821 results about "Navigation safety" patented technology

The invention relates to the technical field of navigation and positioning, in particular to an intelligent positioning and navigation early warning system and method for ships and boats. The method comprises the following steps: collecting and carrying out sea condition sensing and positioning signal evaluation according to a multi-source sensor synchronous data set to obtain a sensor reliability score table; performing target motion situation analysis according to the sensor reliability score table to obtain a fusion situation vector diagram; performing target motion state judgment according to the fused situation vector diagram to obtain a target motion state judgment result; performing multi-target collision risk assessment according to the target motion state judgment result to obtain a risk degree assessment matrix; performing navigation maneuvering performance constraint according to the risk assessment matrix to obtain a route planning maneuvering constraint rule set; and performing route trajectory search according to the route planning manipulation constraint rule set to obtain a navigation trajectory scheme. According to the invention, through the positioning navigation early warning technology, the positioning precision, the navigation safety and the early warning system practicability of the ship in a complex environment are obviously improved.

The invention discloses an intelligent maritime affair security situation awareness system and method, and relates to the technical field of path planning, and the method comprises the steps: obtaining multi-source ship data, carrying out the space-time dimension alignment and feature weight distribution, generating a fusion feature tensor, extracting a spatial topological relation through a graph convolution operation, and capturing a time dependence feature through an LSTM model. Constructing a ship interaction potential energy field model in combination with the spatial topological relation and the time dependence features, calculating a collision risk index in combination with a conflict cone algorithm, generating a dynamically updated sea area risk thermodynamic diagram, and performing situation map construction on the sea area risk thermodynamic diagram and the fusion feature tensor to obtain a dynamic situation map; a ship-environment-event triple relationship is extracted by adopting a hierarchical graph attention network, an interpretable semantic situation map is constructed, a threat matrix is generated through node-level risk assessment in sequence, and an air route planning scheme is obtained through regional-level path optimization; high-risk areas can be avoided, and navigation safety and efficiency 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 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 ship navigation trajectory intelligent prediction method, which belongs to the technical field of ship navigation, and comprises the following steps: dynamically acquiring and fusing multi-source navigation data such as a real-time state of a ship, a historical navigation mode, current and forecast environmental factors and the like; constructing and updating a dynamic feature vector representing the space-time motion behavior and intention of the ship; inputting the vector into a pre-trained integrated deep learning model to generate a probabilistic trajectory prediction sequence; according to the sequence, potential collision and yaw risks are assessed in combination with an electronic chart and traffic rules, and a comprehensive risk assessment index is output; and when the risk exceeds a threshold value or an optimization space exists, generating an optimization route adjustment suggestion considering the safety margin and the navigation efficiency. According to the invention, an integrated deep learning model is adopted, so that the accuracy and reliability of ship trajectory prediction are remarkably improved; and risk assessment and intelligent route optimization can be actively carried out, and the navigation safety and the operation efficiency are enhanced.

The invention discloses a ship path planning method based on deep reinforcement learning. The ship path planning method is suitable for a complex sea area environment. Through environment modeling and dynamic obstacle identification, a detailed state space is constructed, and perception of the environment is enhanced by using an artificial potential field. An ID3QN-PER algorithm is adopted to train a model, an adaptive exploration strategy and a network parameter updating mechanism are designed, and navigation safety and efficiency in a dynamic environment are ensured. The method further comprises the steps of discretizing an action space, designing an obstacle avoidance strategy in combination with a COLREGS specification, smoothing a route through a B-spline technology, adjusting a navigation strategy according to real-time environment data, and improving the real-time performance and the global optimization performance of route planning.

