546 results about "Ship motions" patented technology

The embodiment of the invention relates to the technical field of ship scheduling, in particular to a multi-source data-based intelligent auxiliary decision-making method for ship entering and leaving a port and berthing and leaving the port, and the real-time performance and accuracy of port scheduling decision-making can be improved by fusing multi-source heterogeneous data to construct a ship navigation situation awareness system. The space-time alignment technology effectively eliminates space-time dislocation of environment interference factors and enhances the characterization capability of the ship motion state and environment coupling relation. Quantitative evaluation of multi-dimensional navigation risks is realized based on a ship behavior prediction model, and potential risks such as channel deviation and berthing conflicts can be identified. A collaborative decision-making scheme considering safety and efficiency is generated through a collaborative optimization mechanism of the dynamic scheduling rule base and the risk indexes, and ship trajectory control precision is optimized through dynamic planning of navigational speed adjustment and steering time sequence. Closed-loop linkage of port resource allocation and ship navigation control is achieved through automatic instruction generation and execution, and the reliability and the response speed of the port operation process can be ensured.

The invention relates to the technical field of graph recognition, in particular to a ship type recognition method and system based on transfer learning, and the method comprises the following steps: carrying out the combined preprocessing of a visible light ship image and a synthetic aperture radar image, and separating a ship inherent feature layer and an environmental noise feature layer through an adversarial domain decoupling network; generating an interference-removed cross-domain feature matrix; enhancing space-time consistency features related to the ship motion mode, and outputting an enhanced feature matrix after enhancement; and S2, constructing a differentiable decision tree classifier based on the enhanced feature matrix in S2, selecting a category with the highest probability as a final recognition result to be output, comparing the confidence coefficient of the final recognition result with a preset threshold, and if the confidence coefficient is lower than the preset threshold, triggering incremental learning. The adaptive capacity to complex sea conditions, ship type diversity and shielding conditions is improved, the error recognition rate and the attack resisting success rate are remarkably reduced, and the reliability of the system in the open environment is effectively improved.

A joint simulation and operation operability evaluation method based on wind power operation and maintenance ship motion dynamic response and active compensation gangway ladder motion control comprises the steps that an operation and maintenance ship frequency domain seakeeping model is built, and a hydrodynamic coefficient library is calculated; wave exciting force is reconstructed based on a Cummins time domain motion equation, and ship motion response prediction is realized by adopting implicit generalized alpha time domain composite integral; the dynamic positioning system extracts low-frequency motion through a Kalman filter and optimizes thruster thrust distribution to stabilize the position of the ship. A gangway ladder dynamic model is established based on a Lagrange equation, real-time control over displacement, speed and acceleration of the top end of the gangway ladder is achieved through a feedforward-feedback composite control strategy, and nonlinear output of a hydraulic actuator is optimized. And realizing joint simulation of the coupling system through time domain synchronization and self-adaptive PID (Proportion Integration Differentiation) control. And checking gangway ladder movement based on a preset threshold value and a space boundary condition, and evaluating the operability of the climbing operation. According to the method, the limitation of traditional single-disciplinary analysis is broken through, and the forecasting precision of wind power operation and maintenance operation operability is effectively improved.

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 provides a deep learning-based ship motion trajectory optimization control method, which relates to the technical field of ship control, and comprises the following steps: constructing a ship motion simulation environment, and establishing a dynamic simulation system containing marine environment parameters; acquiring ship operation data; a dual-network structure model is constructed, the dual-network structure model comprises a macroscopic strategy network and a microscopic control network, the macroscopic strategy network generates a course planning strategy at a long time interval, and the microscopic control network generates a specific control instruction at a short time interval; a hierarchical training strategy is adopted to train the dual-network structure model in a ship motion simulation environment, firstly, a macroscopic strategy network is trained to generate an overall course strategy, and then a microscopic control network is trained to realize accurate control; and deploying the trained dual-network structure model to a ship control system, generating an optimal control instruction, and realizing real-time optimization control of the ship motion trail. According to the invention, on the premise of ensuring the control precision, the real-time optimization control of the ship motion trail is realized.

