A fast wake evaluation method is devised for a full-formed ship. The CFD (Computational Fluid Dynamics) technology and artificial intelligence algorithm are used to standardize the wake field and construct the evaluation model. The accuracy of the model is checked by PIV (Particle Image Velocimetry) technology. The artificial intelligence algorithm for the model is chosen through model training. Three artificial intelligence algorithms are tried and the one gives the best fitting accuracy is picked up for the final model. The calculation error of the model is analyzed. Experiments show that for full-formed bulk carriers, CFD software is good for simulating the flow state of the wake field and displaying flow details. The parameters such as wake uniformity can be valuable references for optimizing the stern profile of full-formed ships, designing propellers and energy-saving devices.

To investigate and locate the leakage of a marine lubricating oil cooler, comprehensive analysis is conducted in chemical composition of equipment materials, metallographic structure of welds, scanning electron microscopy of corrosion morphology, and energy spectrum of corrosion products. The microstructure and chemical composition distribution around the corrosion area are obtained. The causes and mechanism of the corrosion are studied to avoid similar corrosion leaks.

Currently, the operation status of ship air coolers is mainly determined according to the temperature of the pressurized airT_A2. An alarm is only triggered when the air cooler is extremely abnormal, resulting in poor timeliness, let alone early warning. To effectively solve this problem, a GA-BP (Genetic Algorithm-Back Propagation) neural-network-based temperature prediction method for pressurized air at outlet of the air coolers is proposed. An air cooler state prediction model is built. By detect the deviation between the measured and predicted values of T_A2, abnormal states of the air cooler can be identified in a timely manner; Genetic optimization algorithms are introduced into the BP neural networks to speed up the convergence and avoid local optimal solutions. To verify the effectiveness of the prediction-based method, multiple sets of state data before and after air cooler cleaning are selected for training and validation. The results show that the GA-BP prediction results are consistent with the actual application of air coolers and could effectively identify abnormal states of air coolers.

Based on the experience of a ship yard designing and building LNG (Liquefied Natural Gas) vessels, this paper reviews the installations of NO96 membrane cargo containment systems on the vessels and the evolution of the design of NO96 system, from NO96 PERLITE, NO96 GW, NO96 L03+ to NO96 SUPER+. The principal specifications of different type of NO96 membrane cargo containment system, the BOR (Boil Off Rate), strength and weight and the relationship between them, are studied. With the design development of NO96 membrane cargo containment system, the BOR has been reduced from the initial value 0.150% to the current value 0.085%. Due to the application of new material and rationalized structure design, the strength and weight of the system have been improved significantly, too.

Noise pollution from traffic is on the increase and may impair the living environment of surrounding area. This paper provides a comprehensive analysis of common traffic noise control measures in terms of noise sources, propagation pathways and noise receptors. The current research progress on noise reduction materials of sound insulation and absorption is discussed. The prospects on future traffic noise prevention and control are given. The study indicates that, in active noise reduction measures, methods such as soundproof windows and acoustic barriers have proven to be effective in reducing the noise levels received by environmentally sensitive receptors. Regarding noise reduction materials, future research may focus on noise reduction performance, lightweight construction, durability, cost-effectiveness, and the development of high-quality materials suitable for various scenarios.

For the digital transformation of enterprises, the first task is to integrate management information systems scattered at different locations with a wide variety of applications. However, as these systems were developed in different periods and adopted different technical solutions, they have different operating platforms and databases, it is difficult to integrate them. Based on an actual case, this paper designs and develops a data governance solution based on the middle office technology, which aims to break through the data barriers and to achieve the goal of turning data into assets. The application of the solution shows that the efficiency of data collection, data processing, data analysis and data application of the case enterprise has been significantly improved, the level of operation and management has reached a higher level, and the digital transformation of efficient services, precise governance and intelligent decision-making has been basically achieved.

