527 results about "Navigation safety" patented technology

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 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 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 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 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 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 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 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 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.

The invention discloses a ship identification and water area safety situation analysis method and system. The method comprises the following steps: acquiring water area environment multi-source data; based on a ship target detection model and a target identity matching method, analyzing the water area environment multi-source data, and identifying a ship target; acquiring a historical ship track based on a ship track interpolation smoothing method according to the ship target and water area environment multi-source data; and obtaining a ship navigation safety situation score by using the ship track prediction model and the composite artificial potential field model according to the ship historical track and the water area environment multi-source data. According to the technical scheme provided by the invention, a ship target is identified through an SDRV method and a target identity matching method, abnormal track points are screened, a historical track is interpolated and smoothed, and ship navigation safety situation scoring is carried out based on track prediction and a composite artificial potential field; multi-source dynamic information of a ship target can be integrated, and ship identification and safety situation analysis are effectively supported.

The invention discloses a ship ballast water pressure loading and unloading control system based on a large model. The ship ballast water pressure loading and unloading control system is used for improving intelligence and efficiency of ship ballast water operation. According to the system, firstly, data such as ship navigation states, ballast water tank water levels and ship body postures are collected in real time through a multi-source sensor; analyzing and reasoning the collected data in the trained large neural network model; the large model outputs an optimal control strategy for ballast water loading and unloading, and the decision control module generates a specific execution instruction to drive a valve or a pump or other devices to complete ballast unloading actions. The system is also provided with a man-machine cooperation interface and a feedback mechanism, and the model can be finely adjusted and optimized according to a sailor instruction or real-time feedback information. Compared with a traditional control method, the method has the advantages that ship stability, energy efficiency and environmental protection requirements are fused through a large model, a ballast scheme can be automatically adjusted, rapid and efficient ballast replacement is achieved, and ship navigation safety and economical efficiency are remarkably improved.

The invention discloses an AGV navigation system used in a storage dynamic environment, relates to the technical field of storage robot control, and is used for solving the problem of poor stable navigation in dynamic storage. According to the method, a closed-loop navigation link of state convergence, confidence evaluation, corridors, setting generation and cooperative gating is constructed, state frames are packaged in unified time, and positioning confidence and availability marks are generated based on the consistency of pose predictive quantity and pose observed quantity, so that credible hierarchical control is realized in shielding, light reflection and personnel forklift passing scenes, and the positioning accuracy is improved. A time sequence occupation constraint set is formed through obstacle tracking, a forbidden area mark is fused, a passable corridor boundary is generated, a speed set value sequence and a curvature set value sequence are generated in a corridor, acceleration change rate constraint and steering change rate constraint are applied, and stable driving control and steering control instructions are output. And the way-giving sequence and the recovery sequence are matched, and the execution state and the occupation state are written back, so that the navigation safety, the stability and the operation continuity are improved.

The invention belongs to the field of ship navigation safety evaluation, and discloses a second-generation complete stability forecasting system and method for real sea navigation of a ship. The method comprises the following steps: acquiring actually measured marine environment parameters for sea wave forecasting, and acquiring ship navigation parameters for second-generation complete stability forecasting of a ship; sea wave forecasting is carried out, wherein regional meaningful wave height and average zero-crossing period forecasting of sea wave data is carried out based on an HP-VIT model; a pure stability loss sensitivity index forecasting model is constructed, and pure stability loss sensitivity index forecasting is completed; and a parameter rolling sensitive index forecasting model is constructed, and regional parameter rolling forecasting is completed. According to the method, rapid forecasting of the second-generation stability of the ship in the real sea environment can be achieved, the research result can provide auxiliary decision-making information for sailing safety guarantee, route planning correction and the like of the container ship, and the method has great significance in guaranteeing the safety of the ship and goods.

The invention discloses an airline design method based on marine climate data and a historical ship trajectory, relates to the technical field of airline design, and aims to solve the problem that an airline design scheme is difficult to adapt to a high-risk section due to neglect of environmental adaptability experience and risk evolution rules contained in a historical ship navigation trajectory. The method comprises the following steps: constructing a global port geographic information database and a shipping channel data set, obtaining historical ship trajectories and marine climate data of starting and target positions, generating an initial route, screening basic approach points with the minimum risk based on risk mutation coefficients and environmental stability of each approach point of a path, and connecting to form a basic route. The ship advances according to the ship route, attitude feedback data and a target position are collected, then an accumulated navigation efficiency difference value is calculated, the consistency of the return flight route is evaluated in combination with feedback, the ship route optimization under the complex marine environment is achieved, and the navigation safety and efficiency are improved.

