In the global energy transportation landscape, maritime transport accounts for over 70% of crude oil shipments, making the reliability of its channels paramount to national energy security. Escalating geopolitical tensions have significantly elevated the risk of disruption to critical maritime channels, posing severe challenges to the stability of energy supply chains. To accurately assess this risk, this study employs complex network theory and integrates Automatic Identification System (AIS) trajectory data with vessel capacity weights to construct a global directed weighted network for crude oil maritime transportation, in which critical channels are abstracted as network nodes. Based on this framework, three progressive attack simulation strategies are designed-single channel disruption, compound scenario failure, and optimal disruption sequence-to systematically investigate the multidimensional vulnerability of the network to channel blockages. The findings indicate: 1) Functional differentiation. critical channels exhibit significant functional differentiation. Some are vital for global efficiency; for instance, removing the Strait of Malacca reduces network efficiency by 1.99%. Conversely, others demonstrate structural suboptimality, as exemplified by the removal of the Panama Canal, which paradoxically increases topological efficiency by 2.57%, revealing quantifiable suboptimal paths within the network. 2) Asynchronous vulnerability. The network exhibits asynchronous responses across performance dimensions. While macro-connectivity remains highly robust against single-point or regional failures, the core structure is highly fragile. For example, the network's K-Core value plummets after attacks targeting only four optimal nodes. 3) Non-linear degradation. Under optimal sequence attacks, global network efficiency follows a "U-shaped" trajectory-initially declining before rebounding beyond its original value due to topological reconfiguration. Notably, the collapse of the core structure occurs significantly earlier than the deterioration of overall transmission functionality, with the latter even showing a paradoxical recovery in the later stages of the attack. This study elucidates the underlying failure mechanisms of the global crude oil maritime transportation network under channel blockages, offering a new analytical paradigm and decision-making basis for ensuring national energy transportation security and enhancing the resilience of global supply chains.

In response to the global sulfur cap regulations established by the IMO MARPOL Convention and the management requirements of China's ship emission control areas, it is imperative to improve the efficiency and accuracy of maritime ship emission monitoring. This study proposes a three-dimensional "Terrestrial-Maritime-Aerial" monitoring technology for ship exhaust emissions, which integrates shore stations, bridges, ships, and mobile platforms into a unified network to address the practical monitoring demands of diverse and complex navigation environments. Additionally, an automatic peak signal recognition algorithm has been developed to enable intelligent remote monitoring of ship exhaust emissions and facilitate precise source tracing. Finally, an integrated intelligent control system was constructed, incorporating intelligent remote monitoring, precise source tracing, and enforcement verification, thereby promoting seamless information flow across multiple aspects of maritime supervision.

To accurately predict the thermal fire hazard of these containers under different stowage methods on a ship, this paper establishes typical working conditions, such as the position of the fire container, stowage height and wind speed. It then simulates the fire scene of lithium battery containers on the deck using FLACS 10.9 and establishes a prediction model for the temperature and heat flow density of the fire flow field using the CatBoost algorithm. The results demonstrate that the air volume within the upper and lower spaces of the lithium battery container is directly proportional to the change in fire temperature. The maximum temperature occurs when the fire layer is in the 7th layer, and the range of damage caused by high temperatures and heat flux is minimised when the fire layer is between the 7th and 8th layers. Increasing the stowage height decreases airflow, resulting in higher maximum fire temperatures, a larger temperature influence range and a longer vertical diffusion distance of heat flux. When the wind speed is in the range of 1-4 m/s, it helps to dissipate heat and reduce the maximum temperature. However, when the wind speed reaches 5 m/s, the oxygen uptake rate of the flame increases, resulting in a higher maximum temperature. When the wind speed reaches 6 m/s, the heat dissipation effect dominates and the maximum fire temperature decreases again. The higher the wind speed, the smaller the area of damage caused by high temperatures and heat flux. Comparing the temperature and heat flux density values predicted by the CatBoost algorithm with the measured samples shows that the model is highly accurate and can identify overheating spots. These research results can inform the determination of lithium battery container accumulation modes and the corresponding fire monitoring.

