A modeling method of ship resident rule based on big data platform

Abstract:
The massive AIS data recorded in the course of shipping contains resident law information. Considering the ship type, the intention of ship activity can be analyzed based on resident law or abnormal behavior can be monitored. At present, the analysis of ship residence law relies on expert artificial reasoning, which makes it difficult to deal with massive data. To solve the above problems, this paper proposes an analysis method of resident rule based on big data platform. It makes full use of the computing power of Spark, analyzes AIS data through density-based clustering algorithm, and gives the resident rule of ships. Experimental results show that the proposed method can quickly analyze the resident rules of multi-scene and multi-type ships.