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 discloses a ship motion track prediction method based on ship track big data. The method comprises the following steps: collecting ship AIS data and preprocessing the ship AIS data to obtain ship track data; a sliding window is adopted to sample ship trajectory data, ship historical trajectory point state features are extracted, and a time interval relation matrix is constructed; the method comprises the following steps: constructing a Transform architecture model which only retains a decoder, discretizing an extracted trajectory feature and a to-be-predicted time interval into corresponding one-hot coding vectors so as to obtain embedded vectors, merging the embedded vectors of the two into a joint feature embedded vector, and stacking ship trajectory point sequences to construct an obtained joint feature embedded matrix, linearly adding the time interval relation embedded matrix and the position feature embedded matrix to obtain an embedded matrix, and inputting the embedded matrix into a Transform architecture model for training to obtain a prediction model; and outputting a ship motion trail prediction result through a decoder. According to the method, the accurate prediction of the non-uniform trajectory sequence is realized, and the problems of non-uniform time interval and low prediction accuracy of the ship trajectory data are solved.

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 further provides an unmanned ship collision avoidance method and device based on the DBSCAN algorithm, and the method comprises the following steps: 1, collecting ship data, and training a six-degree-of-freedom ship motion model through a neural network; step 2, acquiring a wind speed by using a wind speed sensor, and acquiring ocean current information by using an ocean current sensor; step 3, acquiring speed and position information of other ships by using AIS / radar; step 4, collecting ship captain driving information, establishing a ship driving model, and predicting the future motion state of the ship; 5, performing risk analysis on other ships by using a DBSCAN algorithm, and calculating ship collision risks; step 6, according to the step 5, performing corresponding collision avoidance behaviors aiming at the risk; and 7, judging an action execution condition, and returning a reward value and a result. According to the technical scheme, the collision avoidance capability of the unmanned ship can be improved, the generalization capability is good, and the efficiency of collision avoidance and safe navigation of the unmanned ship on the water surface is improved on the whole.

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 an extremely-short-term ship motion attitude prediction method and system based on data enhancement, and relates to the field of ship motion attitude prediction. The method is used for solving the problem that data quality and model generalization performance are affected due to insufficient completeness of input data of a ship motion attitude extremely-short-term prediction model. The method comprises the following steps: performing data expansion on original observation ship attitude data by adopting a Slim-based generative adversarial filling network to obtain an expanded data set; performing data splicing and feature zooming on the original observation ship attitude data and the extended data set; performing time delay correlation analysis on the data after feature scaling to determine optimal sample data, then dividing the optimal sample data into a training set and a test set according to a proportion, and inputting the training set and the test set into a prediction model for training; optimizing parameters of the prediction model according to the prediction value of the training set; and outputting a predicted value by adopting the parameter-optimized prediction model, and carrying out reverse normalization processing to obtain a final prediction result. The method is suitable for extremely short-term ship motion attitude prediction.

The invention provides a ship dynamic stability modeling method and system based on multi-external force coupling, and the method comprises the steps: building a short-peak wave model based on wind wave characteristic parameters under different sea conditions and motion state parameters of a ship, and carrying out the calculation under ground coordinates to obtain a first wave surface equation; a first wave surface equation located in a ground coordinate system is converted into a ship body stable coordinate system through a coordinate and frequency conversion method, a second wave surface equation suitable for the surrounding environment of the ship is obtained, then a fluctuation pressure model caused by sea waves is established, the fluctuation pressure model is corrected through a Smith correction term method, and the ship body stable coordinate system is obtained. A multi-wave superposition fluctuation pressure model is obtained through a harmonic superposition analysis method; then a generalized disturbance force matrix is constructed based on a finite volume method, Archimedes restoring force, inertia hydrodynamic force and viscosity hydrodynamic force are calculated by adopting a specific calculation method, and then a ship motion model is established, so that the accuracy of the model is effectively improved, and the dynamic behavior of the ship can be more accurately predicted under the complex sea condition.

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 provides an unmanned ship target tracking decision control method based on deep reinforcement learning. The method comprises the following steps: establishing an unmanned ship motion model to obtain a current motion state of an unmanned ship; establishing a target tracking task reward function; constructing a deep reinforcement learning network model based on a soft actor-commentator algorithm, and performing initialization; acquiring a current position and a predicted position of the target ship; obtaining an optimal control strategy of the unmanned ship and an updated motion state of the unmanned ship by using a deep reinforcement learning network model; randomly selecting a control strategy to obtain an updated motion state of the target ship, storing the updated motion state of the unmanned ship and the updated motion state of the target ship as training samples in an experience pool, and repeating the above process when the target ship is not tracked; and when the target ship is tracked, randomly sampling from the experience pool to update the parameters of the deep reinforcement learning network model. The intelligent decision-making capability and tracking performance of the unmanned ship are improved, and the application development of the unmanned ship in the civil and military fields is promoted.

