为解决传统船舶运维工作的复杂化与高成本，本文采用数字孪生技术构建智慧船舶运维管控系统，通过对数据的处理与决策，提升管理效率、实现可视化管理。通过传感器采集船舶数据，先通过集成算法对数据进行预处理，后结合数字孪生模型进行深度分析，从而实现对船舶实时的监控和预测维护。该方案在数据预处理方面准确率高达约95%，故障通知时间约为0.06 s，有效提升了船舶运维效率和安全性，同时降低了成本并增强了风险响应能力。可视化界面使管理人员操作和决策更直观，提升用户体验。提供智能船舶运维管控解决方案，集数据采集、处理、分析和反馈为一体，推动智能船舶技术发展，为数字孪生应用提供借鉴。智能化运维有望支持全球航运业数字化转型，提升航运安全、效率。
The rapid advancement of information technology has presented new challenges and opportunities for smart ships. To address the complexity and high costs associated with traditional ship operation and maintenance, this article employs digital twin technology to construct a smart ship operation and maintenance control system. Through data processing and decision-making, it enhances management efficiency and enables visual management. Ship data is collected via sensors, which is preprocessed using integrated algorithms first and then subjected to in-depth analysis in combination with the digital twin model, thereby achieving real-time monitoring and predictive maintenance of the ship. This solution achieves an accuracy rate of approximately 95% in data preprocessing and a fault notification time of approximately 0.06 seconds, effectively improving the efficiency and safety of ship operation and maintenance, while reducing costs and enhancing risk response capabilities. The visual interface makes the operations and decisions of management personnel more intuitive, enhancing the user experience. This article offers an intelligent ship operation and maintenance control solution that integrates data collection, processing, analysis, and feedback, promoting the development of smart ship technology and providing a reference for the application of digital twin. Intelligent operation and maintenance is expected to support the digital transformation of the global shipping industry and enhance shipping safety and efficiency.
2025,47(15): 139-144 收稿日期：2021-8-23
DOI：10.3404/j.issn.1672-7649.2025.15.023
分类号：U664.82；TP391.9
基金项目：国家级大学生创新创业项目（202310390007，202310390026）
作者简介：曾步辉(1973-),男,硕士,高级实验师,研究方向为船舶自动化
参考文献：
[1] 王有哲. 科技助力水运提质升级[N]. 中国水运报, 2023-05-26(3).
[2] 赵蒙, 胡志芳, 胡朔. 加快推进船舶运维信息管理发展措施[J]. 中国水运, 2021(4): 62-66.
ZHAO M, HU Z F, HU S. Measures to accelerate the development of ship maintenance information management[J]. China Water Transport, 2021(4): 62-66.
[3] 李澳, 徐言民, 关宏旭, 等. 基于数字孪生的测试场景架构及应用研究[J]. 舰船科学技术, 2023, 45(24): 171-175.
LI A, XU Y M, GUAN H X, et al. Research on the architecture and application of test scenarios based on digital twin[J]. Ship Science and Technology, 2023, 45(24): 171-175.
[4] 肖龙辉, 裴志勇, 徐文君, 等. 船体结构数字孪生技术及应用[J]. 船舶力学, 2023, 27(4): 573-582.
XIAO L H, PEI Z Y, XU W J, et al. Digital twin technology and its application in ship structure[J]. Ship Mechanics, 2023, 27(4): 573-582.
[5] 杨云帆, 张安通, 殷文慧. 基于多源异构模型和数字孪生的船舶运动仿真[J]. 舰船科学技术, 2023, 45(17): 70-74+92.
YANG Y F, ZHANG A T, YIN W H. Ship motion simulation based on multi-source heterogeneous models and digital twin[J]. Ship Science and Technology, 2023, 45(17): 70-74+92.
[6] BALACHANDAR S, CHINNAIYAN R. Reliable digital twin for connected footballer [C]//International Conference on Computer Networks and Communication Technologies, 2019.
[7] 周少伟, 吴炜, 张涛, 等. 舰船动力系统数字孪生技术体系研究[J]. 中国舰船研究, 2021, 16(2): 151-156.
ZHOU S W, WU W, ZHANG T, et al. Digital twin technical system for marine power systems[J]. Chinese Journal of Ship Research, 2021, 16(2): 151-156.
[8] CHAWLA N V, LAZAREVIC A, HALL L O, et al. SMOTEBoost: improvingpredic-tion of the minority class in boosting[C]//European conference on principles of data mining and knowledge discovery PKDD 2003: knowledge discoveryy in databases, 2003.
[9] 郭旗. 集成数据预处理技术及其在机器学习算法中的应用[J]. 科技与创新, 2023(23): 163-165.
GUO Q. Integrated data preprocessing technology and its application in machine learning algorithms[J]. Science and Technology Innovation, 2023(23): 163-165.
[10] 岳跃申, 郑士君, 黄爱平. 新型船岸一体化管理平台的设计及其功能[J]. 航海技术, 2009(3): 70-72.
YUE Y S, ZHENG S J, HUANG A P. Design and function of a new ship-shore integrated management platform[J]. Navigation Technology, 2009(3): 70-72.
[11] 宋晔琴, 顾丽梅, 张扬. 数字平台何以赋能超大城市敏捷治理——基于组织边界跨越视角的分析[J]. 上海行政学院学报, 2024, 25(1): 19-31.
SONG Y Q, GU L M, ZHANG Y. How digital platforms empower agile governance of mega-cities: An analysis based on the perspective of crossing organizational boundaries[J]. Journal of Shanghai Administration Institute, 2024, 25(1): 19-31.
[12] 荀欢欢. 基于数字孪生的舰船上层建筑运维系统[J]. 舰船科学技术, 2023, 45(3): 133-136.
XUN H H. A ship superstructure operation and maintenance system based on digital twins[J]. Naval Ship Science and Technology, 2023, 45(3): 133-136.
[13] 耿雄飞, 高博, 丁格格. 数字智能驱动的水运行业发展研究[J]. 交通运输部管理干部学院学报, 2023, 33(3): 3-6.
GENG X F, GAO B, DING G G. Research on the development of the water transportation industry driven by digital intelligence[J]. Journal of the Management Cadre Institute of the Ministry of Transport, 2023, 33(3): 3-6.
[14] 殷文慧, 王飞, 孙强, 等. 船用柴油机人工智能故障诊断应用综述[J]. 舰船科学技术, 2023, 45(24): 122-127.
YIN W H, WANG F, SUN Q, et al. A comprehensive review on the application of artificial intelligence in fault diagnosis of marine diesel engines[J]. Ship Science and Technology, 2023, 45(24): 122-127.
[15] HASAN A, ASFIHANI T, OSEN O, et al. Leveraging digital twins for fault diagnosis in autonomous ships[J]. Ocean Engineering, 2024, 292: 116546.
