201 results about "Ship dynamics" patented technology

The invention relates to a high sea condition unmanned ship dynamic anti-interference control method based on intelligent body.The method comprises the following steps that information is collected, multi-source data are integrated through a data fusion algorithm, and multi-mode sensing data are obtained; constructing a body-equipped intelligent model, taking the multi-modal sensing data as input and the unmanned ship control instruction as output, and performing offline training; constructing an interference prediction model to predict the change trend of interference factors under the high sea condition, and performing adaptive interference compensation according to an interference prediction result; a hierarchical control structure is designed, a task planning layer makes a global navigation plan based on risk assessment, a motion control layer adopts a model prediction control and adaptive sliding mode control combined algorithm to convert instructions, and an execution mechanism layer drives an execution mechanism; meanwhile, online self-adaptive optimization is carried out, and the intelligent model with the body is updated and optimized according to feedback information. Compared with the prior art, the dynamic anti-interference capability, the operation precision and the reliability of the unmanned surface vehicle under the high sea condition are remarkably improved, the environment adaptability and the autonomous operation level of the unmanned surface vehicle are enhanced, and the unmanned surface vehicle is suitable for various high sea condition ocean operation scenes.

The invention discloses a large-scale ocean data buoy safety monitoring system and method based on multi-source fusion, and relates to the technical field of buoy monitoring. Buoy equipment operation parameters, ocean environment data and surrounding ship dynamic information are synchronously acquired through a buoy body sensor group, an environment monitoring unit and a ship identification module; time-frequency domain feature extraction and anomaly detection are carried out on the collected data, and a collision risk dynamic evaluation model is constructed in combination with ship trajectory prediction; establishing an equipment health, environment interference and ship threat fusion evaluation system, and performing risk grade division in combination with real-time monitoring data and a historical database; and generating early warning information based on the risk level, and automatically triggering an equipment maintenance instruction and a ship collision avoidance alarm. The method adapts to ship variable-speed steering scenes, threshold values are dynamically adjusted, and adaptability and analysis accuracy of the system are improved.

The invention discloses a ship power system optimization control method and system based on a simulated annealing algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining operation parameters and historical energy consumption data in real time, and constructing a multi-objective optimization function of dynamic weight distribution; generating an initial temperature parameter and a solution set in combination with the navigation state and the environment data; a neighborhood search strategy disturbance solution set is improved, a new solution is evaluated by using a dynamic acceptance probability function, temperature parameters are adaptively adjusted for iterative optimization, and an optimal control parameter combination is output; and an adjustment instruction set is generated after multi-dimensional efficiency verification, and a propulsion device, a generator set and an energy storage module are cooperatively controlled, so that global energy consumption optimization is realized. According to the method, through data driving and intelligent algorithm fusion, energy efficiency and environmental adaptability are improved, and stable operation under complex working conditions is guaranteed. According to the ship power system optimization control method and system based on the simulated annealing algorithm, the energy efficiency level and the environmental adaptability of the ship power system are improved.

The invention relates to the technical field of traffic infrastructure maintenance, and particularly discloses a method for evaluating the anti-collision toughness of a rusted steel bar bridge structure after ship collision, and the method comprises the following steps: firstly, building a rusted bridge pier finite element model based on a rusted steel bar concrete theoretical degradation model; simulating ship collision by adopting an equivalent static method, and determining dynamic damage factors of the ship; structural nonlinear static analysis and nonlinear dynamic analysis are conducted by considering sudden external force to obtain a bridge function loss function, and then a bridge function is calculated; constructing a response surface agent model to perform dynamic response prediction; determining a bridge structure damage state judgment criterion; the vulnerability of the rusted bridge is calculated through Monte Carlo simulation; and according to the bridge function loss function, combining the repair time and the recovery function to calculate a structure anti-collision toughness index. The method provided by the invention fills up the blank of ship collision resistance toughness evaluation of the coastal rusted bridge pier, provides a scientific basis for bridge safety evaluation and maintenance decision, and is of great significance for guaranteeing safe operation of the bridge.

