470 results about "Ship motions" patented technology

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.

A whole ship strain field rapid reconstruction method using ship motion and discrete strain information can rapidly reconstruct a whole ship structure strain field through fusion analysis of ship motion information and finite discrete strain monitoring data, and provides a more reliable reference basis for ship structure safety assessment and auxiliary decision making. The method comprises the following steps: firstly, acquiring ship motion monitoring data, and establishing a wave time-frequency parameter inversion Kalman filtering model based on ship motion information; secondly, according to the wave height obtained through inversion, the wave pressure load on the surface of the ship body is solved by combining phase parameters and relative positions of the wave surface and the ship body; thirdly, establishing a whole ship structure strain field dynamic reconstruction artificial intelligence model fused with a physical information neural network-finite element method, and formulating a layout strategy of measuring points; and finally, performing network parameter optimization on the PINN-FEM strain field reconstruction model by using strain monitoring data of finite discrete measurement points, and improving the overall calculation precision of the model.

The invention relates to a saline intrusion measurement method, system and equipment based on dynamic ship motion conditions and a medium, and aims to solve the technical problem that the salinity inversion precision is reduced due to spatial-temporal dislocation between particle image velocity measurement flow field data and plane laser-induced fluorescence concentration field data caused by ship six-degree-of-freedom motion. According to the method, future poses are predicted by collecting ship motion parameters and environment disturbance parameters, and space-time compensation parameters are generated in combination with a static calibration matrix; performing three-dimensional coordinate transformation and time synchronization on synchronously acquired original flow field and concentration field data by applying compensation parameters, and eliminating space-time deviation caused by ship movement; a three-dimensional salinity distribution field is calculated based on registration data fusion flow field diffusion characteristics and concentration field distribution; and meanwhile, the pose of the measuring equipment is dynamically adjusted through the closed-loop controller so as to optimize the data acquisition quality. According to the method, ship motion interference is effectively inhibited, and accurate characterization of saline invasion frontal surface spatial characteristics is guaranteed.

The invention relates to a ship automatic berthing safety control system, and belongs to the technical field of port ship operation safety control. The system comprises four core modules which are sequentially connected to form closed-loop control: a situation synchronous sensing module generates total-factor dynamic attitude information through space-time synchronization and data fusion of a ship-shore inertial unit; the digital twin deduction module performs real-time simulation based on a ship six-degree-of-freedom motion model, and predicts a ship motion track and berth acting force in a prospective manner; the safety decision and early warning module compares the prediction track with the safe berthing corridor through a track-berthing force coupling algorithm, and outputs a cooperative control strategy; and the multi-terminal cooperative execution module converts the strategy into an instruction and synchronously distributes the instruction to a tug, a navigation bridge and a wharf system. Automatic closed-loop control of the whole process from sensing, prediction, decision making to execution is achieved, and the safety, accuracy and operation efficiency of ship berthing are effectively improved.

The invention relates to the technical field of sea condition prediction, and particularly discloses a sea condition prediction method based on PSO-ShipMotionNet and a spectrum guided optimization strategy, and the method comprises the steps: carrying out the normalization in a single sample; constructing a loss function by using a Goda improved formula of a Jonswap spectrum, and taking MAE as energy-related loss; respectively optimizing the area and the specific frequency band shape by adopting a segmented mixed loss optimizer; using a PSO algorithm to optimize model training parameters; an SRFCA mechanism is introduced, and the ship motion coupling relation and the nonlinear influence of the complex external environment are processed; introducing SRFCA by taking the ResCNN as a reference, and constructing a PSO-ShipMotionNet network in combination with a PSO algorithm; constructing a data set training model by using a boundary element method; and analyzing the influence of the SRFCA and the segmented optimization strategy on the performance of the model. According to the sea condition prediction method based on the PSO-ShipMotionNet and the spectrum guided optimization strategy, the dependence of the model on the number of training samples is reduced to a certain extent, the generalization ability and the physical interpretability of the model are improved, and a new scheme is provided for sea condition parameter prediction.

