29 results about "Ship dynamics" patented technology

This invention discloses a ship dynamic positioning anti-interference control method considering vertical dynamic optimization, comprising: establishing a four-degree-of-freedom nonlinear dynamic model of the ship and obtaining the error vector by combining the desired pose; defining a virtual control law by backstepping and processing the velocity error using a first-order filter; approximating the uncertainties in the model based on an improved Actor-Critic reinforcement learning framework and designing an interference observer to estimate external disturbances; calculating the compensated desired acceleration by combining the approximation output of the uncertainties, the estimated value of the external disturbances, and the velocity error; obtaining the thruster pitch ratio based on the desired acceleration and generating the final control command through a thrust distribution model. This invention can effectively handle model uncertainties and external environmental disturbances in ship dynamic positioning, especially optimizing vertical dynamic performance and improving control accuracy, response speed, and system robustness.

The application discloses a kind of multi-source sensing and space-time matching inland waterway navigability test and evaluation method, belong to waterway engineering and ship test evaluation technical field.Pass through the layout shore base monitoring unit and integrated shipboard perception unit, unified space-time datum is constructed;Based on unified space-time datum, space-time matching fusion is carried out to ship dynamic response data and waterway environment data, forms ship response-environment excitation coupling data set, constructs three-layer navigability performance evaluation system, uses improved fuzzy analytic hierarchy process to determine index weight in combination with three scale method and optimal transfer matrix, uses cloud model to complete index quantization and comprehensive evaluation value calculation, finally determines waterway navigability effect grade according to comprehensive expectation value.The application realizes the quantitative evaluation of multi-source data accurate fusion and subjective and objective fusion, forms standardization test and evaluation process, truly reflects the coupling navigability characteristics of waterway and ship, provides technical support for inland waterway engineering acceptance, navigation safety management and waterway optimization design.

The application relates to the technical field of intelligent control of ships, and discloses a ship intelligent driving control system based on multi-source information fusion, which comprises an interaction and cloud service layer, a perception layer, a decision control layer and an execution layer. The interaction and cloud service layer extracts a route intention and initializes a theoretical load; the perception layer collects cabin visual images and multi-source environment data; a water surface automatic driving domain controller of the decision control layer adjusts a working mode of the perception layer based on a route intention parameter, a multi-source fusion algorithm unit of the decision control layer calculates a ship dynamic eccentricity moment, fuses environment data to calculate a space-time residual error to filter out false targets, and extracts a local water surface lateral disturbance force vector; an autonomous control algorithm unit updates a ship dynamics model online and adaptively based on the theoretical load, the ship dynamic eccentricity moment and the lateral disturbance force vector, generates autonomous berthing and unberthing control instructions, and the execution layer receives the instructions for closed-loop execution. The application improves berthing and unberthing control precision under complex water surface variable load conditions.

This invention provides a system, method, and equipment for unmanned aerial vehicle (UAV) inspection of water depth in port channels and generation of dynamic electronic nautical charts, relating to the technical field of port and waterway safety management. This invention achieves periodic and rapid monitoring of water depth in port channels through the collaborative work of an UAV-borne inspection unit, a data fusion processing center, a dynamic electronic nautical chart generation unit, and a layer distribution and service unit. The UAV scans along an optimized route that integrates ship dynamics and tidal information, collecting laser point cloud data. The processing center fuses and corrects the point cloud data to generate a current digital water depth model, and automatically identifies abnormal shallow areas through intelligent comparison with a dynamic historical benchmark model. The generation unit encapsulates the shallow point information into an incremental electronic nautical chart data package conforming to international standards, forming a dynamic warning layer. The distribution unit pushes the layer to the navigation systems of relevant vessels for overlay display.

The application provides a ship mooring control method and system based on motion analysis, and relates to the technical field of ship mooring control. In view of the technical problems that the existing ship mooring control method mainly depends on position feedback, is easy to cause the single pendulum effect, has the resonance risk, has the response lag and has high energy consumption, the application adopts the fusion monitoring of ship motion state of stereovision and laser radar, constructs the mooring big data covering multiple ship types and multiple environment conditions based on the joint simulation of computational fluid dynamics and ship dynamics, optimizes the parameters of the PID control model with the ship displacement, speed and acceleration as inputs through the genetic algorithm, and finally realizes the closed-loop real-time adjustment of the cable tension relying on the high dynamic winch system. The method improves the control precision and stability of the mooring system, and the robustness and environmental adaptability in the face of complex environment.