The invention relates to the technical field of detection and identification, and discloses a mobile ship dynamic tracking and locking method and system based on target detection and identification, and the method comprises the steps: constructing a manifold embedded network, and mapping the multi-modal features of a ship to a Riemannian manifold space; constructing a continuous evolution modeler of the Shenchang differential equation model, and predicting a continuous evolution trajectory of the features under time, view angle and scale changes; a multi-scale bridging network is constructed, and bidirectional conversion between long and short distance feature representations is realized; constructing a differential geometric attitude encoder, and representing a ship attitude in an SO (3) Lie group space; a feature memory and reconstruction mechanism is realized, and the problem of short-time disappearance target recovery is solved; the components are integrated to construct a unified tracking and locking system. According to the method, the problems of discretization processing limitation, feature representation fragmentation, cross-condition consistency deficiency and the like in the prior art are solved, remarkable technical effects are achieved in the aspects of cross-condition recognition capability, continuous feature evolution capability, short-time disappearance target recovery capability and the like, and the method is suitable for the scenes of Yangtze River shipping monitoring, maritime affair safety supervision and the like.

The invention relates to the technical field of equipment monitoring, in particular to an intelligent monitoring method and system for ship power equipment, and the method comprises the following steps: synchronously collecting the operation time sequence data of the equipment; based on the ship power transmission topological structure, generating a dynamic physical constraint model between devices; and inputting the collected equipment operation time sequence data into the dynamic physical constraint model, calculating a host-gear box torque transmission residual error and a gear box-shafting rotating speed matching residual error sequence, and when the zero-crossing rate sudden change frequency of any residual error sequence exceeds a preset sudden change frequency threshold value in a preset time window, triggering an early warning mechanism. Compared with the limitation that a traditional single-point vibration threshold method cannot cover a multi-node cooperative fault, the cross-equipment coupling fault recognition rate is increased, and the coverage range of the cross-equipment coupling fault is widened.

The invention discloses a hierarchical cooperative motion control method for a ship dynamic positioning and wave compensation embarkation system. The method comprises the following steps: acquiring historical motion data of a ship; the historical motion data of the ship are fused, and the current ship posture and speed are obtained; the prediction model generates a predicted value of the disturbance of the ship in the future preset time; extracting to obtain a low-frequency slow drift motion component; the total output force of the thruster is calculated, and the ship dynamic positioning system controls the thruster to work based on the total output force of the thruster so as to compensate the low-frequency slow drift motion component; a Stewart platform in the wave compensation embarkation system controls supporting legs to work based on the target lengths of the supporting legs so as to compensate low-frequency motion left after compensation of the ship dynamic positioning system and high-frequency motion which is not compensated; and residual disturbance after the Stewart platform is compensated is compensated through the gangway ladder. According to the invention, an energy distribution mode that low-frequency disturbance is compensated by a ship dynamic positioning system and high-frequency disturbance is compensated by a wave compensation embarkation system can be realized.

The invention discloses a ship dynamic collision avoidance decision-making method based on an improved DWA algorithm. The ship dynamic collision avoidance decision-making method comprises the steps of constructing sea chart environment data containing obstacle density through a grid method; establishing a dynamic elliptical ship domain based on a quaternion ship domain (QSD) model, and calculating a distance collision risk degree (NCRI) between ships based on the dynamic elliptical ship domain; improving a DWA algorithm evaluation function, and adding a path yaw evaluation function and a collision avoidance rule evaluation function on the basis of optimizing a speed and azimuth angle evaluation function; fusing the multiple evaluation functions by adopting a weight distribution method; and finally, generating a local collision avoidance optimization route based on environment data, NCRI and an improved DWA algorithm. According to the method, safety boundary dynamic adjustment, rule compliance constraint and ship control characteristic cooperation are realized, and the robustness and practicability of collision avoidance decision making under complex sea conditions are remarkably improved.