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

The invention discloses a path planning method and device for an unmanned ship, a storage medium and equipment, and relates to the technical field of traffic ships, and the method comprises the steps: building an environment map according to the current position information of a ship, the position information of a destination, the state information of the ship and the position information of an obstacle; based on ship motion characteristic constraints, an initial path point sequence is generated through an improved progressive optimal fast exploration random tree algorithm, and the waypoint deviation risk is eliminated. And carrying out motion constraint perception path optimization processing on the initial path point sequence to generate a smooth path which can be directly and efficiently executed by a tracking controller. A plurality of groups of optimized path point sequences are obtained based on ship characteristic parameters with different constraint degrees, the limitation that a path is difficult to adapt to complex sea conditions under a single motion constraint working condition is broken through, finally, path quality multi-target comprehensive evaluation is performed on the plurality of groups of optimized path point sequences, and an optimal target planning path is obtained through screening. The problems of insufficient feasibility and low efficiency of ship path planning are solved.

The invention belongs to the technical field of marine environment monitoring, and discloses a multi-mode deep learning fused marine remote sensing sea wave parameter inversion method and system, and the method comprises the steps: obtaining three-degree-of-freedom motion time calendar data of a ship in a target region and spaceborne synthetic aperture radar (SAR) image data; preprocessing ship motion time calendar data and the SAR image data, and constructing time-space aligned multi-modal input data; and inputting the preprocessed multi-modal input data into a pre-trained sea wave parameter inversion model, and calculating to obtain an inversion value of the sea wave parameter. According to the method, an attention mechanism is innovatively used to realize adaptive alignment and complementation of heterogeneous information in two dimensions of space and time, and an attention module automatically allocates weights according to task correlation; through time-space collaborative feature enhancement, the model can stably extract characterization strongly related to target parameters even under complex sea conditions, so that the inversion precision and robustness of sea wave parameters are remarkably improved.

The invention discloses a ship-based radar and AIS data fusion method and device based on the satellite internet, and relates to the technical field of ship information. The method comprises the following steps: acquiring self-motion, AIS and radar data recorded by a plurality of ships in real time through a low-orbit satellite, and performing target trajectory tracking and data filtering by adopting a first-order linear ship motion model and a self-adaptive extended Kalman filter; the method comprises the following steps: establishing an error model containing a radial error coefficient, an azimuth angle error and an effective detection distance for dynamic system errors of a shipborne radar, and realizing radar-AIS target matching and error correction based on an adaptive differential evolution algorithm and a Hungary algorithm; and finally, performing fusion processing on unknown targets sensed by multiple nodes through a multi-hypothesis tracking method to form a globally unified target observation result. According to the method, shipborne radar data and AIS data can be effectively associated, real-time monitoring of ship targets in a wide-area sea area is achieved, and reliable technical support is provided for marine supervision, safety early warning and track backtracking.

The invention relates to the technical field of logistics tracking, in particular to a shipping logistics tracking method. Comprising the following steps: detecting a signal interruption time point and duration to obtain a signal interruption interval; according to the signal interruption interval, the navigation speed, direction and position which are finally recorded before interruption of the ship are extracted from a historical navigation trajectory database, and an initial state vector is generated; predicting a future transportation path of the ship through the corrected position sequence, and generating a predicted path set; according to the prediction path set, constructing a ship motion track in the three-dimensional virtual scene, and generating a visual track model; obtaining a decision support view by combining the confidence coefficient of the reckoning position sequence; and updating a port scheduling plan in real time through a decision support view, and generating an optimized loading and unloading time sequence. According to the method, the problem that the calculation precision of the position of the ship is not high in a complex scene of signal interruption is solved, and the ship position precision and the prediction precision of a future transportation path are improved.

