29 results about "Ship management" patented technology

The invention discloses an irregular ship scheduling method and system considering uncertain harbor time, and a medium, and belongs to the technical field of shipping management. The method comprises the following steps: collecting ship, cargo, port and interference event data; generating an initial scheduling scheme by adopting a greedy strategy; generating multiple groups of random interference scenes based on Monte Carlo simulation; a two-stage stochastic programming model is constructed, in the first stage, income maximization serves as a target, and an initial plan is generated through damage and repair operator iterative optimization by means of a self-adaptive large neighborhood search algorithm; in the second stage, aiming at each interference scene, a recovery network is constructed for the influenced goods, and an optimal recovery strategy and adjustment cost are solved by utilizing a shortest path algorithm; and finally, outputting a scheduling scheme including a ship path, a navigational speed and a multi-scene recovery strategy through iterative optimization. According to the method, the earnings and the operation efficiency of the shipping companies are improved, the adjustment cost caused by uncertainty is reduced to the greatest extent, the benefits of the shipping companies and cargo owners can be balanced in a complex scheduling problem, and the service quality is improved.

The invention discloses an internet-based ship port entering and leaving scheduling system and method, and relates to the technical field of ship management, and the system comprises an initial ship scheduling unit; and the optimal ship scheduling unit is used for constructing a multi-target reward tensor in combination with a multi-target scheduling constraint condition, processing the multi-target reward tensor by using a sequence based on Monte Carlo tree search, constructing a strategy to generate an initial scheduling set, obtaining a candidate scheduling set through a denoising path optimization mechanism dynamically guided by a target condition, and performing ship scheduling. Selecting an optimal ship scheduling sequence; the multi-ship collaborative network unit is used for constructing a multi-dimensional space-time cascade atlas to identify implicit resource conflicts among ships, and generating a collaborative track by using a multi-ship collaborative track generation algorithm to form a multi-ship collaborative network; and a dispatching instruction generating and pushing unit. According to the invention, balance and accuracy of ship scheduling among multiple targets are ensured, scheduling errors and delay are reduced, and scheduling efficiency and decision quality are improved.

The invention provides a ship berthing and leaving early warning method and system, and relates to the field of port ship management, and the method comprises the steps: obtaining ship position data, converting the ship position data into distribution density, extracting time sequence features, building a motion prediction relation, calculating a congestion risk coefficient, constructing water area risk distribution, and carrying out the route crossing calculation to obtain an avoidance demand value. And determining an avoidance sequence, calculating an avoidance route and a navigation time window, and generating time-sharing avoidance early warning information containing an early warning level and an avoidance instruction. The ship congestion risk can be predicted in advance, the navigation time is reasonably planned, and the navigation safety and efficiency of the port water area are effectively improved.

The application discloses a ship cargo matching system based on an intelligent optimization algorithm, which comprises an order management module, a ship management module, a port management module, a route management module, a freight rate management module and a ship draft management module; the input of the order management module comprises basic information of three types of orders, i.e., a marine oil cargo plate, a large customer cargo plate and sporadic cargo sources; the input of the ship management module comprises static information and dynamic information of a ship; the input of the port management module comprises three-level wharf information; the input of the route management module comprises a sailing distance matrix and a sailing time matrix; the input of the freight rate management module comprises freight rate data between different loading and unloading ports; and the input of the ship draft management module comprises a maximum cargo carrying capacity data table of different ship types under different draft depths. The application has strong dynamic adaptability.

The invention discloses an intelligent production management system for a dry bulk cargo wharf based on digital twinning. The system comprises ten functional modules, namely a ship management module, a business document module, a production scheduling module, a warehouse yard tallying module, a gate wagon balance module, a business charging module, a query statistics module, a system management module, an intelligent application module and a digital twinning application module. Real-time data of wharf equipment, goods, vehicles and the like are collected through the Internet of Things technology, and intelligent production plan arrangement, equipment collaborative scheduling and operation process optimization are achieved in combination with big data analysis and an artificial intelligence algorithm; a digital twin technology is introduced to construct a wharf digital mirror image, the whole process of cargo loading and unloading, storage and transfer is simulated, visual monitoring, intelligent simulation prediction and emergency dynamic response in the production process are achieved, the problems of information isolated island, low resource scheduling efficiency and high operation cost of an existing dry bulk cargo wharf are solved, and the wharf operation efficiency and the intelligent management level are remarkably improved.

