In order to improve the ship in the actual berthing path control accuracy and other issues,

a ship berthing path planning method is proposed based on nonlinear model predictive control (NMPC) combined with moving horizon estimation (MHE), enabling future trajectory prediction and real-time control updates. A Fossen model of the ship is established in the four-degree-of-freedom (4-DOF): heave, pitch, yaw and roll. It adapts to the movement state and environmental changes of the ship on the water surface and improves the accuracy of berthing control. By simulating the simulation experiment of autonomous berthing path planning of papua new guinea and manila international port ships.

The results show that the trajectory error is less than 8.0 m and the berthing position error is only 0.6 m. The effectiveness, generalization and applicability of the proposed algorithm are verified. In order to more accurately simulate the ship's motion response in waves, a 4-DOF ship mathematical model is developed to provide more comprehensive ship motion information for the control system. It further realizes more accurate control and enhances the robustness of the system.

The method provides theoretical and practical support for autonomous berthing in diverse port environments.

To address the challenge of coordinated optimization between collision avoidance planning and motion constraints in the scenario where ships navigate close to dynamic surface targets, a hierarchical path planning method integrating the improved A^* algorithm, rapid reverse search iterative planning (RRSIP), and Hybrid A^* fine-grained planning, aiming to achieve efficient and kinematically compliant dynamic target tracking planning is proposed.

An improved A^* algorithm based on dynamic programming and oriented bounding box obstacle detection is used to plan a global reference path, reducing path length and redundant waypoints. For the dynamic target point, the RRSIP method is proposed, which reuses node information from prior searches via reverse search to quickly iterate and predict the approach point, avoiding global replanning and improving efficiency. Hybrid A^* algorithm is introduced near turning points for local refined planning, quickly generating a feasible path that satisfies ship kinematics and approach heading constraints.

Compared with other typical algorithms, the path length of the improved A^* algorithm proposed is reduced by an average of 4.17% and 1.79% respectively. The RRSIP method reduces the iterative planning time by at least 33.9% compared with the FR method. While ensuring path feasibility, the local Hybrid A^* planning reduces the time consumption by at least 72.1% compared with the global application.

The proposed method can effectively solve the problems of real-time performance and kinematic feasibility in dynamic target tracking, and significantly improve the autonomous tracking capability of ships in scenarios such as tugboat escort and maritime police law enforcement.

To improve the accuracy and robustness of ship trajectory prediction,

an ABiM-Ship network that encodes historical trajectories using a bidirectional selective state space model is proposed. An attention mechanism to explicitly align trajectories with heading and speed is utilized. A two-stage end-to-end joint prediction is designed, first regressing future trajectories, heading, and speed, then refining them using residual correction. Huber loss is introduced to constrain physical errors and stabilize convergence.

The experimental results show that this network outperforms traditional mainstream baselines in terms of average prediction error over short, medium, and long distances, achieving high prediction accuracy. The representation method, two-stage structure, and Huber loss all contribute significantly to performance gains.

The research findings achieve explicit coupling and coarse-to-fine prediction for trajectories, heading, and speed while maintaining linear temporal complexity. They have good reproducibility and scalability, providing a generalizable technical path and engineering reference for intelligent navigation and collaborative scheduling in complex maritime areas with high traffic density.

In order to improve the efficiency of tugboat operation scheduling and energy utilization,

an improved intelligent load forecasting algorithm integrating dynamic data preprocessing and online learning is proposed. Based on a hybrid LSTM-Adaboost architecture, the algorithm addresses the issues of temporal feature degradation and multimodal data fusion through the integration of differentiated LSTM weak predictors and dynamic weight allocation (error sensitivity penalty mechanism), and designs an online learning trigger mechanism (automatic retraining based on prediction error threshold) to achieve dynamic model updating. Additionally, an environmental data collaborative optimization module, including tidal information, is introduced to enhance the adaptability of load forecasting to port conditions. The algorithm is compared with conventional LSTM-Adaboost to validate its effectiveness.

The results indicate that after iterative optimization, the mean squared error of the improved algorithm is reduced by 40.8% compared to the conventional LSTM-Adaboost algorithm, demonstrating higher prediction accuracy and environmental adaptability.

The research results can provide a reference for tugboat energy optimization, safety management, and intelligent scheduling in ports.