The application discloses a kind of unmanned ship dynamics system identification modeling method and device.The method includes, obtains the identification data obtained by unmanned ship maneuvering test;Establish the dynamics equation of unmanned ship maneuvering;Add genetic factor modification parameter estimation value recursion form in RLS algorithm, based on particle swarm optimization algorithm, parameter optimization in the dynamics equation according to the identification data, establish unmanned ship dynamics system identification model.The method introduces genetic factor on the basis of traditional RLS algorithm, and is improved in combination with PSO optimization algorithm, improves the accuracy of model identification.

The embodiment of the application provides a shaft power generation fuel saving system in a ship power positioning mode, which is applied to the technical field of ship propulsion and control, and comprises: a data acquisition module, which acquires ship wave motion sensing data and generates a three-dimensional wave ship body atlas; a thrust loss prediction module, which predicts the instantaneous immersion depth of a propeller according to the atlas and calculates the instantaneous thrust loss amount in a future time window through a deep learning model; a coordinated control and execution module, which controls the dynamic switching of the shaft power generator between the power generation mode and the electric mode according to the comparison result of the thrust loss amount and a threshold value and sends an unloading signal before switching to the electric mode; and a power grid load management unit, which receives the unloading signal and short-term removes an interruptible load. The application realizes active prediction and dynamic compensation of thrust loss under wave disturbance, stabilizes the main engine speed, recovers energy, guarantees the stability of the power grid through load collaborative management, and improves the safety and economy of ship power positioning operation.

The application discloses a kind of ship power scheduling method and system based on multi-sensor data fusion, and the running state data of ship is collected in real time by multiple sensors, and environmental variable data is collected by environmental monitoring unit;Multi-source data is timestamped and aligned and space coordinate is registered, forming multi-source heterogeneous data fusion matrix;The matrix is input into the power demand prediction model trained in advance, and the predicted propulsion power demand and power margin interval are output;Combined with the real-time health status parameters of the ship power system, the optimal power distribution scheme is calculated by multi-objective optimization algorithm;The scheme is issued to each power execution unit, and closed-loop scheduling control is realized.The application improves the economy, safety and environmental protection of ship power scheduling through multi-source data deep fusion, digital twin health assessment and multi-time scale rolling optimization, and is suitable for power scheduling management of various ships.

This invention relates to the field of ship dynamic positioning technology, specifically disclosing a method and system for ship dynamic positioning control applicable to diesel engine-driven vessels. The method includes: S1: acquiring environmental data, calculating the total environmental load force, and determining the upper limit of the thruster and its corresponding upper limit of the diesel engine speed; S2: determining the lower limit of the theoretical diesel engine speed based on the upper limit of the diesel engine speed, and calculating the lower limit of the diesel engine speed and its corresponding lower limit of the thruster thrust based on the lowest stable diesel engine speed; S3: acquiring the initial thrust requirement of the thruster, determining the final thrust of the thruster based on the initial thrust requirement, the upper limit of the thruster thrust, and the lower limit of the thruster thrust, and driving the thruster through the diesel engine according to the final thrust of the thruster to achieve ship positioning control. This invention can fully utilize the high thrust advantage of diesel engines and avoid the shortcomings of slow speed adjustment, balancing thrust requirements and response speed without changing the hardware.

The application discloses a kind of dynamic positioning methods based on high-precision inertial navigation system and global differential satellite positioning system fusion, comprising: the angular velocity and specific force of ship are obtained by high-precision inertial navigation system INS, and the first position estimation is obtained by the integral processing of angular velocity and specific force; the measurement noise of differential position of global differential satellite positioning system DGNSS is obtained, and the Kalman gain of dynamic positioning method based on INS and DGNSS is updated in combination with the first position estimation and second position estimation;According to the PDOP position accuracy factor, the carrier-to-noise ratio quality factor and residual quality factor determine the comprehensive quality factor;And determine the weight size of INS and DGNSS in position estimation, and then realize position estimation.The application is fused by INS and DGNSS, so that the redundancy of dynamic positioning system is higher, the reliability is better, when the differential signal of DGNSS is unavailable, precise ship dynamic positioning operation is realized by INS Seamless mode switching.

