The technological strength of the twentieth century and the current century has had amajor impact on maritime transport, stimulating the development of the cornerstone of world trade ("maritime trade"). Whether in the Pacific, the Atlantic or other oceans, seaports and maritime transport play a very important role in the global economy. However, while maritime transportation is developing, it also increases the number of maritime traffic accidents. After a sea accident occurs, it is necessary to respond to the accident in a timely manner and formulate a reasonable search and rescue plan. Therefore, research on the maritime search and rescue decision algorithm is urgent. The maritime search and rescue decision-making problem involves multiple processes, including drift prediction of search and rescue targets, determination of search areas, and formulation of search plans. This paper takes Bohai Bay as the research area and combines the optimal search theory to study the assistant decision-making algorithm for the maritime search and rescue program. This study mainly analyzes three important concepts including probability, detection probability, and search success rate, and improves the calculation methods of POC and POD variables. By introducing a new method such as "density ratio" and "excluding the study area from the containment area" in the POC and POD calculations, the search success rate is effectively improved. Finally, the real case occurred in Bohai Bay was analyzed. The optimization of the algorithm was verified by comparison with the search and rescue scheme calculated according to the optimal search theory. The algorithm proposed in this paper can support maritime search and rescue decisions, which is of great significance for improving maritime search and rescue capabilities.
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