Research on Ship Traffic Flow Analysis Based on Big Data Technology

In recent years, the global shipping industry has experienced rapid growth, leading to an increasing volume of waterborne traffic, which has imposed higher demands on the management of inland waterways. To enhance maritime safety and navigation efficiency, in-depth analysis of ship traffic flow has become particularly crucial. Leveraging big data technology, this paper designs and implements a ship traffic flow analysis system. The system adopts the Lambda architecture for data processing, utilizing Flink for real-time stream processing of navigation data, and Spark and Hive for offline data analysis. A multi-level storage architecture based on Redis, MySQL, and Hive has been constructed to meet diverse data storage and query requirements. Following the design philosophy of separating the front-end from the back-end, the system's back-end interfaces are built with Spring Boot, while the front-end employs Vue combined with Echarts and Mapbox to achieve intuitive data visualization.