The wave added resistance is calculated based on full-scale ship navigation data, using two methods: numerical calculation and empirical formula, and the calculation accuracies achieved by the two methods are investigated. A bulk carrier is selected for illustration. The ship type information of the bulk carrier is collected. Based on the ship type information, the wave added resistance response curve is calculated; The navigation monitoring data of bulk carriers are collected, and the corresponding wave added resistance response curves for each sampled data are calculated using Python programming. The corresponding wave added resistance values are obtained according to the wave height and wave period of each sampled data usingITTC (International Towing Tank Conference) dual parameter wave spectrum; Then, based on the ISO 15016:2015 regulations, wind, waves, temperature, and water density are corrected, and the shaft power is corrected to the ideal calm water power through wave added resistance correction, water temperature correction, wind resistance correction, etc. Finally, the correction results are compared with the resistance analysis results of the fast model test for bulk carriers, verifying that the SNNM(SHOPERA-NTUA-NTU-MARIC) method is better corelated to the wave added resistance on the actual ship than the slice method used.

A summary of recent researches and achievements in the field of two types of ship wind-assisted propulsion devices-rotor sails and arc-shaped sails are summarized. The literature on the subject is reviewed. The scopes and the methodologies of the works are studied. The contributions of these works to the field are analyzes. The review indicates that the aerodynamic performance and its influencing factors of rotor sails and arc-shaped sails have been researched comprehensively. The development of research methods such as model testing and numerical calculations has reached a mature stage, with numerical calculations generally demonstrating high accuracy in most cases. Further in-depth research on the numerical calculation of aerodynamic performance for rotor sails at high Reynolds numbers is warranted.

Two-stage turbocharging with intercooler system can reduce the power consumption in the process of air compression. The saved energy may be used and, therefore, improve overall efficiency of the system. The derivative uses of the two-stage turbocharging technology for marine two-stroke diesel engines are calculated and analyzed for the purposes of power generation, heating or producing air to the gas film drag reducing system. The impact of Two-stage turbocharging technology on scavenging system and exhaust treatment system in diesel engine is analyzed. The calculation results confirm and quantify the emission reduction effect, as well as the economic benefit. Also the disadvantages of this system are analyzed and avoided by system optimization. It is concluded that the energy saving effect of two-stage turbocharging with intercooler system can be achieved by combining it with some other technologies.

An offset pre-swirl energy-saving device to improve ship propeller's propulsion efficiency is developed and evaluated usingCFD (Computational Fluid Dynamics). For illustration, the device particularly for a new bulk carrier is designed. TheRANS (Reynolds-Averaged Navier-Stokes) method is employed to numerically calculate the resistance on the ship and the wake field at the propeller disk plane. Based on the wake field characteristics, the offset pre-swirl energy-saving device is designed, and its key design parameters are optimized for efficiency of the wake flow rotational energy recovery. Numerical calculation shows that this device can lead to an energy-saving of 3.1%, approximately 20% improvement over conventional pre-swirl energy-saving devices.

The flow field in an cavitation tunnel is simulated by RANS (Reynolds-Averaged Navier-Stokes) equations. The experiments are conducted to verify the RANS-equation simulation of the flow in the cavitation tunnel. The simulations with/without honeycomb/guide vanes are carried out to study the influence of honeycomb and guide vanes on flow field in the test section of cavitation tunnel. It is proved that the honeycomb and guide vanes are effective in keeping water flow even.

In order to improve the information transmission efficiency between the operation site and the bridge and the safety of anchoring operation, an intelligent windlass control system is conducted to upgrade the mechanical system, the hydraulic system and the electric control system. The upgrade windlass control system features one-push-button operation beside the windlass or in the bridge, to automatically do anchor standby, anchor releasing and anchor drawing according to the water depth measurements. The upgraded electronic control system can collect electrical and hydraulic status data through various types of sensors in the windlass and achieve the health status monitoring function. The system has been installed, and proved to be efficient.

The fuel switching process of a large methanol dual-fuel powered container ship is simulated and analyzed to accurately predict the nitrogen purging effectiveness in the fuel supply system and investigate methanol fuel purging patterns. The numerical simulations of the purging process are performed under the ship's actual operation conditions based on the 3D model of the fuel supply system, methanol fuel's physical properties, and purging process flow. The purging performance under constant inlet pressure is predicted and the results are compared with freshwater flushing simulation results. This analysis clarified the effects of different media and pipeline volumes on purging time and methanol residue rates, providing practical references for safely and effectively executing nitrogen purging operations.

