面向海洋气象预报的低时延智能物联网构建

李竟博; 马礼; 李阳; 傅颖勋; 马东超

doi:10.12263/DZXB.20240698

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面向海洋气象预报的低时延智能物联网构建

云边端协同数据管理、分析与系统 | 更新时间：2025-12-08

- 面向海洋气象预报的低时延智能物联网构建
- Construction of Low-latency Artificial Intelligence of Things for Marine Meteorological Forecasting
- 电子学报 2025年53卷第2期页码：301-313
- 作者机构：
  
  1.北京工业大学计算机学院，北京 100124
  2.北方工业大学信息学院，北京 100144
- 作者简介：
  
  [ "李竟博男，1994年出生.山东聊城人，北京工业大学博士研究生.主要研究方向为智能物联网、海洋气象预报.E-mail: lijingbo@emails.bjut.edu.cn" ]
  [ "马礼男，1968年出生.山西朔州人，博士，北方工业大学教授、博士生导师，北京工业大学兼职博士生导师.主要研究方向为分布式系统、智能物联网.E-mail: mali@ncut.edu.cn" ]
  [ "李阳男，1992年出生.河南驻马店人，博士，北方工业大学讲师.主要研究方向为边缘计算、海洋气象预报.E-mail: li_yang@ncut.edu.cn" ]
  [ "傅颖勋男，1986年出生.湖南永州人，博士，北方工业大学副教授.主要研究方向为分布式存储.E-mail: fuyx@ncut.edu.cn" ]
  [ "马东超男，1980年出生.北京市人，博士，北方工业大学教授.主要研究方向为下一代互联网.E-mail: madongchao1980@wo.cn" ]
- 基金信息：
  
  国家重点研发计划(2023YFC3107804);国家自然科学基金(62272007;62001007);北京市自然科学基金(4234083;4212018)
- DOI：10.12263/DZXB.20240698
  中图分类号： TP393;
- 收稿：2024-07-26，
  
  修回：2024-10-24，
  
  纸质出版：2025-02-25
- 稿件说明：
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李竟博, 马礼, 李阳, 等. 面向海洋气象预报的低时延智能物联网构建[J]. 电子学报, 2025, 53(02): 301-313.

LI Jing-bo, MA Li, LI Yang, et al. Construction of Low-latency Artificial Intelligence of Things for Marine Meteorological Forecasting[J]. Acta Electronica Sinica, 2025, 53(02): 301-313.
李竟博, 马礼, 李阳, 等. 面向海洋气象预报的低时延智能物联网构建[J]. 电子学报, 2025, 53(02): 301-313. DOI：10.12263/DZXB.20240698

LI Jing-bo, MA Li, LI Yang, et al. Construction of Low-latency Artificial Intelligence of Things for Marine Meteorological Forecasting[J]. Acta Electronica Sinica, 2025, 53(02): 301-313. DOI：10.12263/DZXB.20240698

摘要

人工智能技术广泛应用于海洋气象预报，且越来越依靠物联网获取海洋环境中多种模态的海量感知数据.针对数据的获取数量和传输速度不足以支撑模型精准预报的问题，提出了面向海洋气象预报的低时延智能物联网构建方案.首先，设计了智能物联网海洋气象预报融合架构，该架构不仅充分适配了人工智能技术和物联网技术，更实现了对海洋气象数据的高效采集、处理与分析，为海洋气象预报提供了有效灵活的底层结构.其次，优化了异构海洋感知设备协同组网方法，通过优化多层耦合网络拓扑，实现了异构海洋感知设备的高效互联，保证了数据收集的全面性和准确性.最后，提出了海洋感知网络低时延路由算法，该算法通过智能路径选择和数据传输优化，减少了信息从感知设备到数据中心的传输延迟，确保预报数据的快速更新.经实验验证，该方案充分利用智能物联网的优势，解决了海洋气象预报中数据获取难和处理延迟长的问题，所提方案的时延均值降低37%，时延中值降低38%，为海洋气象实时和准确的预报提供有力支持.

Abstract

Artificial intelligence technology is widely used in marine meteorological forecasting

and increasingly relies on the internet of things to obtain massive sensory data of multiple modes in the marine environment. Aiming at the problem that the amount of data obtained and the transmission speed are not enough to support the accurate forecast of the model

a low-latency artificial intelligence of things (AIoT) construction scheme for marine meteorological forecasting is proposed. Firstly

an AIoT marine meteorological forecast fusion architecture is designed. This architecture not only fully adapts to artificial intelligence technology and internet of things technology

but also realizes the efficient collection

processing and analysis of marine meteorological data

providing an effective and flexible underlying structure for marine meteorological forecasting. Secondly

the collaborative networking method of heterogeneous ocean sensing devices is optimized. By optimizing the multi-layer coupling network topology

efficient interconnection of heterogeneous ocean sensing devices was achieved

ensuring the comprehensiveness and accuracy of data collection. Finally

a low-latency routing algorithm for ocean sensing networks is proposed. This algorithm reduces the transmission delay of information from sensing devices to data centers through intelligent path selection and data transmission optimization

ensuring the rapid update of forecast data. Experimental verification shows that the proposed scheme fully utilizes the advantages of the AIoT and solves the problems of difficult data acquisition and long processing delay in marine meteorological forecasting. The mean delay of the proposed scheme is reduced by 37% and the median delay is reduced by 38%

providing strong support for real-time and accurate marine meteorological forecasting.

关键词

Keywords

references

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