Illegal Sea Project Discovery Algorithm based on Hash Table and DBSCAN Algorithm

Marine cage culture does not need to be reclaimed, so it has a certain degree of conceal ability, which leads to the situation that some individuals or even enterprises install cages to carry out aquaculture in the open sea without approval. These behaviors not only violate the national legal system of marine natural resources management and cause pollution to the marine environment, but also have an inestimable impact on the safety of maritime shipping and marine tourism. The existing supervision methods, such as manual or simple density statistics of ships in the grid, have low work efficiency, huge labor cost and high misjudgment rate. In view of the above drawbacks, this paper proposes an illegal sea project detection algorithm based on hash table and DBSCAN algorithm. Through DBSCAN clustering analysis of spatial location and time in ais information, the clustering status of ships is separated. Further, hash table is used to calculate and save the relevant latitude and longitude distance data in advance to optimize the calculation time of DBSCAN algorithm. Finally, the spatial scope of the clustered coordinate points is judged, and the operating scope of ships in the open sea is distinguished, so as to narrow the key areas of government ships and drones, and improve the ability of maritime supervision.