To objectively and systematically understand the current status of reliability testing of maritime autonomous surface ships (MASS),

the current research status from three aspects: testing methods, testing technologies, and evaluation systems, and discusses the future development trends are analyzed. Specifically, it includes: conducting a visual analysis of 134 related papers using CiteSpace and VOSviewer to systematically sort out the research directions and development trends in the field of ship collision avoidance capability testing; sorting out the uses, advantages and disadvantages, and research status of the three major testing platforms: real ship testing, model testing, and virtual simulation testing; in-depth discussion on the development trends, feature comparisons, and challenges faced by the three mainstream testing scenario generation technologies based on expert knowledge, random sampling, and artificial intelligence; summarizing the evaluation indicators from four dimensions: data authenticity, scene complexity, risk, and generation efficiency; and on this basis, looking forward to future research directions.

The results show that virtual simulation testing has the advantages of low cost and high coverage and has become the main testing method. The ship collision avoidance capability testing method based on artificial intelligence has development potential in high-risk edge scenarios and ship interaction games, but the current research still faces challenges such as idealized motion models, lack of multi-ship dynamic game mechanisms, single evaluation indicators, and difficulties in virtual-to-real migration.

The research on testing scenario generation and deduction based on artificial intelligence has important research value and significance for promoting the testing of MASS.

To investigate the carbon fiber reinforced polymer (CFRP) hull lightweighting effect on the environmental impact,

Life cycle assessment on an 11 m CFRP high-speed vessel is conducted, focusing on atmospheric pollution indicators: global warming potential (GWP) and ozone depletion potential (ODP).

The results demonstrate that the fiber content adjustment-based lightweight design algorithm can achieve a 12.5% reduction of hull structure mass while maintaining structural safety by increasing fiber content from 40% (original case) to 55% (lightweight design case) approximately. However, due to the significantly higher environmental burden of carbon fiber production compared to resin, the manufacturing phase saw increases of 10.24% in GWP and 14.37% in ODP. Conversely, the operational phase benefited from reduced fuel consumption due to lightweighting, saving 323.98 t of fuel over 25 years, which decreased GWP and ODP by 4.13% and 4.19%, respectively.

The operational phase ultimately offset the negative environmental impacts of the manufacturing phase. Critical insights for green ship design and maritime industry decarbonization strategies is provided.

Aiming at the problem of the impact of center body position on nozzle cavitation intensity and flow morphology,

based on the CFD-Fluent, the Mixture multiphase model is employed, the k-ε turbulence model, and the Schnerr-Sauer cavitation model, numerical simulations are performed for nozzle flow fields with varying center body positions. The validity and reliability of the methodology are confirmed through comparison with prior research results.

The nonlinear regulatory effect of the center body's axial position on the internal flow field and cavitation intensity within the nozzle is systematically quantified. It clearly identified the junction of the nozzle throat and diffuser section as the optimal position most prone to triggering cavitation effects. Cavitation intensity and mass transfer rate peaked when the center body is located at this position. Cavitation is absent on the center body when positioned inside the nozzle. When center body located outside the nozzle, the downstream extent of the cavitation zone changed minimally, and cavitation intensity gradually diminished as the center body moved further downstream. Furthermore, based on the large eddy simulation (LES) method, an in-depth analyze the complex unsteady flow structures and large-scale radial diffusion characteristics of the vapor phase downstream of the nozzle under the optimal cavitation position condition is further conducted. Significant unsteady features are observed at a location 10 nozzle diameters (10D) downstream, where the vapor phase distribution expanded radially to four times the nozzle diameter (4D).

The research clarifies the regulatory mechanism of center body position on cavitation intensity, providing a theoretical basis for optimizing cavitating nozzle design. The findings contribute to enhancing the efficiency of industrial processes reliant on cavitation, such as cleaning and fragmentation, and offer theoretical support for developing adjustable center body structures to enable real-time control of cavitation intensity.

To optimize the propulsion efficiency of trailing suction hopper dredgers (TSHD) in two typical operating conditions: low-speed operation and self high-speed navigation,

The ducted pitch propeller and the ducted pitch propeller are designed based on the graph method, and the performance difference of the two cases is compared. A multi-objective optimization platform is established, utilizing the Reynolds-Averaged Navier-Stokes (RANS) method and the non-dominated sorting genetic algorithmⅡ (NSGA-Ⅱ) to conduct an optimization study of the fixed-pitch ducted propeller that balances both operating conditions.

