538 results about "Ship control" patented technology

The invention belongs to the technical field of ship path tracking, and discloses a ship path tracking method based on adaptive line-of-sight guidance and fuzzy adaptive PID, and the method comprises the steps: presetting a path point, and obtaining a real-time coordinate; obtaining an actual expected heading angle # imgabs0 # and a ship observation heading angle # imgabs1 #, subtracting the actual expected heading angle # imgabs0 # and the ship observation heading angle # imgabs1 # to obtain a heading deviation # imgabs2 #, and calculating a heading deviation rate # imgabs3 #; the course deviation and the course deviation rate are input into a fuzzy self-adaptive PID motion control module, and gain parameters are output through a fuzzy control algorithm; the gain parameter and # imgabs4 # are input into a PID controller to calculate a ship rudder angle # imgabs5 #; inputting # imgabs6 # into the motion mathematical model to update the motion state, and forming a closed-loop updated motion state to enable the ship to approach a preset path point; and judging a ship path after the state information is updated. The expected course of the ship is calculated in real time and the sideslip angle of the ship and the control parameters of the ship are adjusted in real time through self-adaptive sight guidance, and the control parameters are automatically adjusted in real time by means of the fuzzy self-adaptive PID motion control module to achieve accurate motion control, so that the ship travels according to a preset planned path.

The invention discloses a ship control auxiliary method based on machine learning, and relates to the technical field of ship intelligent control, and the method comprises the steps of ship data collection, fuel oil energy consumption prediction, dynamic navigational speed planning and energy-saving feedback control. According to the method, the fuel energy consumption is predicted by adopting a prediction method combining multi-mode and disturbance self-adaption, modeling multi-dimensional data is unified, perception of environmental disturbance is enhanced, accurate prediction of the fuel energy consumption under complex navigation conditions is realized, and the reliability of subsequent dynamic navigational speed planning is effectively improved; dynamic speed planning is carried out by adopting a speed planning method combining multiple targets and segmented constraints, three core factors of fuel energy consumption, navigation time and safety risks are comprehensively considered, a multi-target cost function is constructed, segmented constraints are introduced, the physical feasibility and risk control of speed adjustment are guaranteed, the speed is optimized through a reinforcement learning mechanism, and the speed planning efficiency is improved. Intelligent self-adaptive navigational speed adjustment is achieved, and the energy-saving capacity, the navigation efficiency and the operation safety of the ship under the complex navigation condition are improved.

The invention relates to the technical field of intelligent ship control, in particular to an intelligent ship moving control method and system based on a self-adaptive algorithm. The method comprises the following steps: acquiring hardware equipment data required by ship movement control; configuring a network topology structure of a computer network system, winch equipment and an encoder according to hardware equipment data, and constructing an electrical ring network; carrying out initialization setting and communication test on the electrical ring network, and establishing a data sharing mechanism between devices to obtain system initial configuration data; collecting the winding and unwinding length and tension parameters of a steel wire rope through a sensor of winch equipment based on initial configuration data of the system, and constructing a real-time monitoring model of the position and posture of the ship; and performing ship state monitoring by using the real-time monitoring model to generate ship real-time state data. According to the invention, the electrical ring network with a redundant backup mechanism is adopted, the communication real-time performance and reliability of the system are improved, and the stability of control signal transmission is ensured.

The invention provides a high-efficiency ship automatic control system, and the system comprises a comprehensive control platform which carries out the unified integration of subsystems, sets a target function, and carries out the data sharing, collaborative optimization and global control among the subsystems. The intelligent sensing network module is used for collecting historical data and real-time data; the self-learning control module is used for constructing a control strategy generation model; the ship digital model module is used for constructing a digital model for simulating ship navigation so as to test different ship control strategies; the decision execution module is used for generating and executing a control instruction; and the safety monitoring module detects abnormal behaviors of the ship and gives an alarm. According to the invention, a plurality of subsystem modules can be integrated in a unified manner, data sharing, collaborative optimization and global control are realized, an optimal control strategy can be provided, the cost and risk of actual navigation are reduced, and the control precision, response speed and operation efficiency of a ship are improved.

