为实现全国港口运输距离的动态更新，降低人工依赖性，更及时高效地计算水路运输周转量，本文基于水运业务大数据，开展了港口间运输距离测算模型的构建与应用研究。首先，对船舶自动识别系统（Automatic Identification System, AIS）数据与进出港报告数据的内容、特征进行了系统分析，明确了两类数据之间的关联性。其次，提出一种基于水运大数据的融合关联分析方法，结合航线可达性与运输频次，采用DBSCAN聚类算法识别核心港口节点，构建基于实际航行路径的港口运输距离测算模型。最后，选取典型内河港口与沿海港口进行实证分析。结果表明，该模型计算结果与测量结果的误差在5%以内，具有较高的准确性。研究成果可为水路运输规划、运力调度及周转量统计提供科学依据，同时为智慧港口建设与航运大数据应用提供技术参考。

In order to realize the dynamic update of the national port transportation distance, reduce the manual dependence, and calculate the waterway transportation turnover more timely and efficiently, this paper carries out the construction and application research of the calculation model of the inter port transportation distance based on the big data of the waterway transportation business. Firstly, the contents and characteristics of the AIS (Automatic Identification System) data and the port entry and exit report data are systematically analyzed, and the correlation between the two types of data is clarified. Secondly, a correlation analysis method based on water transportation big data fusion is proposed. Combined with route accessibility and transportation frequency, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is used to identify the core port nodes, and the calculation model of port transportation distance based on the actual navigation path is constructed. Finally, typical inland ports and coastal ports are selected for empirical analysis. The results show that the error between the model calculation results and the measured results is less than 5%, which has high accuracy. The research results can provide scientific basis for waterway transportation planning, capacity scheduling and turnover statistics, and provide technical reference for smart port construction and shipping big data application.