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In this paper, an area decomposition algorithm is presented that is suitable for multiple vessels and multiple aircraft participating in maritime searches over a large region. The algorithm can decompose the entire sea region to be searched (deemed as polygonal area) into nonoverlapping subpolygons (search subareas) according to the sizes of the areas covered by various search facilities while considering their search capabilities as well as their corresponding commence search points. The algorithm draws on the concept of a polygon division algorithm in computational geometry. The main novelty in this study is the optimization of the classic polygon division algorithm by introducing a ''maximizing-minimum-angle'' strategy, which can effectively compensate for the deficiency of the traditional algorithm, as reflected in the area decomposition result. This improved algorithm can produce rectangle-like subareas, especially for a rectangular search region, which is commonly used in maritime search operations. For nonrectangular search regions, a rightangle division can be achieved so that the shapes of the search subareas are more conducive to planning specific search routes for search facilities. Fast maritime search coverage over a large region can be achieved. The effectiveness of the algorithm is validated by comparing decomposition results before and after the improvement.

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Mental health monitoring of seafarers is an important part of achieving normal development of the ocean shipping industry. In this paper, a dual subjective–objective testing scheme is proposed to achieve a more effective and intelligent assessment of seafarers' mental health status. Firstly, a new seafarers' mental health test scale (SMHT) is revised based on fuzzy factor analysis and the test data of 283 marine practitioners are analyzed using SPSS v24 software; secondly, this paper proposes an intelligent framework module for immersive subjective emotion extraction based on natural language processing, namely semantic summary extraction (SSE), speech emotion extraction (SEE), using hybrid scoring mechanism to obtain semantic and emotion matching values and assist the seafarer mental health scale to obtain the final correction score. The results showed that the assessment results of the SMHT scale exhibited good reliability (Cronbach's alpha of 0.852 $$\in (0.80{-}0.90)$$ ∈ ( 0.80 - 0.90 ) and retest reliability R of $$0.873\in (0.85{-}0.90)$$ 0.873 ∈ ( 0.85 - 0.90 ) ) and scale association validity (for SCL-90, ($$\text{r}= 0.468{-}0.841)>0.45$$ r = 0.468 - 0.841 ) > 0.45 ). In addition, the calibration rate of the subject-object dual test method was improved by approximately 12.05% compared to the traditional mental health scale. Finally, the advantages and disadvantages of this solution were compared with mental health testing techniques such as CAT, machine learning, SCL-90, and fMRI, and the method demonstrated more accurate psychological testing results, providing a simple and intelligent solution for standardized psychological testing of seafarers.