To address the challenges posed by dense shipping traffic and the difficulty of identifying navigation risks in open waters, this paper introduces an Especial Cautious Navigation Angle (ECNA) model for ships, grounded in ship collision avoidance geometry. The ECNA model dynamically identifies the range of navigation angles where collision risks may arise between ships. Building upon this model, a comprehensive scoring algorithm is proposed to assess ship collision risks in open waters. This algorithm not only effectively tracks the evolving risk of collisions but also prioritizes ships with the most imminent danger of collision. Experimental results demonstrate that the ECNA model can accurately define the range of collision risk navigation angles. Furthermore, the scoring algorithm provides a quantitative analysis of the development trends in collision risks and offers continuous monitoring of these risks during navigation in open waters. The proposed model and algorithm exhibit strong practical applicability and operability in identifying ship collision risks in both open and dense navigable areas. These findings not only offer valuable guidance for real-world collision risk identification but also contribute to the theoretical advancement of ship collision risk analysis, presenting a novel solution to this pressing issue.

Port approach channels concentrate vessel conflicts, waiting, and speed adjustments that can increase fuel use and CO2 emissions, yet real-time wind is rarely represented as both an emission driver and a scheduling constraint. This study develops a wind-constrained vessel traffic organization framework and a Pareto-based multi-objective proximal policy optimization algorithm (Pareto MO-PPO). A wind-aware propulsion model links vessel speed, transit time, relative wind, and CO2 emissions, while wind-dependent engine-load limits restrict the feasible speed range. A preference-conditioned actor-critic network learns policies across the efficiency and emission trade-off, and an external archive retains non-dominated policies. The framework was evaluated in a Caofeidian Port scenario. Relative to first-come, first-served (FCFS) scheduling, the balanced policy reduced emissions by 11.41% while increasing system time by 7.49%. Detailed results show how wind-aware Pareto policy learning can provide port operators with explicit operating choices rather than a single fixed weight solution.