2300results about "Marine craft traffic control" patented technology

Disclosed in the present invention is a dynamic spatial-temporal graph attention method for ship trajectory prediction, comprising the following steps: spatial graph learning: using a graph neural network to aggregate neighbor embeddings of a target entity to learn a local structure representation thereof, then calculating the effect of neighbor entities and a collaborative relationship on the target entity by means of a knowledge-aware graph attention mechanism, and finally fusing semantics and attributes from different neighbors by means of an information aggregation operation to refine the embedding of the target entity; temporal graph fusion: using a temporal graph attention layer to learn a global temporal dependency relationship of ship trajectory data, and providing interpretability for a model, then using a GRU-based sequence encoder to replace positional encoding in a conventional Transformer architecture, and capturing more local temporal dependency relationships while providing a more reasonable inductive bias for an input sequence; and feature decoding: decoding a trajectory vector having spatial-temporal attributes, generating future relative position coordinates of a ship, and achieving high-precision ship trajectory prediction.

The invention relates to the technical field of channel safety management, and more specifically relates to a channel multi-source data fusion digital twinborn risk early warning system comprising a multi-source data acquisition module used for acquiring channel specific data types, comprising AIS data, radar data, video monitoring data, hydro meteorological data, channel surveying and mapping data, ship report data, shore-based sensor data and unmanned aerial vehicle patrol data, and comprehensive data support is provided for channel management through space-time alignment, dynamic weight adjustment and conflict resolution; a high-fidelity digital twin is constructed, fluid dynamics and a ship behavior model are fused, and a real channel scene is restored; intelligent risk early warning and traceability are realized by using deep learning, and decision scientificity is improved; efficient virtual-real interaction is realized through multi-terminal pushing and sand table deduction, and a management closed loop is formed; a cloud-edge-end framework and the like are adopted to optimize system performance, and real-time performance and safety in a large-scale scene are guaranteed.

The invention provides a multi-network integration ship-shore communication system and an intelligent ship remote driving control system based on a ship Internet of Things, and the hardware part of the multi-network integration ship-shore communication system comprises an integrated circuit board, a power module, an edge calculation module, a communication module, and an extensible communication interface, wherein the power module, the edge calculation module, the communication module, and the extensible communication interface are installed on the integrated circuit board. The communication module comprises a 4G / 5G communication module and / or a satellite module and / or a microwave module and / or a VHF module and / or an AIS module and / or a Beidou satellite module, the edge calculation module is connected with the power supply module, the communication module and the extensible communication interface, and the software part comprises a multi-network integration management software module. And the multi-network integration management software module is installed in the edge computing module. The method has the advantages that the multi-link aggregation system is used, the network transmission rate and stability are improved in the application scene of ship inland river shipping, complementation with a satellite communication system can be formed, and the service stability is further enhanced.

The invention discloses a ship collision avoidance optimization method under the condition of uncertain obstacle ship motion information, and the method comprises the steps: constructing a membership function for measuring the ship collision risk degree, so as to construct a ship collision risk degree mathematical model, and obtaining the current ship collision risk degree; based on international maritime collision avoidance rules, according to a preset ship collision risk degree range, ship collision avoidance danger stages and ship encounter situations and way-giving relations corresponding to the ship collision avoidance danger stages are divided and obtained, and support is provided for formulating an autonomous collision avoidance strategy for the intelligent ship under different collision avoidance situations by combining a DWA collision avoidance algorithm and a VO collision avoidance algorithm; aiming at the condition that the ship navigation state has discontinuous output, a CNN-GRU ship navigation state prediction network is provided, the ship navigation state and the uncertainty of the state are predicted based on AIS historical data, and the uncertain distribution of the position of the ship navigation state is obtained, so that under the condition that a moving obstacle cannot be continuously perceived, the ship navigation state can be accurately predicted. It can still be ensured that the intelligent decision-making system provides effective collision avoidance decision support for the intelligent ship, the uncertainty of the ship position is fully considered, and the ship field is optimized.