The in-depth reinforcement learning technology is introduced into the highway traffic management and a traffic smoothing control system is designed and physically developed. The system controls the speed of vehicles by adjusting the speed limit value of The in-depth reinforcement learning technology is introduced into the highway traffic management and a traffic smoothing control system is designed and physically developed. The system controls the speed of vehicles by adjusting the speed limit value of road sections according to the traffic situation in bottleneck area. The system consists of three functional modules: dynamic speed limit initiation, speed limit determination and update-information board dynamic releasing. The mainstream deep reinforcement learning algorithm DDQN (Double Deep Q-Network) is used in the system, and a traffic smoothing control strategy based on DDQN is developed to improve the performance of the algorithm by the destination network and experience replay technologies. Based on the CTM(Cellular Transmission Model), a traffic flow scenario at a road section of Ningxia expressway is simulated, and the control strategy is verified in such environment. The experiment shows that the system can improve the traffic efficiency in the bottleneck area in terms of volume of traffic and vehicle transit time.road sections according to the traffic situation in bottleneck area. The system consists of three functional modules: dynamic speed limit initiation, speed limit determination and update-information board dynamic releasing. The mainstream deep reinforcement learning algorithm DDQN (Double Deep Q-Network) is used in the system, and a traffic smoothing control strategy based on DDQN is developed to improve the performance of the algorithm by the destination network and experience replay technologies. Based on the CTM(Cellular Transmission Model), a traffic flow scenario at a road section of Ningxia expressway is simulated, and the control strategy is verified in such environment. The experiment shows that the system can improve the traffic efficiency in the bottleneck area in terms of volume of traffic and vehicle transit time.

This paper proposes a distributed energy storage control strategy for the coordination of distributed energy storage in ship integrated electric propulsion systems containing AC and DC bus-bars. Simulation models of the parts of the ship’s integrated electric propulsion system are built, including power generation system, energy storage system, etc.; A distributed energy storage control strategy is designed for the system, which achieves the automation of the energy storage system by collecting information such as bus voltage and energy storage status; A system simulation model of the ship’s integrated electric propulsion system is constructed and operated. The simulation results show that the fluctuation of DC bus voltage is less than 2%. The system’s AC and DC bus voltage can meet the operational requirements. The distributed energy storage management method proposed in this paper can effectively manage energy storage systems and achieve stable operation.

The impact of errors in measurements of swing amplitude, cycloid length, and arm of force on the calculation results of the metacentric height h_GM of the ship in inclining tests is studied. The 5 000 ton barge “Shipping Barge 1” is analyzed for illustration. The influencing factors for the ship’s inclining test are analyzed. A mathematical model based on error propagation theory is built for analyzing the influence of measurement errors in swing amplitude, cycloid length, and arm of force on h_GM under the test conditions, The sensitivity analysis is conducted to clarify the degree of influence of each factor. The results are verified through calculation of some typical ship cases. The research results indicate that the swing error has the greatest impact on the calculation results of h_GM; The larger the h_GM, the greater the calculation error of h_GM the swing amplitude change causes.

At present, the city's video image surveillance system can no longer meet the growing demands for urban digital operation and application due to technical bottlenecks such as forwarding strategy, technical architecture and software and hardware updating. This paper, with technologies such as distributed management, integrated media services, video source forwarding, all-round data aggregation and multi-mode data sharing, devises a set of digital management system suitable for million-level video images. The theoretical basis of the technical architecture is presented through the current situation analysis, problem analysis and strategy analysis. The feasibility of the technical architecture is demonstrated by a pilot project. The compatibility of the system with the equipment commercially available today is examined an for reference of future development.

To address the safety risks of leakage, phase imbalance, and explosion associated with ammonia fuel injection operations, for the injection scenario of a certain type of ammonia powered tugboat tanker truck, a collaborative plan for process optimization and risk prevention is developed. Based on CFD (Computational Fluid Dynamics) numerical simulation (SST k-ω turbulence model), the diffusion law of ammonia leakage is quantified, and a three-level dynamic safety control zone is designed to solve the multiphase flow balance problem of liquid ammonia injection. By means of a nitrogen dual loop purging system with real-time monitoring technology based on laser absorption spectroscopy, a leak prevention and control closed-loop mechanism is formed. The research results indicate that this scheme can effectively control the risks of leakage, explosion, and low temperature during the process of liquid ammonia refueling, providing a highly applicable and safe technological paradigm for ammonia fuel ship refueling, and supporting the low-carbon transformation of the shipping industry.

