332 results about "Ship control" patented technology

The invention discloses a dynamic sea condition ship-machine cooperative attitude control system and method for sea grass transplantation, and belongs to the crossing field of marine ecological restoration and intelligent ship control. The system comprises a mother ship attitude control layer, a communication management layer, an underwater operation end and a sensor network layer, the mother ship attitude control layer, the communication management layer, the underwater operation end and sensor network layer and the mother ship attitude control layer are sequentially connected through lines; the method integrates inertial navigation, visual measurement, event-triggered communication and adaptive filtering technologies, realizes high-precision attitude synchronization and stable operation of the ship and the underwater operation device in sea conditions of four levels and below, and can be widely applied to marine ecological engineering scenes such as seaweed bed restoration, coral transplantation and submerged vegetation reconstruction. And the operation efficiency and the environmental adaptability are effectively improved.

The invention discloses a ship attitude prediction method and system based on FT-Mama architecture. The method comprises the following steps: collecting training data of multiple modes; performing feature extraction on the training data of various modes to obtain time domain features and Fourier transform frequency domain features corresponding to the training data of various modes; according to the time domain feature and the Fourier transform frequency domain feature, training a preset FT-Mama multi-modal fusion model to obtain a ship attitude prediction model; obtaining to-be-processed multi-modal ship data, inputting the to-be-processed multi-modal ship data into the ship attitude prediction model, and outputting a ship attitude prediction result; wherein the training data comprises ship attitude data, sea condition data and meteorological data. The method can effectively improve the ship attitude prediction precision under the complex sea condition, reduces the prediction delay, provides reliable look-ahead information for boarding compensation control, and can be widely applied to the technical field of ship control.

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

The invention discloses an unattended screw ship unloader material and ship control method and system, and the method comprises the steps: collecting multi-dimensional sensing data including visual images, material weight, material types and spatial poses in real time through multiple types of sensors disposed on a screw ship unloader; performing space-time registration and fusion processing on the acquired multi-dimensional sensing data to obtain an operation scene situation sensing result for comprehensively representing the real-time state of the ship unloading operation scene; based on the operation scene situation perception result, driving a screw ship unloader digital twin model to perform real-time simulation to obtain a simulation result including a future material accumulation spatial form, equipment operation performance parameters and / or potential risk probability; and according to a simulation result of the digital twinborn model, an optimization control instruction is generated based on a preset decision rule, so that self-adaptive adjustment of ship unloading operation parameters and intelligent disposal of abnormal working conditions are realized.

The invention discloses an unmanned ship-oriented task priority scheduling and resource awareness management method, and belongs to the technical field of unmanned ship control. The method comprises the steps of obtaining a task request generated by an unmanned ship system, inputting the task request into a task classifier for task classification and priority allocation, performing real-time monitoring on resources of the unmanned ship system based on a resource perception and threshold grading model, judging an alarm interval where the resources of the current unmanned ship system are located, and sending the alarm interval to the unmanned ship system. Obtaining a target scheduling strategy; allocating each task in the task request to a corresponding thread pool for execution, dynamically adjusting the scheduling of each task according to a multi-thread pool cooperation and preemption mechanism, and dynamically optimizing the load and channel of the unmanned ship system based on a load balancing and channel selection mechanism; feedback information during execution of each task is collected, a target scheduling strategy is adjusted through an adaptive optimization mechanism, and scheduling and resource allocation of each task are updated in real time. The method improves the resource utilization rate of the unmanned ship system.

The invention discloses an unmanned ship control method based on deep reinforcement learning in a multi-task scene. The method comprises the following steps: constructing dynamics and kinematics models of an unmanned ship; the method comprises the following steps: constructing a high-fidelity simulation environment based on Isaac Sim and a parallel training framework thereof, and respectively designing a state space and an action space for various unmanned ship tasks; reward functions are respectively designed; constructing an unmanned ship control strategy, and designing an algorithm framework of a centralized importance sampling and shearing strategy optimization mechanism based on an end-to-end deep reinforcement learning algorithm; and for different task scenes, multiple times of training-verification are performed on the strategy network through a PPO algorithm, and the trained strategy network is used for realizing multiple control tasks of the unmanned ship. According to the method, a high-fidelity simulation environment is constructed through Isaac Sim and a parallel training framework thereof, parallelization support which is crucial to deep reinforcement learning training is achieved, and various actual control tasks of the unmanned ship can be achieved.