The invention relates to the technical field of anti-collision systems, in particular to an offshore wind power construction safety pre-warning system based on AIS (automatic identification system) data, which comprises a ship dynamic uncertainty modeling module for acquiring AIS signal updating frequency and positioning precision marks of a target ship and generating a ship future position probability ellipse. According to the invention, the position coordinate, the speed value and the course angle of the ship are obtained in real time based on the AIS data, the reliability and the motion trend of ship position prediction are determined by combining the AIS signal updating frequency and the positioning precision mark, and the future position probability ellipse of the ship is accurately generated; a protection area is dynamically constructed, comprehensive calculation is carried out in combination with the spatial relation between a ship future position probability ellipse and the positions of surrounding fan pile foundations, and a hourly dynamic collision risk index sequence is obtained; and further according to the statistical deviation between the current motion state of the ship and the standard parameter of the function partition, combining with path difference analysis to obtain a navigation abnormity comprehensive index.

The invention relates to a distributed unmanned ship cluster collaborative awareness and control integration method and system in a high sea condition environment. The method comprises the steps of equipment installation and distributed communication network construction; task target and environment information is collected and subjected to preprocessing, space-time calibration and data fusion, fused data is transmitted and shared through a distributed communication network, and a distributed Kalman filtering algorithm is used for joint data processing; on the basis of an unmanned ship dynamics model containing interference factors, a model prediction control algorithm is adopted to carry out rolling optimization control input, hierarchical path planning is carried out through an improved A * algorithm and a dynamic window method, cooperation among unmanned ships is kept through a cooperative control protocol, and control parameters are automatically adjusted according to sea conditions; and constructing a collaborative optimization objective function, adjusting collaborative perception and collaborative control strategies according to the minimum value of the objective function, and performing closed-loop feedback optimization on the collaborative perception process. Compared with the prior art, the sensing accuracy, the control stability and the collaborative operation efficiency of the unmanned ship cluster in a high sea condition environment can be effectively improved, and the ability of the unmanned ship cluster to cope with a severe sea environment is remarkably enhanced.

The invention discloses a fault detection method for a ship power condenser system based on multi-scale feature fusion, relates to the fault detection method for the ship power condenser system based on the multi-scale feature fusion, and belongs to the field of fault diagnosis and safety monitoring of ship power systems. The objective of the invention is to solve the problems of insufficient detection precision and high false alarm rate of a traditional fault diagnosis method. The method comprises the following steps: step 1, acquiring input and output information of a condenser system; 2, constructing a multi-scale feature extraction model; 3, obtaining a trained multi-scale feature extraction model and parameters; 4, inputting online data of the condenser system into the trained multi-scale feature extraction model, and outputting predicted values of the liquid level and the vacuum degree of the condenser by the trained multi-scale feature extraction model; the residual generator obtains an online prediction error signal based on the prediction value; and step 5, performing fault diagnosis based on the online prediction error.

The invention discloses a dynamic obstacle avoidance control method for an unmanned ship, and the method comprises the steps: multi-sensor data fusion and environment modeling, dynamic obstacle trajectory prediction, deep reinforcement learning path planning, nonlinear model prediction control, and final planning algorithm summarization. Obstacle avoidance control is carried out through multi-sensor data fusion and environment modeling, dynamic obstacle trajectory prediction, deep reinforcement learning path planning, nonlinear model prediction control and final planning algorithm summarization, and the method comprises multi-modal sensor fusion, LSTM trajectory prediction, deep reinforcement learning decision and nonlinear model prediction control. High-precision real-time obstacle avoidance under a complex water area is achieved, the whole method combines dynamic modeling and optimization control, the path safety, efficiency and energy utilization rate are remarkably improved, and the method is suitable for the fields of agricultural unmanned feeding, unmanned water quality intelligent detection and the like.

The invention relates to the technical field of maritime navigation and intelligent shipping, in particular to an electronic chart shipping relation network construction and dynamic updating method and system based on a chart database, and the method comprises the steps: obtaining electronic chart static data and shipping dynamic information, and carrying out standardized preprocessing to unify a space reference and an attribute format; defining node types and edge relation types of channel space elements based on the graph database, and constructing a shipping relation network; shipping dynamic information is accessed in real time, and the weight and the attribute of the side relation are dynamically updated to reflect the real-time state of the navigation channel; and receiving a user path planning query request, executing a path planning algorithm fused with the navigation constraint condition based on the updated network, and generating and outputting an optimal route, thereby effectively solving the problems of weak data association, difficult cross-scale fusion and poor dynamic adaptability of a traditional electronic chart, realizing intelligent management and accurate path planning of a channel relationship, and improving the user experience. And the shipping efficiency and safety are improved.

