一种融合多源异构数据的港口航道拥堵监测预警方法
By fusing multi-source heterogeneous data and using ship flow transfer equations, the problem of traffic flow coupling and correlation in port and waterway monitoring was solved, realizing global monitoring and collaborative scheduling of the port and waterway network, improving the timeliness and foresight of early warnings, and reducing the probability of port congestion.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DALIAN UNIV OF TECH
- Filing Date
- 2026-05-12
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies in port and waterway monitoring fail to fully consider the coupling relationship of traffic flow between multiple waterways, resulting in fragmented and one-sided monitoring results that are difficult to fully reflect the overall traffic situation of the port and waterways. Furthermore, traditional early warning methods cannot dynamically adapt to real-time changes in traffic flow, leading to delayed early warning responses and making it difficult to provide sufficient response windows for scheduling decisions.
By employing a multi-source heterogeneous data fusion method, a unified spatiotemporal grid for the port is constructed through the spatiotemporal fusion of data from the Automatic Identification System (AIS), shore-based radar, hydrological and meteorological data, and terminal operations data. A ship flow transfer equation is established, the interaction and impact of traffic flow and the congestion propagation mechanism are quantified, the congestion index is dynamically calculated, and a hierarchical early warning mechanism is constructed to achieve global monitoring and coordinated scheduling of the waterway network.
It enables global monitoring of the port and waterway network, improves the comprehensiveness and real-time nature of monitoring results, significantly enhances the timeliness and foresight of early warnings, provides unified information support for port scheduling decisions, and reduces the probability of congestion.
Smart Images
Figure CN122176960B_ABST