1. The name of the design product: order information and delivery management graphical user interface for electronic equipment. 2. The use of the design product: for an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: interface change state diagram 1. 5. Other faces of the electronic device are conventional designs, and other views are omitted. 6. The use of the graphical user interface: this interface is used to display order information and delivery, and the interface shown in the main view is the initial interface. In the interface shown in the main view, click "to be shipped" to enter interface change state diagram 1; in interface change state diagram 1, click "shipping processing" to enter interface change state diagram 2; in interface change state diagram 2, click "shipping processing" to enter interface change state diagram 3; in interface change state diagram 3, click "please select" on the right side of any "estimated delivery date" to enter interface change state diagram 4; in interface change state diagram 4, click "cancel", then click "please select" on the right side of any "express name" to enter interface change state diagram 5; in interface change state diagram 5, click "cancel" to enter interface change state diagram 6, and in interface change state diagram 6, click "submit" to enter interface change state diagram 7.

The invention relates to the field of ship management and control, in particular to a ship point cloud multi-scale self-supervision identification method and equipment. According to the pre-trained ship depth detection model, feature extraction is carried out through the image multi-scale feature extraction module and the point cloud multi-scale feature extraction module, and the image multi-scale feature extraction module is pre-trained based on an existing database; and the comparison learning module carries out multi-scale comparison identification on the point cloud multi-scale features of the ship target sample and the pre-training image multi-scale features so as to realize accurate self-supervision detection of the to-be-identified ship. Therefore, the defect that a ship point cloud detection method based on deep learning tends to be over-fitted easily due to the fact that a current ship target point cloud database is small in data volume and a model is difficult to fully learn feature representation of the target from a small amount of training data is effectively overcome, and the target detection accuracy is improved.

The application discloses a ship-shore information synchronization method in a Web-based ship management platform, which comprises the following steps: starting synchronization according to a timing synchronization time and a synchronization data range, initiating a synchronization request by a ship-side server, acquiring an upgrade information package by the ship-side server and installing the upgrade information package, judging whether the ship-side server identification is consistent with the MAC address of the ship-side server, stopping synchronization and performing synchronization in a manual mode if the ship-side server identification is not consistent with the MAC address of the ship-side server, otherwise, acquiring a shore base database file package by the ship-side server, judging whether the data package in the ship-side server is consistent with the shore base database file package, updating the data package in the ship-side server according to the shore base database file package if the data package in the ship-side server is not consistent with the shore base database file package, acquiring the data package of the ship-side server in the synchronization data range and regarding the data package as a to-be-updated data package, transmitting the to-be-updated data package to a shore base server, and updating the shore base server according to the to-be-updated data package. The method improves the data consistency and the data reliability between the shore base server and the multiple ship-side servers.

The invention discloses a liner shipping period recovery method and system considering container recovery and carbon emission reduction, and a medium, and belongs to the field of shipping management. Firstly, various kinds of basic data of liner operation are obtained; then, an optimization model with the purpose of minimizing the total cost is constructed, and the model comprehensively considers the fuel cost, capital operation cost, carbon emission tax cost, container delay cost and cost for canceling the affiliation of a port; an improved genetic algorithm fusing reinforcement learning and a simulated annealing mechanism is designed to solve the model, the algorithm can dynamically and adaptively adjust the crossover rate and the mutation rate, and a disturbance strategy is introduced to avoid falling into local optimum; and finally, outputting a liner shipping period recovery scheme with the lowest cost. According to the method, economic benefits and environmental benefits of the liner company are fully considered, the interference recovery cost is reduced, carbon emission is reduced, and the competitiveness of the liner company is enhanced.