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 remote sensing image ship motion, and discloses a wake matching-based ship motion parameter estimation method, which comprises the following steps of: constructing a ship wake model comprising an infrared sea surface model, a ship wake height field and a ship wake temperature field; setting multiple navigational speeds, multiple navigations and multiple sea condition parameters by using the model to generate a ship wake infrared image, and constructing a ship wake database; a deep learning convolutional neural network and a multi-task loss function are adopted to extract a wake to be estimated; and constructing a multi-stage image matching model based on the feature point extraction module, the feature vector description module and the feature point matching module, and matching the database and the batch to-be-estimated trails by using the multi-stage image matching model. According to the method, the infrared sea surface model mixedly calculates two sea surfaces, so that the repeatability of the sea surfaces is reduced while the authenticity is ensured; by simulating the wake temperature field and the wake height field, the authenticity of the wake is improved, the constructed wake data set can be reused, and efficient estimation of ship motion parameters is realized.

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 provides an extremely-short-term ship motion attitude prediction method and system under a multi-dimensional coupling characteristic aiming at a multi-dimensional coupling challenge encountered in extremely-short-term ship motion attitude prediction, and relates to the field of ship motion attitude prediction. The method comprises the steps of collecting original attitude information of a ship; analyzing the relationship between each pair of variables in the original attitude information through a Spearman coefficient and a maximum information coefficient to obtain strong correlation characteristics; carrying out load correlation analysis on the strong correlation characteristics and historical data, and constructing an input characteristic sequence; training and evaluating the GRU network model of the integrated attention mechanism by adopting the input feature sequence, and adjusting network parameters of the GRU network model of the integrated attention mechanism to obtain an optimal GRU network model of the integrated attention mechanism; and predicting the attitude information of the ship by adopting the optimal GRU network model integrated with the attention mechanism to obtain a prediction result. The method is suitable for ship motion data characteristics under different sea conditions.

The invention discloses a reinforcement learning-based multi-unmanned ship formation hunting control method, which comprises the following steps of: constructing a multi-unmanned ship motion system influenced by environmental interference, and constructing a hunting formation error variable; constructing a virtual controller of the unmanned ship by adopting a backstepping method based on the encircling formation error variable; constructing a leader ship formation hunting controller based on a reinforcement learning method in combination with a neural network; constructing a following ship formation hunting controller based on an adaptive backstepping method in combination with the Lyapunov theory and a neural network; and according to the leading ship formation hunting controller and the following ship formation hunting controller, formation hunting control of the target ship is realized. The problem that the control performance of the multiple unmanned ships is insufficient due to the interference of complex marine environments such as wind, waves and flow in the process of efficiently executing a hunting task in the formation of the multiple unmanned ships, the cooperative relationship between the chasing unmanned ships cannot be fully reflected, the formation of the multiple unmanned ships is difficult to control, and the hunting efficiency of the multiple unmanned ships is improved is solved. And the control is difficult to realize at the cost as low as possible.

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 provides a ship motion prediction and compensation method based on domain adaptive transfer learning, and aims to solve the prediction and compensation problems of rolling motion of a service workboat in offshore wind power construction under complex sea conditions. According to the method, ship rolling angle data are collected in real time through a sensor, and time similarity quantitative division is carried out on the data to divide the data into source domain motion data and target domain motion data. And then, processing the data by using a domain adaptive transfer learning model, and predicting ship rolling motion at a future moment. The prediction result and actual data are subjected to inverse kinematics solution to obtain the expansion and contraction amount of the six electric cylinders of the Stewart platform, the expansion and contraction amount is fed back to a ship control system, the ship attitude is adjusted in real time, and the operation safety and efficiency are improved. According to the method, model parameters can be dynamically adjusted according to feature differences under different sea conditions, the prediction precision is remarkably improved, the method is suitable for rolling motion prediction and compensation under variable sea conditions, and improvement of the safety and stability of SOV operation is facilitated.

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.