Collision accidents pose a serious threat to the navigation and operational safety of fishing vessels. To enhance the decision-making capability of fishing vessel operators, this paper proposes an intelligent collision avoidance decision-making method based on the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). This method determines the encounter situation of the vessel in accordance with the COLREGS requirements and calculates the degree of collision risk, represented by the Collision Risk Index (I_CR), based on the Distance to the Closest Point of Approach (d_CPA) and the Time to the Closest Point of Approach (t_CPA). By investigating the collision avoidance behaviors of multiple fishing vessel operators, the Goodwin marine observation results were revised to establish the Safe Distance Approach (S_DA) specific to fishing vessels. Furthermore, the traditional Artificial Potential Field (APF) method was improved and integrated with a Genetic Algorithm (GA) to ensure that fishing vessels can avoid collisions safely and effectively. Finally, simulation experiments were conducted to validate the effectiveness of the proposed algorithm. The results demonstrate that the proposed method overcomes the limitations of the single artificial potential field approach in previous vessel collision avoidance decision-making systems. It achieves optimal steering amplitude and determines the appropriate timing for returning to the original course, thereby providing a safe and collision-free decision-making solution that complies with COLREGS. This study can serve as a valuable reference for practical collision avoidance operations by deck officers.

Ship trajectory prediction has become increasingly important in marine traffic management, shipping safety, and related fields. Current methods for ship time-series prediction exhibit certain limitations when handling multi-feature data inputs in water traffic scenarios, as they fail to adequately capture the correlations among features or focus on the critical information within time-series data. To address these shortcomings and further improve the accuracy of ship trajectory prediction, this study proposes a method named GCAU, which integrates an improved Graph Convolutional Network (GCN) with a Recurrent Attention Unit (RAU). First, Graph Convolutional Networks are employed to capture interdependencies between features, thereby enhancing the model's capability to extract feature correlations. Second, an attention gate is incorporated into the Recurrent Attention Unit (RAU), enabling selective emphasis on time-level features. Finally, the study evaluates four different ship time-series prediction methods across three distinct scenarios. The results demonstrate that GCAU outperforms the other methods in all tested scenarios, achieving lower values in Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The proposed method can effectively enhance the accuracy and stability of ship trajectory prediction, thereby providing more reliable decision support for maritime traffic management and other related applications.

Ship collisions pose significant risks to ship structures and the safety of lives on board. Rapid analysis of the extent of structural damage from collisions can provide a critical basis for emergency risk mitigation and damage control and rescue operations. While the Finite Element Method (FEM) can accurately calculate the degree of structural collision damage, it is time-consuming and requires numerous parameters, making it unsuitable for rapid damage assessment under limited input conditions. Using a dataset of 202 ship collision accidents that occurred between 2015 and 2019, this study applies statistical analysis to establish relationships between damage factors and damage levels. A Bayesian network model is developed to analyze the risk of collision damage grades under the combined influence of multiple factors. The results demonstrate that the proposed method agrees well with actual accident cases and can rapidly estimate the collision damage level of ship structures even when only limited parameters are available.

Reasonable maritime route planning contributes to enhancing both the safety and economic efficiency of ship navigation. To address the challenges associated with route planning in complex waters, this paper proposes a method for extracting maritime traffic routes based on ship behavior patterns. Using Automatic Identification System (AIS) data, characteristic points of ship behavior patterns are identified through threshold judgment and a sliding window approach. A clustering algorithm is then applied to determine centroid points within each cluster, which represent the distribution of point sets. Finally, connection rules are established to sequentially link these centroids, thereby generating maritime traffic routes. Experimental analysis was conducted using AIS data from the Beibu Gulf waters. The results indicate that the routes extracted by this method align closely with the recommended routes published in the Navigation Guide for Ships in the Guangxi Area of the Beibu Gulf.

Complex intersection waters, characterized by heavy ship traffic and frequent route crossings, exhibit complex traffic dynamics and a high risk of collisions, making route optimization in such areas highly important. Traditional route optimization methods tend to focus more on the formulation of traffic rules and traffic control measures. While effective, these approaches often rely on the subjective experience of maritime managers and lack an objective basis. To address these limitations, this paper proposes a route optimization method based on historical ship trajectory data. The ship traffic network is extracted through trajectory clustering and image processing techniques. A node similarity model is constructed, and a clustering algorithm is applied to partition the overall network into multiple local traffic networks. Route optimization is then achieved by merging nodes and reconstructing the network within each community. Experimental results demonstrate that the proposed method reduces the complexity of ship traffic by 50% and the risk of ship collisions by 30% compared to pre-optimized conditions. These improvements significantly enhance navigation safety, alleviate the regulatory burden on maritime authorities, and provide valuable insights for the planning of ship routing systems.