The invention discloses an intelligent navigation system and method for a ship in the technical field of ship navigation, and the system comprises a sensing module which is used for collecting the surrounding environment information and the state information of the ship in real time; the data processing and fusing module is used for preprocessing, calibrating and fusing the surrounding environment information and the self state information of the sensing module; the route optimization module is used for formulating a navigation plan and a collision avoidance strategy; the navigation control module is used for autonomously controlling the ship according to the output of the ship route optimization module; and the communication monitoring module monitors the navigation state of the ship and the change of the surrounding environment through real-time communication between the ship and a shore-based control center, other ships and a maritime affair management department. According to the method, data of different sensing units are associated, and the associated data are fused, so that mutual verification and complementation can be realized, and the accuracy and reliability of target identification are improved.

The invention relates to a ship safety monitoring method, system and device based on data fusion, and relates to the technical field of ship navigation safety, and the ship safety monitoring method comprises the steps: collecting multi-source heterogeneous data; preprocessing the multi-source heterogeneous data; carrying out fusion processing on the multi-source heterogeneous data based on an information entropy theory; a ship safety domain model is constructed, trajectory prediction is executed, a collision risk index is calculated, and the ship collision risk is evaluated; and performing anomaly detection on the ship equipment operation data based on manifold learning and a thermodynamic model. According to the invention, a multi-source heterogeneous data fusion technology is adopted, multi-dimensional data sources are integrated, and omnibearing ship safety situation awareness is constructed. The early warning time is prolonged to 8-10 minutes, which is far better than the early warning time of 3-5 minutes in the prior art. The system thoroughly solves the problem that subsystems in a traditional ship monitoring system operate independently and do not communicate with one another, the crew information acquisition cost is reduced, and the number of times of interactive operation is reduced by 42%.

The invention provides a ship remote real-time early warning system based on cloud computing, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting ship operation data, environment state data and external interaction data; the data preprocessing module is used for generating standard processing data; the data grouping module is used for dividing a priority data group and a common data group; the data transmission module is used for transmitting the data to the cloud analysis module; the cloud analysis module is used for carrying out multi-source data aggregation processing on the priority data set and the common data set, carrying out real-time evaluation on the ship operation state, identifying abnormal behaviors and predicting emergencies, and generating corresponding final early warning instructions including an avoidance operation instruction and an air route adjustment suggestion; the early warning feedback module is used for sending the early warning instruction to the ship terminal; the local response module is used for executing instant triggering operation on the instruction classification data; according to the invention, autonomy and accuracy of remote real-time early warning of the ship are improved.

The invention discloses a ship navigation risk assessment and decision support method and system. The method comprises the steps that a ship navigation area is expressed as a to-be-assessed area, navigation information and historical accident data of the to-be-assessed area are acquired, key risk factors influencing ship navigation safety are determined based on the navigation information, and the key risk factors are classified; building an accident scene based on the classified key risk factors and historical accident data, evaluating the accident scene, calculating a risk value of the evaluated accident scene, and dividing the accident scene into different grades according to the risk value; and constructing a risk control operation flow chart, and determining a risk value of the to-be-evaluated area based on the risk control operation flow chart and the accident scene. The method is suitable for risk assessment of different scales and complexities, the risk is comprehensively and accurately assessed, meanwhile, adaptability and flexibility to variable environments are kept, and the navigation safety performance and effect are improved.

The invention discloses a maritime traffic flow analysis method based on big data, and relates to the technical field of information, and the method comprises the steps: collecting data related to maritime traffic, and carrying out the preprocessing; performing adaptive dynamic division on the sea area by using the processed data to generate a real-time updated water area boundary; based on the self-adaptive division result, analyzing a ship behavior mode to obtain an anomaly detection result; analyzing interaction among the ship groups according to an abnormal detection result, and predicting potential congestion and conflicts; based on the group behavior prediction result, ship path planning is optimized, and an optimal route suggestion is generated; by constructing a space-time diagram convolutional network model and comprehensively considering relative positions, speeds and rendezvous frequencies of ships, potential navigation conflicts can be accurately predicted; according to the method, the optimal route suggestion based on multi-objective optimization is provided for the ship by optimizing path planning, so that the sailing efficiency and the economical efficiency are improved to the maximum extent on the premise of ensuring the safety.