The invention discloses an unmanned ship control system based on a digital twin and deep learning technology in the technical field, and the system comprises a perception information fusion module which collects real-time data in real time, and transmits the real-time data to a digital twin module; the digital twin module predicts the change of the marine environment in a period of time in the future through the real-time data collected by the perception information fusion module, and the prediction result provides a basis for subsequent path planning and navigation control; the deep learning module is used for extracting a feature recognition result in the marine environment, and the feature recognition result is used for optimizing path planning and navigation control of the unmanned ship; and the controller execution module is used for receiving a prediction result from the digital twin module and a feature recognition result from the deep learning module, realizing accurate control of a main engine, a steering engine and a load of the unmanned ship, and realizing high-precision and self-adaptive control of the unmanned ship by simulating and predicting a marine environment in real time. And the adaptability and robustness of the unmanned ship in a complex environment are improved.

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

The invention 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 the technical field of ship control, in particular to a fishing boat control system based on data analysis, which comprises a traction load analysis module, a fluctuation condition division module, an attitude disturbance estimation module, a propulsion power regulation module and a rudder angle correction output module. According to the method, by analyzing the traction force load sequence value and the moment arm length of the fishing net traction point, calculating the left and right side offset torque and marking the action direction, the attitude adjustment precision of the ship is optimized, the operation sea condition level is adjusted according to data, and the ship can be subjected to more reasonable attitude estimation and adjustment under the dynamic working condition; the wind pressure change rate and the pitch angle rate are combined to judge the direction resultant force trend, and the navigation attitude is adjusted according to the sea condition grade, so that the ship can more accurately resist the influence of sea surface storm waves and maintain the course stability, and the rudder angle is corrected by adjusting the propulsion power in real time, limiting the adjustment amplitude of the propulsion power and correcting the rudder angle according to the real-time data of the navigation attitude. And the propulsive efficiency and the navigation precision are optimized.

The invention relates to the technical field of ship control, and particularly provides an energy optimization method and device for a pure electric tug, a storage medium and a control system. According to the method, the operation navigation data, collected in real time, of the pure electric tug can be input into the pre-constructed operation classification model, and the real-time operation state is obtained. If the real-time operation state of the pure electric tug is a heavy load dragging state, determining the current propulsion power demand and the total power demand of a propulsion system in the pure electric tug, and inputting the operation navigation data, the current propulsion power demand and the total power demand into a pre-constructed power distribution model, and performing power distribution according to a power distribution coefficient output by the model. Therefore, the power distribution response can be quickly and accurately made based on the intelligent power distribution model, and the intelligent ship is realized. Moreover, electric energy supply of the propulsion system can be guaranteed preferentially, so that sufficient power supply of the propulsion system can be guaranteed, and the operation efficiency of the pure electric tug can be improved.

The invention relates to the technical field of unmanned ship control, and provides an unmanned ship cluster formation cooperative control method applying a large language model. According to the method, multi-source information such as unmanned ship operation data and marine environment data is collected, deep customization training is carried out based on a large language model, and by combining transfer learning and reinforcement learning technologies, the method has autonomous learning and intelligent decision-making capabilities. During task execution, the model receives unmanned ship states, task requirements and marine environment information in real time, intelligently judges task types, plans formation forms by using an optimization algorithm and reasonably allocates tasks. For example, in a seabed surveying and mapping task, an optimal formation scheme and an optimal surveying and mapping route are determined according to surveying and mapping area characteristics and unmanned ship performance, task progress and environment change are continuously monitored, and a cooperation strategy is rapidly adjusted. According to the method, the cooperative control capability of the unmanned ship cluster formation in the complex marine environment is effectively improved, and compared with a traditional method, the method has higher autonomous learning, intelligent decision making and dynamic cooperative capabilities.