The conventional air compression stations for gas supply in large-scale ship yards simply maintain a preset pressure in main pipe and leave the pressure at user ends letting it be. Such a system is weak in adapting to the changes at user ends. This paper introduces the user end information dynamically into existing air pressure control system to improve the adaptability. The relationship between main tube pressure setting and actual air pressure at user end is studied and the load changes at user ends are investigated. A control strategy based on big data analysis, which dynamically predict load changes and adjust the pressure setting value of the air compression station, is developed. The developed control system integrates multiple technologies such as cloud computing technology, energy applying application in the form of mini program, data modeling and analysis, and grey prediction models.

Ships characterized as same type can be significantly different in technical parameters. For reasonably evaluate ship's fuel efficiency, they should be divided in a better way. This paper studies the way of grouping ships belonging to a shipping company for fuel efficiency evaluation. The ships involved covering bulk carriers, container ships, and tankers. A "two-step" ship grouping method is proposed. The unsupervised machine learning algorithm weighted K-Means is employed to divide the company's ships into groups according to nine ship attributes of the vessels, and the number of clusters is determined using the elbow method. Based on the obtained cluster number, the K-Means model is applied again to group the ships and assign category labels. The ship data are further processed using the supervised machine learning algorithm weighted XG-Boost according to the above categories to gain the capability of handling incomplete/bad quality data and providing categorizing probability. The enhanced model is used to process the ships belonging to other companies, the accurate attributes of which may be available. The effectiveness of the proposed method is verified through actual fuel consumption estimation.

A comprehensive analysis is conducted on the Environmental Steward services and corporate environmental management. Through literature reviews and case studies, the current issues in corporate environmental management, the features and functions of the Environmental Steward services and development status of the services are studied. Measures are proposed to enhance the integration of the Environmental Steward services with corporate environmental management. The analysis concludes that the Environmental Steward is an effective innovative mode for enhancing corporate environmental management capabilities. The legal guidance for the standardization of Environmental Steward services is needed and the government and regulatory agencies can provide the guidance through powerful regulations and close supervision and guidance. The providers of the Environmental Steward services should enhance their own capabilities, actively update their knowledge base, and track and apply various new technologies, including cloud computing and big data, to achieve precise management and to improve work efficiency. Enterprises should actively assume environmental responsibilities and ensure the practical implementation of the technical services provided by Environmental Steward. Through the joint efforts of the government, consulting firms and enterprises, the Environmental Steward can play an effective role in corporate environmental management and effectively enhance the quality and efficiency of corporate environmental management.

This paper Explores the application of RAG (Retrieval-Augmented Generation) technology in building a large-scale shipping professional knowledge base, providing support for the intelligent development of the shipping industry. The principle of RAG technology is introduced systematically. The limitations of traditional RAG knowledge bases in text slicing, retrieval accuracy, and global understanding are analyzed. Advanced solutions such as introducing reordering models, database MPC (Multi-Process Communication) Server architecture, and super large context models are proposed to establish a shipping professional knowledge base. The research results indicate that the construction of a shipping professional knowledge base based on the RAG big model can significantly enhance the information retrieval and knowledge generation capabilities in the shipping professional field, providing strong support for the digital transformation of the shipping industry.

In order to improve the operational management level and asset utilization efficiency of container asset management enterprises, considering the uncertainty of the behavior in returning container upon lease termination and the influence of other internal and external factors, a predictive analysis is conducted on the behavior of returning container upon lease termination. An intelligent prediction model is developed based on customer historical data and comprehensive variables such as regional container return limits and monthly container return limits of ports for particular container types. The container return volume is predicted through specific calculation methods. The practical application results have proved that the model can perform prediction and analysis of action of returning container upon lease termination, assist container asset management enterprises in predicting the future evolution trend of lease termination, help them adjust management and operation strategies in a timely manner, and improve operational management efficiency and asset utilization.