The invention discloses a ship intelligent collision avoidance decision-making method and system, and relates to the technical field of ship intelligent navigation, and the method comprises the steps: collecting radar data and AIS information of a ship; building a ship dynamic prediction model according to the radar data and the AIS information, predicting the future navigation trajectory of the target ship through a Kalman filtering algorithm, and calculating the meeting parameters of the two ships in combination with the navigation parameters of the ship; based on the meeting parameters, establishing a risk assessment matrix, and performing risk level judgment on the current navigation situation by using a fuzzy neural network algorithm; based on the risk level judgment result, collision avoidance maneuvering parameters are calculated through an adaptive particle swarm optimization algorithm, and a collision avoidance action scheme is output. According to the invention, the whole process automation from information perception, situation prediction, risk assessment to decision execution is realized, the workload of navigation personnel is greatly reduced, and the navigation safety level is improved.

The invention relates to the field of channel management, in particular to an intelligent channel management method and system based on multi-source data fusion. The invention discloses an intelligent navigation channel management system based on multi-source data fusion. The system comprises a combined ship identification module, a positioning frequency updating module, a collision risk assessment module and an intervention management module. According to the invention, the system can sense multi-dimensional environmental parameters such as illumination intensity, visibility, AIS signal quality, radar echo signal-to-noise ratio and the like in real time by constructing a combined ship identification model containing AIS, radar, vision and infrared, and dynamically adjust the fusion weight of each sensor to compensate for the deficiency of a single sensor; according to the method, the robustness of ship dynamic monitoring is improved, high-precision tracking can be kept in severe weather, the data redundancy contradiction of a traditional multi-sensor system is avoided, and therefore continuous and stable target state input is provided for follow-up risk analysis.

The invention discloses an autonomous collision avoidance method and system for an unmanned ship in a mixed traffic mode, and the method comprises the following steps: obtaining the current position coordinate information, course information and surrounding environment information of the unmanned ship in real time through a ship positioning system and a ship recognition system on the unmanned ship; combining the target point, constructing a path plan and calculating the energy consumption of a navigation path, selecting a first navigation path, identifying and classifying static obstacles and dynamic obstacles, monitoring the static obstacles in real time, calculating and evaluating a detour difference, adjusting the path according to a collision avoidance rule, and generating a second navigation path; predicting the movement direction of the surrounding dynamic obstacles, and judging whether a collision risk exists or not; predicting a future position and adjusting the course to generate a third navigation path; and respectively evaluating the electric quantity threshold, the energy consumption threshold, the battery electric quantity and the energy consumption value, and adjusting an energy use strategy in real time. According to the invention, the accuracy of the navigation path is improved, and the safety of the unmanned ship in a complex environment is improved.

The invention discloses an intelligent ship situation awareness prediction method and system, and relates to the technical field of intelligent ship situation awareness and trajectory prediction, and the method comprises the steps: carrying out the time-space synchronization of collected operation environment data of a ship through a timestamp alignment method, and generating a ship situation data set; based on the ship situation data set, the course angle change rate and the Euclidean distance of the obstacle relative to the ship are extracted, and weighted fusion is carried out on the dynamic collision probability and the approaching time of the obstacle to obtain an obstacle threat degree index; inputting the obstacle threat degree index into a ship trajectory prediction model, and outputting the minimum meeting distance between the ship and each obstacle; and carrying out risk grade division on the minimum encounter distance according to an international maritime collision avoidance rule, outputting a graded early warning signal, and formulating a navigation strategy to execute obstacle avoidance and route adjustment. According to the method, the technical problems of risk perception lag, single and rough avoidance action and insufficient trajectory prediction accuracy in the prior art are effectively solved.

The invention relates to the technical field of combined fleet navigation, and discloses a combined fleet intelligent navigation data processing system, which comprises a data acquisition unit used for acquiring navigation data and ship lock data of each ship of a combined fleet in real time, fusing the preprocessed navigation data and ship lock data to obtain a comprehensive data set, through dynamic data fusion and self-adaptive grid division, the spatial-temporal characteristics of a flow velocity abrupt change area during opening and closing of a ship lock are captured in real time, the limitation that a traditional algorithm depends on a static environment is broken through, a dynamic power distribution strategy is generated based on flow field characteristics and navigation data, and precise power matching is achieved in combination with a propeller efficiency priority and a self-adaptive weight algorithm. The response time is shortened, the cooperation efficiency is improved, course deviation is avoided, a multi-stage early warning and time difference judgment mechanism is constructed, the power margin is evaluated in real time, the weight, the navigational speed and the formation are adjusted according to the risk level, it is guaranteed that power distribution adjustment of the whole fleet does not lag behind water flow changes, and normal course of the fleet is guaranteed.