The invention belongs to the technical field of ship control motion prediction, and discloses a ship control motion real-time prediction method and system based on a fusion technology. According to the method, standardization processing is carried out on ship motion data and marine environment data, the marine environment data and the ship motion data are enabled to be input into a constructed ship motion prediction model based on a Transform architecture in the same numerical range, a sliding time window technology is used, online gradient updating is carried out, and the method adapts to new data features; a physical loss item is introduced, and an improved ship motion prediction model based on a Transform architecture is obtained; and carrying out optimization verification to finish nonlinear ship control motion prediction. According to the method, the defects of a traditional model in the aspect of nonlinear processing can be overcome, the reliability of a data driving model can be improved through physical constraints, and powerful technical support is provided for operation control and safe navigation of intelligent ships in the future.

The invention belongs to the technical field of ship maneuvering performance prediction, and discloses a ship maneuvering hydrodynamic derivative intelligent forecasting method and system. The method comprises the following steps: compressing a plurality of extracted ship parameters such as a ship three-dimensional geometrical shape, a ship control movement speed and an angular speed into low-dimensional representation data by using a defined auto-encoder, and constructing a physical information neural network model based on the preprocessed compressed low-dimensional representation data; introducing an improved total loss function, and optimizing the physical information neural network model; based on the physical information neural network model with the improved total loss function introduced, a combined training strategy of an optimization algorithm is adopted for further optimization, an optimized physical information neural network model is obtained, and verification and iterative optimization are conducted on the optimized physical information neural network model PINN model. According to the method, high prediction precision can be kept under different working conditions, and good adaptability is shown in complex sea conditions.

The invention provides an automatic cruise path planning method for a water pollution monitoring and treatment unmanned ship, and the method comprises the following steps: constructing a global virtual gridding two-dimensional space model, inputting a two-dimensional contour of a cruise water area into the global virtual gridding two-dimensional space model, and obtaining a global virtual grid array; corresponding grid types are defined for all virtual grids of the global virtual grid array according to the field cruise water area, wherein the virtual grid types comprise cruise grids, obstacle grids and obstacle avoidance cruise grids; and introducing a positioning reference coordinate system for the global virtual gridding two-dimensional space model, mapping actual geographic position coordinates with each infinitesimal element point in the global virtual grid array, and determining a water bank boundary envelope domain set of the cruise water area. The method can adapt to a complex water area environment, and a water pollution monitoring and treatment unmanned ship control compensation algorithm with obstacle avoidance and self-adaptive adjustment capabilities is provided, so that the safe operation efficiency and comprehensiveness of the water environment are improved.

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 provides a multi-unmanned-ship cooperative control propulsion system and method based on Roland-C positioning, and relates to the field of unmanned ship control, the system comprises a Roland-C positioning system, a data processing module, a path planning module, a cooperative control module and a propulsion module, the Roland-C positioning system is used for providing real-time position information and course data of multiple unmanned ships, and the Roland-C positioning system is used for providing real-time course data of the unmanned ships; and the data processing module is used for transmitting the processed position information and environment data to the path planning module and the cooperative control module, dynamically adjusting the tracking propulsion path of the unmanned ship according to the optimal route, and generating a control instruction to enable the propulsion module to control the unmanned ships to keep formation and cooperatively work. Through a cooperative control strategy with strong robustness, formation stability and safe navigation of the unmanned ship are ensured so as to realize high-precision positioning, intelligent path planning and robustness cooperative control in a complex water area environment.

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 provides an unmanned ship six-degree-of-freedom prediction method and system based on visual analysis, and relates to the field of automatic control of unmanned ships, and the method comprises the steps: obtaining the multi-modal data of an unmanned ship through collecting the visual data, inertial measurement unit data and historical navigation data of the unmanned ship; fusing and preprocessing the obtained multi-modal data, performing feature extraction on visual data, performing time sequence analysis on inertial measurement unit data and historical navigation data, performing position and attitude prediction by using a multi-task learning framework, and constructing a six-degree-of-freedom prediction model of the unmanned ship; adjusting parameters and a data fusion strategy of the six-degree-of-freedom prediction model according to real-time environment data, and integrating multi-modal data processing, the six-degree-of-freedom prediction model and a self-adaptive real-time correction mechanism into an unmanned ship control system so as to predict the six-degree-of-freedom state of the unmanned ship and perform accurate navigation control. And high-precision, real-time and intelligent attitude and position prediction of the unmanned ship is realized.