The results show that the pitch ratios of the ducted propellers obtained based on the graph method are very close for both operating conditions, allowing a compromise propeller design to achieve good efficiency in both conditions. Furthermore, compared to the ducted fixed-pitch propeller, the ducted controllable-pitch propeller has higher requirements for the disk area ratio, and under dredging conditions, the fixed-pitch propeller exhibits higher efficiency. Through optimization, the two optimal ducted propeller designs obtained show efficiency improvements of 5.65% and 5.59% under dredging condition, and increases of 7.70% and 8.09% under high-speed navigation condition, respectively.

It provides assistance and reference for subsequent research.

In order to study the potential application of the diesel fuel direct coal liquefaction diesel (DDCL) and polyoxymethylene dimethyl ethers (PODE) mixed fuel in marine diesel engines,

the volume of fluid (VOF) method is adopted to simulate and study the influence of different fuels, nozzle hole conical and the angle between the nozzle hole and the needle valve axis on the cavitation flow in the nozzle.

The results show that the cavitation intensity in the nozzle hole with a larger angle between the needle valve axis is greater, while there is no obvious cavitation in the nozzle hole with an angle less than 60°. The gradually converging nozzle hole can effectively suppress cavitation and has good flowability and low turbulence intensity. With the increase of PODE content, the density of the mixed fuel increases, the cavitation, turbulence intensity and flow loss in the nozzle hole decrease, and the effective flow area increases. The mass flow rate of DDCL is lower than that of petrochemical diesel. After blending the same volume of PODE, the mass flow rate of the former increases by 6.2% and is higher than that of petrochemical diesel.

The blended fuel of PODE-coal direct liquefaction diesel can reduce the internal flow loss of nozzle orifices, improve the flow performance of the orifices, and increase the mass flow rate.

To enhance the real-time computational capability of diesel generator set simulation models under dynamic conditions such as sudden load changes, and to address the issues of computational complexity and insufficient dynamic response timeliness in traditional mechanistic models during ship deployment,

a physics-mechanism-inspired multilayer perceptron (MLP) data-driven modeling method is proposed. By constructing a dual-hidden-layer network topology mapped to the electromagnetic-electromechanical transient process of generators, the approach achieves coordinated rapid calculation of the DC bus voltage and current of diesel generator sets.

The model effectively captures the nonlinear dynamic characteristics of diesel generator sets, improving computational efficiency while maintaining the accuracy of mechanistic models.

The research providing rapid-deployable technical support for real-time situational awareness and intelligent management of ship power systems.

To enable accurate assessment and online diagnosis of the DC-link capacitor health state in neutral point clamped (NPC) three-level inverters,

DC-side parameter identification with a focus on capacitor parameter degradation characteristics is investigated. To address the low accuracy and strong specificity of traditional capacitor-parameter identification methods, this paper proposes a digital-twin-based approach for DC-link capacitor identification in NPC three-level inverters. The method first analyzes the capacitor operating characteristics to determine the identification parameters, then builds a mathematical model and constructs a digital-twin NPC inverter using a fourth-order Runge-Kutta solver. A particle swarm optimization algorithm is employed to continuously update and refine the identification parameters until the digital twin matches the real system outputs.

Simulation studies verify the effectiveness of the proposed capacitor-health identification method under various conditions and confirm the consistency between the digital-twin model and the physical system. Results show that the method accurately identifies capacitor values, equivalent series resistance, and related load parameters, with identification errors generally within 5%.

The research results provide a reference for the parameter identification of DC-side capacitors in inverters.

Aiming at the problem of unstable bus voltage output caused by Marine condition disturbance switching during the operation of ship direct current (DC) microgrid photovoltaic power generation units,

a photovoltaic power generation control strategy based on double integral sliding mode controller is proposed, including establishing an engineering model of photovoltaic cells and adopting a new exponential approach law and hyperbolic tangent switching function.

Simulation verification shows that the controller shortens the startup time to 0.002 s, reduces the overshoot to 2%, and effectively eliminates the steady-state error. Compared with traditional proportional-integral (PI) control, the startup time is reduced by 67% and the ability to resist load disturbances is enhanced by 41.2%.