The invention relates to the technical field of intelligent ship control, and particularly discloses an area control integration method and system based on an intelligent ship engine room, original operation data and multi-source communication data of equipment in each area of the engine room are collected, a unified real-time state vector is generated through protocol analysis and standardization processing, and a plurality of redundant processing nodes are processed in a unified mode. Synchronizing and verifying state data through a high-speed channel, constructing a global consistent state synchronization set, disassembling composite logic into atomic operation units with state constraints according to an upper-layer instruction and the synchronization set, packaging the atomic operation units into a private instruction sequence bound with the state, executing the instruction sequence by a main node, and executing the state synchronization set by a slave node; seamless switching to a standby node is realized through independent hardware monitoring when a fault occurs, and the standby node continuously executes based on the continuity of a state synchronization set, so that high-reliability uninterrupted operation of area control is realized; according to the method, the problems of instruction semantic distortion and single-point fault risk in heterogeneous equipment integration are solved, and the control accuracy and the intelligent level are improved.

The invention discloses a water surface unmanned ship control method and system under path tracking, and relates to the technical field of attitude control, and the method comprises the steps: building a kinetic model of a water surface unmanned ship; establishing a path tracking error equation for describing a system error of the unmanned surface ship relative to an expected path under a Serret-Frenet coordinate system; constructing a path tracking controller based on a linear active disturbance rejection controller; inputting the system error and a water surface unmanned ship control signal into a fuzzy RBF neural network, and outputting an optimal control parameter of a path tracking controller by taking minimization of the system error as a target; updating a path tracking controller according to the optimized control parameters; and re-estimating external disturbance by using the updated path tracking controller, outputting a control signal for optimizing the water surface unmanned ship, and controlling the water surface unmanned ship to advance along an expected path. According to the invention, the attitude control precision under strong ocean current and changeable weather is improved, and the path tracking precision of the water surface unmanned ship is greatly improved.

The invention discloses a geometric constraint fitting point cloud filtering method for sea surface three-dimensional reconstruction, and belongs to the technical field of computer vision and three-dimensional reconstruction. The objective of the invention is to solve the problem of insufficient subsequent three-dimensional reconstruction precision caused by interference of reflection noise, mismatching points and the like in sea surface point cloud. The method specifically comprises the following seven steps: firstly, acquiring sea surface original point cloud through three-dimensional data acquisition equipment; a filtering technology is adopted to obtain a to-be-fitted point cloud; fitting a quadric surface through an improved RANSAC (Random Sample Consensus) algorithm to solve an initial parameter; constructing a comprehensive error function, and optimizing the model through gradient descent; effective inner points are screened through quadratic term coefficient constraint and a distance threshold value; and finally, iterating until a termination condition is met, and outputting an optimal effective point cloud. The method is high in noise rejection rate, the point cloud fits the sea surface form, and high-quality data support can be provided for sea surface fitting, sea wave simulation and unmanned ship control.

Embodiments for a battery pack for an electric marine vessel are described herein. In a particular embodiment, a battery pack for an electric marine vessel comprises a waterproof chassis, a high voltage battery comprising a plurality of battery cells disposed within the chassis, positive and negative high voltage connectors disposed on a front flange of the chassis, a manual disconnect connector disposed on the front flange of the chassis, and a signals connector disposed on the front flange of the chassis, the signals connector configured for signal communication with at least a power distribution unit and a vessel control unit. A battery management system disposed within the chassis and coupled to the signals connector comprises a battery management controller, a plurality of relays operable by the battery management controller, and a plurality of sensors electrically coupled to the battery management controller.