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 discloses an unmanned ship navigation path intelligent optimization system and method based on ocean data, and particularly relates to the technical field of unmanned ship navigation. The accuracy of monitoring data of the sensor in a marine environment is continuously evaluated through a series of steps of preliminary calibration, data acquisition, filtering processing, abnormal marking, feature extraction and the like of the unmanned ship sensor, and real-time evaluation and time period division of the quality of the monitoring data are realized through comprehensive analysis of abnormal features of sound velocity and conductivity. The abnormal degree is predicted in the abnormal monitoring time period, and for the condition of high abnormal degree, a safety avoidance strategy is automatically set through a path planning system, and the route and the navigational speed of the unmanned ship are dynamically optimized, so that a risk area is avoided, the navigation precision and safety are improved, the reliability of sensor data is remarkably improved, and the safety of the unmanned ship is improved. The influence of data deviation on path planning is reduced, and the task success rate and the navigation safety of the unmanned ship in a complex marine environment are enhanced.

The invention provides an inter-fleet dynamic electric quantity balance and collaborative replenishment system, which relates to the technical field of shipping transportation, and comprises an intelligent scheduling module used for obtaining a data set containing electric power conditions, navigation parameters, sea condition influences and task progress requirements of each ship based on ship motion states, ship equipment is monitored in real time to obtain data and analyze environmental influence to obtain adjustment parameters, then the total energy consumption contribution of a single ship is calculated, the total loss of a fleet is quantified to fuse to obtain the initial amount of total energy consumption and correct to obtain the navigation efficiency, and then a power output adjustment strategy is formulated for a main propulsion ship according to the navigation efficiency and an electric power balance target. And finally, a collaborative supply path is planned by taking the low-power ship as a core and combining multi-party information. According to the invention, efficient coordination of deployment and supply of the fleet on power management is realized, the transportation efficiency is improved, the cost is reduced, and the navigation safety is enhanced.

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 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.

The invention relates to the technical field of ship navigation system safety, and provides a Beidou satellite navigation deception detection method and system based on multi-source information, and the method comprises the steps: obtaining Beidou satellite navigation data, AIS information, navigation radar data and sea chart data; performing denoising processing on the Beidou satellite navigation data, and performing space-time compensation on the AIS information; clutter suppression is carried out on the navigation radar data; calculating ship body position deviation, and verifying the ship body relative position according to the ship body position deviation; calculating the real position of the ship body and the Beidou positioning deviation, and verifying the absolute position of the ship body; performing hull course consistency verification; and performing Beidou satellite navigation deception detection according to the ship body relative position verification, the ship body absolute position verification and the ship body course consistency verification. According to the invention, multi-dimensional detection is carried out on key information such as the position and course of the ship, so that the probability that the ship falls into danger due to wrong navigation signals is greatly reduced, and the navigation safety of the ship is guaranteed.

The present invention provides an unmanned ship laser ranging optimization method and system based on dynamic environment perception, which relates to the field of navigation and obstacle avoidance technology of unmanned ships. The method includes: sensing the surrounding environment data in real time through sensors to obtain obstacle position, speed and heading information; based on the dynamic characteristics of the environment, using an adaptive algorithm to dynamically adjust the measurement frequency and angle of the laser rangefinder to optimize the measurement accuracy; combining the environmental data with the laser ranging results, using a path planning algorithm to optimize the obstacle avoidance route, and adjusting the heading in real time; adjusting the laser ranging parameters according to dynamic feedback information to ensure the stability of navigation and obstacle avoidance performance. The present invention can effectively improve the laser ranging accuracy of unmanned ships in complex dynamic environments, optimize navigation safety and obstacle avoidance capabilities, and has broad application prospects.

The invention discloses a ship collision avoidance decision method based on two-stage safety reinforcement learning, and the method comprises the following steps: constructing a first-stage reinforcement learning ship collision avoidance decision model which is used for enabling a ship to take collision avoidance actions when the ship has other ships or obstacles within a first threshold value around the ship; a second-stage safety reinforcement learning ship collision avoidance decision model is constructed and used for limiting the ship action space to ensure the ship navigation safety when the ship has other ships or obstacles within a surrounding second threshold value; under a first threshold condition, adopting the trained first-stage reinforcement learning ship collision avoidance decision model to carry out collision avoidance decision in the ship running process; and when the ship and other ships or obstacles are in a second threshold condition, adopting the trained second-stage safety reinforcement learning ship collision avoidance decision model to carry out collision avoidance decision in the ship driving process. According to the method, the safety and economy of ship navigation are improved, and a safety guarantee is provided for a reinforcement learning collision avoidance algorithm.

