Research on Epidemic Big Data Monitoring and Application of Ship Berthing Based on Knowledge Graph-Community Detection

The COVID-19 epidemic has been raging overseas for more than three years, and inbound goods and people have become the main risk points of the domestic epidemic. As the main window for China to exchange materials and personnel with foreign countries, under the dual pressure of the global economic downturn and the China-US economic confrontation, ports’ pressure and responsibility to ensure material transportation and foreign trade are particularly heavy. However, the risk screening of ship and crew epidemic information based on manual methods is extremely time-consuming and labor-intensive, and it is difficult to take into account the efficiency and accuracy requirements of the port's own business and disease control and traceability. To this end, this study proposes an epidemic risk screening method based on knowledge graphs. This method is based on shipping big data and community discovery algorithms, analyzes the geospatial similarity of ship information, crew information and real-time epidemic policy information, and quickly establishes a structure. Map data, quickly screen high-risk ships and crew members, and access the business system to arrange nucleic acid testing tasks. When the time cost is only one thousandth of that of manual labor, the detection accuracy rate approaches and exceeds the accuracy level of manual screening, with an average precision advantage of 8.18% and an average time advantage of 1423 times. It is further found that it is more capable of performing heavy screening tasks than humans, and its AUC decline rate with the increase of the amount of measured data is only 34% of that of the manual method. The research results have been initially applied in Ningbo Port, which has greatly improved the informatization level and screening efficiency of Ningbo Port's risk screening during COVID-19 epidemic.