Yigit Yagmur, Nguyen Long D, Ozdem Mehmet, Kinaci Omer Kemal, Hoang Trang, Canberk Berk, Duong Trung Q
Department of Computer Engineering, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey.
Duy Tan University, Da Nang, Vietnam.
Sci Rep. 2023 Jul 29;13(1):12310. doi: 10.1038/s41598-023-39366-1.
Numerous ports worldwide are adopting automation to boost productivity and modernize their operations. At this point, smart ports become a more important paradigm for handling increasing cargo volumes and increasing operational efficiency. In fact, as ports become more congested and cargo volumes increase, the need for accurate navigation through seaports is more pronounced to avoid collisions and the resulting consequences. To this end, digital twin (DT) technology in the fifth-generation (5G) networks and drone-assisted data collection can be combined to provide precise ship maneuvering. In this paper, we propose a DT model using drone-assisted data collection architecture, called TwinPort, to offer a comprehensive port management system for smart seaports. We also present a recommendation engine to ensure accurate ship navigation within a smart port during the docking process. The experimental results reveal that our solution improves the trajectory performance by approaching the desired shortest path. Moreover, our solution supports significantly reducing financial costs and protecting the environment by reducing fuel consumption.
全球众多港口都在采用自动化技术来提高生产力并实现运营现代化。此时，智能港口成为处理日益增长的货量和提高运营效率的更重要范例。事实上，随着港口变得更加拥堵且货量增加，通过海港进行精确导航以避免碰撞及相关后果的需求愈发凸显。为此，第五代（5G）网络中的数字孪生（DT）技术与无人机辅助数据收集相结合，可提供精确的船舶操纵。在本文中，我们提出一种使用无人机辅助数据收集架构的DT模型，称为TwinPort，以提供一个用于智能海港的综合港口管理系统。我们还提出了一个推荐引擎，以确保船舶在智能港口停靠过程中准确导航。实验结果表明，我们的解决方案通过接近所需的最短路径提高了轨迹性能。此外，我们的解决方案支持通过降低燃料消耗显著降低财务成本并保护环境。