The invention relates to a self-adaptive task allocation and cooperative control method for a distributed unmanned ship cluster, and the method comprises the following steps: enabling an unmanned ship to collect the state information and environment information of the unmanned ship, constructing a state perception model with integrated cognition, and evaluating the navigation safety level and cruising ability of the unmanned ship; the unmanned surface vehicle determines an evaluation index according to a task type according to a state sensing model of the unmanned surface vehicle and information interaction between the unmanned surface vehicle and an adjacent unmanned surface vehicle, calculates fitness to different tasks, and dynamically negotiates and distributes the tasks based on a game theory and a negotiation mechanism; a reinforcement learning algorithm is introduced, the unmanned surface vehicles interact with the environment to adjust an action strategy according to reward feedback, meanwhile, learning experience is shared among the unmanned surface vehicles, and a cooperative control strategy is optimized. Compared with the prior art, the method fully considers the individual difference of the unmanned surface vehicles and the dynamic change of the marine environment, realizes the adaptive distribution and efficient cooperative control of tasks, and improves the operation efficiency, reliability and safety of the unmanned surface vehicle cluster in the complex marine environment.

The invention discloses an automatic obstacle avoidance method and system for an unmanned ship based on Beidou artificial intelligence, and relates to the field of computer vision, and the method comprises the steps: collecting a visible light image and a near-infrared image of a water surface environment, and carrying out the reflection suppression processing; performing feature fusion on the image to generate an environment perception graph; obstacle features are extracted through a cascade cavity convolution structure, and an obstacle pixel-level semantic segmentation result is obtained; constructing an obstacle motion prediction model, and obtaining a predicted obstacle motion track; acquiring motion track data of the unmanned ship based on Beidou positioning and electronic chart data; establishing a space-time collision detection model, calculating collision parameters, and generating a collision data set; an obstacle avoidance path is generated through a dynamic situation field method, and a rudder angle instruction and a propeller rotating speed instruction are obtained. The unmanned ship has the advantages that accurate sensing, motion prediction and intelligent obstacle avoidance of the unmanned ship on water surface obstacles are realized through Beidou positioning and technology, and the sailing safety and intelligent level are improved.

The invention relates to the field of navigation risk prediction, in particular to a data fusion and integration method and system based on navigation multi-source data, and the method comprises the steps: constructing a correlation analysis model; acquiring source data of different scene types in real time; inputting different types of source data into the correlation analysis model, and extracting risk features of the source data through the correlation analysis model; setting a hierarchical clustering model, inputting each risk feature into the hierarchical clustering model, and analyzing a correlation coefficient between each risk feature and historical navigation data through the hierarchical clustering model; determining a weight coefficient of the risk feature according to the correlation coefficient, wherein the weight coefficient is used for reflecting the importance level of the risk feature in navigation risk prediction; and constructing a data framework of the navigation risk data, and integrating the risk features and the weight coefficients thereof into the data framework. And making a corresponding risk management strategy. A ship operator can process navigation multi-source data more effectively, accuracy of navigation risk prediction is improved, navigation safety is guaranteed, and a navigation plan is optimized.

