In large shipyards, the management of equipment, which are used for building a variety of 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 that arises because of the nature and size of shipyards is the management of moving vehicles. 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, roam irregularly around a yard, identifying their working status without being onsite is difficult. Location information alone is not sufficient to determine whether a vehicle is working, moving, waiting, or resting. 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 information from forklifts equipped with GPS and Internet of Things (IoT) devices. The system provides visual information on the status of individual forklifts and helps in the efficient management of their movements within large shipyards.

中文翻译：

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机器学习方法分析船厂不规则移动叉车的状态

在大型造船厂，用于建造各种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而出现的一个特殊困难是移动车辆的管理。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，在院子里不规则地漫游，在没有现场的情况下识别它们的工作状态是很困难的。仅靠位置信息不足以确定车辆是在工作、移动、等待还是休息。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，从配备 GPS 和物联网 (IoT) 设备的叉车收集信息。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，从配备 GPS 和物联网 (IoT) 设备的叉车收集信息。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，从配备 GPS 和物联网 (IoT) 设备的叉车收集信息。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。