The application discloses a kind of for underwater pipeline sinking Multi-source sensing data fusion adaptive model monitoring method and system, belong to underwater pipe laying technical field.This method first obtains underwater pipeline sinking whole process three-dimensional associated data, constructs environment structure collaborative analysis model to three-dimensional associated data feature extraction, obtains initial fusion parameter set, and input time sequence weight self-evolution decoupling model, obtains pipeline sinking adaptive parameter set, inputs the working condition adaptive control model to pipeline sinking adaptive parameter set, in combination with preset pipeline sinking safety threshold generates real-time control matrix, finally based on real-time control matrix generates sinking collaborative control instruction, and is issued to crane hydraulic control system, ship power positioning system and anchorage adjusting device.This method can ensure the high purity and reliability of initial fusion parameters, realize the intelligent decoupling and denoising of initial fusion parameters, efficiently generate the optimal control matrix, realize the adaptive monitoring and control of underwater pipeline sinking whole process.

This invention belongs to the field of marine propulsion technology and provides a method and system for scheduling hybrid power systems based on digital twins and distributed collaboration. The method includes collecting and processing multi-source data to obtain multi-dimensional synchronous data vectors, constructing a multi-step prediction model to obtain predicted multi-dimensional synchronous data vectors, building an operating cost model and a preset strategy library to generate power control commands for optimal hybrid power allocation, and constructing a virtual digital twin model and a distributed collaborative model. The method and system for scheduling hybrid power systems based on digital twins and distributed collaboration provided by this invention increase the safety and reliability of decision execution by performing simulation operation and safety verification before decision execution through the digital twin model. Simultaneously, the distributed collaborative model enables each power unit to have local autonomous decision-making capabilities to cope with sudden extreme situations, improving the robustness and fault tolerance of hybrid power scheduling in dynamic marine environments.

The embodiment discloses a ship power positioning output feedback control method based on Hamilton theory, obtains state error interconnection and damping distribution passivity control law based on a three-degree-of-freedom port controlled Hamilton model of a ship horizontal plane, obtains auxiliary state variables and disturbance estimation values by constructing a reduced-order extended state observer, estimates and compensates total disturbance in real time, constructs a non-convex constraint operator of a propeller control input, establishes a dynamic auxiliary system to obtain a dynamic auxiliary system state vector, compensates input saturation error by using the dynamic auxiliary system, obtains actual application control instructions after non-convex projection based on an adaptive event triggering mechanism, finally obtains unconstrained ideal control instructions, and obtains actual control input based on the adaptive event triggering mechanism, so that high-precision trajectory tracking and propeller service life extension are realized.

The application discloses a new energy ship power equipment operation and maintenance analysis method, through collecting multi-physical field operation, microstructure and historical failure data and correcting and standardizing processing time sequence deviation, a high-quality coupled data sample set is generated. By constructing a physical constraint layer embedded with a physical field coupling equation and a fault mechanism knowledge graph and injecting a model training loss function, the obtained multi-physical condition calculation model can accurately depict the multi-field coupling characteristics and fault evolution law, and improve the complex working condition adaptation ability. By extracting multi-dimensional features and weighted fusion dimension reduction, a core fault feature vector is generated, and the fault essence is accurately captured. Through the meta-learning algorithm, the pre-training model is fine-tuned to obtain a fault analysis large model, which improves the small sample scene analysis accuracy and generalization ability, and can clearly trace the fault evolution path, define the coupling critical condition and establish the micro-macro mapping relationship. Through the feedback of the real ship, the model and the knowledge graph are updated in a closed loop, and the operation and maintenance accuracy is ensured to be stable for a long time.