A method for assisting the management of a vessel comprising at least one tank configured to contain liquefied gas and a vapor phase treatment system capable of sending boil-off gas exiting the tank to a propulsion engine of the vessel or to a gas combustion unit on board the vessel and capable of extracting a portion of the liquid phase contained in the tank and of evaporating this portion in order to send it to the propulsion engine. The method comprises: generating at least one tank management scenario defining an evolution of the pressure of the gas phase contained in the tank along a path of the vessel; computing a cost function that at least depends on a total amount of boil-off gas generated in the tank along the path; and displaying to a user the tank management scenario as a function of the computed cost function.

The utility model discloses a bulk cargo ship sewage discharge monitoring device which comprises a pipe body, a frame body, a stirring assembly, an adjusting assembly and a monitoring frame, the frame body is arranged at the top end of one side of the pipe body, the stirring assembly is arranged in the pipe body, the adjusting assembly is arranged on the side of the stirring assembly, and the monitoring frame is arranged on the other side of the pipe body. According to the bulk carrier sewage discharge monitoring device, the sensor monitors sewage discharge, the sensor is a flow sensor and can monitor sewage discharge flow in real time, the monitoring frame is arranged on the other side of the pipe body, and the water quality sensor is arranged at the bottom end of the monitoring frame and can monitor water quality parameters of sewage in real time; data are transmitted to a monitoring center of a ship management department or a supervision mechanism through a terminal, remote real-time monitoring is achieved, a clamping plate is arranged in a pipe body and used for blocking large particle impurities in a water body, and the clamping plate is matched with a blade-shaped stirring blade, so that the impurities are smashed, and the purposes of preventing blockage and reducing detection inaccuracy caused by interference are achieved.

A marine vessel management system to reduce the burden on a marine vessel rental business operator includes a marine vessel, an information processor, and a portable terminal device operable by a marine vessel user. When the marine vessel is leaving a port and / or when the marine vessel is returning to the port, the portable terminal device receives information about a state of the marine vessel via the information processor, and displays the received information about the state of the marine vessel.

The application discloses an Internet-based ship in-out port scheduling system and method, and relates to the technical field of ship management. The system comprises: an initial ship scheduling unit; an optimal ship scheduling unit, which is used for combining multi-target scheduling constraints, constructing a multi-target reward tensor, processing the multi-target reward tensor by using a sequence based on Monte Carlo tree search, constructing a strategy to generate an initial scheduling set, and obtaining a candidate scheduling set through a denoising path optimization mechanism guided by a target condition to select an optimal ship scheduling sequence; a multi-ship coordination network unit, which is used for constructing a multi-dimensional space-time cascade graph to identify implicit resource conflicts between ships, and generating a coordinated track by using a multi-ship coordination track generation algorithm to form a multi-ship coordination network; and a scheduling instruction generation and pushing unit. The application ensures the balance and precision of ship scheduling among multiple targets, reduces scheduling errors and delays, and improves scheduling efficiency and decision quality.

This invention discloses a BeiDou terminal monitoring method and system. The ingenious solution monitors the power supply of a BeiDou terminal equipped with a solar panel, compares the monitoring data with a preset battery charging characteristic curve, and outputs the judgment result. Using the preset battery charging characteristic curve as a reference, the power supply monitoring of the BeiDou terminal is not only fast and effective, but also allows for a good preliminary judgment on whether there are any anomalies in the battery or solar panel. Furthermore, when the power supply monitoring result indicates an abnormality in the BeiDou terminal's charging, this solution further intervenes through a ship management platform to determine whether the solar panel is obstructed, causing the anomaly. This solution is flexible and reliable, and plays a positive role in achieving maritime situational awareness.

