285 results about "Ship dynamics" patented technology

The invention discloses a starting control system and method for a main and auxiliary power hybrid ship, and relates to the field of ship power control, and the system comprises a multi-source data collection module which is used for obtaining a multi-source state data set of a ship power system; the fusion processing module is used for performing feature alignment processing on the multi-source state data set based on a multi-modal sensing data fusion algorithm to generate a hybrid power starting parameter set; the dynamic weight distribution module is used for constructing a dynamic weight distribution matrix according to the hybrid power starting parameter set; the cooperative control module is used for generating a main and auxiliary engine combined starting instruction set based on an improved cooperative control algorithm; and the execution and monitoring module is used for executing the main and auxiliary engine combined starting instruction set in a preset time window and monitoring the transient response characteristic of the propulsion system. According to the starting control system and method for the main and auxiliary power hybrid ship, the starting stability and the energy utilization efficiency of the hybrid ship are improved.

The invention provides an unmanned ship dynamic path planning method and system based on deep reinforcement learning, and relates to the technical field of path planning, and the method comprises the steps: obtaining the initial position, target position and marine environment information of an unmanned ship; inputting the initial position, the target position and the marine environment information of the unmanned ship into a pre-constructed deep reinforcement learning model, and based on the deep reinforcement learning model, using a state value function to evaluate the long-term return of the unmanned ship taking different actions in each state, determining an optimal action strategy of the unmanned ship according to the long-term return, and obtaining a local path planning result of the unmanned ship; based on a preset path optimization algorithm, performing smooth optimization on the local path planning result to obtain a dynamic global path of the unmanned ship; and controlling the course and speed of the unmanned ship according to the dynamic global path, so that the unmanned ship travels to a target position along the dynamic global path.

The invention discloses a ship dynamic path adjusting method combining visual navigation and radar data. The ship dynamic path adjusting method comprises the following steps: S1, collecting data; s2, carrying out pretreatment; s3, performing data fusion to generate an environment perception model; s4, combining the generative adversarial network and the space-time diagram neural network to construct an environment prediction model; s5, performing initial path planning by using a heuristic search algorithm to generate an initial path; s6, refining and optimizing the path by using a fast extension random tree algorithm to generate an optimal path; s7, generating future possible environment change according to the environment prediction model, and calculating a coexistence reserve path for each prediction scene in combination with the environment perception model; s8, when the current path is predicted to have risks in the future, switching to a preset standby path in advance; and S9, automatically generating a navigation control instruction, and transmitting the instruction to a ship automatic driving system. According to the invention, intelligent optimization of the ship path is realized through a multi-sensor fusion and environment prediction method.

The invention discloses a ship intelligent collision avoidance decision-making method and system, and relates to the technical field of ship intelligent navigation, and the method comprises the steps: collecting radar data and AIS information of a ship; building a ship dynamic prediction model according to the radar data and the AIS information, predicting the future navigation trajectory of the target ship through a Kalman filtering algorithm, and calculating the meeting parameters of the two ships in combination with the navigation parameters of the ship; based on the meeting parameters, establishing a risk assessment matrix, and performing risk level judgment on the current navigation situation by using a fuzzy neural network algorithm; based on the risk level judgment result, collision avoidance maneuvering parameters are calculated through an adaptive particle swarm optimization algorithm, and a collision avoidance action scheme is output. According to the invention, the whole process automation from information perception, situation prediction, risk assessment to decision execution is realized, the workload of navigation personnel is greatly reduced, and the navigation safety level is improved.

An intelligent safety supervision system applied to a ship is provided. An image acquisition module is configured to acquires high-definition images in real time. An automatic recognition module is configured to obtains ship dynamic and static data. A ship server to-performs feature recognition on the ship dynamic and static data to obtain a data processing result, to-transmits the ship dynamic and static data and the data processing result, and receives alarm indication information. An alarm module outputs an alarm. A ship client displays the data processing result, and determines whether to transmit the alarm indication information according to the data processing result. A communication module receives and transmits the ship dynamic and static data and the data processing result. A shore-side supervision system includes a ship safety supervision big data analysis platform for performing secondary feature recognition on the ship dynamic and static data, so as to obtain a secondary data processing result.

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 ship automatic mooring control strategy optimization method and system based on multi-source data, and the method comprises the steps: firstly obtaining multi-source perception data of a target ship, carrying out the preprocessing of the data, forming a standard data set, extracting dynamic feature parameters from the preprocessed data through a sliding time window, and obtaining a target ship automatic mooring control strategy; comprising ship dynamic offset, environment interference intensity and mooring energy consumption distribution parameters. A dynamic optimization strategy generation model is called, and an optimization parameter set is generated according to the difference between the ship dynamic offset and the safety threshold value, the matching degree between the environment interference and the interference threshold value and the deviation between the mooring energy consumption and the optimization target. According to the set, parameters of an executing mechanism of the automatic ship mooring control system are dynamically adjusted, a control instruction is generated, meanwhile, the ship attitude offset and the cable tension fluctuation amount during mooring are monitored in real time, finally, feedback correction is conducted on the optimized parameter set based on the monitoring result, and a corrected control strategy is obtained. Therefore, the continuous optimization of the automatic mooring control strategy of the ship is realized, and the safety and efficiency of mooring are effectively improved.