A kind of ship power condenser system fault detection method based on multiscale feature fusion, the present application relates to ship power condenser system fault detection method based on multiscale feature fusion, belongs to ship power system fault diagnosis and safety monitoring field.The present application aims to solve the problem of insufficient detection accuracy and high false alarm rate of traditional fault diagnosis method.Process is as follows:step one, obtain the input and output information of condenser system;Step two, construct multiscale feature extraction model;Step three, obtain trained multiscale feature extraction model and parameters;Step four, input the online data of condenser system into trained multiscale feature extraction model, and trained multiscale feature extraction model outputs the predicted value of condenser liquid level and vacuum degree;Residual generator obtains online prediction error signal based on predicted value;Step five, fault diagnosis based on online prediction error.

This invention relates to the field of intelligent navigation and safety protection for ships, and discloses a deep learning-based dynamic coupling prediction method for ship collision risks, comprising the following steps: acquiring environmental feature tensors and target ship dynamic attributes, constructing an environmental disturbance risk manifold space and calculating hydrodynamically corrected encounter parameters; abstracting the monitored sea area into a heterogeneous spatiotemporal map, using the corrected encounter parameters as edge connection strength, and outputting deep features that integrate physical laws through a cross-modal attention mechanism; constructing a continuous dynamic system using neural ordinary differential equations, mapping the initial state and outputting the future pose and continuous attitude deflection derivative in the continuous time domain; converting the deflection derivative into continuous prestress control commands for physical protection equipment, and dynamically reconstructing the UAV trajectory based on manifold curvature. This invention establishes a complete closed loop from digital situational awareness to physical equipment response, achieving precise early warning and adaptive flexible protection in complex sea areas.

The application relates to a dynamic positioning self-disturbance rejection control method based on an adaptive extended Kalman filtering algorithm, and relates to the field of ship dynamic positioning control.The application is aimed at solving the problems that the existing ship dynamic positioning self-disturbance rejection control method cannot guarantee response speed while meeting the control stability requirement, the positioning speed is slow, and the service life of an executing mechanism is short.The application comprises the following steps: obtaining a ship motion model by using a low-frequency motion model of a dynamic positioning ship and a high-frequency motion model of the dynamic positioning ship in a state space form; designing a nonlinear state error feedback control law to obtain a propeller applied thrust value; discretizing the ship motion model to obtain a discretized ship motion model; and designing a dynamic positioning self-disturbance rejection controller based on an adaptive extended Kalman filtering method by using the discretized ship motion model and the propeller applied thrust value.The application is used for ship dynamic positioning self-disturbance rejection control.

The embodiment of the application provides a shaft power generation fuel saving system in a ship power positioning mode, which is applied to the technical field of ship propulsion and control, and comprises: a data acquisition module, which acquires ship wave motion sensing data and generates a three-dimensional wave ship body atlas; a thrust loss prediction module, which predicts the instantaneous immersion depth of a propeller according to the atlas and calculates the instantaneous thrust loss amount in a future time window through a deep learning model; a coordinated control and execution module, which controls the dynamic switching of the shaft power generator between the power generation mode and the electric mode according to the comparison result of the thrust loss amount and a threshold value and sends an unloading signal before switching to the electric mode; and a power grid load management unit, which receives the unloading signal and short-term removes an interruptible load. The application realizes active prediction and dynamic compensation of thrust loss under wave disturbance, stabilizes the main engine speed, recovers energy, guarantees the stability of the power grid through load collaborative management, and improves the safety and economy of ship power positioning operation.

This application discloses a method for the operation and maintenance analysis of power equipment for new energy ships. It generates a high-quality coupled data sample set by collecting multi-physics field operation, microstructure, and historical fault data, followed by time-series deviation correction and standardization. By constructing a physical constraint layer embedding physical field coupling equations and a fault mechanism knowledge graph, and injecting a model training loss function, the resulting multi-physics condition calculation model can accurately characterize multi-field coupling characteristics and fault evolution patterns, improving adaptability to complex operating conditions. By extracting multi-dimensional features and weighted fusion for dimensionality reduction, a core fault feature vector is generated, accurately capturing the essence of the fault. A large-scale fault analysis model is obtained by fine-tuning the pre-trained model using a meta-learning algorithm, improving the analysis accuracy and generalization ability for small sample scenarios. This allows for clear tracing of fault evolution paths, definition of coupling critical conditions, and establishment of micro-macro mapping relationships. Real-ship feedback enables closed-loop updates of the model and knowledge graph, ensuring long-term stability of operation and maintenance accuracy.

