In large shipyards, the management of equipment used to build ships is critical. Because orders vary year to year, shipyard managers are required to determine methods to make the most of their limited resources. A particular difficulty arises in the management of moving vehicles because of the nature and size of shipyards. In recent years, shipbuilding companies have attempted to manage and track the locations and movements of vehicles using global positioning system (GPS) modules. However, because certain vehicles, such as forklifts, move irregularly around a yard, identifying their working status without being onsite is difficult. Simple location information alone is insufficient to determine whether a vehicle is working, moving, waiting, or resting. Status information acquisition requires intelligence algorithms to distinguish GPS data. This study proposes an approach based on machine learning to identify the work status of each forklift. We use the DBSCAN and k-means algorithms to identify the area in which a particular forklift is operating and the type of work it is performing. We developed a business intelligence system to collect data on forklifts equipped with GPS and Internet of Things devices. The system provides visual information on the status of individual forklifts and helps in the efficient management of their movements within large shipyards.

在大型造船厂，用于建造船舶的设备的管理至关重要。由于订单每年都在变化，船厂经理需要确定方法来充分利用他们有限的资源。由于造船厂的性质和规模，移动车辆的管理出现了一个特别的困难。近年来，造船公司尝试使用全球定位系统 (GPS) 模块来管理和跟踪车辆的位置和运动。然而，由于某些车辆，例如叉车，在院子里不规则地移动，在没有现场的情况下识别它们的工作状态是很困难的。仅靠简单的位置信息不足以确定车辆是在工作、移动、等待还是休息。状态信息的获取需要智能算法来区分 GPS 数据。本研究提出了一种基于机器学习的方法来识别每台叉车的工作状态。我们使用 DBSCAN 和 k-means 算法来识别特定叉车正在运行的区域及其正在执行的工作类型。我们开发了一个商业智能系统来收集配备 GPS 和物联网设备的叉车的数据。该系统提供有关各个叉车状态的视觉信息，并有助于在大型造船厂内有效管理其移动。我们开发了一个商业智能系统来收集配备 GPS 和物联网设备的叉车的数据。该系统提供有关各个叉车状态的视觉信息，并有助于在大型造船厂内有效管理其移动。我们开发了一个商业智能系统来收集配备 GPS 和物联网设备的叉车的数据。该系统提供有关各个叉车状态的视觉信息，并有助于在大型造船厂内有效管理其移动。