Container port loading and unloading efficiency calculation method driven by AIS big data

Abstract

The invention discloses a container port loading and unloading efficiency calculation method driven by AIS big data. Firstly, a berthing area model of a target port is established, a ship berthing event of the target port is accurately recognized by utilizing data of a ship automatic identification system (AIS), and the ship access frequency of the target port and the berthing duration of a berthing ship are calculated. Secondly, the unique value (such as the number of shore tackles needing to be equipped) of the operation parameters for loading and unloading the ship berthing in the port is determined according to a preset value method; and finally, the berthing duration of the ship at the target port, the operation parameter values of loading and unloading of the ship at the port and the historical throughput data of the target port at a certain time period are combined to calculate the loading and unloading efficiency value of the target port in the time period. The method can accurately evaluate the influence of uncertain factors on the port loading and unloading efficiency, greatly improves the evaluation precision, and breaks through the technical bottleneck that the real-time loading and unloading efficiency of the container port is difficult to accurately calculate in the prior art. 本发明公开了一种AIS大数据驱动的集装箱港口装卸效率计算方法。首先通过建立目标港口的泊位区域模型，利用船舶自动识别系统(AIS)数据，精确地识别目标港口的船舶靠泊事件，并计算目标港口的船舶访问次数和靠港船舶的靠泊时长。其次，根据预设的取值方法确定靠港船舶装卸的操作参数的唯一取值(例如所需配备的岸吊数量等)。最后，综合目标港口船舶的靠泊时长、靠港船舶装卸的操作参数取值以及目标港口的某时段的历史吞吐量数据，可测算该时间段内目标港口的装卸效率值。本发明可准确评估不确定因素对港口装卸效率的影响，大幅度提升评估精度，突破现有技术难以精确计算集装箱港口实时装卸效率的技术瓶颈。