The invention provides a combined fleet steering cooperation method and system based on intelligent control, and relates to the technical field of data processing, and the method comprises the steps: calculating a cooperative steering deviation value of an actual track and a preset reference path; if the collaborative steering deviation value exceeds a preset collaborative deviation threshold value, a real-time safe navigation boundary is generated through a universal dynamic point set convex hull updating algorithm; calculating a hydraulic pressure correction parameter and a rudder angle compensation amount according to the spatial position relationship between the cooperative steering deviation amount and the real-time safe navigation boundary; and converting the hydraulic pressure correction parameter into a pressure regulation instruction, and driving a pressure self-adaptive regulation mechanism of the hydraulic system to execute a compensation steering action. According to the invention, the steering deviation is reduced, and the navigation safety in a complex environment is improved.

The invention discloses a ship navigation safety risk identification method based on multi-source environment information, and the method specifically comprises the following steps: S1, constructing a ship navigation state knowledge graph, obtaining the basic data of ship navigation, and carrying out the building of the ship navigation state knowledge graph through the preprocessed basic data; s2, acquiring real-time navigation state data of the ship, and performing comprehensive safety risk identification according to the real-time navigation state data of the ship; and S3, optimizing a ship navigation strategy according to a comprehensive safety risk identification result, and finally issuing a ship end to carry out an auxiliary driving decision. According to the method, ship navigation basic data is obtained through the shore-based platform, and a data basis is provided for the platform to identify ship navigation environment risks; risk identification is carried out based on basic data obtained by a shore end, a navigation optimization strategy is provided in combination with a ship navigation state, the navigation optimization strategy is issued to a ship end handheld terminal for alarm reminding, and advanced alarm and navigation optimization are carried out on environmental safety risks existing in ship navigation.

The invention discloses a rotor unmanned aerial vehicle visual navigation method and system for an unknown forest region, and the method comprises the steps: firstly, combining a depth camera with a Sobel gradient filtering mechanism, and effectively eliminating interference information; secondly, pixel-level segmentation and three-dimensional positioning of the trunk are realized by segmenting a cascade model of a convolutional network and a residual network, visual information is accurately mapped to a world coordinate system, and the problem of positioning drift caused by similar textures in a forest scene is solved; based on starting point-target point-terminal point minimum jerk trajectory planning, a smooth trajectory is generated through polynomial optimization and quadratic programming, flight jitter is inhibited fundamentally, and the mechanical loss is reduced while the image acquisition quality is guaranteed; and finally, through real-time target point updating and trajectory dynamic correction of the depth camera, a sensing-planning-execution closed loop is formed, so that the unmanned aerial vehicle has dynamic obstacle avoidance and path online optimization capabilities, autonomous crossing is finally realized, and navigation safety and stability in a complex environment are significantly improved.

The invention relates to the technical field of ship navigation safety, in particular to a real-time ship trajectory tracking and early warning method, which comprises the steps of multi-source data acquisition and preprocessing, trajectory prediction model construction, collision risk assessment, early warning triggering and the like. By integrating AIS, radar and meteorological data, accurate trajectory prediction is realized by adopting Kalman filtering and extended Kalman filtering algorithms, and the method adapts to a complex environment based on dynamic weight factors and fuzzy logic control. Meanwhile, a multi-level early warning mechanism and redundant communication design are provided, and timely transmission of early warning information is ensured. The collision risk assessment model is optimized by using the deep neural network, and the early warning precision is improved. The real-time performance, accuracy and dynamic adaptability of ship trajectory tracking and early warning can be improved, and the efficient and safe requirements of modern shipping are met.

The invention belongs to the technical field of inertial navigation, and discloses a modulation inertial navigation real-time health monitoring method based on observation enhancement, which ensures the navigation safety of a carrier in a long-endurance task. According to the method, redundant configured inertial navigation system resources are fully utilized, relative postures, speeds and positions between two sets of single-axis rotation modulation inertial navigation systems are used as constraints for observation, an error model of the inertial navigation system is optimized, and a residual normalized joint error state adaptive state monitoring filter is established; a health state monitoring criterion is established, a threshold value is determined in real time based on estimation output of a filter, meanwhile, an azimuth gyroscopic drift variation fitting method based on the compass effect is designed to achieve separation estimation of azimuth gyroscopic drift, and then real-time monitoring of the health state of the inertial device is achieved. The method does not need the assistance of external information, and provides powerful guarantee for the safety and reliability of long-endurance navigation.