The invention provides an intelligent pleasure boat navigation system based on interactive pointing control and a method thereof. The system comprises a monitoring display module; a single-point navigation instruction receiving module; a route planning module; a multi-point navigation indication processing module; the real-time coordination and broadcast module is a user interaction interface module; the invention further discloses an intelligent pleasure boat navigation method. By adopting an interactive pointing control technology, a dynamic neighborhood potential field path planning algorithm and a multi-target optimization algorithm, the problems of the traditional navigation system in the aspects of safety, efficiency, user experience and multi-ship management are solved, the safety, efficiency and user experience of navigation are remarkably enhanced, the capability of multi-ship collaborative operation is realized, and the system has a wide application prospect. The method is an important progress of intelligent water traffic management in the future.

The invention discloses a land-sea-air-space holographic sensing and collaborative decision-making system based on multi-mode edge intelligence. The system comprises a sensing layer which is used for carrying out three-dimensional monitoring on a ship driving process, the sea, the sky, a shore base and a space formed by the ship driving process, the sea, the sky, the shore base and the sea, the sky and the shore base; the network layer is used for constructing a global communication basis, preprocessing monitoring data, judging whether the ship has an abnormal condition or not, and realizing data transmission of the ocean, the sky, a shore base and a space formed by the ocean, the sky and the shore base; and the application layer is used for judging whether an abnormal condition exists in the ship driving process or not based on the data of the ocean, the sky, the shore base and the space formed by the ocean, the sky and the shore base transmitted by the network layer, performing collision early warning decision based on the abnormal condition, and performing dynamic channel capacity prediction. And navigation guidance and route tracking in extreme weather are carried out based on environmental risk early warning, underwater obstacle collision is avoided, and early warning information and decision information are fed back to a sensing layer.

The invention discloses an intelligent channel management method and system based on digital twinning. The method comprises the following steps: acquiring GIS data and historical monitoring data of a target channel, and constructing an intelligent channel digital twin model; importing channel monitoring data in a preset time period, performing dynamic simulation, and determining and updating a data updating strategy of the model in real time; monitoring the ship in the target channel in real time through the model, and identifying the behavior state of the ship; in combination with ship behaviors and meteorological and hydrological data, the navigation risk is assessed, and a risk assessment result is generated; and optimizing a data updating strategy according to an evaluation result. According to the method, the intelligence of channel management and the safety of ship navigation are improved.

The invention provides a ship trajectory prediction method based on a graph attention mechanism and an electronic chart, and relates to the technical field of intelligent ships. By fusing dynamic AIS trajectory data and static navigation channel geographic information, high-precision modeling and prediction of the future motion trajectory of the ship are realized. According to the method, a traditional time sequence-based trajectory modeling mode is expanded into multi-modal joint modeling, channel structure vector representation is introduced for the first time, structural information of a channel environment is extracted on the basis of a graph neural network, and deep fusion is performed on the structural information and historical trajectory data of a ship; and constructing a trajectory expression mode containing time, space and environment triplex semantics at the same time. In the aspect of model architecture, a graph attention mechanism is adopted to enhance the node representation capability in a channel sub-graph, and meanwhile, a time sequence modeling module is introduced to model a trajectory evolution rule, so that the prediction precision and generalization capability of the model in a complex water area environment are effectively improved, and high-quality modeling, interpretable prediction and intelligent support of the ship trajectory are realized.

The invention relates to the technical field of wireless communication, in particular to an intelligent wireless communication control method applied to ships and boats. The method comprises the following steps: acquiring radar original point cloud data, an AIS ship attribute data set, a Beidou navigation state data set and night vision target feature data through a multi-dimensional sensor, and constructing ship multi-source standardized preprocessing data; therefore, the functions of multi-source heterogeneous data fusion, dynamic channel adaptive modulation, distributed relay communication guarantee, real-time air route dynamic planning, intelligent instruction generation and the like are realized; the problems of high data isolation, large transmission delay, rigid navigation path, slow obstacle avoidance response, insufficient formation collaboration and the like in the existing ship communication control technology are solved, and the communication stability, the navigation safety and the overall combat effectiveness of the ship formation in a complex dynamic environment are remarkably improved.