This invention proposes a port carbon emission prediction method and system based on a large model, relating to the fields of port environmental monitoring and carbon emission prediction technology. The method generates a multimodal data stream by simultaneously collecting port video, ship dynamics, and meteorological data. Visual feature vectors and temporal feature vectors are extracted using the visual encoding and temporal encoding modules in a pre-trained multimodal spatiotemporal prediction model, respectively. These are then fused across modalities through the attention mechanism of the fusion prediction module to generate port carbon emission intensity prediction data. The prediction data is transformed into an evolutionary simulation report containing trend curves and decomposition of key influencing factors. The system supports receiving scenario adjustment commands in natural language through a user interface, driving the model to dynamically extrapolate and generate updated reports, thus achieving high-precision, interpretable prediction and interactive scenario analysis of port carbon emissions.

This invention relates to a method and system for real-time thrust allocation in dynamic positioning of large ships, comprising the following steps: S1, establishing a six-degree-of-freedom dynamic model of the ship in three-dimensional space; S2, using a sensor system to monitor external disturbances such as wind, waves, and currents in real time, and processing the monitoring information through a data fusion algorithm to estimate the external disturbance forces acting on the ship; S3, integrating the maximum thrust limit, total power limit, and turning angle limit of the propellers into a set of mathematical inequality constraints; S4, based on model predictive control theory, combining the dynamic model, the estimated external disturbance forces, and the mathematical inequality constraints, constructing an optimization problem with the objective of minimizing ship positioning error and propeller energy consumption, and obtaining the optimal thrust allocation scheme for each propeller in real time by solving this problem. This invention has the effects of improving positioning accuracy, improving power system efficiency, enhancing the real-time performance and adaptability of the system, and ensuring the safety of the power system.

The present invention discloses a method and system for ship transportation safety supervision based on AI and big data, specifically related to the field of ship traffic supervision, and is used to solve the problems of identifying the position of hidden ships, difficult to dynamically identify blind spots in supervision, and insufficient traffic risk warning ability in the existing ship traffic supervision process; collecting AIS ship dynamic data through base stations to generate a real-time traffic state set, marking suspected hidden ships after detecting the loss of ship AIS signals, analyzing the steering and avoidance behaviors of surrounding ships, inferring the hidden probability position domain through reverse cross-positioning, performing spatio-temporal correlation verification on hidden targets and inferring the range of their motion parameters, and at the same time identifying the migration path and range of blind spots in supervision based on continuous monitoring periods, generating a dynamic supervision risk area in the electronic nautical chart and implementing traffic supervision warnings for ships entering the area, so as to realize the dynamic identification of hidden ship activities and traffic safety supervision.

This invention provides a method, apparatus, equipment, and storage medium for ship maritime calibration based on multi-UAV collaborative hovering. It collects ship parameters in real time and preprocesses them to generate a ship dynamic state vector. A world coordinate system is established with the bottom of the ship's mast as the origin, and a real-time coordinate system is established with the ship's center of mass as the origin. Based on this dynamic state vector, a homogeneous transformation matrix between the two coordinate systems is constructed and updated in real time. This dynamically converts the UAV's relative hovering pose, pre-configured in the ship's real-time coordinate system, into a target pose in the world coordinate system and distributes it to each UAV. This drives multiple UAVs to synchronously adjust their hovering positions with the ship's movement, forming a dynamic calibration reference network around the ship that maintains a constant geometric relationship with the mast camera. Then, using the real-time known world coordinates of each UAV as reference points, the camera's extrinsic parameters are calculated, achieving high-precision, full-circumferential dynamic calibration of ship cameras in a maritime environment.