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

A marine vessel includes a vessel steering system including a boat controller configured or programmed to include an automatic vessel steering mode and a manual vessel steering mode as vessel steering modes. When a password to release a locked state is input to a multifunction display in a locked state where the vessel steering mode is fixed in the automatic vessel steering mode, the boat controller is configured or programmed to shift the automatic vessel steering mode to the manual vessel steering mode.

The invention discloses a ship intelligent energy efficiency optimization method and system, and relates to the technical field of ship energy efficiency optimization control, and the method comprises the steps: collecting the operation data of a ship navigation process, and carrying out the preprocessing of the operation data; inputting the preprocessed operation data into a ship energy efficiency evaluation model, and outputting a current energy efficiency index value of the ship; based on the energy efficiency index value, solving a ship energy efficiency optimization objective function through a particle swarm optimization algorithm to obtain an optimal navigational speed parameter and an optimal course parameter; and issuing the optimal navigational speed parameter and the optimal course parameter to a ship control system, and adjusting the ship navigation state. According to the system, the ship navigation safety is ensured, meanwhile, the fuel consumption is effectively reduced, the overall energy efficiency level is improved, and the high practical value and the intelligent degree are achieved.

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 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 automatic ship driving control method and system based on artificial intelligence, and relates to the technical field of ship control, and the driving control steps are as follows: S1, obtaining a current two-dimensional coordinate position of a ship based on a satellite map, and obtaining wind direction data of the current two-dimensional coordinate position in a ship route based on a weather bureau, obtaining a ship route state construction data set; and S2, establishing a ship running model, predicting position and attitude changes of the ship in a future route in combination with real-time wind direction data, calculating a ship displacement control quantity in advance through a control algorithm, and counteracting offset generated by wind direction factors. According to the invention, the ship can timely and accurately return to a correct route, the navigation accuracy of the ship in a complex environment is greatly improved, fine adjustment of ship navigation parameters is realized, unnecessary energy waste is avoided, deviation from a target route is avoided, and the risks of collision and stranding are increased.

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 a ship seaborne collision confrontation semi-physical simulation system, and relates to the technical field of simulation, the semi-physical simulation system comprises a simulation host, an impact ship simulation system and a collided ship simulation system, the impact ship simulation system and the collided ship simulation system are respectively connected with the simulation host, and an operator can simulate the ship seaborne collision confrontation through the impact ship simulation system and the collided ship simulation system. According to the invention, the system simulates ship collision and collided ship driving, thereby carrying out chasing and collision simulation, and has the prominent advantage that people are in the loop, and the simulation host is internally provided with a collision confrontation core solver formed through a theoretical evaluation method, an empirical formula, an agent model and other methods. By combining with a two-ship simulation system, a sea collision complete process simulation method of two-ship control navigation before collision, multi-dimensional impact feedback presentation at the moment of collision, damage evaluation after collision and the like can be realized, a battlefield environment can be really simulated, a ship collision effect can be accurately evaluated, and the ship collision simulation system has the characteristics of strong immersion and strong interactivity; the ship marine collision confrontation training platform can be used for training marine law enforcement officers.

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 ship control system fault scheduling method, system and device and a storage medium, and relates to the technical field of ship control, and the method comprises the steps: dividing a ship control system into a plurality of sub-control systems, and determining a software configuration table and a hardware configuration table of each sub-control system according to a ship cycle task; constructing a software fault tree minimum cut set and a hardware fault tree minimum cut set according to historical full-cycle data of the ship control system, the software configuration table and the hardware configuration table; calculating the fault occurrence probability of each sub-control system at the current moment based on the software fault tree minimum cut set and the hardware fault tree minimum cut set; and respectively virtualizing software and hardware of the ship control system, and carrying out flexible recombination scheduling when the fault occurrence probability exceeds a preset probability threshold. According to the method, the sub-control systems are divided, potential faults of each sub-system are accurately positioned and analyzed, and flexible recombination scheduling can quickly respond to and process the faults without interrupting system operation.

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.