Open AccessDesign scheme of ship risk prediction model from the perspective of artificial intelligence
Author(s) -
Qunsheng Ruan,
Fang Lin Luo,
Zhiliang Li,
Manying Shi,
Zhiling Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1982/1/012062
Subject(s) - scheme (mathematics) , computer science , matching (statistics) , plan (archaeology) , perspective (graphical) , graph , big data , naval architecture , risk analysis (engineering) , industrial engineering , data mining , artificial intelligence , engineering , marine engineering , theoretical computer science , medicine , mathematical analysis , statistics , mathematics , archaeology , history
Vessels sailing at sea are full of risks, accidents occur frequently, and it is easy to cause heavy casualties and property losses. To this end, this article focuses on how to build a high-precision and rapid ship risk prediction model, and proposes a design plan for a ship risk prediction model based on big data and artificial intelligence. The plan includes a basic data layer, a technical business layer, and an application layer, with a focus on The technical research scheme of the graph representation model construction of the ship risk big data, the dynamic graph matching algorithm and the ship risk prediction is elaborated in detail, and finally the experimental design method of the scheme is given. The design scheme proposed in this paper can provide reference and inspiration for the research and development of marine risk system.