The invention discloses an AI-fused cloud platform ship intelligent management method and system, belongs to the technical field of ship management, and aims to solve the problems of data islanding, weak real-time decision-making ability and lack of adaptive optimization of a traditional system. The system adopts a data acquisition layer, an edge intelligent layer, a cloud decision-making layer and an application interaction layer architecture. The data acquisition layer acquires ship navigation, equipment, energy and environment data through a sensor; the edge intelligent layer preprocesses the data, diagnoses faults through threshold detection and lightweight AI, and transmits the data in a grading manner; the cloud decision-making layer generates an optimal plan by means of deep learning, reinforcement learning and digital twinning; the interaction layer is applied to visualize information, and operation information and actual effect data are fed back to optimize the AI model. According to the invention, data intercommunication of the whole ship is realized, decision real-time performance and comprehensive operation efficiency are improved, and communication and operation and maintenance costs are reduced.

The present application relates to the field of ship management and control, and particularly relates to a ship point cloud multi-scale self-supervised identification method and device. A pre-trained ship depth detection model extracts features through an image multi-scale feature extraction module and a point cloud multi-scale feature extraction module, and the image multi-scale feature extraction module is pre-trained based on an existing database. A contrast learning module performs multi-scale contrast identification on the point cloud multi-scale features of a ship target sample and the pre-trained image multi-scale features, thereby achieving accurate self-supervised detection of a ship to be identified. Thus, the present application effectively overcomes the problem that the amount of data in a current ship target point cloud database is small, and a model is difficult to fully learn the feature representation of a target from a small amount of training data, which easily leads to the defect that a ship point cloud detection method based on deep learning tends to overfitting, thereby improving the target detection accuracy.

The application provides a ship automatic lock control method and system, and mainly relates to the technical field of ship management; the method mainly comprises the following steps: collecting a picture at a ship lock, and recognizing ship information from the picture; judging whether the ship is a to-be-locked ship according to imported lock information and the ship information, if yes, performing lock control through gear detection and boundary line detection, and if no, guiding the ship to return to an anchorage through a broadcast device voice. The application can collect the picture at the ship lock and recognize the ship information, determine the to-be-locked ship through the ship information and the lock information, and automatically control the ship to pass through the lock through the gear detection and the boundary line detection, so as to reduce the pressure of the staff and the error rate.

To reduce the burden of ordering and receiving asphalt mixtures, and to streamline the ordering and receiving process by reducing manpower. [Solution] The delivery device 300 includes a receiving unit 371 that receives order data for asphalt mixture and request data for product test sheets transmitted from an information processing terminal 100; an instruction receiving unit 372 that receives instructions for quality control data, manufacturing data and dispatch data related to the order data based on the order data and request data received by the receiving unit 371; a database 301 that links the quality control data, manufacturing data and dispatch data received by the instruction receiving unit 372 together and stores them as shipment management information; and an instruction transmitting unit 374 that transmits manufacturing instructions and dispatch instructions for asphalt mixture based on the instructions for quality control data, manufacturing data and dispatch data received by the instruction receiving unit 372.

The utility model provides a container type mobile power supply control and monitoring system for a ship. The container type mobile power supply control and monitoring system comprises a PLC-based central control unit; the communication management unit is connected with the central control unit through a switch, and the communication management unit is respectively connected with the ship management unit, the ship network, the liquid cooling unit, the battery management unit and the DC / AC inverter so as to receive and forward the data of the ship management unit, the ship network, the liquid cooling unit, the battery management unit and the DC / AC inverter; the fuse state detection unit, the insulation state detection unit, the battery switch state detection unit, the DC / AC emergency stop unit, the alarm unit and the relay are connected with the central control unit; and the human-computer interface is connected with the switch to realize human-computer interaction. According to the utility model, through centralized control of the PLC, the system realizes efficient automatic management, and human intervention is reduced.