The invention discloses an electric ship remote intelligent control method and system based on cloud-side cooperation, and relates to the technical field of cloud-side cooperation computing, and the method comprises the steps: carrying out the feature extraction of images around a ship and time-space aligned multi-modal data, obtaining visual features, motion features and environment features, carrying out the feature fusion, and generating an environment situation vector; inputting the environment situation vector into a channel boundary detection model, analyzing the emergency degree of the current situation, generating a collision avoidance instruction, and generating a ship control decision through a three-level collaborative decision; combining a ship control decision with ship control characteristics, simulating a traffic development situation in a navigation area, comparing with historical ship navigation conflict data, and identifying potential conflict data; through collaborative fusion of the ship-end decision-making unit, the shore-end decision-making unit and the cloud-end decision-making unit, an optimal control strategy is generated in combination with respective advantages, intelligent decision-making of global optimization in a complex environment is realized, and adaptability and safety of ship control are enhanced.

The invention discloses an intelligent ship control system which comprises a ship interaction data acquisition module, a ship state extraction module, a navigation experience screening module, a multi-ship cooperative control agent training module, a ship parameter adaptive adjustment module and a ship control module. The invention belongs to the field of intelligent control, and particularly relates to an intelligent ship control system. According to the scheme, core features are extracted through double-branch extraction, and a dynamic transition probability similarity coefficient is introduced, so that similarity judgment adapts to interaction strength; for positive sample pairs, ship low-dimensional features of similar behaviors are forced to approach, and for negative sample pairs, the feature distance is increased, so that a collision avoidance strategy can migrate across ship types; navigation experience screening is carried out through safe sampling guided by a reference path, and dangerous data interference is avoided; extreme risk punishment is added to strengthen fitting of the close-range collision sample; and an environment perception dynamic period is introduced, an adjusted safety reward is set, and environment change is dynamically adapted, so that the ship control effect is improved.

The embodiment of the invention provides a ship control method and device, electronic equipment, a readable storage medium and a chip, and the method comprises the steps: determining a first distance range and a second distance range corresponding to a target ship; determining an obstacle tracking detection result obtained based on perception fusion in the first distance range; determining information parameters of a plurality of ships in the ship automatic identification system within the second distance range; determining a sailing ship according to the obstacle tracking detection result and the information parameters; determining a relative position type of at least one sailing ship corresponding to the target ship; determining a traffic flow value of the current driving water area of the target ship according to the information parameters; and according to the relative position type and the traffic flow numerical value, switching a driving mode of the target ship, wherein the driving mode comprises a tracking mode and a ship following mode. According to the scheme, the safety of tracking or following the ship by the automatic driving ship in the inland river environment is improved.

The invention provides an unmanned surface vehicle course tracking control method with signal quantization based on a double-expansion observer, and the method comprises the steps: building a third-order response type unmanned surface vehicle course control mathematical model according to the characteristics of a ship and the sea condition information of a surrounding environment; designing a double-expansion observer model for simultaneously estimating matched interference and non-matched interference in the ship course tracking control system and observing uncertain items and external interference in the ship control system; based on a backstepping method and a dynamic surface control algorithm, a course controller of the unmanned surface vehicle is designed, and the calculation complexity brought by a virtual control law is reduced; based on the Lyapunov stability theory, the stability of the designed unmanned surface vehicle course tracking control system with state quantization and input quantization is proved, all signals in a closed-loop system are consistent and bounded in a semi-global mode, and the tracking error is arbitrarily small by adjusting parameters of a controller.

The invention discloses a ship crew working state monitoring method, and belongs to the technical field of ship control. The method comprises the following steps: acquiring environmental data of a ship and physiological data and behavior data of a target crew in the ship; performing time-frequency analysis on the environment data, the physiological data and the behavior data to obtain state time-frequency characteristics of the target crew; performing fusion time sequence modeling based on the state time-frequency characteristics to obtain a working state model of the target crew; the working state model is called to adjust early warning rules, the working state parameters are evaluated through the adjusted early warning rules, the working state monitoring result of the target crew is obtained, the early warning rules comprise a threshold rule, a trend rule and an association rule, and the working state parameters are obtained based on the physiological data and the behavior data. According to the method, by fusing time sequence modeling and dynamically adjusting the early warning rule, the early warning accuracy and practicability are improved, and a powerful guarantee is provided for safe operation of the ship.

