摘要
传统人工监管方式难以适配大规模AIS(Automatic Identification System)轨迹数据的分析需求。本文构建了适配AIS数据质量特性的预处理体系,提出深度时空自编码网络(ST-AE)模型,通过CNN与Bi-LSTM协同融合架构及注意力机制捕捉轨迹时空依赖关系,结合轻量化设计实现检测精度与实时性的平衡,并配套设计模型训练策略与自适应异常判定机制。基于AIS数据集构建实验环境,选取滑动窗口+3σ阈值、随机森林、常规LSTM自编码等主流方法与本文模型进行对比,结果表明ST-AE模型在准确率、召回率及实时性上均优于其他3种方法,可有效适配多种船舶异常行为检测需求。研究结果可为海事智能监管提供了可靠的技术支撑。
Traditional manual supervision methods are difficult to adapt to the analysis requirements of large-scale Automatic Identification System(AIS)trajectory data.In this study,a preprocessing system adapted to the quality characteristics of AIS data was constructed,and a deep spatio-temporal autoencoder(ST-AE)model was proposed.The ST-AE model captures the spatio-temporal dependencies of trajectories through the synergistic fusion architecture of Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(Bi-LSTM)combined with an attention mechanism.Meanwhile,a lightweight design is integrated to achieve a balance between detection accuracy and real-time performance.Corresponding model training strategies and adaptive anomaly determination mechanisms were also developed to support the model.An experimental environment was established based on AIS datasets,and three mainstream methods,namely sliding window+3σthreshold,random forest,and conventional LSTM autoencoder,were selected for comparative experiments with the proposed ST-AE model.The results indicate that the ST-AE model outperforms the other three methods in terms of accuracy,recall,and real-time performance,and can effectively meet the detection requirements of various ship abnormal behaviors.This study provides reliable technical support for maritime intelligent supervision.
作者
董海亮
张梁
阳建中
甘兆斌
DONG Hailiang;ZHANG Liang;YANG Jianzhong;GAN Zhaobin(Beibu Gulf University,Qinzhou 535011,China;Civil Aviation Flight University of China,Guanghan 618307,China)
出处
《舰船科学技术》
2026年第5期146-150,共5页
Ship Science and Technology
基金
2026年度广西自然科学基金(2025JJH160108)
2023年度钦州市科学研究与技术开发计划项目(20233141)
2025年度钦州市科学研究与技术开发计划项目(20251706)
2024年度广西高等教育本科教学改革工程项目(2024JGB275)。
关键词
AIS轨迹数据
异常轨迹检测
深度时空自编码网络
时空融合
注意力机制
海事智能监管
AIS trajectory data
abnormal trajectory detection
deep spatio-temporal autoencoder network
spatiotemporal fusion
attention mechanism
maritime intelligent supervision