The invention relates to the technical field of maritime affair safety monitoring, in particular to an intelligent ship identification management method and system, which ensures the comprehensiveness of data sources and provides a rich basis for subsequent analysis by collecting original data from a plurality of heterogeneous sensors. Original data are fused to generate a fusion feature vector, effective integration of multi-source data is achieved, ship feature representation is more comprehensive and accurate, and a reliable basis is provided for judgment of unfamiliar ships; whether the target ship is a strange ship or not is judged based on the fusion feature vector, manual experience judgment is replaced with data-driven judgment logic, and subjectivity and negligence are avoided; risk assessment is carried out on the unfamiliar ship, and corresponding early warning is triggered, so that seamless connection of identification and early warning is realized, and rapid risk response to the unfamiliar ship is ensured; and according to the risk level and the real-time state information, a disposal suggestion is generated and linkage response is started, so that a complete process from identification to disposal is formed, and automation and high efficiency of ship management are improved.

This utility model discloses an intelligent ship management and monitoring system, comprising an intelligent shore-based management and monitoring system and several sub-intelligent ship management and monitoring systems. The intelligent shore-based management and monitoring system and the sub-intelligent ship management and monitoring systems establish a real-time communication link through a data interaction system. The sub-intelligent ship management and monitoring systems serve as the core execution layer, while the shore-based management and monitoring systems serve as the remote monitoring layer. Each sub-intelligent ship management and monitoring system includes a ship digitization system, a communication and navigation system, a ship management system, a video management and monitoring system, and a data interaction system, achieving hardware interconnection via industrial Ethernet and CAN bus. This utility model, through hardware system integration, high-speed data transmission, and intelligent early warning mechanisms, achieves comprehensive monitoring and ship-shore collaborative management of ship equipment, navigation status, and ship management.

This application provides a method for designing ship piping system models. A control system diagram template is selected based on the system type. Each component in the control system diagram template is matched with a corresponding standard model block, and each standard model block is matched with a configuration table and parameter constraints. The parameter constraints of the ship piping system are obtained, and the parameter constraints of the standard model blocks are adjusted according to these constraints. The configuration table is reset, and each standard model block is adjusted accordingly. The geometric information of the adjusted model blocks is then obtained. A three-dimensional model of the ship piping system is obtained based on the geometric information of multiple model blocks. This application can be used in the design of piping systems for most civil ships, reducing the number of times designers need to modify layout schemes, improving design efficiency and quality, and avoiding design errors caused by lack of experience. The combination of parameter constraints and templates completes the standardized design of three-dimensional models for ship management, facilitating widespread use.

The invention provides a ship management system and method based on the Internet of Things, relates to the technical field of ship management, and solves the technical problems of backward environmental protection supervision means and low ship management efficiency in an existing ship management system. The system comprises a data acquisition module used for acquiring ship operation data through an Internet of Things sensor and performing cleaning and standardization processing on the data to obtain preprocessed data; the environmental protection excitation module is used for calculating a green index representing the environmental protection performance of the ship according to the preprocessed data, and implementing differential excitation and intelligent scheduling on the ship based on the green index; and the safety supervision module is used for carrying out safety monitoring and risk early warning on the ship operation environment through a visual identification and data fusion technology. The method and the device are used in the whole-process management process of ship operation.

Disclosed in the present invention is an AI-model-based personalized user interface design system, comprising: a capture module, which captures key nodes, key data blocks and state information of service function modules; an identification module, which marks a role, job title and involved approval nodes of each user in a ship management system, and matches marked information with data of the service function modules; a maritime regulation module, which performs reminding and determination on post requirements of the user; and an AI analysis module, which checks the captured information with content of the post requirements in the maritime regulation module, sends a supplementary reminder message to the user after receiving prompt information, performs analysis in view of historical operation records of the user and historical operation records of users at the same post on a platform, and sorts the service function modules according to the post requirements and work habits of the user, so as to obtain a personalized interface. The personalized interface which is output by the system is a personalized office interface that is output via an AI model and is completely based on data such as post requirements and work habits of users.

