A BASM-Integrated LLM Reasoning Method for Multidimensional Vessel Anomaly Attribution and Decision Support

Abstract

The attribution of anomalous vessel behaviors is crucial for maritime traffic supervision and safety decision-making. However, existing approaches often lack the capability to systematically interpret multidimensional anomaly features and reveal their underlying behavioral mechanisms, limiting the transparency and practical value of anomaly attribution. To address these challenges, we propose a multidimensional anomalous vessel behavior attribution and decision-support framework based on large language models (LLMs). Specifically, the framework first employs Behavioral Anomaly Semantic Mapping (BASM) to transform multidimensional anomaly features into structured semantic units governed by logical constraints; it then leverages knowledge-guided reasoning (KGRP-PCoT) with LLMs to perform multi-step inference, enabling systematic attribution from low-level anomaly observations to high-level behavioral mechanisms. Experiments conducted on AIS data from the Wusongkou waters demonstrate that the proposed framework significantly outperforms traditional methods. Quantitative evaluations show that the framework achieves a BLEU-4 score of 0.91 and a BERTScore of 0.98 when integrated with advanced LLMs like DeepSeek. Furthermore, the ablation study confirms that the proposed BASM and KGRP-PCoT mechanisms improve the BERTScore from approximately 0.86 to 0.98. It not only reveals the causal mechanisms underlying anomalous behaviors more accurately but also improves logical consistency and regulatory compliance, confirming its practical utility and decision-support value.