A motion state observation method for autonomous navigation of a ship. The method uses a determination method for system matrixes Ad, Bd, Ed and Hd, and a determination method for a related measurement covariance matrix R and noise covariance matrix Q, and uses a discrete Kalman filtering algorithm to perform motion state observation and prediction for autonomous navigation of a smart ship. The method fully uses ship position and heading measurement information of a smart ship during autonomous navigation, uses a ship motion model and the discrete Kalman filtering algorithm, and selects an effective state observation variable, thus calculating reasonable and effective observer parameters. The state observation method can effectively calculate information such as the actual position, heading, speed and angular velocity of a smart ship at each time instant during autonomous navigation, and predict motion information at a next time instant, thus helping to reduce the difficulty of smart ship motion controller design, ensuring effective implementation of navigation control strategies, reducing ineffective thrust output and rotation of propulsion systems, and improving the autonomous navigation capability of smart ships.

The invention relates to the technical field of waterway traffic inland ship berthing planning, in particular to a ship berthing pose planning method based on an end-to-end deep learning model, which comprises the following steps: acquiring environment and position information in a ship berthing process by adopting data sensing and sensing equipment, and acquiring a ship berthing pose based on GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit) and VDR (Vessel Navigation Recorder) equipment which are deployed on a ship; obtaining complete motion state information of the ship in real time; constructing an end-to-end deep learning model, inputting the state of the current ship and berth into the end-to-end deep learning model to obtain a target rudder angle, a propulsion instruction and a ship heading, and generating a manipulation instruction sequence of the whole berthing process; and meanwhile, a dynamic correction mechanism of environment disturbance and motion feedback is introduced, environment disturbance information and ship motion feedback information are collected, and the adaptability to uncertain factors such as wind current in the berthing process is improved. According to the method, manual trajectory decomposition is not needed, and the more intelligent and comprehensive ship berthing pose planning method is achieved.

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 compensation operation simulation method for a telescopic active wave compensation trestle, and the method comprises the steps: taking sea condition parameters as input, combining a wave frequency response factor of a ship, and simulating the six-degree-of-freedom wave frequency motion of the ship; establishing a series coordinate system connected with a compensation mechanism on the telescopic active wave compensation trestle, and constructing a forward motion equation and a reverse solution equation of compensation motion; according to the compensation target position of the tail end of the telescopic active wave compensation trestle, the six-degree-of-freedom motion amplitude of the ship is combined, and the motion stroke of a compensation mechanism is reversely solved; differentiating the motion stroke of the compensation mechanism, calculating the speed and acceleration of the compensation mechanism, and determining the statistical value of the motion time sequence of the compensation mechanism; and finally, judging whether the telescopic active wave compensation trestle can compensate the ship motion caused by the sea condition or not according to the statistical value. Whether the compensation mechanism design of the trestle can compensate the ship motion under the sea condition or not can be judged according to the simulation result.

The invention discloses an intelligent cabin light adjusting system, which belongs to the technical field of intelligent illumination of ship cabins, and comprises a ship motion sensing module, a human body data collection module and an environment detection module, the ship motion sensing module, the human body data collection module and the environment detection module respectively collect corresponding data and carry out data integration; and then the integrated data enters the intelligent control unit and the intelligent light adjusting module in sequence, and finally the intelligent lamp group is controlled. According to the cabin intelligent light adjusting system, light can be intelligently adjusted, the energy efficiency is improved, the energy consumption is reduced, operation is convenient, the user experience is improved, and the overall energy-saving effect of a ship cabin is improved.

The invention relates to the technical field of ship motion control, and particularly discloses a ship dynamic positioning control method and system for a full-water-depth sea area, and the method comprises the steps: S1, measuring the water depth in real time, and calculating the water depth-to-draft ratio through combining with the ship draft; s2, according to the water depth-to-draft ratio, a water depth self-adaptive ship body dynamic model is updated; s3, calculating real-time optimal control force by utilizing an optimal quadratic index according to the updated water depth self-adaptive ship body dynamic model; and S4, performing dynamic positioning control according to the real-time optimal control force. The dynamic positioning system can be compatible with control requirements of deep water and shallow water operation areas, and the problems of control drift, instability divergence and the like caused by inertia increase, damping increase and thrust loss of a ship body in a shallow water area are solved.