The invention relates to an unmanned ship dynamic channel modeling method in an offshore water surface environment, and the method comprises the steps: constructing a three-dimensional offshore water surface simulation scene which comprises a sea surface, a wharf facility, an unmanned ship cluster and a communication node; establishing a wireless channel propagation model based on a ray tracing method, and simulating a propagation mechanism of direct, reflection and diffraction paths; establishing a motion characteristic model of the unmanned ship under the action of waves, and obtaining six-degree-of-freedom pose data of the unmanned ship; generating multiple frames of scene copies in batches by adopting an intelligent batch processing dynamic reconstruction method; dynamically correcting an antenna pattern in each frame of scene copy according to the pose data so as to couple the influence of hull motion on antenna radiation characteristics; importing the corrected antenna pattern into the wireless channel propagation model to generate a dynamic channel response; simulation is executed, and channel data obtained through simulation are collected and analyzed. According to the method, the motion characteristics of the unmanned ship in waves and the wireless channel propagation mechanism can be accurately coupled, and the accuracy of dynamic channel prediction is improved.

The invention belongs to the technical field of ship power equipment health management, and provides a ship main engine fault early warning method and system based on an LSTM dynamic threshold model. The method comprises the following steps: preprocessing collected three-axis vibration original data to remove noise or interference signals in the collected data; by constructing the space-time matrix, the time for capturing the vibration of the marine main engine is associated with the space difference, and the fault recognition precision of the marine main engine is further improved; through the double-layer LSTM model, the state of the marine main engine at multiple times in the future is predicted; and through a dynamic threshold updating mechanism, the problem that the false alarm rate is high or a fault cannot be found in time is solved. The fault probability is calculated through the dynamic threshold value and the residual error, and a high-precision fault probability basis is provided for subsequent multi-level early warning. By setting multi-level early warning, the early warning signal of the corresponding level can be accurately triggered according to the fault probability, and the misinformation risk caused by single-level setting is effectively avoided.

The invention discloses a high-precision ship accurate identification method fusing redundant features. The method comprises the following steps: step (1), measuring the longitude, latitude and position of a ship; step (2), ship type extraction based on a high-resolution optical remote sensing image; step (3), decoding and extracting ship AIS related information; step (4), acquiring point cloud feature data of passing ships in the navigation channel by using LiDAR; step (5), extracting ship videos, photos and ship plate names based on the monitoring video; step (6), carrying out space-time registration and unified coordinate transformation on the multi-source data; step (7), ship dynamic feature extraction; (8) data fusion, ship license plate name identification and type matching; and (9) identifying and tracking the ship. According to the invention, space-time registration is carried out through carrying out multi-dimensional data fusion based on Beidou, optical remote sensing, ship AIS data, LiDAR and ship video monitoring, and the accuracy and robustness of ship identification are effectively improved.

The invention relates to the technical field of ship information monitoring, and discloses a ship information acquisition and monitoring system based on AIS signals. The system comprises a ship dynamic information acquisition module which receives AIS base station ship real-time position data, extracts latitude and longitude coordinates, a navigational speed and a course angle, and generates a ship dynamic trajectory sequence in combination with a timestamp; a sailing state feature extraction module identifies acceleration, deceleration and steering behaviors of the ship according to a speed change rate and a course angle change rate in the track sequence, and marks state switching points to generate a sailing state feature set; the regional sailing density calculation module divides sea area grids, counts the number of ships in each grid and staying duration, and generates a regional ship density distribution diagram; the abnormal behavior detection module compares the navigation behavior with a preset normal mode, and screens abnormal behavior points to form a set; and the risk area labeling module integrates the high-density area and the abnormal behavior points, calculates the aggregation degree, generates a ship navigation risk thermodynamic diagram, and realizes ship navigation monitoring and risk early warning.