The invention provides a navigation mark early warning method and system based on multi-factor fuzzy comprehensive judgment. The method comprises the following steps: firstly, determining key factors influencing navigation safety, and distributing weights according to the importance of each factor; secondly, environment data are collected in real time through a sensor, data of nearby ships are collected through an automatic identification system (AIS), and cleaning and formatting processing are carried out; thirdly, on the basis of the judgment factors and the weights, constructing a fuzzy relation matrix, inputting data collected in real time, processing the data through a fuzzy logic algorithm, and outputting a fuzzy vector; and finally, according to the fuzzy vector, the current risk level is evaluated, and an early warning signal is generated and broadcasted through communication equipment. The system comprises a data collection module, a data preprocessing module, a fuzzification module, a fuzzy reasoning module, an optimization module, a defuzzification module and an early warning generation module. The fuzzy relation matrix is optimized by combining the genetic algorithm and the particle swarm optimization algorithm, and the accuracy and robustness of the early warning system are remarkably improved.

The invention provides an OFDM signal receiving and processing system and method suitable for an NAVDAT system. According to the method, the characteristics that NAVDAT system signals have synchronous headers, pilot frequencies and the like are fully considered, firstly, the synchronous headers are used for carrying out correlation operation on received data to search a frame starting point, and a signal frame to be demodulated and decoded is obtained in combination with a constant false alarm detection algorithm; and subsequently, carrying out symbol synchronization to determine the FFT window position. Secondly, in combination with a discrete pilot signal, estimating an integral multiple frequency offset error, namely coarse synchronization, and further realizing estimation of a decimal multiple frequency offset error, namely fine synchronization, so as to realize frequency synchronization. And then, channel estimation is carried out by using the pilot signal and adopting a least square method to obtain the channel frequency response of the pilot, the frequency response of the complete signal is obtained through interpolation, and the rough estimation result is further processed through the designed RCAFCN network to obtain accurate channel estimation, so that the original OFDM signal is recovered. And finally, extracting a data part in the OFDM signal according to a frame structure, realizing soft demodulation through a log-likelihood ratio mode, decoding data of a signaling part by using a polarization code, and decoding data of a service part by using a low-density parity check code, thereby realizing complete demodulation and decoding. The method provided by the invention can effectively reduce inter-symbol interference and inter-carrier interference existing in OFDM signals, compensate channel distortion, reduce noise influence, improve decoding precision, improve communication quality, realize optimization of NAVDAT system baseband processing, and guarantee sailing safety of ships on the sea by combining with an existing marine broadcast system.

The invention relates to a polar region ship navigation risk management and control method and system, and the method comprises the following steps: obtaining polar region natural environment original data, predicting missing data points through a random forest, and obtaining a high-density environment risk data sample in a space-time dimension; a Bayesian network model of a polar region ship navigation process is constructed based on accident risk factors, model parameters are calculated based on environment observation data, and navigation safety assessment in a multi-risk mode is realized by constructing a risk assessment model; and for the most important risk type after navigation safety assessment, comprehensive risk management and control of polar region ship navigation are realized in combination with ship seaworthiness, ship navigation window period, air route and risk management and control measures. Compared with the prior art, the method has the advantages that environment and climate real-time characteristics are fully considered, risk assessment and comprehensive risk management and control are realized through subjective and objective combination, and the like.

The invention discloses a method for improving channel traffic volume based on an underwater map, and belongs to the technical field of water transportation traffic, and the method comprises the following steps: S1, constructing an underwater high-precision map, deploying an unmanned ship and an underwater robot, and collecting underwater topographic data by using a multi-beam sounding technology to construct a high-precision underwater map; s2, establishing a dynamic channel planning model, collecting hydrological data and ship traffic data of different time and places of a channel by using a big data analysis and machine learning algorithm, and generating a dynamic channel use scheme; and S3, integrating a ship navigation and management system, developing ship navigation terminal equipment, and integrating the dynamic channel planning model with a monitoring system of a channel management department. According to the invention, channel resource utilization is optimized, intelligent dynamic planning is realized, ship passing efficiency, navigation autonomy, navigation safety and system stability and reliability are improved, channel management scientificity is enhanced, and cooperation of ships and management departments is enhanced.