The invention discloses a navigation safety early warning method and system considering river geological disasters. The surge height of a ship is determined mainly based on the distance between a geological disaster point position coordinate and a ship positioning coordinate in a geological disaster point influence water area in combination with a surge prediction parameter corresponding to a current water level, and the ship rolling angle is determined according to the surge height of the ship and the ship width. And carrying out ship navigation safety early warning based on the ship rolling angle. According to the method, a quantitative analysis method and an evaluation system for the influence of the landslide surge on the ship navigation safety are theoretically improved, and the accuracy and timeliness of early warning information in actual operation are improved.

The invention relates to the technical field of sea area intelligent shipping, in particular to a ship dynamic risk group identification method and system based on spectral clustering, which are used for identifying high-risk ship interaction groups and evaluating spatial distribution characteristics of the high-risk ship interaction groups. According to the method, the collision risk of each pair of ships is calculated in real time by obtaining AIS dynamic data, and a risk weight matrix is constructed by indexes; introducing modularity as a ship clustering cluster number estimation standard, and realizing ship group division based on a spectral clustering algorithm; further combining modularity optimization and risk potential field calculation, and determining and identifying the positions of a dynamic high-risk group and a water area hot spot area; according to the method, potential conflict groups can be dynamically extracted in a complex navigation environment, accurate modeling of risk structures among ships is achieved, intelligent and prospective support is provided for maritime affair supervision and risk intervention, and therefore ship navigation safety is guaranteed.

The embodiment of the invention discloses a low-altitude wind shear early warning method and device based on multi-source meteorological data. A specific embodiment of the method comprises the steps of generating a first region of interest for a target region according to a contour topographic map; determining a second region of interest according to the real-time rainfall distribution diagram and the real-time air temperature distribution diagram; performing region optimization on the first region of interest according to the real-time wind field graph to obtain a third region of interest; generating a fourth region of interest according to the second region of interest and the third region of interest; and according to the fourth region of interest and a pre-trained low-altitude wind shear prediction model, generating wind shear region early warning information. According to the embodiment, accurate prediction of the wind shear is realized, so that early warning is performed on the low-altitude aviation equipment, and the navigation safety of the low-altitude aviation equipment is ensured.

The invention relates to the technical field of ship intelligent navigation, and provides a dynamic rendering engine implementation method and system based on multi-source data fusion, and the method comprises the steps: collecting multi-source data in real time, carrying out the preprocessing, and obtaining multi-modal data; performing time alignment and coordinate transformation on the multi-modal data through space-time registration, performing prediction through adaptive Kalman filtering, calculating a residual error according to prediction data, and performing sensor anomaly detection according to the residual error; carrying out weighted fusion according to the state of the sensor, carrying out Mercator projection transformation on fused data to obtain rendering coordinates, and carrying out detail level marking according to the rendering coordinates; performing alarm judgment according to the detail level marks, and performing rendering sorting according to the alarm judgment; and carrying out load detection and rendering parameter adjustment through a frame rate control module according to the rendering sequence. According to the invention, the whole process optimization from data acquisition and processing to visual display is realized, the navigation precision and reliability are improved, and the ship navigation safety is improved.

The invention relates to the technical field of ship group intelligent control, and discloses a ship motion trail optimization control system based on deep learning, which comprises control devices deployed on ships, receiving navigation state information of adjacent ships through a ship automatic identification system, generating asymmetric virtual energy-saving potential fields, superposing the asymmetric virtual energy-saving potential fields to form a total potential field, and outputting the total potential field; according to the method, a ship group spontaneously forms a dynamic navigation manifold with relatively low overall energy consumption, and through the asymmetric potential field design of bow repulsive force and stern gravitational force, linkage course correction caused by traditional collision avoidance is converted into collaborative energy-saving motion, so that the energy-saving effect of the ship is improved. Meanwhile, navigation safety is ensured in combination with a post-arbitration mechanism, and environment self-adaptive compensation is achieved through micro-disturbance inversion ocean currents.