The strategy significantly enhances the dynamic response speed and robustness, and is applicable to the dynamic working conditions of ships, addressing the shortcomings of traditional control methods under sudden light changes and load disturbances.

In order to apply the dynamic inclinometers based on low-cost micro electro mechanical systems (MEMS) inertial measurement units to ships conviniently, it is necessary to overcome the significant impact of the ship sway and surge motion in moored condition, which are periodic acceleration changes of several seconds to tens of seconds, on inclinometer measurements.

The algorithm for inclinometers in the situation is studied. Gyroscope measurements are used for attitude quaternion update. Then the horizontal accelerometer measurement values without the effects of roll and pitch are obtained. On this basis, the error analysis and impact analysis of sway and surge is carried out. The horizontal accelerometer measurement values are put through a low-pass filter with zero-phase-delay. Then Kalman filtering is performed using the low frequency component of horizontal accelerometer measurement values as the observation of the Kalman filter, and horizontal attitude errors and angular velocity measurement errors as state variables. Attitude closed-loop correction is conducted to make the MEMS dynamic inclinometer keeping the expected accuracy in a long time when the ship is moored.

An experiment is conducted using a certain type of MEMS dynamic inclinometer to validate the algorithm of reducing the effect of sway and surge. The measurement accuracy reached 0.2°(1σ) with an acceleration amplitude of 0.8g in the experiment,

verifying the effectiveness of the algorithm. Key words: low pass filter; Kalman filter; dynamic inclinometer; sway and surge

Aims to establish a cloud-edge-device collaborative intelligent management and control system to enhance production process controllability, shorten construction cycles, and strengthen decision support capabilities.

Driven by production plans and guided by process flows, a three-level cloud-edge-device collaborative architecture is designed. By constructing a physical-information fusion environment in the ship block workshop, a "plan-resource-execution" linkage mechanism is established. An improved genetic algorithm (IGA) combined with simulated annealing is proposed for the dynamic scheduling model, alongside the development of a multi-source heterogeneous data fusion engine to achieve full-factor visual management and control.

After system implementation, the ship block construction cycle is reduced by 19.7% compared to traditional models, production anomaly response time is shortened by 75%, and the equipment load balancing index is optimized by 28%.

The proposed cloud-edge-device collaborative management and control model effectively resolves the dynamic matching dilemma between planning and execution in ship block workshops. The established "perception-analysis-decision-execution" closed-loop system provides a reusable implementation framework for intelligent ship manufacturing, promoting the digital transformation of the shipbuilding industry.

To quantitatively analyse the impact of digital transformation on enhancing the performance of shipbuilding enterprises and to understand its underlying mechanisms,

the research employs principal component analysis and regression models to assess the influence of digital transformation on corporate performance, based on its digital transformation level data from 2011 to 2023.

It reveals that for every one standardised unit increase in an enterprise's digital transformation level, its total output value grows by 30.4%. The combined contribution from the four application systems-design, manufacturing, management, and supply chain-remains relatively balanced. The research indicates that digital transformation is an indispensable pathway for the development of the shipbuilding industry. The key to successfully achieving digital transformation lies in enabling data sharing across design, procurement, manufacturing, and management systems. Crucially, realising data sharing hinges on establishing an enterprise data standards system and developing an enterprise data model that describes organisational behaviour, characteristics, status, and performance.

The proposed comprehensive measurement method for digital transformation levels, based on the depth of core system application, provides a reference for manufacturing enterprises to evaluate transformation effectiveness and optimise resource allocation.

To overcome the limitations of the traditional random sample consensus (RANSAC) algorithm in cylindrical segmentation, a novel method is developed for segmenting point clouds of ring-ribbed shells by integrating structural features and statistical methods.

Initially, the model surface area feature is utilized to estimate the proportion of inliers, thereby enhancing the accuracy of initial parameters. Subsequently, principal component and radius constraints are introduced to enhance the accuracy of cylinder identification and reduce the number of iterations. Then, a weight function-based correction method is applied to mitigate outlier interference, thereby improving the accuracy of cylinder fitting. Finally, the DBSCAN algorithm clustered the point clouds of ring-ribs, and an improved RANSAC algorithm identified localized features, thus achieving precise measurement of component dimensions.