The invention discloses a channel flow velocity measurement error compensation method based on multi-source fusion, and the method comprises the following steps: S1, synchronously collecting flow velocity, position, attitude and environment data, including ADCP radial flow velocity, GNSS position / speed and IMU attitude angular velocity, through a multi-source sensor, aligning multi-source data based on a time synchronization mechanism, and achieving microsecond time synchronization based on PPS time service; according to the method, RTK high-precision positioning and ADCP bottom tracking data are integrated, the water surface elevation and the water bottom distance are inversed in real time, and the actual water depth and the riverbed erosion and deposition rate are dynamically calculated; edge calculation, motion compensation and external compass correction technologies are fused, the flow velocity and flow direction measurement precision is improved, and platform motion and attitude interference is eliminated; multi-source data local fusion processing and real-time generation of a flow velocity thermodynamic diagram and a water level change curve are realized, remote transmission and visualization are supported, and comprehensive and accurate hydrological data support is provided for maritime affair intelligent supervision, navigation safety early warning and digital channel construction.

The invention provides a deception detection method and device for ship navigation and PNT trusted terminal equipment. The method comprises the following steps: acquiring shore-based trusted service data, the credibility of a navigation system, global navigation satellite GNSS data and multi-class navigation sensor data; determining a first deception probability in a preset time period according to the multi-class navigation sensor data and a preset data threshold value of the multi-class navigation sensor; according to the multi-class navigation sensor data, the GNSS data, the shore-based trusted service data and the credibility, performing fusion positioning through Kalman filtering to obtain trusted fusion positioning; determining a second deception probability within a preset time period according to the trusted fusion positioning and the GNSS data; determining a target deception probability according to the first deception probability, the second deception probability and the credibility; and outputting the target deception probability. Through the method, the ship navigation spoofing attack can be effectively detected, the spoofing probability is output, and the navigation safety is improved.

The invention belongs to the technical field of ship and ocean engineering, and discloses a ship seakeeping intelligent forecasting method and system based on a physical information neural network. According to the method, a CFD-RAO method is combined with PINNs, and rapid and high-precision prediction of ship seakeeping is realized by constructing a coupling model of a physical equation and a neural network loss function. According to the invention, rapid and high-precision prediction of ship seakeeping is realized. The method not only can effectively cope with multi-degree-of-freedom coupling motion under complex sea conditions, but also has high real-time performance and adaptability, and provides more scientific and reliable technical support for ship design and navigation safety.

The invention discloses an intelligent ship autonomous collision avoidance whole process path planning method, which comprises the following steps: establishing an error distribution model of a fusion target according to dynamic information and static information of a target ship, and analyzing to obtain a classification model of the target ship; according to the classification model of the target ship, combining a quaternary ship domain model to establish a ship domain model of dynamic and static obstacles; establishing local path planning based on an artificial potential field method, establishing a repulsive force field around an obstacle in the ship domain model, establishing a gravitational field at a target point, introducing a global path return mechanism, and establishing a local collision avoidance path planning algorithm considering the return mechanism; gravitational force self-adaptive adjustment is achieved based on the current ship path deviation degree and the current ship navigation deviation degree, a dynamically-optimized local collision avoidance path planning algorithm considering a self-adaptive homeward voyage mechanism is formed, and path planning of the ship is conducted. After collision avoidance of the ship, the ship is controlled to return to the global route reasonably and efficiently, and the navigation safety and efficiency of the ship are guaranteed.

The invention relates to the technical field of computer vision, in particular to a method for generating a blind person navigation obstacle avoidance map. The method comprises the following steps: driving a blind guiding robot to a preset inspection point, and collecting a surrounding environment image in real time; generating a three-dimensional point cloud according to the environment image, and constructing an environment map; extracting feature points based on the surrounding environment image, performing feature matching, and outputting a feature matching result; evaluating an inter-frame motion state by using a feature matching result; determining pose features of the blind guiding robot according to the inter-frame motion state; semantic segmentation is carried out by using the pose features, and an obstacle area is determined; acquiring an obstacle image of the obstacle area, and fusing the obstacle image with the surrounding environment image to generate an obstacle map; and constructing a dynamic occupancy grid based on the obstacle map. According to the invention, real-time updating and intelligent obstacle avoidance functions of the blind navigation map are realized based on the computer vision technology, and the navigation safety and the path planning accuracy are improved.