The invention discloses an aquatic product oxygenation unmanned ship intelligent control method and system based on multi-sensor fusion, and relates to the technical field of unmanned ship control, and the method comprises the following steps: synchronously collecting a variety of data, and generating high-precision pose estimation through extended Kalman filtering fusion; discretizing the fish pond into grids, and dynamically calculating the weight of each grid according to the real-time dissolved oxygen value and a preset threshold value; generating an initial bow-shaped path based on the starting point and the ending point, optimizing the path in real time, and adaptively adjusting the scanning line direction and spacing; when grid dissolved oxygen is lower than a threshold value, an oxygenation pump is started, oxygenation flow is dynamically adjusted according to dissolved oxygen deviation, and the larger the deviation is, the higher the flow is; a double-paddle differential model is adopted to track the course and the speed, and three-level response is triggered when an obstacle is encountered; through 5G data and video streams, when the electric quantity is lower than a threshold value, a Dijkstra algorithm is used to plan a shortest obstacle avoidance path for homeward voyage. According to the method, the dynamic probability distribution diagram is constructed by fusing real-time dissolved oxygen data, and oxygenation targeting control is realized in combination with a path cost function.

The invention discloses an intelligent plug-in communication system and method for a ship battery-replaceable container type battery, and relates to the technical field of ship electrification. The system issues a handshake signal when a battery compartment is connected to a ship electric energy busbar socket, and automatically identifies an identity message of the battery compartment and the ship working condition of a ship communication master control system; acquiring attitude operation data of the ship according to a sensor, and performing normalization processing after the integrity verification and the parameter rationality verification are passed; environmental disturbance evaluation is carried out by constructing a navigation disturbance prediction index Dna, when sea condition environmental disturbance is unstable, a dynamic power demand correction Pad is constructed, and a cabin body coupling distribution index Pba is constructed in combination with a battery cabin state data set to carry out battery cabin balance evaluation; and generating disturbance sensitive information, correction qualified information and power suppression information according to an evaluation result, and transmitting the information to a ship control system to execute corresponding power distribution and operation adjustment, thereby realizing dynamic optimization and stable operation of ship power distribution under complex sea conditions.

The invention discloses an environment adjusting method and device for a ship control cabin and electronic equipment, and belongs to the technical field of ship control. The method comprises the following steps: acquiring activity data of an operator in a ship control cabin and environment information of the control cabin; extracting working state information of an operator from the activity data; the working state information and the environment information are input into a parameter prediction model, environment adjustment parameters output by the parameter prediction model are obtained, the parameter prediction model is obtained through a plurality of training samples, and the training samples comprise sample working state information and sample environment information of a sample operator; the environment adjusting parameter label is located in a sample control cabin where a sample operator is located; and based on the environment adjustment parameters, calling a mirror image model of an operator to perform analogue simulation, and setting the control cabin through the environment adjustment parameters under the condition that a simulation result of the mirror image model reaches a preset simulation condition. The method can fully consider the requirements of different operators on the environment.

The invention discloses a control method of a marine wind power boosting rotor, and relates to the technical field of ship energy conservation and control, and the method comprises the steps: determining constraint conditions of marine wind power boosting rotor control; according to the wind power environment parameters, the operation condition of the wind power boosting rotor for the ship at the t moment is determined, and the optimal rotating speed of the wind power boosting rotor for the ship is determined; constructing a target function controlled by the marine wind power boosting rotor, and calculating the optimal rudder angle and the optimal propeller power of the ship when the target function reaches the minimum by utilizing an optimization method according to the optimal rotating speed at the moment t, the rudder angle of the ship, the propeller power, the motion state parameters and the wind power environment parameters; and the rotating speed of the wind power boosting rotor and the rudder angle of the ship are adjusted according to the optimal rotating speed, the optimal rudder angle and the optimal propeller power. According to the method, cooperative improvement of rotor energy efficiency and ship control stability is achieved by controlling boundary constraint and self-adaptive adjustment of a complex wind field.

