Maritime UAV path planning is a key link in realizing the intelligence of maritime emergency transportation, providing key support for fast and flexible maritime accident disposal and emergency material supply. However, most of the current UAV path planning methods are designed for land environments and lack the ability to cope with complex marine environments. In order to achieve effective path planning for UAV in marine environments, this paper proposes a Directional Drive-Rotation Invariant Quadratic Interpolation White Shark Optimization algorithm (DD-RQIWSO). First, the directional guidance of speed is realized through a directional update strategy based on the fitness value ordering, which improves the speed of individuals approaching the optimal solution. Second, a rotation-invariant update mechanism based on hyperspheres is added to overcome the tracking pause phenomenon in WSO. In addition, the quadratic interpolation strategy is added to enhance the utilization of local information by the algorithm. Then, a wind simulation environment based on the Lamb–Oseen vortex model was constructed to better simulate the real scenario. Finally, DD-RQIWSO was subjected to a series of tests in 2D and 3D scenarios, respectively. The results show that DD-RQIWSO is able to realize path planning under wind environments more accurately and stably.

Accurate ship-speed prediction under heterogeneous wind, wave and current forcing is important for voyage optimisation, energy-efficiency assessment and operational decision support. This study is framed as a single-vessel, single-voyage AIS–metocean diagnostic study rather than as an operational model with demonstrated cross-voyage or cross-vessel generalisation. The study compares a non-learning Physics model, a data-driven model (DDM), and a physics-informed hybrid-driven model (PHDM) in which a simplified physics-based speed-loss baseline is corrected by a data-driven residual learner. Self-organising map (SOM) regimes are used as diagnostic environmental strata rather than as a general-purpose sea-state classification. Using the predefined C1–C5 scenarios of the SHANGHAI EXPRESS voyage dataset, the exploratory post-hoc best-learner comparison gives an average MAPE of 3.0168% for PHDM, compared with 7.5794% for the Physics model and 6.6594% for DDM. In C4, MAPE decreases from 13.4291% for the Physics model and 12.9513% for DDM to 5.4178% for PHDM. These results provide the primary scenario-wise description of model performance within the analysed voyage. In C1, the comparison is metric-dependent: the Physics model has lower RMSE and MSE, whereas PHDM has lower MAE and MAPE. A fixed-reference stress test uses chronological partitioning, matched learner identity, and non-position feature sets to examine the stability of the scenario-wise pattern. Positive PHDM gains are observed for all fixed learners in C4 and for most fixed learners in C5, whereas the pattern differs across C1–C3. These findings apply to the analysed vessel, voyage period, validation design, and SOM-derived environmental regimes.

The global shipping industry is evolving towards deep integration of digital transformation, intelligent upgrading, and green development. Meanwhile, recent geopolitical shifts have introduced heightened uncertainties into international shipping, compounding the challenges and escalating the demands for weather routing services for ocean-going ships. This paper provides a systematic review and expert perspective on China’s current status and key challenges in ocean-going weather routing services. Based on operational insights from China’s national meteorological service synthesized with a review of current trends and the literature, it further explores the future development of China’s ocean-going weather routing services and technologies from multiple dimensions: enhancing maritime weather observation capabilities, developing advanced weather routing service models, upgrading autonomous and controllable global satellite communication systems, promoting intelligent navigation technologies to facilitate shipping’s low-carbon transition, and expanding meteorological support capabilities for Arctic shipping routes. The analysis identifies critical gaps and proposes strategic directions, offering a unique contribution to understanding the trajectory of weather routing services within China’s specific national context from the perspective of its primary national service provider.