The invention relates to the technical field of intelligent management and control of ships, and discloses an intelligent management and control method and system on a large and medium-sized ship terminal, and the method comprises the steps: detecting meteorological conditions in real time through meteorological and hydrological collection equipment, providing important navigation information, predicting severe weather to ensure the navigation safety, and carrying out the route planning through combining the meteorological condition data; a camera is used for monitoring a key area of the ship, mastering the operation condition of the ship in real time, carrying out safety monitoring on the ship, and carrying out key event detection; the edge gateway is used as a connection and conversion medium between different networks; the position, identity and speed information of the ships is automatically sent and received through the AI automatic recognition system, and communication and recognition between the ships and between the ships and a shore-based management mechanism are helped. By installing meteorological and hydrological acquisition equipment, cameras, edge gateways, AIS and other production data acquisition equipment on large and medium-sized ships, intelligent management and control of real-time hydrological and hydrological information monitoring, unmanned driving recognition and personnel behavior recognition are achieved.

The invention discloses a ship intelligent energy efficiency optimization method and system, and relates to the technical field of ship energy efficiency optimization control, and the method comprises the steps: collecting the operation data of a ship navigation process, and carrying out the preprocessing of the operation data; inputting the preprocessed operation data into a ship energy efficiency evaluation model, and outputting a current energy efficiency index value of the ship; based on the energy efficiency index value, solving a ship energy efficiency optimization objective function through a particle swarm optimization algorithm to obtain an optimal navigational speed parameter and an optimal course parameter; and issuing the optimal navigational speed parameter and the optimal course parameter to a ship control system, and adjusting the ship navigation state. According to the system, the ship navigation safety is ensured, meanwhile, the fuel consumption is effectively reduced, the overall energy efficiency level is improved, and the high practical value and the intelligent degree are achieved.

The invention discloses a local anti-pursuit method for an unmanned surface vehicle based on aggregation of spatial-temporal characteristics. At the present stage, a local navigation algorithm of an unmanned surface vehicle is mainly used for carrying out local collision avoidance path planning on dynamic obstacles in static and uniform linear motion, and the unmanned surface vehicle may be pursued due to behaviors such as out-of-control operation or malicious blocking, so that the navigation safety of the unmanned surface vehicle is greatly threatened. The invention provides an anti-pursuit path planning method suitable for a complex water surface environment, and the method comprises the steps: simulating a multi-source shipborne sensor to generate a simulation signal, providing environment perception data, constructing a coding structure of aggregated spatial-temporal characteristics, and distinguishing dynamic and static obstacles in a scene, and constructing an end-to-end navigation path planning model by adopting a value distributed reinforcement learning algorithm. According to the invention, the motion characteristics of the obstacle in the environment can be identified, so that the decision-making ability and the navigation robustness of the unmanned ship in a complex environment are enhanced.

The invention relates to the technical field of ship navigation risk assessment, in particular to a ship navigation risk assessment system based on multi-source heterogeneous data fusion, which comprises a multi-source data integration module, a spatial-temporal feature mapping module, a dynamic risk detection module, a linkage decision control module and a feedback optimization module. According to the method, standardized operation data is generated through multi-source data cleaning and fusion, a spatial-temporal feature distribution map is generated by using a multi-dimensional dynamic clustering algorithm, a risk index set is extracted in combination with adaptive boundary adjustment and a nonlinear optimization algorithm, and accurate path planning and real-time regulation are realized. In addition, a global sensitivity analysis framework and an early warning module are introduced into the system, and the ship navigation safety and reliability are improved. According to the method, the risk prediction accuracy can be remarkably improved, the navigation accident probability is reduced, the navigation efficiency is optimized, and safe operation of the ship is guaranteed.