Comprehensively extract and effectively integrate multi-modal maritime safety information to provide intelligent navigation decision support for mariners. [Solution] For the data processing end, a multimodal fusion network is proposed that can train various input information including latitude and longitude information of navigation AIS, text information of NAVTEX, vector information and raster information of electronic charts, and RGB information and HOG information of video images using various networks, and a Concat fusion structure based on multiple attention mechanisms MHA is introduced into the parallel structure of the multimodal network to fully extract the features of the multimodal information and effectively fuse them. For the result display end, a Transformer model based on generative pre-training is proposed, and a multimodal decision support method is proposed by understanding the context of the multimodal information of the fusion features.

The invention discloses a ship navigation risk dynamic early warning method and system based on multi-source information fusion, and relates to the technical field of ship navigation control, and the method comprises the steps: carrying out the time-space registration and fusion processing of multi-source data obtained in real time, and building an environment model comprising a ship, surrounding ships, static obstacles and environment elements; fusing the risk distribution of all the risk influence factors based on an environment model, and generating a dynamic risk field reflecting the risk situation of the whole water area; performing collision avoidance decision analysis based on the dynamic risk field; the environment complexity value is fed back to the generation process of the dynamic risk field, and the risk distribution form of the dynamic risk field is changed by adjusting calculation parameters of risk fusion; and risk monitoring is carried out based on the changed dynamic risk field, and when it is detected that the risk level of the area where the ship is located exceeds an early warning level threshold value, early warning information including collision avoidance suggestions is generated in combination with a recommendation strategy.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention discloses a ship lock navigation scheduling optimization method and system based on an AI algorithm, and belongs to the technical field of ship lock navigation scheduling, and the method comprises the steps: collecting ship data, hydro meteorological data and ship lock state data, and obtaining original collection data; the method comprises the following steps: preprocessing original acquisition data to obtain processing data, and integrating the processing data according to an acquisition timestamp to obtain ship monitoring data; performing feature extraction on the ship monitoring data to obtain key features related to ship lock navigation scheduling; key features are extracted from ship monitoring data, a ship lock navigation scheduling optimization model is established, and a ship lock navigation scheduling scheme is formulated by determining a target optimization function and constraint conditions; and solving the ship lock navigation scheduling optimization model through a genetic algorithm and a particle swarm optimization algorithm to obtain an optimal ship lock navigation scheduling scheme.

The invention discloses an unmanned ship cluster cooperative communication method and system, particularly relates to the technical field of unmanned ship cluster communication, and is used for solving the problem of poor unmanned ship cluster cooperative communication. According to the method, a cooperative communication mechanism with real-time sensing and self-adaptive capabilities is constructed, and effective links meeting communication distance and link weight conditions are screened out by collecting spatial position, speed and course information of the unmanned surface vehicle, constructing a standardized state vector and generating and dynamically maintaining a communication topological graph; monitoring a link load in real time, calculating a node congestion sensing factor, triggering path optimization, selecting an optimal path based on a distance and the load, and adjusting a forwarding rate; a scheduling factor is constructed by combining a task time limit, an action node range and a delay sensitivity level, a priority score is generated, a communication task is scheduled to an optimal link and an optimal time period according to a task priority, and space-time collaborative scheduling optimization of an unmanned ship cluster according to a task level is realized.

The invention relates to an AIS (Automatic Identification System)-based ship abnormal state monitoring method, device and equipment and a medium, which are applied to the technical field of ship abnormal monitoring, and the method comprises the following steps: acquiring ship operation information; determining a to-be-monitored ship based on the ship operation information and a preset rule; acquiring historical operation information of all ships in a preset time period, data points of the to-be-monitored ship and meteorological information, wherein the data points comprise speed and position; determining an abnormal judgment navigational speed based on the historical operation information and the meteorological information, wherein the abnormal judgment navigational speed comprises a floating navigational speed and a slow navigational speed; determining the number of abnormal point locations based on the abnormal judgment distance and the abnormal judgment navigational speed, wherein the number of abnormal point locations comprises the number of drift abnormal point locations and the number of slow speed abnormal point locations; determining abnormal information of the to-be-monitored ship based on the data points, the abnormal judgment navigational speed and the number of abnormal point locations; and giving an alarm based on the abnormal information. The method has the effect of improving the timeliness and accuracy of abnormal state monitoring.

