (OMNeT++ simulating a 12 node topology) and a multi vendor network in the laboratory. Typical faults such as fiber breakage and wavelength conflicts were considered for comparative analysis.

Results

The experiment showed that the dynamic weighting mechanism can reduce the volatility of evaluation results by 42%

and the fault localization time (2.73±0.41 s) was reduced by 44.5% compared to traditional methods. The optimization of resource allocation latency is 38.6 ms

which is reduced by 51%. The fault missed detection rate in a cross vendor environment is only 0.8%

which is better than the static weight method (5.7%) and the traditional Telecommunications Management Network (TMN) method (23.6%). The stability of CMI index in sudden traffic scenarios reaches±0.11

meeting the threshold requirement of ±0.15.

Conclusion

This method significantly improves the accuracy and environmental adaptability of intelligent evaluation of optical networks through hierarchical quantization and dynamic weighting mechanism. It provides a standardized evaluation tool for device selection and technological evolution. In the future

further integration of digital twin technology is needed to enhance the ability to emulate the physical layer disturbances.

关键词

Keywords

references

朱一鸣 , 林显浩 , 迟楠 , 等 . 面向5G/6G移动前传网络的关键技术 [J ] . 光通信研究 , 2025 ( 3 ): 240078 .

Zhu Y M , Lin X H , Chi N , et al . Key Technologies for 5G/6G Mobile Fronthaul Network [J ] . Study on Optical Communications , 2025 ( 3 ): 240078 .

邬惠峰 . 抢抓人工智能浪潮推动软件产业高质量发展 [J ] . 政策瞭望 , 2024 ( 11 ): 37 - 40 .

Wu H F . Seize the Wave of Artificial Intelligence and Promote the High-Quality Development of Software Industry [J ] . Zhengce Liaowang , 2024 ( 11 ): 37 - 40 .

商兴宇 , 刘小欧 , 杨明川 . 人工智能原生网络发展趋势研究 [J ] . 信息通信技术与政策 , 2025 , 51 ( 2 ): 9 - 17 .

Shang X Y , Liu X O , Yang M C . Study on Artificial Intelligence Native Network [J ] . Information and Communications Technology and Policy , 2025 , 51 ( 2 ): 9 - 17 .

黄旭 , 成梦虹 , 成芝言 . 基于SRv6的算网基础管控体系研究 [J ] . 光通信研究 , 2023 ( 5 ): 38 - 44 .

Huang X , Cheng M H , Cheng Z Y . Research on Basic Management and Control System of Computing Force Network based on SRv6 [J ] . Study on Optical Communications , 2023 ( 5 ): 38 - 44 .

张新全 , 余少华 . 信息化前景和网络演进探讨 [J ] . 光通信研究 , 2021 ( 2 ): 1 - 6 .

Zhang X Q , Yu S H . Discussion on the Prospect of Informatization and Network Evolution [J ] . Study on Optical Communications , 2021 ( 2 ): 1 - 6 .

李鸿 , 刘武 , 罗鸣 . 基于机器学习的光网络监测与优化方法 [J ] . 光通信研究 , 2024 ( 3 ): 230046 .

Li H , Liu W , Luo M . Optical Network Monitoring and Optimization Methods based on Machine Learning [J ] . Study on Optical Communications , 2024 ( 3 ): 230046 .

华为技术有限公司 . 自动驾驶网络解决方案白皮书 [R ] . 2023 .

Huawei Technologies Co., Ltd. . White Paper on Autonomous Driving Network Solutions [R ] . 2023 .

ITU-T G.7712-2022. Controlframeworkfor ASON [S ] .

尹林 , 王富 , 王小龙 , 等 . 面向算力互联的快速光交换技术研究 [J ] . 光通信研究 , 2024 ( 5 ): 240032 .

Yin L , Wang F , Wang X L , et al . Fast Optical Switching for Computing-Power Interconnects [J ] . Study on Optical Communications , 2024 ( 5 ): 240032 .

王堂超 . 基于ACTN和SDOTN标准的SDN跨域管控研究 [J ] . 光通信研究 , 2024 ( 2 ): 220085 .

Wang T C . Research on SDN Cross-Domain Management and Control based on ACTN and SDOTN Standards [J ] . Study on Optical Communications , 2024 ( 2 ): 220085 .

陈亮 , 余少华 . 6G移动通信关键技术趋势初探 [J ] . 光通信研究 , 2019 ( 5 ): 1 - 8 .

Chen L , Yu S H . Preliminary Study on the Key Technologies of 6G Mobile Communication [J ] . Study on Optical Communications , 2019 ( 5 ): 1 - 8 .

吴瑕 , 周焰 , 杨龙坡 , 等 . 模糊动态AHP导弹识别算法 [J ] . 光电工程 , 2009 , 36 ( 9 ): 6 - 11 .

Wu X , Zhou Y , Yang L P , et al . Missile Recognition based on the Fuzzy Dynamic AHP Algorithm [J ] . Opto-Electronic Engineering , 2009 , 36 ( 9 ): 6 - 11 .

赵崇尧 , 孙耀华 , 冯昕澳 , 等 . 基于OMNeT++的低轨卫星时间确定性网络仿真平台设计 [J ] . 电信科学 , 2025 , 41 ( 5 ): 43 - 59 .

Zhao C Y , Sun Y H , Feng X A , et al . Design of Time-Deterministic Network Simulation Platform for Low-Orbit Satellites based on OMNeT++ [J ] . Telecommunications Science , 2025 , 41 ( 5 ): 43 - 59 .

薛晓良 , 苏海冰 , 舒怀亮 , 等 . FPGA在辐照环境下的故障注入系统研究 [J ] . 光电工程 , 2019 , 46 ( 12 ): 180549 .

Xue X L , Su H B , Shu H L , et al . Research on Fault Injection System of FPGA in Irradiation Environment [J ] . Opto-Electronic Engineering , 2019 , 46 ( 12 ): 180549 .

刘可为 , 谢俊杰 , 吴冰冰 , 等 . 通感一体化光网络的技术、应用与展望 [J ] . 光通信研究 , 2025 ( 2 ): 240046 .

Liu K W , Xie J J , Wu B B , et al . Technology, Application and Prospect of Integrated Sensing and Communication of Optical Networks [J ] . Study on Optical Communications , 2025 ( 2 ): 240046 .

王友祥 , 唐雄燕 . 6G网络架构和关键技术展望 [J ] . 中兴通讯技术 , 2023 , 29 ( 5 ): 21 - 27 .

Wang Y X , Tang X Y . Prospect of 6G Network Architecture and Key Technologies [J ] . ZTE Technology Journal , 2023 , 29 ( 5 ): 21 - 27 .

浏览量

下载量

CSCD

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

混合线速率的虚拟光网络映射成本优化方法

基于拓扑链路识别的光网络流量数据合成算法

基于最短路径敏感度的光网络关键链路识别

边缘计算与云计算协同的SOON实现机制（特邀）

一种基于网络风险的路由波长分配算法