The invention relates to a ship-borne unmanned aerial vehicle recycling method and system based on visual navigation and six-degree-of-freedom compensation. The method comprises the following steps: extracting infrared, polarization and fluorescence frequency domain features and fusing to obtain an anti-shielding composite descriptor; constructing a ship dynamics differential model, discretizing the ship dynamics differential model by adopting an explicit multi-step method, and predicting six-degree-of-freedom disturbance data of the ship; a vision and inertia combined cost function is constructed, a vision residual item is calculated, the continuity of the motion track of the unmanned aerial vehicle is restrained, and the optimized real-time pose of the unmanned aerial vehicle is obtained; decomposing the six-degree-of-freedom disturbance data into a body coordinate system compensation amount, setting a backstepping sliding mode control law containing feed-forward compensation, and generating an anti-disturbance trajectory instruction; and air data are collected in real time, the rotor rotating speed is corrected according to the air data, the electromagnetic adsorption force is calculated, and the ship-borne unmanned aerial vehicle is recycled. And the navigation stability under complex sea conditions is enhanced.

According to the SARIMA prediction-based inland river navigation draught optimization management and control method for the ship with the overrun draught, a multi-source time sequence data set is obtained and preprocessed to obtain an input time sequence set, and the input time sequence set is processed through a constructed SARIMA model to obtain a water level prediction sequence; a ship dynamic draught prediction sequence is obtained through the constructed ship dynamic draught model based on water level prediction sequence processing, a draught safety margin judgment operator is constructed to carry out draught safety margin evaluation and risk grading, and finally a draught optimization management strategy is solved through the draught optimization management model and issued. A ship draught control closed-loop chain of prediction-calculation-evaluation-optimization-feedback is formed, through deep coupling of SARIMA time sequence prediction, ship dynamic draught calculation and draught optimization management, prospective recognition and dynamic regulation and control of draught safety margin of a draught overrun ship are achieved, and under the premise of guaranteeing navigation safety, the ship draught safety margin is effectively controlled. And the water depth utilization efficiency of the inland waterway and the ship loading efficiency are greatly improved.

The invention discloses an intelligent control method and system applied to a marine floating target, and the method comprises the steps: collecting target ship information and marine environment data in real time through a multi-mode sensor array, and carrying out the time-space alignment and fusion processing, and obtaining an environment state vector; based on the ship kinematics principle, wind and flow disturbance force is decomposed into resultant force and moment vectors under a ship body coordinate system; evaluating the danger degree of the current navigation environment through the dynamic risk index, carrying out navigation risk intelligent grading, and triggering a grading control strategy; a self-adaptive controller is designed based on the Lyapunov stability theory, anti-interference dynamic adjustment is carried out on the target ship, and track deviation caused by environmental interference is eliminated; when an obstacle or a high-risk sea condition is detected, intelligent obstacle avoidance and path re-planning are carried out, and an optimized path for avoiding the obstacle or a surge area is generated in real time; and calculating the minimum safety distance based on the ship dynamics model, and implementing a graded braking strategy to perform dynamic safety braking on the target ship.

The invention discloses a navigation aid information real-time transmission and analysis system based on a navigation mark, and relates to the field of navigation mark telemetering, the system is composed of a plurality of functional modules, and the system comprises a data acquisition module for acquiring dynamic data, meteorological data and hydrological data of ships near the navigation mark; the edge calculation module is used for generating a ship real-time track according to the ship dynamic data, obtaining a ship prediction track based on the ship real-time track and the hydrological data, and calculating a deviation degree based on a preset ship channel and the ship prediction track; the risk prediction module carries out risk analysis on the deviation degree according to meteorological data and carries out route adjustment according to a risk analysis result, and the route adjustment module generates an optimized route by applying an A * algorithm and considering ship dynamic constraint and channel limitation and then sends an instruction in time through the VHF radio module, so that risks of ship deviation from a channel, collision and the like are effectively avoided; the safety of sailors, ships and goods is greatly guaranteed.

