12 results about "Maritime safety" patented technology

The invention discloses an offshore small target radar detection method and system, and relates to the technical field of radio detection. The method comprises the following steps: acquiring radar echo data, a visible light image, an infrared image, an AIS signal and navigation attitude data in real time; performing sea clutter suppression and pre-filtering on the radar echo data, and correcting target plots in combination with a historical radar plot set to obtain a radar single-source target track set; performing target detection on the visible light image and the infrared image to obtain an optical target detection set and an infrared target detection set; the radar single-source target track set, the optical target detection set, the infrared target detection set, the AIS signals and the navigation attitude data are associated and fused, and finally deception feature recognition and consistency verification are performed to obtain an updated fused target track set; according to the method, man-made confrontation and cheating behaviors on the sea can be accurately identified, and reliable support is provided for safety monitoring on the sea, maritime affair supervision and emergency response.

The invention relates to the technical field of ship trajectory prediction, in particular to a cross-scene ship trajectory prediction fusion method based on a Transform algorithm. The method comprises the steps that a multi-source data input layer integrates basic navigation, port, sea transportation, inland river exclusive and environment and interaction data; the data preprocessing and link filtering layer cleans data noise and screens high-probability OD pairs; the multi-dimensional feature engineering layer constructs 15-dimensional feature vectors including sea transportation, inland river and interaction features; the core model is based on a Transform encoder-decoder architecture, integrates multi-head attention, time-space diagram attention and multi-mode bias, and adopts double-branch output and mixed loss function optimization; and the result post-processing and application output layer adopts a differentiation strategy for short-term and long-term prediction, and supports multi-ship collision avoidance path generation and derivative application at the same time. According to the invention, multi-scene coverage of sea transportation and inland waterway is realized, short-term and long-term prediction precision is well improved, and the method can be applied to tasks such as maritime affair safety scheduling and inland river intelligent shipping.

The invention discloses an NAVDAT, meteorological fax and NAVTEX collaborative maritime information broadcasting system, which comprises a shore-based intelligent fusion gateway, a cloud server, a cloud server, a cloud server and a cloud server, and is characterized in that the shore-based intelligent fusion gateway is used for analyzing received maritime safety information and mapping the received maritime safety information into a unified maritime event information model; based on the maritime event information model, broadcast signals conforming to NAVDAT, meteorological fax and NAVTEX system technical standards are generated in parallel, and a unified association identifier is embedded into the broadcast signals originating from the same information model; and the shipborne fusion terminal receives and analyzes the broadcast signal, automatically associates and recombines information from different systems according to the unified association identifier, and performs fusion display by taking an event as a unit. According to the invention, deep collaboration of the NAVDAT system, the weather fax system and the NAVTEX system in a protocol layer, a data layer and an application layer can be realized, and the integrity, the cognitive efficiency and the decision support capability of maritime affair safety information are remarkably improved.

The application relates to the technical field of detection and recognition, and discloses a mobile ship dynamic tracking and locking method and system based on target detection and recognition, which comprises the following steps: constructing a manifold embedding network to map ship multi-modal features to a Riemann manifold space; constructing a continuous evolution model of a neural differential equation to predict the continuous evolution trajectory of the features under time, perspective and scale changes; constructing a multi-scale bridging network to realize bidirectional conversion between features representing long and short distances; constructing a differential geometric pose encoder to represent the ship pose in an SO(3) Lie group space; realizing a feature memory and reconstruction mechanism to solve the problem of short-time disappearing target recovery; and integrating the above components to construct a unified tracking and locking system. The application solves the problems of discretization processing limitations, feature representation fragmentation and cross-condition consistency loss in the prior art, and remarkable technical effects are achieved in terms of cross-condition recognition capability, continuous feature evolution capability and short-time disappearing target recovery capability, and the application is suitable for scenes such as Yangtze River shipping monitoring and maritime safety supervision.

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.

The present application belongs to the technical field of flow field intelligent prediction based on numerical simulation, and particularly relates to a ship surface fluid pressure field intelligent prediction method, program, device and storage medium based on a combination of a reduced-order model and a neural network. The present application maps the unstructured grid data obtained by numerical simulation into structured grid data by using a surrogate model, ensuring the uniformity of the subsequent data processing format. On this basis, the main modal of the surface pressure field and the corresponding grid node normal vector is extracted by the reduced-order model. Then, the full-link neural network is used to realize the rapid prediction of the new ship type basis coefficient, so that for the new ship type, only the design parameters need to be input without inputting the ship grid, the rapid prediction of the ship surface load distribution, the external normal vector distribution and the pressure resistance can be realized, and reliable technical support can be provided for the ship structure strength design and safe navigation at sea.