Experimental results show that the proposed method effectively addresses the intelligent recognition and dimensions measurement of components in various parts of the ring-ribs, significantly improving the recognition speed and accuracy of cylindrical shell and ring-ribs. The precision, recall, and overall accuracy of cylindrical shell reach 96.9%, 99.5% and 96.4% respectively, with a computational speed increase of approximately 4.6 times. The measurement error for ring-rib component dimensions is within 0.2%.

Compared with traditional methods, the proposed method offers significant advantages in the accuracy and computational efficiency of point cloud segmentation.

In order to formulate a reasonable control strategy for electrothermal de-icing of wind turbine blades,

an experimental approach utilizing electrothermal heating component prototypes has been employed to investigate the influence of factors such as ice thickness (5 mm and 20 mm), heating power (ranging from 400 W to 1 000 W), and ambient temperature on the ice-melting process within an environmental chamber set at temperatures between -20 ℃ and -5 ℃.

The results show that for each 1 ℃ decrease in ambient temperature, an additional approximately 40 W of power is required to sustain the same final temperature, revealing a linear coupling relationship between heating power and ambient temperature with respect to the final temperature of the heated surface. Furthermore, when the ice thickness is 5 mm, the duration of the gradual temperature rise phase during ice melting extends from 2.5 minutes to 10.0 min, and a heating power of 800 W or higher becomes necessary for effective ice melting when the ambient temperature falls below -15℃. As the ice thickness increases to 20 mm, the heat absorption by the ice layer itself increases by 3.2 times, leading to a proportional extension of the ice-melting time by 55%.

Therefore, in practical engineering applications, it is imperative to dynamically adjust the heating power based on real-time data on ambient temperature thresholds and ice thickness, while also optimizing the control logic by taking into account the critical conditions for ice shedding and the temperature abrupt change characteristics during the ice-melting stagnation phase.

To address the severe scouring challenges faced by offshore wind power infrastructure,

the scouring problem of the four-pile jacket foundation of offshore wind power under the action of ocean currents through numerical simulation. Using FLOW-3D software is studied, adopting the large eddy simulation (LES) turbulence model and the sediment transport model, the validity of the numerical model was verified through comparisons with experimental results, the scouring process of the four-pile jacket foundation under the action of a single steady flow is simulated. The development and changes in the scour pit morphology around the foundation over time are analyzed, and the effects of different flow velocities, incoming flow angles, and pile spacings on the scouring of the four-pile foundation are studied.

The results show that the group pile effect is central to the scouring characteristics of four-pile jacket foundations. The incident flow angle alters the shielding interactions among piles, leading to an asymmetric distribution of scour morphology. Pile spacing modulates the intensity of interference between adjacent piles; as the spacing increases, the group pile effect gradually weakens, and the scour pattern transitions from a unified, interconnected scour hole to relatively independent local scour holes. The maximum scour depth is primarily governed by flow velocity and exhibits only minor variation with changes in pile spacing.

The research findings provide a reference for the scouring of four-pile jacket foundations in offshore wind farms.

To effectively reduce fatigue damage, a reasonable dynamic cable design is required.

An optimization model based on an improved hybrid particle swarm optimization algorithm is established. It employ MATLAB to develop a genetic-chaotic particle swarm dynamic factor optimization algorithm and utilize the Orcaflex software for the overall design and optimization of platform dynamic cables. The optimization problem of deepwater dynamic cables is treated as the objective function, with parameters such as cable length, buoyancy block and counterweight block positions, and spacing as optimization variables. Building upon the foundation of the standard particle swarm algorithm and integrating genetic algorithms, it effectively prevent dynamic cable optimization parameters from falling into local optima. Chaotic initialization of initial particles is applied to ensure a uniform distribution in high-dimensional solution spaces. Dynamic inertia weight factors and learning factors are introduced to balance global and local search capabilities during optimization. Adhering to the Pareto principle, It formulate an objective function to facilitate multi-objective constrained optimization. The improved optimization algorithm shows better performance in terms of convergence, accuracy, and convergence speed.

It quickly and effectively balances the relationship between the maximum axial tension and the minimum bending radius of the cable and pipe, achieves the optimal design.

It provides strong support and guidance for practical engineering applications.

Offshore wind speed observations often suffer from data gaps, limiting the accuracy of wind resource assessment and wind farm operation.