In order to reduce the emission of polluting gases from auxiliary generators during ship berthing, and in response to the growing adoption of shore power infrastructure, this paper incorporates the distribution of shore power into the berth scheduling plan of container terminals. Building upon the traditional berth scheduling model, relevant constraints for shore power allocation and carbon emission reduction targets are introduced, establishing a mathematical model that integrates ship-in-port activities, shore power usage, and carbon emissions. To solve the model effectively, an Improved Bat Algorithm (IBA) incorporating a stagnation mutation strategy is proposed. The inertia weight method is employed to update individual optimization speeds, preventing the algorithm from converging to local optima. Case studies show that considering shore power distribution increases the complexity of the berth scheduling problem. When the number of ships does not exceed 25, the mathematical model can be solved accurately with optimal solution quality; however, when the number increases to 30, the model cannot be solved within a reasonable time frame. In comparison, the IBA achieves efficient solutions for all test cases with significantly shorter computation times. The maximum deviation between IBA results and the exact model solutions is only 2.37%. Furthermore, compared to traditional Genetic Algorithms and the basic Bat Algorithm, IBA demonstrates superior performance in terms of solution quality and computational efficiency, with an average increase in computation time of only about 10 seconds compared to the basic bat algorithm. A matching analysis between the shore power retrofit ratio of berths and ships revealed that under a fixed dock berth retrofit ratio, terminal costs decrease as the ship retrofit ratio increases. However, once the two ratios reach equilibrium, the rate of cost reduction levels off and remains largely stable. These results indicate that optimal cost savings are achieved when the shore power retrofit ratios of berths and ships are appropriately matched. Ensuring a balance between supply and demand can effectively prevent resource waste and enhance the efficiency of shore power utilization.

In view of the slow response speed of fuel cells, which limits their ability to promptly respond to dynamic power loads, a composite energy storage power supply is employed to address this issue. Using wavelet transform technology, the steady component of the load is allocated to the fuel cell, while the fluctuating portion is assigned to the composite power supply. Based on Pontryagin's minimum principle, an energy management strategy is formulated with the supercapacitor's energy as the state variable, the output power of the lithium battery as the control variable, and the root mean square current of the lithium battery as the cost function. A simulation model of the ship power system is built in Matlab/Simulink to validate the proposed energy management strategy. The results demonstrate that the proposed control strategy enables stable output power from the fuel cell and achieves rational power distribution according to the charge-discharge characteristics, capacity, and current state of charge of both the supercapacitor and the lithium battery. Compared to hybrid ships without supercapacitors and traditional fixed filter strategies, the proposed approach reduces the rate of current change in the lithium battery and extends the service life of the fuel cell and lithium battery.

To address the high safety risks and frequent accidents associated with oil tanker loading and unloading operations, this paper proposes a data-driven risk assessment method based on Bayesian networks. Guided by systems engineering theory, a three-layer Bayesian network evaluation model comprising 34 nodes is constructed. Using the inference principle of the expectation-maximization algorithm, the conditional probabilities of the network nodes are computed to quantify risk levels within the model. The rationality and reliability of the model are verified through sensitivity and effectiveness analyses. Validation using data from 20 actual tankers demonstrates that the model's output aligns with risk levels assessed by port security personnel and can accurately evaluate the risks during oil tanker loading and unloading operations. The proposed model and method are applicable for assessing safety risk levels in oil tanker operations and can serve as a reference for safety evaluations of loading and unloading operations for other types of dangerous goods carriers.

From the aspects of navigation service, reservation navigation, green and low carbon, the service level index system of inland navigation hub under the reservation mode of 6 first-level indicators and 22 second-level indicators is constructed. Secondly, the game comprehensive weighting method is used to combine the qualitative weights and quantitative weights determined by the interval two-tuple linguistic method and the CRITIC method respectively. Then, based on the matter-element extension theory, the service level evaluation model of inland navigation hub under the reservation mode is constructed. Finally, taking the Three Gorges navigation hub of the Yangtze River as an example, the empirical analysis is carried out to verify the scientificity and feasibility of the model. The research conclusion has a good reference value for improving the service level of inland navigation hub.