An image-based floating marine object detection and distance measurement system for a small vessel of less than 10 tons is disclosed. The system comprises: an infrared stereo camera and an IR projector provided on a vessel; and an image display system which is connected to the infrared stereo camera and the IR projector through a control API, includes an image correction filter for correcting blurriness due to light reflection, shadow, and sea fog, detects the floating marine object (a discarded fishing net, a rope, a buoy, and marine litter) by a deep learning-based object detection model on the basis of pre-training data of the floating marine object, measures a distance between a camera of a vessel of less than 10 tons and the floating marine object, and displays an image to prevent vessel collision, damage to a propulsion shafting system, and an accident of entanglement between a vessel and floating marine debris through a forward hazard situation alert and a corresponding response. The system uses an infrared stereo camera and an IR projector provided on a small vessel of less than 10 tons in which hull motion, sea fog, and light reflection due to wind and wave heights exist on the sea, so as to detect a floating marine object posing a hazard during vessel navigation, by a vision recognition technology using a floating marine object recognition distance measurement algorithm and a deep learning-based floating marine object recognition model using an AI technology, prevents vessel collision, damage to a propulsion shafting system, and an accident of entanglement between a vessel and floating marine debris through a hazard situation alert and a corresponding response, monitors marine litter, and is used as a marine safe navigation assistance system for a small vessel that is vulnerable to the floating marine object.

The invention belongs to the technical field of maritime safety and intelligent navigation, and particularly relates to a maritime high-rise protection distance prediction analysis method based on a radar technology, which comprises the following steps of: establishing a space grid by acquiring a detection efficiency attenuation field around a maritime high-rise, and extracting a radar signal attenuation value, a space change gradient and a distance-height ratio of each grid unit; after normalization and nonlinear transformation, the radar interference risk is determined according to the range of three transformation results, the real-time ship density and the ship encounter threat value of each grid unit are calculated based on the real-time ship AIS data, and the navigation conflict risk is determined; weighting and fusing the two corresponding risks into a comprehensive risk, and dynamically delimiting the boundary of the grading protection area according to the comprehensive risk; and monitoring the ships entering the area, and generating and sending a navigation instruction according to the position risk levels of the ships. According to the method, through grid analysis, multi-feature fusion and a dynamic decision-making mechanism, the complex radar interference and navigation conflict mixed risk caused by the towering object is effectively dealt with.

The application discloses a water ship traffic flow complexity calculation method, device, equipment and medium, and the method comprises the steps of obtaining ship AIS data of a target water area; determining ship clustering clusters in the target water area according to the ship AIS data by using an improved DBSCAN algorithm; performing ship state feature clustering on the ship clustering clusters to obtain state feature probability distribution of the ship clustering clusters; and determining ship traffic flow complexity of the target water area based on information entropy according to the state feature probability distribution. The improved DBSCAN algorithm is innovatively obtained based on a ship field model, so that the algorithm is more suitable for navigation practice; the uncertainty of the water traffic flow state is described by information entropy according to the state feature probability, the method for measuring the complexity of the water ship traffic flow in the field of water traffic engineering is expanded, and the application has important theoretical and practical significance for promoting the informatization of maritime safety supervision.

The application discloses a maritime accident prediction method and device based on an interpretable integrated machine learning, relates to the technical field of maritime safety risk analysis, and comprises the following steps: acquiring accident investigation data and preprocessing, balancing the data through a ten-fold stratified oversampling method and training through cross-validation, determining a performance-optimal model by using a test set and performing interpretable analysis, and explaining the influence of features on accident prediction results. Through the construction of a closed-loop "data processing-model optimization-explanation output" process, the SMOTE oversampling, the ten-fold stratified cross-validation training and the heterogeneous base model integrated learning strategy are adopted, the processing capacity for the data imbalance problem of the accident category is improved, the data leakage problem of oversampling is avoided, and the possible bias of a single model is overcome. The interpretable analysis of the model prediction result can quantitatively show the contribution of each feature to the prediction from the global and local aspects, reveals the nonlinear relationship and interaction effect among the features, and provides a transparent explanation for the model decision.

The invention discloses an intelligent gateway for a heterogeneous maritime affair broadcast protocol, which comprises a data access layer, an intelligent processing layer and a protocol output layer, and is characterized in that the intelligent processing layer comprises a natural language processing module and is configured to perform semantic analysis on input maritime affair safety information and extract key elements including time, places and event types; the content association engine is configured to establish an association relationship among multi-source information for the same maritime event based on the key elements; the multi-protocol converter is configured to generate broadcast signals conforming to the NAVDAT protocol, the meteorological fax protocol and the NAVTEX protocol according to the incidence relation; and a guidance instruction generator configured to generate a standardized guidance instruction associated across the protocol. According to the intelligent gateway for the heterogeneous maritime affair broadcast protocol, intelligent understanding and automatic association of maritime affair information can be achieved, the problem that multi-system fusion depends on manual operation is solved, and unified, efficient and enhanced information service is provided.

This invention relates to the interdisciplinary field of maritime safety supervision in transportation and artificial intelligence, and discloses an intelligent auxiliary method and system for Port State Control (PSC) inspections of liquefied natural gas (LNG) vessels. The method includes structured processing and relational mapping of multi-source basic data, semantic storage of maritime regulatory texts in parent-child segmentation, and generation of a specialized knowledge base data with a five-dimensional relational mapping network. Responding to the identification information of the vessel to be inspected, the method calls upon the specialized knowledge base data and performs calculations using a risk analysis model combining a rule engine and machine learning classification to generate personalized inspection checklist data labeled with risk level, high-risk inspection areas, and inspection priorities. Through the synergy of the method and system, the method addresses pain points such as inefficient regulatory retrieval for LNG vessel PSC inspections, reliance on experience for defect determination, and cumbersome report generation, significantly improving inspection efficiency and accuracy, standardizing the inspection process, and enabling personalized training for professionals and continuous system optimization.