大数据分析下双向通航港口船舶自适应调度算法

Adaptive ship scheduling algorithm for two-way navigation port based on big data analysis

为了提高双向通航港口船舶的通行能力,进行双向通航港口船舶自适应调度,提出基于大数据信息融合的船舶调度算法。采用网格区域分割模型进行双向通航港口航道分割,构建船舶调度的大数据信息传递模型,对船舶的调度数据进行信息融合滤波处理,抑制冗余数据的干扰,采用模糊波束集成方法进行双向通航港口船舶运行大数据的聚类分析,根据数据的分布属性特征进行船舶航行调度,提高船舶调度的自适应性。仿真结果表明,采用该方法进行双向通航港口船舶自适应调度能提高港口的吞吐能力和船舶航行的畅通能力,且对双向通航港口船舶大数据的集成信息处理能力较强。

In order to improve the capacity of two-way navigable port ships, adaptive dispatching of two-way navigable port vessels is carried out. This paper proposes a ship scheduling algorithm based on big data information fusion, which uses the grid region segmentation model to segment the two-way navigable port and waterway, and constructs the big data information transmission model for ship scheduling. The ship scheduling data is processed by information fusion filtering,the interference of redundant data is suppressed, and the fuzzy beam integration method is used to cluster analysis of big data, which runs in two-way navigable port. According to the distributed attributes of the data, ship navigation scheduling is carried out to improve the self-adaptability of ship scheduling. The simulation results show that, this method can improve the throughput and navigation capacity of the port, and the integrated information processing ability of big data, which is a twoway navigable port, can be improved by the adaptive ship scheduling in the two-way navigable port.