The application belongs to the technical field of ship management monitoring and management, and particularly relates to a non-invasive calibration system for ship pipeline pressure instrument based on ultrasonic measurement. The system comprises a control module, a transmission driving module, an ultrasonic wave transmission module, an ultrasonic wave timing module and an ultrasonic wave receiving module. The control module comprises an STM32 main control chip. The transmission driving module is composed of an NPN triode, a current limiting resistor and a coupling capacitor. The ultrasonic wave transmission module comprises a timer chip and a timer power supply circuit. The ultrasonic wave timing module comprises a TDC time-to-digital converter, a high-precision digital timing chip and a peripheral circuit. The ultrasonic wave receiving module comprises an ultrasonic wave transducer, an impedance matching and signal coupling circuit, a multi-stage amplification circuit, a band-pass filter circuit and a peak detection and comparison circuit. The application is mainly used for completing non-invasive pipeline liquid pressure instrument field calibration and avoiding the application limitation of traditional pressure instrument calibration and detection scheme in the corresponding scene.

The invention provides an intelligent ship comprehensive monitoring and safeguard system and method, and the method comprises a data collection module which is used for collecting the operation data of all kinds of equipment on a ship; the equipment control module is used for setting control interfaces on the cab workstation and the cabin control room workstation to carry out centralized remote control on various types of equipment; the data storage module is used for storing operation data and maintenance data of various types of equipment; the equipment fault diagnosis module is used for acquiring equipment operation data, evaluating the health state of the equipment through a correlation analysis method and a deep learning model, and predicting possible faults of the equipment; the equipment maintenance module is used for generating an equipment maintenance plan and regularly generating a monthly maintenance plan and an annual maintenance plan of the whole ship; and the mobile terminal management module is used for distributing a maintenance plan and responding to a mobile terminal user request according to the mobile terminal user permission. Through the scheme, the ship management control efficiency can be improved, and the maintenance cost can be reduced.

This invention discloses a method for detecting anomalies in ship navigation behavior based on meteorological perception, belonging to the field of ship management technology. The main steps include: First, spatiotemporal alignment and standardization of ship AIS data and marine meteorological data are performed. Then, static attributes, dynamic trajectories, and meteorological environmental information are feature-encoded, and the dynamic and meteorological features are dynamically fused through a learnable meteorological gating unit. The fused feature sequence is input into a Transformer model to capture long-term temporal dependencies and predict the ship's state at the next moment. Finally, by calculating the prediction residuals and comparing them with an adaptive threshold dynamically generated based on historical error statistical distribution, accurate determination of ship behavior anomalies is achieved. This invention realizes the modeling of the dynamic correlation between ship behavior and the marine meteorological environment, thereby effectively distinguishing between reasonable risk avoidance behavior caused by severe weather and genuine abnormal behavior, significantly reducing the false alarm rate.

The invention relates to a ship operation map intelligent interaction guiding method, device and equipment and a medium, and is applied to the technical field of ship operation, and the method comprises the steps: obtaining user information, user demands and user historical records; analyzing the user demand based on the user information and the user historical record, and determining a question type and a keyword; determining a retrieval result based on the question type and the keyword; and carrying out personalized display on the retrieval result on a map. The method has the effect of improving the user information acquisition efficiency.

The invention relates to the technical field of shipping management information, and discloses an intelligent shipping management system which comprises an event perception and conversion module, an event bus and persistence module, a dynamic graph construction and analysis module, an intelligent process self-adaption engine module, a state projection and query service module and a unified application and interaction module. The dynamic graph construction and analysis module constructs and maintains an event causal association graph in real time. And the intelligent process self-adaptive engine module calculates a quantified cumulative influence index, and when the cumulative influence index exceeds a preset action threshold value, the engine generates a command to actively intervene the business process and sends the command to the event sensing and conversion module to form a control closed loop. The problems that a traditional system is lagged in response and lacks risk prediction capacity are solved, and prospective analysis and dynamic self-adaptive control over shipping business risks are achieved.