The invention discloses an intelligent detection navigation system and method for an inland ship. The method comprises the following steps: step 1, acquiring multi-modal sensing data in the navigation process of the inland ship; 2, constructing an initial cognitive map; step 3, carrying out dynamic modeling on each semantic state node by adopting an improved liquid neural network, embedding symbolic logic constraints in state transition, and generating a navigation control bias steering quantity; step 4, calculating a tension value of a causal directed edge and executing a structure evolution operation to generate a self-evolution cognitive map; 5, constructing a candidate path set; step 6, calculating an atlas winding degree index of each candidate path; 7, constructing a path decision vector, executing a path backstepping subduction operation, and screening to obtain a target optimal path; and 8, converting the target optimal path into a navigation control instruction, and outputting the navigation control instruction to a ship control system. According to the invention, cognitive map modeling and neural network reasoning are fused, and intelligent navigation control of inland ships is realized.

The invention relates to the technical field of ship operation forecasting, and discloses a neural network training and application method for engineering ship workability forecasting. According to the method, a position sensor device and an environment sensor device are arranged on an engineering ship, and the current position and surrounding environment monitoring data of the ship are obtained in real time; an operation plan acquisition device is arranged in the ship control system to acquire current operation plan data; generating an input feature set from the data, inputting the trained neural network model, and calculating to generate an workability forecasting result; and comparing the forecast result with a preset judgment standard, and judging the operation state. According to the method, multi-source data are integrated, complex association among the data is mined by using the neural network model, accurate forecasting of the workability of the engineering ship is realized, a scientific basis is provided for operation decision making, the method is adaptive to a complex operation environment and diversified tasks, and engineering ship operation is assisted to be carried out more efficiently and safely.

The invention belongs to the field of ship control, and provides a self-adaptive active stabilization method and system for a ship. The ship is configured to be provided with a plurality of water pressing plate executing mechanisms, each water pressing plate executing mechanism comprises a water pressing plate assembly and an executor connected with the water pressing plate assembly, and the method comprises the following steps that ship body attitude information of the ship is obtained; calculating a target moment according to the hull attitude information; generating an execution instruction corresponding to each actuator according to the target torque; and the executor responds to the execution instruction so as to drive the connected water pressing plate assembly to execute water pressing movement. According to the self-adaptive active stabilization method and system for the ship, the ship can be effectively controlled to reduce shaking, and the navigation stability, comfort and safety of the ship are improved.

The invention discloses an autonomous berthing method for a double-water-jet-propeller water surface unmanned ship, and the method comprises the steps: defining a state space, an action space, a reward function and a neural network structure element, employing a reinforcement learning method, and according to the current radar observation information of the unmanned ship, the target berth position information, and the current speed information of the unmanned ship; according to the method, the unmanned ship control output information is obtained, the problems that a traditional manual design berthing collision avoidance rule, a physical motion model, a machine learning method, an environment model and the like are greatly influenced by the environment, and intermediate steps are multiple and complex are solved, the calculation efficiency and the one-time planning berthing success rate are effectively improved, and therefore the berthing efficiency is overall improved.

The invention discloses a ship autonomous collision avoidance decision-making method and system based on cognitive entropy near-end strategy optimization, and belongs to the field of intelligent ship control. The method comprises the following steps: acquiring ship and environment information, and constructing a state vector containing a collision risk index; the method comprises the following steps of: inputting a cognitive entropy into a cognitive entropy near-end strategy optimization decision model, introducing a cognitive entropy regular term under a near-end strategy optimization framework to regulate and control exploration and balance of a strategy, realizing smooth transition from high exploration to high utilization, and outputting a propulsive force / rudder angle instruction; after the instruction is executed, calculating a multi-component reward based on target proximity, track deviation, fused / collision risk and COLREGs compliance; and iteratively optimizing network parameters by utilizing rewards and state feedback. The problems that exploration and utilization of reinforcement learning in ship collision avoidance are difficult to balance and convergence efficiency is low are solved, and decision safety, compliance and training stability are improved.

The invention discloses a sailing task scheduling method and device for sailors and electronic equipment, and belongs to the technical field of ship control. The method comprises the steps of obtaining a to-be-executed task of a ship and working capability information of each crew in the ship; decomposing the to-be-executed task into a plurality of sub-tasks according to coupling constraints among task units in the to-be-executed task, and determining a scheduling process of the sub-tasks; according to the work capability information and the scheduling process, matching at least one target crew for the sub-task, and generating a corresponding execution scheme; and obtaining working state information of the target crew, and performing task scheduling on the target crew according to the working state information and the execution scheme. According to the invention, crew resources can be efficiently allocated and utilized, and the task execution efficiency is improved.

