With the development of autonomous navigation for unmanned ships, identifying ship collision avoidance behavior has become a key factor in their independent decision-making. To address the inefficiency and misjudgment issues of existing ship trajectory recognition algorithms, this paper proposes a data mining model based on the steering point of a sliding window for ship collision avoidance. When the model identifies a ship's steering point, it first evaluates the change characteristics of the heading at adjacent time points in the ship's Automatic Identification System (AIS) data using a fixed sliding window. Then, the slope change of the trajectory points at adjacent moments is calculated for verification, and the earliest turning point of the heading change within the window is marked. Finally, a variable sliding window is used to maintain the heading change and error parameters during the trajectory change process, determining whether the steering point is a collision-avoidance steering point. The model is experimentally compared with the Douglas-Peucker (DP) algorithm. The results show that the model can effectively identify whether a ship's steering is collision avoidance behavior, resolve the issue of the DP algorithm misjudging steering points due to data fluctuations, and extract the earliest steering point during the ship collision avoidance process to assist in collision avoidance decision-making. This model can be applied to the research and development of intelligent collision avoidance decision-making systems, ensuring the safety of ship navigation.

To improve the comprehensive evaluation system of ports and move beyond the single evaluation mode of "throughput only," this study investigates a comprehensive evaluation index system for world-class ports. Firstly, the concept and connotation of world-class ports are interpreted using the "Theme-Objective-Path-Service" analysis framework. A comprehensive evaluation index system for world-class ports is proposed, centered around weighted throughput, ship service efficiency, connectivity, economic contribution, and green and security levels. The index weights are determined using the Analytic Hierarchy Process, and a fuzzy comprehensive evaluation model for world-class ports is established. Based on this model, 34 global sample ports are selected for comprehensive evaluation. The evaluation results show that the ports of Singapore and Shanghai rank among the top two in terms of comprehensive scores, placing them in the world's leading lineup. Nine other ports, including Rotterdam, Ningbo-Zhoushan, Busan, and Qingdao, rank among the top ports globally with scores above 80. Finally, suggestions for conducting comprehensive evaluations of world-class ports are proposed, focusing on establishing world-class port evaluation standards and strengthening the application of big data from the Automatic Identification System (AIS).

A stochastic programming model is devised for the multi-base, multi-drone location and routing problem, considering the simultaneous movements of drones and ships as well as ship movement uncertainty. A decoding algorithm is developed to divide a sequence into sub-routes using ship-based and drone-based strategies. Furthermore, a bi-stage heuristic algorithm is proposed, combining a genetic algorithm and Tabu search. In the bi-stage algorithm, the first stage addresses ship movement uncertainty and employs Tabu search to solve the drone base station location problem. The second stage uses the genetic algorithm to route the drones for detection based on the location results. Numerical experiment results show that, in the same application scenario, the drone-based (D) strategy can optimize flying distance by 7% while reducing computing time by 50% compared to the ship-based (S) strategy. Considering ship movement uncertainty can reduce flying distance by 10% for the drone base station location solution. Flying distance is sensitive to the number of available drones. For example, in a scenario with two base stations and 3-5 drones, adding one drone may increase flying distance by 15%. Speeding up the drones by 5% may reduce flying distance by 5%. This method can effectively generate multi-UAV inspection paths that meet the requirements of moving ships, providing technical support for maritime supervision.

To ensure the safety of navigation in the bridge area, this paper proposes a ship automatic monitoring method based on the fusion of vision and AIS (Automatic Identification System). The ship contour information in the image is extracted by the YOLOv5 (You Only Look Once version 5) target detection algorithm and the Canny algorithm. A distance, azimuth, and height measurement model of the visual target in the bridge area is constructed to achieve the three-dimensional positioning of the ship. An abnormal behavior detection model is established using the ship navigation situation data from the fusion of vision and AIS to automatically identify and monitor monitoring of dangerous ships in the bridge area. The experimental results show that: In cases of single and multiple ships, the accuracy of visual and AIS data association is 98.45% and 91.29%, respectively; The method can effectively monitor the motion state of ships in the bridge area. This paper provides an effective method for ensuring the safety of ships and bridges.

In order to study the safe deployment of tugboats under the uncontrolled situation of large LNG (Liquefied Natural Gas) ships in the dock-constrained waters, and to ensure the safety of port terminal operations, the study adopts the CFD (Computational Fluid Dynamics) analysis method, based on the wind flow interference model, and calculates the hydrodynamic parameters and determines the emergency tugboat deployment strategy by simulating the drift process of the large LNG ship under different working conditions. Taking Wenzhou Xiaomendao as a case study object, the study focuses on analyzing the ship motion process of Qmax LNG ship under the working condition of sudden loss of control, and calculates the drift distance, which is used to guide the emergency deployment of tugboats. The results of the study show that the emergency deployment strategy of tugboat under different working conditions can be clarified by CFD analysis. This study provides scientific support for the safety of large LNG vessels in port transportation and provides an effective basis for emergency decision-making for port management to ensure the safe operation of ports and shipping industries.

To provide integrated communication-navigation means for maritime distress and safety communication, China has established the BDMSS (BeiDou Message Service System) based on the BeiDou system's regional short message capability. After five years of operation, BDMSS has been successfully recognized by the IMO (International Maritime Organization) as a GMDSS (Global Maritime Distress and Safety System) service provider. This paper outlines the evaluation and recognition procedures for mobile satellite systems used in GMDSS, as defined by the IMO and the IMSO (International Mobile Satellite Organization). It examines BDMSS's application process for IMO recognition and the technical and operational assessment conducted by IMSO. Key assessment points for BDMSS, including availability, restoration and spare satellite arrangements, priority mode, on-site demonstration scenario design, and additional considerations, are analyzed. The results enhance understanding of the international approval and evaluation criteria for potential GMDSS mobile satellite systems. Furthermore, they provide valuable references for future revisions of IMO Resolution A.1001(25) and for incorporating international maritime requirements into the design of new mobile satellite systems.