Abstract:Vessel monitoring system (VMS), as a comprehensive application system integrating global satellite positioning, electronic map, network communication and database technology, aims to acquire the ship's position and operating status information in near-real time, and transmit them to the onshore monitoring center to realize the information interaction. More than sixty thousand fishing vessels have installed VMS so far. The VMS data mainly contains time, position, speed, direction and rate of turn with a temporal resolution of 3 minutes and a spatial resolution of 10 meters which can be analyzed deeply using data mining technology to identify the status of vessels, calculate fishing effort, search fishing ground and so on. DBSCAN is a density-based spatial clustering algorithm designed to find high-density regions segmented by low-density. Compared with distance-based clustering algorithms, it can identify noise data and find clusters of arbitrary shapes. In this paper, we used VMS data of Zhelinyu 12870 and Zhesanyu 66666 in 2017 and DBSCAN to extract the hauls of set gillnet. The speed of hauling gillnet is quite different from that during tracking or setting the net, so they could be extracted them by threshold. Firstly, we obtained the speed threshold of vessel's fishing condition by the statistics of navigational speed and extracted the points whose speed is in the range of threshold value. Secondly, the time difference of adjacent points was carried out to obtain time interval between hauls. Thirdly, the DBSCAN algorithm was used to cluster the fishing points around the set gillnet, which determines the starting time and the ending time of each fishing haul. The difference of the starting time and the ending time of each haul between clustering result and fishing log were calculated, and 15 minutes were token as a reasonable error-tolerant range. The comparisons showed that the accuracy of the hauls was above 80%.