To further standardize the management of ship dismantling, it is essential to evaluate and analyze relevant policies as a basis for improving the full life cycle management system of ships. This paper reviews and analyzes 42 policy documents issued by governmental and industrial organizations over the past five years. Based on the content analysis of these policies, a Policy Consistency Index model (abbreviated as the "P Index Model") is established. The ship dismantling policies are categorized into three types: macro guidance, supplementary refinement, and guidance and encouragement. Representative policy documents are selected as evaluation targets, and their rationality is empirically analyzed. The results indicate that China's ship dismantling policies require improvements at three levels. At the national level, it is recommended to establish a coordinated ship dismantling management mechanism involving relevant regulatory departments and to develop unified planning and industry support policies. At the local level, local governments should promptly formulate and implement specific measures to ensure effective policy enforcement. At the industry level, associations need to enhance communication and collaboration with local governments, particularly in establishing consensus on the qualifications of compliant ship dismantling companies, to jointly promote the standardized development of the industry.

In accordance with the MARPOL Annex Ⅵ Nitrogen Oxide (NO_x) Technical Code issued by the International Maritime Organization (IMO), this paper proposes a simplified on-board test scheme for measuring NO_x emissions from marine diesel engines. The scheme employs the carbon balance method to calculate exhaust flow, requiring only the measurement of NO_x and CO₂ concentrations in diesel exhaust at selected load points, and adopts the default fuel element content values provided in the IMO Technical Code. The results show that the weighted average NO_x deviation of the simplified method is approximately 2% compared with the standard cycle, while also reducing the complexity of emission testing equipment and shortening the testing process. The findings provide a theoretical basis for streamlining NO_x emission on-board tests.

In response to increasingly stringent maritime emission regulations, vessels using alternative fuels—such as Liquefied Natural Gas (LNG) and methanol—are being gradually deployed. However, the number of ports offering bunkering for these new fuels remains significantly lower than those supplying conventional fuels, making route planning and operational management more challenging for alternatively fueled vessels. Shipping companies urgently need to rationally design routes and support the operation of alternative fuel bulk carriers within the context of scarce refueling infrastructure. To this end, this study develops a mixed-integer nonlinear programming (MINLP) model aiming to minimize total emissions, incorporating constraints such as total voyage duration and adjustable speed ranges. To address the nonlinear nature of the model, a heuristic algorithm based on variable neighborhood search is proposed to efficiently obtain near-optimal solutions. Finally, through case studies under both single-voyage and continuous-voyage scenarios, the collaborative optimization of refueling strategies and speed adjustments is thoroughly analyzed, demonstrating the effectiveness of the proposed model and algorithm.

This study aims to apply deep reinforcement learning to address the challenges of trajectory planning and control for unmanned surface vehicles. In trajectory planning, the Q-learning algorithm is employed to generate trajectories in real-world aquatic environments. For the design of the reward function, factors such as shallow water areas are taken into account, with an emphasis on minimizing the number of turning points along the path. For trajectory tracking control, we integrate the Soft Actor-Critic (SAC) algorithm with the Proportional-Integral-Derivative (PID) control method to alleviate the difficulties of manual parameter tuning associated with conventional PID controllers. This hybrid approach also mitigates the interpretability limitations often found in pure deep reinforcement learning methods. Comparative experiments involving the traditional PID algorithm, Genetic Algorithm (GA), and Deep Deterministic Policy Gradient (DDPG) algorithm demonstrate the superiority of the proposed SAC-PID method. Simulation results show that the planned trajectories effectively incorporate multiple factors, including travel distance, shallow water regions, and number of turning point, the SAC-PID method achieves outstanding performance in trajectory tracking.

To address challenges such as significant scale variations, high aspect ratios, dense arrangements, and complex backgrounds in ship target detection from remote sensing images, this paper proposes an improved YOLOv7-based algorithm. Using YOLOv7 as the baseline network, the prior anchor generation algorithm is optimized for the dataset. A long-edge representation method combined with circular smooth labeling is introduced to detect ship targets with uncertain rotation directions. The YOLOv7 network is enhanced by embedding both the GAM (Global Attention Mechanism) and SimAM (Simple Attention Mechanism) modules, which effectively suppress interference from complex background regions in remote sensing images and improve target detection accuracy. Additionally, the coordinate loss function is optimized to accelerate model convergence. Experimental results on the DOTA-ship and HRSC2016 datasets for both single-class and multi-class detection tasks show mAP values of 86.1%, 97.7%, and 87.1%, respectively-representing improvements of 7.8%, 4.6%, and 7.9% over the original YOLOv7 model. These results validate the effectiveness and superiority of the proposed method.