The present invention discloses a method for rapidly predicting harbor waves and the dynamic response of moored ships, comprising: S1: constructing a harbor wave numerical model; S2: constructing a mooring numerical model of a preset ship type; S3: using the harbor wave numerical model and the mooring numerical model to establish a training set, the training set including nearshore wave condition parameters, nearshore wind field condition parameters, harbor wave parameters, and moored ship dynamic response data; S4: using the nearshore wave condition parameters and the nearshore wind field condition parameters as inputs and the harbor wave parameters and the moored ship dynamic response data as outputs to train a neural network model; S5: inputting nearshore wave observation data and nearshore wind field observation data into the neural network model to obtain harbor wave and moored ship dynamic response prediction data. The present invention can overcome the shortcomings of the prior art and achieve rapid prediction of harbor waves and moored ship dynamic responses.

The invention discloses a ship dynamic high-precision positioning and abnormal behavior recognition method, and particularly relates to the technical field of navigation positioning. The method comprises the following steps: performing time sequence synchronization and noise elimination on satellite signal propagation time ranging data, carrier phase data and ship attitude data received by a ship; outputting a reflection path coupling relation parameter by analyzing a time-varying coupling relation between the rolling and pitching states of the ship body and a sea surface specular reflection path; by establishing a multi-path deviation correction model, error correction values of satellite signal propagation time ranging data and carrier phase data are calculated; ship track motion state data are output by extracting ship space attitude change features and track geometric morphology features; and updating the ship trajectory motion state data by using the corrected satellite signal propagation time ranging data and carrier phase data to obtain ship positioning trajectory data, and outputting a ship abnormal behavior identification result. And the detection accuracy and reliability of the abnormal behaviors of the ship are improved.

According to the dynamic positioning method and device for the fishery ship, the electronic equipment and the storage medium provided by the invention, filtering and information fusion are carried out by collecting the current motion state and the surrounding environment parameters of the ship, and the ship pose state estimation information and the environment disturbance estimation information are output; based on the rolling time domain model and the multi-target performance index function, according to the current constraint target, the ship pose state estimation information, the environment disturbance estimation information and preset propeller use energy consumption parameters, an optimal control sequence corresponding to the current control period is output, and according to the optimal control sequence and a propeller current efficiency matrix, the propeller is controlled. And determining current thrust output information of each thruster, and controlling each thruster to output thrust according to the priority of the thruster and the current thrust output information of each thruster. The method can realize automatic fishery ship dynamic positioning, improves the dynamic positioning efficiency and accuracy of the fishery ship, and can be widely applied to the technical field of ship motion control.

The invention discloses an intelligent ship navigation system based on a space-time prediction risk field and deep reinforcement learning, relates to the technical field of ship navigation, and aims to solve the problem that a conventional navigation system is insufficient in dynamic risk assessment and prospective planning in the prior art. The technical key points are that the system comprises a multi-source data acquisition module, a dynamic risk mapping module, an intelligent path planning module, a visual interaction module, a risk early warning module and a self-checking module. According to the system, multi-source data such as weather, ocean current and ship dynamics are collected in real time, a predictive risk field is generated by using a long-short-term memory network, a route is optimized in combination with an adaptive deep reinforcement learning algorithm, and risk distribution and a recommended route are visually presented in a multi-scale three-dimensional form; the system can early warn a high-risk area in real time, and the reliability is ensured through a self-checking function. According to the invention, the sailing safety and the intelligent level of the ship in a complex environment are obviously improved.

The invention discloses a ship dynamic path planning method and system based on an improved DRRT algorithm, and relates to the technical field of ship automatic sailing, and the method comprises the steps: obtaining the two-dimensional plane environment parameters of a ship sailing sea area, determining the boundary range of the sea area, collecting the position and contour data of a static obstacle, and collecting the state information of a dynamic obstacle in real time, determining a starting point and a target point of ship path planning; an improved DRRT algorithm is adopted, sampling points are determined through target and path point bias sampling, the extended step length is determined through step length adaptive adjustment, a path node set is determined through path node trimming and reconnection, and an initial path is generated through cubic B-spline smoothing; obstacles are monitored, whether part of path results are invalid or not is judged, if the failure phenomenon exists, an improved DRRT algorithm is adopted for re-planning, and a dynamic path is obtained. According to the invention, the comprehensive requirements of the real-time performance, the safety and the path quality of the path planning in the intelligent navigation process of the ship can be met.