A ratio-based interpolation method for reconstructing missing wind speed data using ERA5 reanalysis and floating LiDAR observations is proposed. Taking 100 m wind speed data from a coastal buoy as a case study, the method is evaluated across annual scale, seasonal variability, wind speed levels, and typical extreme weather events.

Results show that the method effectively captures temporal wind speed trends, with an annual average correlation coefficient of 0.839. However, it tends to underestimate wind speed magnitudes, with errors increasing notably under high wind conditions, especially during convective summer periods and typhoon events. Compared to traditional linear regression methods, the ratio method performs better in maintaining trends and controlling errors, and it demonstrates greater stability and robustness under conditions of severe wind speed fluctuations or extreme weather.

Overall, the ratio method demonstrates good applicability in stable wind environments and is suitable for long-term wind resource evaluation and data reconstruction. Nevertheless, its accuracy under extreme weather remains limited, suggesting the need for integration with high-resolution simulations or multi-source data fusion approaches.

To review the current state of research on autonomous navigation decision-making and control technologies for intelligent unmanned surface vehicles, and to clarify the technical bottlenecks and development trends under scenarios of varying complexity,

a systematic investigation is conducted into the development history of key technologies for unmanned surface vehicles both domestically and internationally. It review addresses the differing technical requirements between low-to-medium complexity and high-complexity application scenarios, covering path planning, line-of-sight guidance, autonomous collision avoidance, automatic docking and undocking, multi-agent cooperative control, and autonomous recovery. It evaluates existing technological shortcomings and provides recommendations for future development.

Analysis indicates that autonomous navigation technology for open waters has matured and is gradually being implemented in engineering applications. However, core technologies for complex waters and complex missions still face developmental bottlenecks.

Looking further ahead, we propose establishing a standardized simulation and real-vessel testing evaluation system tailored to real-world scenarios. It will accelerate the rapid iteration and implementation of key technologies, thereby supporting the advancement of autonomous navigation decision-making and control technologies for unmanned surface vehicles in China.

Existing autonomous berthing technologies rely on precise mathematical ship models and mostly employ empirical formulas for modeling. However, in actual berthing scenarios, influenced by environmental factors and speed, these methods cannot accurately reflect the current ship maneuvering status in real-time, leading to limited berthing control accuracy. To address the aforementioned problems,

a ship autonomous berthing control method based on physics- informed neural networks (PINN) is proposed. The method constructs a real-time dataset using a sliding window and identifies ship maneuvering parameters in real-time through the physics-informed neural network. An adaptive controller based on gain scheduling is designed to dynamically adjust control gains using the identified parameters, realizing precise ship berthing.

Experimental results demonstrate that the PINN network can converge rapidly under dynamic conditions and accurately identify ship parameters, with a goodness of fit reaching 0.97. In berthing experiments, the method ensured that the terminal heading deviation and lateral error converged to a minimal range, achieving smooth and safe docking, with a heading error of 0.13°.

The method effectively resolves the failure of traditional control algorithms caused by model mismatch under unknown ship parameters and complex working conditions, offering a safe and interpretable adaptive berthing control solution.

To systematically review the technological evolution of unmanned surface vehicles (USVs) and explore the path of their convergence with intelligent ships, aiming to overcome the performance bottlenecks of individual USVs regarding endurance, computing power, and communication.

It reviews the centennial evolution of USVs, tracing the transition from radio remote control to fully autonomous navigation, and from single-agent operation to swarm collaboration. It provides an in-depth analysis of four core technologies: environmental perception, decision planning, motion control, and communication links. On this basis, the study focuses on the convergence trend between USVs and large intelligent ships, analyzing the "mothership-drone" cross-domain collaborative operational mode and the cloud-based management system driven by digital twins.

It indicates that current USV technology is undergoing an intelligent transition from "perception-avoidance" to "cognition-gaming". Furthermore, the "mothership-drone" collaborative mode, by combining the platform advantages of large ships with the high maneuverability of USVs, effectively resolves the challenges of individual USV operations in complex deep-sea environments and the "last mile" maneuvering difficulties for large intelligent ships entering and leaving ports, thereby achieving complementary advantages.

Collaborative mode represents a mainstream paradigm for future maritime operations. However, continuous breakthroughs are still required in areas such as regulatory adaptability, communication network security, and green energy propulsion. The findings provide theoretical references for constructing a new integrated air-surface-underwater intelligent maritime equipment system.