A novel tail fin propulsion mechanism is developed for large-scale BCF (Body and Caudal Fin) biomimetic fish. This mechanism employs the crank-rocker principle to convert steady motor rotation into sinusoidal oscillations, enabling stable tail fin undulation and precise dual-joint angular adjustments through a single power source. This mechanism is characterized by compact structure and simplified control. The prototype of the large biomimetic fish adopts a metal skeleton-skin configuration, effectively resolving assembly precision issues in large-scale motion systems while enhancing mechanical reliability. Self-propulsion tests in a towing tank are conducted to measure swimming speeds by tail fin oscillations, validating the propulsion mechanism's efficacy. The achievement provides technical support for applications of large biomimetic fish in marine monitoring and underwater rescue.

In order to satisfy the core requirements of the highway engineering firms for internal operation data exchange and sharing in construction, management, maintenance, operation and safety and for the respond of external data demands, an resource capacity open platform for construction, management, maintenance, operation and safety is designed based on the API (Application Programming Interface) gateway principle with secondary development. The work is focused on the following: sorting and classifying the businesses associated with the highway construction and operation; design of the overall framework of the platform and the structure of resource service directory and system engineering. The transformation of existing business system is carried out to make it fit to the open platform. The internal and external data sharing and opening are realized.

To enhance the safety of maritime operations, an in-depth analysis of cybersecurity risks faced by oceangoing vessels is conducted, along with the exploration of corresponding mitigation strategies. The study systematically categorizes major cybersecurity risks for oceangoing vessels, including external hacker intrusions, malware attacks, internal human errors, and inherent system vulnerabilities. Seven targeted mitigation strategies are proposed, emphasizing the adoption of advanced cybersecurity technologies and solutions-particularly the integration of AI (Artificial Intelligence) and ML (Machine Learning) technologies to strengthen threat detection capabilities. The strategies also highlight the need to intensify cybersecurity training for crew members to improve overall security awareness, implement rigorous data backup and recovery mechanisms, and employ dedicated networks and isolation technologies to reduce risks from external attacks.

In order to imediately detect unsafe actions of crew members and to reduce the risk of accidents caused by crew misoperation, a method for identifying unsafe actions of crew members based on YOLOv5-Lite algorithm is proposed. The Fire Module structure is introduced into the YOLOv5 algorithm to achieve channel transformation, which also reduces the number of image recognition model residual modules and the size of the model, and, in turn, to improve the operational efficiency of the model. As an example, an image recognition model for detecting persons in operation area who is not wearing a safety helmet is established. The model collects image data, generates an HTST (Helmet Tumble Smoke Tired) dataset, and performs image processing. The model is trained to achieve the identification of movements of people. The output of the model is compared with the recognition results obtained with algorithms YOLOv3 and YOLOv4 to verify the effectiveness of the method. The results show that the method for identifying unsafe behaviors of crew members based on YOLOv5-Lite algorithm can effectively identify unsafe behaviors of crew members with a mAP value of 95.70%. YOLOv5-Lite algorithm has better stability and detection capability than that of YOLOv3 and YOLOv4. The FPS (Frames Per Second) detection speed is fast, meeting the demand for real-time identification of unsafe behavior of crew members.

The ship mast vibration caused by excitation source coupling is analyzed as an element of ship system. The case of a Panamax bulk carrier is analyzed for illustration. The mast vibration prediction method is derived based on the ship'sglobal-structure-mast-coupled finite element model established in ship design phase. During sea trials, multi-channel vibration measurement technology is applied to conduct full-scale vibration measurement of the mast. The accuracy of the numerical predict method is verified against the full-scale measurement data. The results show that the error of the vibration predicting method can be controlled within 3%.

To address schedule management challenges in resource-constrained ship outfitting projects, a progress management model is developed based on global project resource scheduling. This model utilizes the AHP (Analytic Hierarchy Process) to decide resource allocation priorities for subtasks, establishing a resource scheduling framework under constrained conditions. Network planning techniques and priority rule-based heuristic algorithms are applied to separately allocate resources for critical and non-critical tasks. Trial results demonstrate that this model enables proactive resource configuration interventions, effectively improves resource utilization efficiency, and resolves issues of resource shortages or wastage.

