Research on the Cultivation Model of Decision-Making Ability for Maritime Talents Driven by AIS Data in the Era of Intelligent Shipping

Xiaona Liu; Genyuan Wang; Xiaowen Li

doi:10.70088/1fp08739

Authors

Xiaona Liu Hainan Vocational University of Science and Technology, Haikou, China Author
Genyuan Wang Hainan Vocational University of Science and Technology, Haikou, China Author
Xiaowen Li Hainan Vocational University of Science and Technology, Haikou, China Author

DOI:

https://doi.org/10.70088/1fp08739

Keywords:

intelligent shipping, ais data, maritime education, decision making, teaching model

Abstract

To address the strategic need for maritime professionals transitioning to shore-based monitoring and intelligent decision-making in the era of smart shipping, traditional maritime education models exhibit significant structural contradictions in data-driven thinking and advanced decision-making training. Consequently, there is an urgent demand for innovative pedagogical approaches that align with modern technological advancements. This study proposes a novel decision-making competency development model centered on Automatic Identification System (AIS) data analysis, aiming to enhance situational awareness and operational proficiency through data technology empowerment. By integrating Kalman filtering and Douglas-Peucker algorithms to establish a high-availability AIS data foundation, employing deep reinforcement learning (DDPG) for dynamic collision avoidance simulation, and utilizing K-means clustering algorithms for historical trajectory pattern mining, we have developed a comprehensive tripartite teaching pathway encompassing "algorithm validation-scenario implementation-data evaluation." This structured approach ensures that theoretical knowledge is seamlessly translated into practical expertise. Practical assessments demonstrate that trainees achieved an average 22.4% improvement in risk prediction accuracy in complex waters and a 15.8% reduction in dynamic decision response time, effectively bridging the technological gap between traditional training methods and intelligent shipping requirements. Ultimately, this model not only provides a robust quantitative evaluation framework for cultivating high-caliber maritime professionals but also offers critical talent support for the broader transition from a conventional shipping industry to a globally competitive, intelligent maritime powerhouse.

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