The invention discloses a state intervention method and device for sailors and electronic equipment, and belongs to the technical field of ship control. The method comprises the following steps: acquiring navigation information and environment information of a ship and state information of workers in the ship; according to the navigation information and the state information, determining a task allocation scheme of the staff; obtaining a state evaluation result of the worker according to the environment information and the state information; the task allocation scheme is optimized through the state evaluation result, the working state of the worker executing the task allocation scheme is obtained, state intervention is carried out on the worker according to the working state, and the working state is determined based on the state index and the working efficiency. According to the method, personalized accurate evaluation and optimization intervention are carried out on the states of the sailors, so that the safety and efficiency of ship operation and the physical and psychological health of workers are improved.

Embodiments for a battery pack for an electric marine vessel are described herein. In a particular embodiment, a battery pack for an electric marine vessel comprises a waterproof chassis, a high voltage battery comprising a plurality of battery cells disposed within the chassis, positive and negative high voltage connectors disposed on a front flange of the chassis, a manual disconnect connector disposed on the front flange of the chassis, and a signals connector disposed on the front flange of the chassis, the signals connector configured for signal communication with at least a power distribution unit and a vessel control unit. A battery management system disposed within the chassis and coupled to the signals connector comprises a battery management controller, a plurality of relays operable by the battery management controller, and a plurality of sensors electrically coupled to the battery management controller.

The invention relates to a shipping green platform construction method and device of an AI large model, equipment and a medium. The method comprises the following steps: collecting multi-dimensional shipping data; preprocessing the multi-dimensional shipping data to generate a standardized multi-modal data set; based on the standardized multi-modal data set, constructing and training a multi-modal AI large model; based on the trained multi-modal AI large model, generating a shipping green decision scheme; and pushing the shipping green decision scheme to a ship control system, a port management system and a shipping enterprise management platform to obtain a decision scheme execution result. By adopting the method, energy conservation and emission reduction in the shipping process can be realized, the operation efficiency and the management level of shipping services are improved, and green transformation of the shipping industry is promoted. And meanwhile, the requirements of shipping enterprises on intelligent carbon emission optimization decision making and whole-process supervision are met.

The invention discloses a PID (Proportion Integration Differentiation) tracking control method based on error perception nonlinear gain, which is used for high-precision tracking control of an MIMO (Multiple Input Multiple Output) nonlinear system. Comprising the following steps: 1) constructing an adaptive gain by using current error information, historical error information and prediction error information; 2) realizing intrinsic integral saturation resistance through nonlinear integral design; and 3) complete autonomous operation can be realized without accurate system dynamics priori knowledge or manual adjustment. According to the method, the nonlinear compensation capability and the robust adaptive performance are enhanced while the structural simplicity of the PID controller is kept. Through rigorous Lyapunov analysis, the stability of the method to a non-linear system in a general form is verified, and the method shows excellent performance in both adjustment and time-varying reference tracking scenes. The method can be widely applied to the fields of linear motor servo systems, sea surface ship control, unmanned vehicle dynamics control and the like needing high-precision tracking control.

The invention relates to the technical field of ship control and navigation simulation, in particular to a ship trajectory visualization method and system based on combination of STM32 and Simulink, and the method comprises the following steps: an inertial measurement module and an ultra wide band positioning module collect attitude position information of a ship; the attitude position information is transmitted to an STM32 chip, the STM32 chip fuses the three-axis angular velocity, the three-axis linear acceleration and the three-axis geomagnetic data through a quaternion fusion algorithm to obtain a quaternion calculation result, and the quaternion calculation result and the position information in the three-dimensional space are fused to obtain fused data; the STM32 chip is used for fusing data, the fused data are packaged into data frames, the STM32 chip is used for transmitting the data frames to a ship model in a Simulink platform through a MAVLink protocol, the ship model is used for extracting the attitude angle and space coordinate information of the ship based on the data frames, and the visual motion trail of the ship is generated based on the attitude angle and space coordinate information of the ship. According to the invention, visual display and control algorithm verification are realized, and a semi-physical simulation closed-loop process is completed.