In order to meet the demand for intelligent development in the field of tunnel engineering traffic monitoring in China, a traffic monitoring platform based on microservice architecture is designed to address the problems of low emergency rescue efficiency, insufficient intelligent control, and inadequate data analysis capabilities of traditional monolithic traffic monitoring systems. The traffic monitoring platform adopts a layered architecture design and modular design. By integrating GIS (Geographic Information System), video, Internet of Things, digital twins, and data governance services, sub platforms such as road network monitoring, tunnel monitoring, and emergency response are built to connect rescue parties quickly, improve the emergency rescue efficiency and enhance the intelligent control. The system can predict traffic situations through big data analysis and effectively improve the platform’s intelligence level. The platform has been deployed in Qinghai and Chongqing, and the results show that it can significantly improve the intelligence level of traffic management through full factor data perception and full process business control.

FreeCAD is used as the parametric modeling tool to generate a ship hull form. OpenFOAM is used as a numerical simulation tool to find the resistance of the ship hull. Regression analysis is used as the data processing method to learn the relationship between parameters and resistance and, in turn, the impact of each parameter on the resistance. The optimal parameters for the best resistance performance are decided. An engineering ship is examined and optimized as an example. The results show that the inflow section of the target ship has a significant effect on the resistance and the resistance is reduced through optimizing the parameters.

This paper studies the demands of traffic management industry for video incident detectors and the application of the detectors in traffic engineering projects at development stages and highlights the developments of mainstream AI (Artificial Intelligence) video incident detectors in traffic engineering market. The video incident detector has developed in four stages, from standard definition video incident detector, high-definition video incident detector, cloud desktop video incident detector to AI video incident detector. The detection effectiveness and recognition efficiency and other function-alities have been improved through the stages, but with the ever-escalating requirements, the technical and application bottleneck is gradually emerging. With the development of AI and cloud computing technology, deep involvement of AI technology will be the main development trend of video incident detector and they will develop the capability to dynamically perceive the status of traffic incidents and traffic flow in real time and to actively predict traffic safety incidents and traffic conditions.

The hydrodynamic characteristics and mooring safety of the deep-water aquaculture platform ZHENYU No.2 is studied with the fishing net modeled based on Froude similarity criterion. The external force on the platform under the extreme wind speed and flow velocity is tested, and the frequency response of the platform to regular waves is studied through experiments and numerical simulation. The forces on the mooring system when moving and moored under the combined action of wind, wave and flow are tested to get the characteristic values of platform hydrodynamics and the forces acting on the mooring system. The results of numerical simulation and test show that, when ignoring the influence of the fishing net, wave itself has little effect on the motion of the platform. The safety factor of mooring system under cross-wind, cross-wave and cross-current action meets the requirements of relevant guidelines of classification society with a certain safety margin.

Sound barrier installation projects usually have the characteristics of short effective construction period, complex construction environment and high safety risks. The sound barrier installation project for Shanghai metro line 1 which has been keeping in service has completed on schedule and without any accidents. This paper reports the experiences of the project management. The hazard identification analysis table and targeted preventive measures are prepared, covering catenary grounding, material loss, welding electric shock, falling from high place and object strike. The measures for improving efficiency are devised for all tasks of the project, such as design, process, quality and machinery.

With the globalization trend of the chemical industry, the operational efficiency and safety resilience of liquid chemical terminals have attracted much attention, and traditional management models are no longer suitable for the needs of modern logistics. This paper, based on real-life application scenarios, designs and develops a digital twin platform for smart liquid chemical terminals. The platform adopts a layered architecture, including data source layer, base engine layer, general capability layer, architecture fusion layer, and scenario empowerment layer. Implementation of integrated, visualized, and intelligent management and control of terminal operations at all levels is realized. Among them, the dock environment monitoring subsystem integrates multiple heterogeneous sensors to achieve real-time and comprehensive monitoring of the dock operation environment; The production safety management subsystem integrates multiple key systems to build a comprehensive safety protection system; The intelligent operation and maintenance subsystem of the dock utilizes digital twin technology to achieve real-time mapping of equipment status and intelligent operation and maintenance management. The system has been developed and integrated in a liquid chemical terminal shore area, achieving succesful results.

