融合数据质量增强和时空信息编码网络的船舶海上轨迹预测方法  

Ship Maritime Trajectory Prediction Method Integrating Data Quality Enhancement and Spatio-Temporal Information Encoding Network

作　　者：石悦 罗贺[1,2,3] 蒋儒浩 王国强[1,2,3] SHI Yue;LUO He;JIANG Ruhao;WANG Guoqiang(School of Management,Hefei University of Technology,Hefei 230009;Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education,Hefei University of Technology,Hefei 230009;Anhui Province Engineering Research Center for Intelligent Management of Aerospace System,Hefei University of Technology,Hefei 230009;College of Electronic Engineering,National University of Defense Technology,Hefei 230037)

机构地区：[1]合肥工业大学管理学院,合肥230009 [2]合肥工业大学过程优化与智能决策教育部重点实验室,合肥230009 [3]合肥工业大学安徽省空天系统智能管理工程研究中心,合肥230009 [4]国防科技大学电子对抗学院,合肥230037

出　　处：《模式识别与人工智能》2025年第1期51-67,共17页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金面上项目(No.71971075,72271076,71871079);安徽省自然科学基金项目(No.2308085QG233)资助。

摘　　要：高精度的海上船舶轨迹预测是降低船舶碰撞风险、提升船舶搜救效率的重要基础.海上航行环境的多变性使船舶轨迹数据在时间和空间上具有高度复杂性,现有方法对船舶轨迹数据的质量及运动信息关注度不足,难以充分捕捉轨迹中的时空特征和关联信息.因此,文中提出融合数据质量增强和时空信息编码网络的船舶海上轨迹预测方法(Ship Maritime Trajectory Prediction Method Integrating Data Quality Enhancement and Spatio-Temporal Information Encoding Network,DQE-STIEN).首先,基于船舶轨迹数据的特征,设计结合哈希映射分类及局部离群哈希值异常检测的数据质量增强算法,对问题数据进行质量增强.然后,针对多属性的船舶轨迹数据,设计具有双编码通道的时空信息编码网络,充分提取并整合船舶轨迹数据中的位置信息与运动特征.最后,基于时空信息编码提取数据中的时空关联信息,并经解码生成完整的轨迹预测结果.在5个不同区域的AIS数据集上的实验表明DQE-STIEN性能较优.同时,DQE-STIEN具有一定的泛化性,也能有效分析能源、销售、环境和金融等领域的时序数据.High-precision maritime vessel trajectory prediction is crucial for reducing collision risks and enhancing search and rescue efficiency.The dynamic maritime environment renders vessel trajectory data highly complex in both temporal and spatial dimensions.Existing methods exhibit insufficient attention to the quality and movement information of vessel trajectory data,making it challenging to fully capture the spatio-temporal features and correlations effectively.To address these issues,a ship maritime trajectory prediction method integrating data quality enhancement and spatio-temporal information encoding network(DQE-STIEN)is proposed.First,based on the characteristics of vessel trajectory data,a data quality enhancement algorithm is designed by combining hash mapping classification and local outlier factor-based anomaly detection using hash values to improve the quality of problematic data.Then,a spatio-temporal information encoding network with dual encoding channels is tailored for multi-attribute vessel trajectory data to extract and integrate positional information and movement features comprehensively.Finally,the spatio-temporal associations within the data are encoded and decoded to generate complete trajectory prediction results.Experimental results on AIS datasets from five different regions demonstrate the superior performance of DQE-STIEN.Moreover,DQE-STIEN exhibits certain generalizability,making itself effective for analyzing time-series data across various fields such as energy,sales,environment and finance.

关 键 词：轨迹预测 时空信息编码 数据质量增强 双编码通道 混合预测模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]