The invention relates to the technical field of ship power and propulsion control. The invention provides a multi-source cooperative propulsion control method and system based on hybrid power and a straight wing steering oar. The method comprises the following steps: collecting ship motion state parameters and environment parameters in real time; constructing a multi-objective joint optimization model, and solving an optimal power distribution proportion and an optimal thrust vector under the current working condition by adopting a dynamic programming algorithm; based on the optimal power distribution proportion, a fuzzy PID controller is adopted to dynamically adjust the torque coupling ratio of the diesel engine and the motor; according to the optimal thrust vector calculation result, vector synthesis is carried out on the propulsive force and the steering torque; and predicting the motion state of the ship based on an extended Kalman filter, and correcting the weight coefficient of the multi-target joint optimization model on line in combination with the change rate of the channel boundary distance. The problem that adaptability to complex working conditions is insufficient due to the fact that a traditional control strategy does not fully fuse ship motion states and environment parameters is solved.

The invention provides a ship path tracking and rolling comprehensive control method and device based on a straight-wing propeller, belongs to the technical field of ship motion control, and solves the technical problem that path tracking and rolling control precision and efficiency of a ship are insufficient due to environmental disturbance. Constructing a four-degree-of-freedom ship motion equation, constructing a four-degree-of-freedom ship motion model with interference and measurement noise, reconstructing a ship motion state by adopting a Kalman filter, calculating a path tracking error by utilizing a self-adaptive sight guidance algorithm, and generating an expected course angle; determining a longitudinal thrust, a rolling stabilizing moment and a course control thrust; and the revolution speed and the rotation swing angle of the straight wing propeller are optimized, and the rotation swing angle is recalculated. The method is suitable for the ship provided with the straight-wing propeller, and high-precision path tracking and efficient rolling stabilization are achieved by controlling the revolution speed and the swing angle of the propeller.

The invention discloses a submarine cable construction ship extremely-short-term motion response prediction method and system, and relates to the technical field of ship motion control in ocean engineering. Comprising the following steps: acquiring six-degree-of-freedom time sequence data based on a ship-borne inertial measurement unit of a submarine cable construction ship, and constructing a data set; the data set is preprocessed; constructing a model CNN-RSBU-BiLSTM (Convolutional Residual Shrinkage-Bidirectional Long Short Term Memory) with fusion of the convolutional residual shrinkage; the model CNN-RSBU-BiLSTM is trained by means of the data set obtained after preprocessing is carried out on the model CNN-RSBU-BiLSTM; and inputting the six-degree-of-freedom time sequence data acquired and preprocessed in real time into the trained model, and outputting an extremely short-term six-degree-of-freedom motion response prediction result of the submarine cable construction ship. According to the method, the multi-scale feature extraction capability of the one-dimensional convolutional neural network, the noise reduction enhancement effect of the residual shrinkage building unit and the digging capability of the bidirectional long-short-term memory network on the time sequence dependency relationship are combined, so that accurate prediction of the extremely-short-term six-degree-of-freedom motion response of the submarine cable construction ship is realized, and the prediction accuracy is effectively improved.

The invention discloses an AIS information and video data fusion method. The method comprises the following steps: collecting and preprocessing AIS and video data; constructing a constant acceleration model to interpolate the AIS data, and projecting AIS latitude and longitude coordinates to a video pixel coordinate system to obtain a target trajectory; detecting a video ship by using a YOLOX model, and tracking through a MotionTrack algorithm to obtain a video trajectory; ship motion features of the tracks in the AIS and the video data are extracted respectively; constructing a comprehensive measurement function fusing the Euclidean distance, the course and the navigational speed similarity; performing data matching by adopting a Hungary algorithm to obtain a matching result of the AIS and the track in the video data; verifying a matching result through abnormal value elimination and category consistency verification; and according to a matching result, fusing and displaying the matched AIS information and the video image. The AIS information and the visual target information are comprehensively utilized, and the information matching result is determined by using multiple clues, so that the fusion accuracy is improved, and the precision and reliability of multi-source information fusion in maritime affair monitoring are improved.