To address the congestion issues on highways caused by significant traffic flow variation to improve the road transportation efficiency and reduce the operational costs of traffic management departments, this paper conducts vehicle profiling research based on vehicle travel behavior and trajectory characteristics. The key vehicles that frequently appear on the road network are identified and occasional driving data are ignored; A travel behavior portrait system is constructed. The system operates based on a three-level labeling system, covering six core dimensions including vehicle information, travel frequency, duration, speed, distance, and travel preferences. It produces comprehensive and multidimensional vehicle portraits for peak hours and off-peak hours; An improved Analytic Hierarchy Process (AHP) algorithm and entropy weight method are used to decide the weights of lower level labels and calculate the score of the top level labels; The vehicle portraits are categorized into 6 categories by the K-Means clustering algorithm based on their scores; The PrioritSpan algorithm is used to mine the typical path patterns of the six types of vehicles at different time periods, generating sets of travel patterns for different types of vehicles. The effectiveness of the system is proved by experiments.

This paper introduces the key points of soil environmental management of industrial enterprises in three stages, namely, the construction period, the operation period and the shutdown period, analyzes the possible risk problems, and puts forward the preventive measures that can be taken, such as soil hidden danger investigation and strengthening self-monitoring, and strictly controlling the discharge of hazardous substances. The soil pollution potential investigation and self-monitoring carried out by a ship building and repairing enterprise are explained as an example. The hidden dangers of soil pollution scope were identified and rectified timely through hazardous waste disposal measures so as to reduce the risk of soil pollution.

In order to anticipate springing or whipping driving hull into the limit of the fatigue strength and ultimate strength in mega container ships and, meantime, enrich the relevant full scale ship data, a hull structural safety monitoring system considering the hydro-elastic effect of springing and whipping is proposed to monitor the structural safety of the ship. The application of the system on the mega container ship is introduced. By reasonably setting sensing points in the main structure and other relevant parts of the ship, and fusing the parameters of stress, acceleration and waves, it can provide early warning in case the structural stress is tend to beyond the threshold value, and can assist in decision making to avoid the risks caused by human decision errors during navigation. Springing vibration analysis of the monitoring data from the full-scale ship is carried out to provide reference for researches on the hydro-elastic effect of mega ships.

Regions in Shanghai are classified based on their characteristics in terms of usage—production area, living quarter or ecological zone. The land use distribution pattern and its evolution characteristic in Shanghai in the period of 2010—2020 are studied from two aspects of spatial layout and quantitative structure using the land use transition matrix and land use dynamic degree. The research shows the facts as follows: The change of space distribution in Shanghai was significant in the period of 2010—2020. The area of ecological zone was relatively small, mainly covered by Shanghai Jiuduansha Wetland National Nature Reserve. However, it had expanded and further expansion is expected. The area of ecological production space and production ecological space reduced in the process of development, and the living and production space expanded. Among four types of spaces, the transition from production ecological space to living production space accounted for the largest proportion, the conversion from ecological production space to production ecological space follow. The ecological space had been transferred out to some extent in the 10 years, but the amount of transfer in is larger, so the overall area has increased. As for the periods from 2010 to 2015 and from 2015 to 2020, the ecological space area showed increasing trends in both periods. The area of ecological production space and production ecological space continued to decrease, while the area of living and production space continued to increase. The intensity of overall land use change was at a low level in both periods.

The traditional turbocharger state trend prediction method has the problems of low accuracy and high subjectivity dependence. This paper proposes a LSTM (Long Short-Term Memory)-based turbocharger state trend prediction method. The turbine speed is taken as the evaluation index of turbocharger operation status. The strategy of turbocharger status data screening is devised to reduce the impact of operating conditions and external factors on the characteristics of turbocharger state change. The state trend of the turbocharger is predicted by using the cycle nodes in the LSTM hidden layer and the time sequence similarity search. The performance of LSTM trend prediction model is analyzed and verified. The results show that the LSTM-based turbocharger state trend prediction method has better tracking capability and significantly higher accuracy than those can be expected from the least square method.