To enhance the cognitive capabilities of waterborne transportation systems for intelligent ship navigation, this study first reviews the development of electronic map systems designed for the new generation of shipping systems and summarizes the technical requirements for future map functionalities in waterborne transportation. Subsequently, the Pan-information-based Navigation Scenario Map (PNSM) is proposed as a core component of the "Navigation Brain System". The elements, characteristics, and conceptual foundations of the PNSM are analyzed, with emphasis placed on its capabilities in object-oriented modeling and computer-cognitive representation of navigational environment data. Key technologies underpinning the PNSM are further discussed, including spatiotemporal object modeling of scenario elements, hierarchical organization of all elements, dynamic association among objects, adaptive cross-domain computing for scenario data, cognitive modeling of scenario semantics, intelligent information services, and multi-dimensional dynamic visualization of the scenario map. The application process and practical effectiveness of the PNSM are illustrated using data from the Intelligent Shipping Project in the Water Network Areas of Zhejiang Province, China. Research results demonstrate that the PNSM can effectively integrate, organize, correlate, and visualize water traffic data, enabling comprehensive cognitive representation of all elements within the waterborne transportation system and their interrelationships. It provides essential technical support for the advancement of next-generation shipping systems, including intelligent navigation technologies and standardized frameworks.

Ship trajectory prediction and behavior recognition can help effectively assess navigational risks and provide an important basis for decision-making in collision avoidance and traffic management. To improve the accuracy of ship trajectory prediction and behavior recognition, this paper studies a multi-task Informer model for simultaneous trajectory prediction and behavior recognition. Based on the Informer framework, the model incorporates a multi-task learning approach. It addresses the issue that inaccurate ship behavior records in AIS data cannot be directly used as model inputs by designing a multi-task loss function that jointly trains behavior recognition and trajectory prediction in parallel. During training, an adaptive updating strategy for the loss function-based on homoscedastic uncertainty-is designed to automatically allocate weights to the losses of the two tasks. Evaluated using real AIS data from the Taicang sector waters, the multi-task Informer model reduces trajectory prediction loss by 40.2% and 14.7% compared to LSTM and Informer models, respectively. In behavior recognition, the multi-task model improves accuracy by 11.7% and 5.95% compared to LSTM and Informer models, respectively. The results demonstrate that the multi-task model effectively enhances the performance of ship trajectory prediction while achieving accurate recognition of ship behavior.

Addressing the requirements for ship’s autonomous navigation and high-precision path following control, this paper proposes a fuzzy control method with partitioned integral regions based on the Integral Line-of-Sight (ILOS) guidance law for path following control of autonomous navigation ships. Based on the established ship motion model, an extended Kalman filter is employed to estimate ship states such as speed and heading using GNSS measurement data. Fuzzy controllers are designed to dynamically adjust the look-ahead distance parameter according to different navigation conditions, thereby improving tracking performance. The 700 Twenty-foot Equivalent Unit (TEU) battery-powered container ship built by COSCO Shipping Group is taken as the plant for path following experiments on a simulation platform. Comparative analyses with results from different algorithms show that the proposed control algorithm achieves superior performance and smoother rudder angle manipulation. This method can provide reference for path following control of autonomous navigation ships.

Aiming to address the issues associated with traditional artificial cargo hold clearing operations, such as high risk, low efficiency and an insufficient level of automation, this paper introduces the Intelligent Cargo Hold Clearing Robot (ICHCR) system for bulk carriers. Based on dual-mode control, the ICHCR system achieves unmanned operation and robot-shore collaborative intelligent cargo hold clearing. The article presents an intelligent robot hardware system for cargo hold clearing and a cloud control platform for immersive operation. Intelligent control methods for the ICHCR are investigated, including perception and localization inside the cargo holds, and dual control modes of cloud control and autonomous navigation operation. Other core technologies include robot-shore cooperative cargo hold clearing. Finally, the system was applied and validated on a 70,000-tonne Panamax bulk carrier in a grain port. Experimental results demonstrate that the ICHCR system improves the safety of cargo hold clearing operations, optimizing the overall process and reduces time consumption, meeting the requirements of safety and efficiency.