The invention discloses a large ship autonomous berthing path planning method, and relates to the technical field of channel control. The method comprises the steps of collecting and processing data, establishing a ship motion model, setting a path planning target, selecting and solving an optimization algorithm, verifying and adjusting a path, obtaining hydro meteorological historical data and real-time sensing data of port wind, wave and flow, ship basic parameters and under-actuated dynamic characteristic data, and preprocessing the data. Obtaining a ship motion model; minimum berthing time, minimum energy consumption and maximum safety are set, and a comprehensive objective function is obtained; defining constraint conditions of kinematics, dynamics, port environment and collision avoidance; solving by using an optimization algorithm, performing dynamics simulation verification and collision detection verification on the planned path, adjusting parameters of the optimization algorithm, and modifying a target function or a constraint condition; according to the method, the optimal berthing path can be planned, the collision risk is effectively avoided, the safety and efficiency of large ship berthing are improved, and the method has universality and flexibility.

The invention discloses a ship motion attitude dynamic prediction method fusing CNN and BiLSTM, and the method comprises the steps: S1, obtaining ship navigation data, and carrying out the preprocessing of the ship navigation data; s2, establishing a ship navigation three-degree-of-freedom nonparametric model, and obtaining a regression function of the nonparametric model; s3, establishing a ship motion attitude non-parameter identification prediction model, training the ship motion attitude non-parameter identification prediction model, and optimizing a plurality of hyper-parameters by adopting an improved white shark optimization algorithm to obtain an optimized ship motion attitude non-parameter identification prediction model; and S4, dynamically updating the optimized ship motion attitude non-parameter identification prediction model by adopting an incremental learning method so as to realize dynamic prediction of the ship motion attitude. According to the method, a bidirectional long-short-term memory neural network BiLSTM is adopted as a basic model, a time sequence dependency relationship is more comprehensively captured in combination with past and future information, and an improved white shark optimization algorithm IWSO is adopted to perform hyper-parameter optimization on a non-parameter identification prediction model so as to select an optimal hyper-parameter combination, so that the WCBI-SAT can maintain relatively high prediction performance, and the prediction efficiency is improved. And the method has relatively high generalization ability and stability.

The invention relates to the field of drilling ship manufacturing, in particular to a data acquisition method and system of a driver for oil exploitation. Six-degree-of-freedom motion data of a ship motion reference unit, pressure data of a hydraulic cylinder of a marine riser tensioner, hook load data of a drill string hung by a top driving system, torque data and radial acceleration data are synchronously collected, and collected multiple paths of signals are aggregated to form synchronous multi-source vibration data flow. According to the method, multiple types of vibration, load and pressure signals are synchronously collected and aggregated and segmented under the unified time reference in the operation process of the drilling ship, so that the consistency of multi-source data on the time scale is ensured, and the real dynamic coupling relation can be reflected by subsequent calculation; in the signal processing stage, key acceleration and pressure fluctuation information are extracted, and the actual working state of the marine riser system is represented through dynamic response characteristics constructed by the peak position, the peak amplitude and the power spectral density.

The invention discloses a moored ship motion state prediction and anti-collision early warning system, particularly relates to the field of traffic control and anti-collision early warning, and is used for solving the problems that ship motion state accurate prediction and anti-collision scheduling response lag cannot be realized in existing port mooring operation. According to the system, ship trajectory, port operation and environment monitoring data are acquired in real time through a data acquisition module, and a fusion data stream with consistent time-space attributes is generated; constructing a space-time diagram structure by using a feature extraction module, extracting traffic flow dynamic features, and calculating a short-term conflict probability, a trajectory convergence risk value and a mooring risk value; the risk early warning module generates a collision risk index based on risk level mapping and triggers graded early warning; the mooring decision-making module sets a key control leg according to the early warning level and generates a dynamic decision-making scheme; the anti-collision scheduling module executes actual channel passing, speed adjustment and track change, and dynamic coordination and safety management and control of port mooring anti-collision are achieved.

