As global economies continue to expand, maritime trade is projected to double by the year 2050. In this context, the development of a new generation of maritime intelligent supervision systems, which rely on precise information perception and machine learning-based technologies, is crucial for ensuring the safety of maritime navigation. Traditional methods for detecting abnormal ship behavior often suffer from inadequate feature extraction capabilities, leading to ineffective and inaccurate anomaly detection. To address this issue, this paper introduces a Transformer-GSA encoder detection model. This model enhances ship information data through BCE-GAN, extracts multi-layer convolutional features, and utilizes the Transformer-GSA module to capture the dependencies between trajectory features and time-series data, thereby effectively identifying abnormal ship behaviors. An evaluation conducted using AIS data from the Yantai-Qingdao route in January 2021 demonstrates that the model achieves an accuracy of 96.26%, with a reduced parameter count of 12.11M. These results confirm the model’s efficacy and superiority in detecting abnormal ship trajectories, contributing to the reduction of maritime accidents and illegal activities, alleviating the management burden on duty personnel, and enhancing the quality of intelligent ship traffic services and port operation efficiency under the maritime Internet of Things architecture.

Existing models for assessing ship collision risk involve complex calculations that complicate the simultaneous qualitative and quantitative analysis of the factors affecting ship navigation safety. Therefore, these models often exhibit slow generation of the risk index and evaluation results with reduced accuracy. To resolve these issues, we model the ship collision risk based on the cloud model theory. Specifically, we select “distance of closest point of approach (DCPA)” and “time to closest point of approach (TCPA)” as the main factors affecting the ship collision risk and analyze the data of DCPA, TCPA, and collision risk index (CRI) based on their cloud models. By combining these analyses with a double-condition-single-rule generator, we construct a cloud model for ship collision risk and finally develop a cloud model-based inference engine system to assess ship collision risk. This engine allows us to establish different ship collision risk analysis models according to the scenario encountered by the ship, which can be used to verify the feasibility of the proposed algorithm for ship collision risk modeling. Through comparisons with traditional ship collision risk models, the proposed ship collision risk model is found to be superior owing to its simple implementation, accurate results, and shorter time required to generate the risk model. The model established in this study enables the crew to determine the key objects to be avoided in case of potential collision with multiple ships. At last,analysis and research of cloud model ship collision risk based on global sensitivity and uncertainty are done to reduce the dimension of the risk parameters and show the main factors of unstable collision risk,therefore,the uncertain results in the calculation of the degree of danger are avoided, some reasonable suggestions are proposed for real navigation safety. the maritime pilot can make correct decisions promptly to reduce or avoid the occurrence of collision accidents.