The invention relates to a ship port resource coordinated scheduling method and device, and belongs to the technical field of port resource optimized scheduling, and the method comprises the steps: building a target function with the maximization of the anchor ground utilization rate, the maximization of the berth utilization rate and the minimization of the ship waiting time as targets; solving the objective function by using MILP and a particle swarm algorithm to obtain an initial scheduling scheme meeting constraint conditions; the initial scheduling scheme comprises anchor ground distribution, berth distribution, berthing time and leaving time; clustering the initial scheduling scheme based on the multi-core weighted distance of the initial scheduling scheme to obtain a clustered scheduling scheme; and optimizing the clustered scheduling scheme based on a game theory model to obtain a target scheduling scheme. According to the ship and port resource coordinated scheduling method provided by the invention, global optimization of ship berth allocation, channel use and anchor ground scheduling is realized, and high efficiency and safety of ship operation are ensured while channel resource conflicts are reduced.

The invention discloses a ship navigation risk assessment and decision-making method and system, a medium and a product. The method comprises the following steps: acquiring ship navigation parameters, and meteorological data and ocean data within a future preset duration; determining a meteorological influence value and an ocean influence value according to the meteorological data and the ocean data; inputting the sailing speed value, the load tonnage value, the meteorological influence value and the ocean influence value into a preset sailing risk assessment function to obtain a sailing risk value in a future preset duration; when the navigation risk value exceeds a preset risk threshold value, searching a reachable port; determining harbor information of reachable harbors according to a preset harbor database; determining an optimal reachable port based on the wind sheltering condition, the berth state and the geographic position; calculating an optimal wind-sheltering path between the ship and the optimal reachable port and the sailing duration and sailing fuel consumption of the optimal wind-sheltering path; and visually displaying the optimal wind sheltering path, the sailing duration and the sailing fuel consumption. By implementing the method, the ship navigation decision making efficiency is improved.

The invention discloses a bridge ship collision early warning system based on artificial intelligence, and relates to the field of intelligent traffic infrastructure safety protection. The method is used for solving the problems of difficulty in multi-source data fusion and low risk identification accuracy in a complex water area. The method comprises the following steps: firstly, uniformly mapping a radar point cloud, an AIS signal and a visual trajectory to a coordinate system taking a pier as a center through a Lie group transformation algorithm, and constructing a ship motion state tensor; then, ship behavior characteristics are extracted through a space-time diagram convolutional network, and intention probability distribution and behavior deviation degree are output; thirdly, an orthogonal constraint variational auto-encoder is adopted to decouple the autonomous control behavior and the environment disturbance factor, and a collision risk quantized value is generated according to the norm ratio of the autonomous control behavior and the environment disturbance factor; and finally, constructing a dynamic alarm threshold value according to the behavior deviation degree and the disturbance characteristics, realizing graded early warning, and driving a behavior pattern library to update through a feedback mechanism to form a self-adaptive closed-loop early warning system.

The invention discloses an intelligent navigation system for a ship, and the system comprises an obtaining module which is used for obtaining the navigation data of the ship within a preset time period in the navigation process of the current route; the preprocessing module is used for preprocessing the navigation data to obtain preprocessed navigation data; the evaluation module is used for inputting the preprocessed navigation data into a pre-trained route evaluation model to obtain a first evaluation risk value; the optimization module is used for comparing the first assessment risk value with a preset assessment threshold value and judging whether to adjust and optimize the current route according to a comparison result; by acquiring data of the ship in the sailing process in real time and preprocessing the data, the system can find potential sailing risks in time, such as channel congestion and meteorological condition deterioration; based on evaluation of the data, the system can intelligently adjust the ship route and avoid a dangerous area, so that the safety of ship navigation is remarkably improved.