The invention discloses six-degree-of-freedom wave compensation embarkation equipment suitable for complex sea conditions and a control method of the six-degree-of-freedom wave compensation embarkation equipment. The six-degree-of-freedom wave compensation embarkation equipment comprises a base platform fixed to an operation ship deck; a lower platform of the Stewart parallel mechanism is connected with the base platform, and an upper platform of the Stewart parallel mechanism is connected with the lower platform through six hydraulic cylinders; the embarkation gangway ladder system is mounted on the upper platform; the hydraulic servo driving system is used for driving the six hydraulic cylinders; the intelligent sensing system is used for collecting ship motion data and relative pose data of the target platform; the intelligent control system is electrically connected with the hydraulic servo driving system and the intelligent sensing system; wherein the intelligent control system is configured to execute a compound control algorithm based on a nonlinear extended state observer and nonlinear model predictive control so as to drive the hydraulic servo driving system and compensate ship motion. The embarkation equipment provided by the invention has the advantages of high precision, high stability and strong robustness, and can realize accurate and stable compensation of complex sea conditions.

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.

The invention relates to a transverse movement berthing control method considering main thrust idling, which comprises the following steps of: dividing transverse movement berthing control into a controller part and a thrust distribution part, designing a three-degree-of-freedom controller for ship movement by utilizing a nonlinear disturbance observer in the aspect of the controller, and calculating force and moment required for transverse movement of a ship; in the aspect of thrust distribution, the discontinuity of a main thrust idle speed area and rudder force is considered, a thrust action range is decomposed into nine action ranges according to the thrust state of double main thrusters, multi-domain switching logic is designed to complete thrust distribution of control force, and therefore closed-loop control over ship transverse moving berthing is achieved. A simulation result proves the effectiveness of the method.

The invention relates to a multi-signal fusion real-time deduction method for three-dimensional wave parameters along with a ship, and a deduction device consists of a state sensor, a one-way stress sensor, a vertical acceleration sensor, a shunt, a terminal junction box, a signal acquisition instrument, a host, analysis software, a signal transmission cable and the like. The deduction method comprises the processes of system state matrix discretization, transfer function calculation, Kalman filtering deduction model construction, convergence analysis, result comparison and the like, and three-dimensional time-frequency parameters of waves are deduced in real time through fusion analysis of ship motion and actual measurement data of a key position stress response sensor. Real-time information of surrounding waves can be provided for more sailing ships, more comprehensive reference basis can be provided for ship sailing and ship type optimization, and the method has practical significance and market value.

The invention relates to the technical field of ship stabilization control, and discloses a rudder fin combined stabilization system cooperative control method comprising the following steps: 1, building a ship motion model; step 2, designing a fuzzy adaptive PID controller; 3, constructing a rudder fin interaction model; and 4, introducing a prediction control algorithm, and optimizing a control input sequence based on a model prediction principle. By establishing a ship motion model, designing a fuzzy self-adaptive PID controller, analyzing rudder fin interaction and introducing a predictive control algorithm, the problems that an existing rudder fin combined stabilization system is insufficient in cooperative control, in the ship navigation process, a ship is affected by external factors such as sea waves and sea wind, the ship is prone to swing motion, and the stability of the ship is poor are solved. Therefore, the stabilization effect and the course stability of the ship are improved.

The invention discloses an unmanned ship target tracking constraint control method based on an exponential time-varying performance function, and relates to the technical field of state tracking control, in particular to the technical field of performance constraint control, comprising the following steps: step (1), establishing an unmanned ship motion three-degree-of-freedom mathematical model; step (2): constructing an exponential time-varying function; (3) constructing an exponential time-varying performance function; (4) introducing a time-varying auxiliary function; (5) designing a transfer function; (6) designing a fixed time performance constraint heading controller based on an exponential time-varying performance function; (7) designing a fixed time performance constraint surging controller based on an exponential time-varying performance function; and (8) performing simulation verification. According to the method, mainly starting from the design of a novel time-varying performance function, the problem that the state error of the traditional performance function constraint control at the initial moment is limited by the performance boundary is solved, so that the transient performance and the steady-state performance of the state tracking error of the unmanned ship are remarkably improved; and the overall control performance of unmanned ship target tracking control is further improved.