This invention discloses a ship arrival prediction intelligent agent system and method based on the fusion of XGBoost and a large language model, belonging to the field of intelligent shipping technology. The system includes real-time collection of ship dynamic data, port information, marine meteorological data, and ship static attributes; inputting the feature set into a pre-trained XGBoost regression model to generate structured analytical results; retaining only semantic summary features; generating readable explanatory text through a large language model fine-tuned for port scheduling; and returning the generated explanatory text to the user via a web frontend. This invention achieves high-precision prediction of long-haul ETA through multi-source heterogeneous data fusion, enhanced missing value processing, and rich temporal feature engineering; it overcomes the limitations of existing "black box" models, enabling dispatchers to understand the prediction basis and attribute deviations; it achieves a leap from "information provision" to "decision support"; and it ensures that core shipping data does not leave the internal network, meeting high-level security protection requirements.

The application discloses a ship fuzzy PID control method based on dynamic population cost induced particle swarm optimization, and comprises the following steps: establishing a ship maneuvering motion mathematical model of a ship power positioning system in static water and a ship maneuvering motion mathematical model in wind and wave; designing a fuzzy PID controller, and correcting the actual position of the ship in a fixed coordinate system through the fuzzy PID controller; optimizing the fuzzy parameters of the fuzzy PID controller of the ship power positioning system by using an improved particle swarm algorithm, designing a fuzzy control system optimized by a particle swarm, and realizing the positioning control of the ship based on the fuzzy control system optimized by the particle swarm. The method solves the problem that the selection of the membership function and the setting of the fuzzy rule and other parameters in the existing fuzzy controller are all obtained from artificial experience, and the conventional PSO control method is prone to local optimization, thereby reducing the work efficiency and control efficiency of the user and affecting the performance of the ship power positioning control.

The embodiment of the application relates to the field of ship electrical engineering, and provides a green electricity supply ship and power receiving ship dynamic cooperation and safety prevention and control method and device, computer equipment and storage medium, the method comprises the following steps: first, developing a power supply and power receiving cooperation control protocol for ship-shore low-delay communication to deploy a communication architecture, then constructing a multi-dimensional perception hazard identification model to output multi-dimensional perception hazard warning information, and adopting a two-level cooperative safety prevention and control architecture to construct a power supply and power receiving field large model to output two-layer hazard warning information, then modeling a Markov decision model and solving it to output sailing posture control information to control the execution of the charging docking operation, and finally, under the condition that the warning levels represented in the various hazard warning information meet the target level, the execution of the charging docking operation is stopped. The implementation of the method improves the cooperation efficiency and safety of the charging docking operation process.

This invention discloses a method and system for dynamic path planning of ships based on an improved DRRT algorithm, belonging to the field of ship automatic navigation technology. The method includes: acquiring two-dimensional planar environmental parameters of the ship's navigation area, defining the sea area boundary, collecting the position and contour data of static obstacles, collecting the state information of dynamic obstacles in real time, and determining the starting point and target point of the ship's path planning; employing an improved DRRT algorithm, determining sampling points through target and path point bias sampling, determining the expansion step size through adaptive step size adjustment, determining the path node set through path node pruning and reconnection, and generating an initial path through cubic B-spline smoothing; monitoring obstacles to determine if some path results are invalid, and if invalidity is found, replanning using the improved DRRT algorithm to obtain the dynamic path. This invention can meet the comprehensive requirements of real-time performance, safety, and path quality in intelligent ship navigation.

This invention relates to the field of ship dynamic positioning control technology, specifically to a coupling optimization method for the coordinated control of hybrid power and DPS positioning. The method involves: establishing a multi-input multi-output nonlinear power-positioning mathematical coupling model to achieve precise quantification and conversion of positioning errors into power parameters; analyzing positioning errors and power requirements using a dual-mode positioning sensor; dynamically adjusting the output power of the diesel engine and battery using a model predictive control algorithm to ensure the power response speed reaches a preset value; and correcting closed-loop errors using a Kalman filter algorithm. This invention achieves precise coupling between hybrid power output and DPS positioning requirements, improving the positioning accuracy and stability of ships in complex sea conditions at sea, while simultaneously reducing fuel consumption and equipment wear of the power system.