The invention relates to the technical field of marine environment governance, in particular to a DQN enteromorpha salvage optimal path optimization method based on adjacent target guidance, which comprises the following steps: S1, integrating enteromorpha drift trajectory prediction and ship dynamics response, constructing a time-space coupled salvage weight correction matrix, and dynamically reflecting the matching degree of drift displacement and steering capability; s2, screening a current optimal unaccessed unit as an adjacent target based on the correction matrix, predicting an enteromorpha position when a ship arrives, and generating a guide action set limited by a minimum steering radius; and S3, constructing a dual-channel DQN network, extracting task accessibility by a main channel, describing a drift change trend by an auxiliary channel, and generating an optimal salvage path adaptive to a complex drift environment through a dynamic attenuation fusion mechanism. According to the method, by constructing the enteromorpha-ship coupling model and the acceleration sensing guiding mechanism, collaborative improvement of feasibility, stability and environmental adaptability of path planning is achieved.

The invention discloses an intelligent ship power control system which comprises a data collection module, a power control strategy adjustment trigger module, a ship navigation state recognition model construction module, a ship power strategy generation module and a power intelligent control module. The invention relates to the technical field of ship power control, in particular to an intelligent ship power control system, which innovatively introduces a power control strategy adjustment triggering method of a dual judgment logic mechanism, and effectively realizes intelligent updating of a ship power control strategy. The stability of an identification result is improved through identification model construction of a dynamic hyperbox expansion mechanism of overlapping risk avoidance and a membership function construction method of boundary dynamic regulation and control; a multi-stage strategy generation method is designed, an improved optimization algorithm which introduces a population search direction control factor and a self-adaptive reverse guidance mechanism is adopted, the global optimization ability of the control strategy is improved, and fine control over the ship power strategy in the complex navigation environment is achieved.

The embodiment of the invention provides an AIS data quality evaluation method and device, electronic equipment, a readable storage medium and a chip, and the method comprises the steps: obtaining AIS data of a plurality of ships; determining a sea area type corresponding to each ship; determining a data updating interval threshold value corresponding to each ship according to the regional channel attribute; determining an update frequency score; determining a data integrity score according to the ship static data and the ship dynamic data; determining a time synchronism score according to the device time; determining a signal stability score according to the signal strength of the AIS data; determining an initial weight; determining at least one emphasis index according to the ship type and the regional operation attribute corresponding to each ship; determining a dimension weight according to the emphasis index and the plurality of initial weights; and determining the comprehensive quality index of the ship according to the dimension weight and the dimension score. Through the scheme of the invention, multi-dimensional analysis of the AIS data is realized, and the accuracy of AIS data quality evaluation is improved.

The invention discloses a marine traffic flow probability prediction method based on multi-source AIS-meteorological data fusion, and belongs to the field of marine traffic safety management, and the method comprises the following steps: S1, obtaining a standardized multi-source data set; s2, fusing the dynamic statistical characteristics of the AIS ship and the meteorological environment characteristics through a ship motion transfer function, and constructing a space-time patch considering a short-term ship motion trend and a long-term sea condition trend; s3, based on the space-time patch, fusing patch Transform time sequence modeling and adaptive dynamic graph space correlation learning to obtain a space-time fusion feature; and S4, outputting the traffic flow state prediction value and the probability distribution of the traffic flow key indexes. The marine traffic flow probability prediction method based on multi-source AIS-meteorological data fusion breaks through the limitation that a traditional method is single in data dimension, insufficient in space-time correlation capture and lack of probability confidence, and high-precision probability prediction of the marine traffic flow under the complex sea condition is achieved.