The invention relates to the technical field of ship construction, and discloses an intelligent diagnosis and early warning method for ship final assembly construction quality potential safety hazards. The method comprises the following steps: receiving real-time sensing data of a plurality of detection nodes in ship construction, and analyzing the real-time sensing data through a pre-trained quality feature extraction model to generate a hidden danger feature vector and a risk level; and dynamically generating a hidden danger diagnosis strategy according to the sequence, wherein the hidden danger diagnosis strategy comprises a detection equipment scheduling instruction and a defect repair priority sequence. And utilizing the pre-trained hidden danger ship dynamic model to predict a defect diffusion trend, adjusting a dynamic response threshold value, optimizing, updating and repairing a priority sequence through an adaptive genetic algorithm, and outputting a quality early warning instruction to a ship construction management system. The method can accurately diagnose and early warn potential quality safety hazards, reasonably dispatch resources, improve the ship construction quality and safety, reduce the cost, and promote the intelligent development of the ship construction industry.

The invention provides a multi-tugboat collaborative berthing controller under complex multiple constraints. The multi-tugboat collaborative berthing controller comprises a finite time kinematics module, an instruction optimization module and a finite time dynamics module. The finite time kinematics module receives position and course information and expected position and course information of a target tugboat in the finite time kinematics module, and the finite time kinematics module outputs expected speed to the instruction optimization module; the instruction optimization module is used for receiving an expected speed from the finite time kinematics module and position and course information of a target tugboat from the finite time dynamics module; the instruction optimization module generates an optimized speed signal according to the contact force constraint between tugboats and ships, the speed constraint of the tugboats and the ship dynamic constraint and outputs the optimized speed signal to the finite time dynamics module; according to the method, the calculation pressure is greatly reduced, and the resource consumption is effectively reduced.

The invention provides an unmanned ship path tracking control method based on lightweight self-adaptive sight guidance. The method comprises a self-adaptive sight guidance algorithm, a foresight distance is adjusted in real time according to the current position and path deviation of an unmanned ship, ideal course information is obtained, and the path convergence speed and algorithm calculation efficiency are improved. And in the virtual simulation platform, marine environment information is combined, and unmanned ship dynamic model parameters are obtained through experiments. And constructing an objective function taking the path tracking error and the rudder angle change as evaluation indexes, and optimizing PID controller parameters by adopting an improved sparrow search algorithm. According to the algorithm, a dynamic additive updating strategy is introduced, so that the global search capability and the convergence stability are improved, and the optimal PID parameter is ensured to be obtained. The method has the advantages of being high in response speed, small in overshoot and high in path tracking precision in the complex marine environment, the real-time control performance and the environment adaptability of the unmanned ship can be remarkably improved, and the method is suitable for the unmanned ship path tracking control requirements in various task scenes.

The invention discloses an extended-range hydrogen fuel power ship propulsion system, which relates to the technical field of ship propulsion and comprises an acquisition module, a quantum annealing optimization module and a quantum chip, the acquisition module is used for eliminating noise interference of acquired hydrogen energy ship power parameters through a Kalman filtering algorithm and generating a standardized data packet, and the quantum chip is used for receiving the standardized data packet and outputting the standardized data packet. The method comprises the following steps: mapping hydrogen energy ship power parameters into an Isin model, generating a control instruction according to a hydrogen fuel consumption rate, an equipment life attenuation coefficient and a navigation task priority weight, inputting the control instruction into a simulation model of a proton exchange membrane by a proton membrane regulation and control module, and dynamically regulating a hydrogen production rate and fuel cell power, according to the system, the efficient hydrogen energy closed-loop monitoring unit and the three-level safety protocol are adopted, the response speed and precision are improved, the stability and safety are remarkably enhanced, it is ensured that a ship can reliably operate under the complex working condition, and potential safety risks are reduced.

The invention discloses a large-scale ocean data buoy safety monitoring system and method based on multi-source fusion, and relates to the technical field of buoy monitoring. Buoy equipment operation parameters, ocean environment data and surrounding ship dynamic information are synchronously acquired through a buoy body sensor group, an environment monitoring unit and a ship identification module; time-frequency domain feature extraction and anomaly detection are carried out on the collected data, and a collision risk dynamic evaluation model is constructed in combination with ship trajectory prediction; establishing an equipment health, environment interference and ship threat fusion evaluation system, and performing risk grade division in combination with real-time monitoring data and a historical database; and generating early warning information based on the risk level, and automatically triggering an equipment maintenance instruction and a ship collision avoidance alarm. The method adapts to ship variable-speed steering scenes, threshold values are dynamically adjusted, and adaptability and analysis accuracy of the system are improved.

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

The invention discloses a ship power system optimization scheduling method and system based on a genetic algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining real-time operation parameters of a ship power system, building an initial population of the genetic algorithm based on the operation parameters, and carrying out the optimization scheduling of the ship power system based on an energy consumption characteristic index and an emission characteristic index. And calculating the comprehensive fitness value of each individual, performing selection operation, interlace operation and mutation operation on the initial population to generate a new population, repeatedly performing population iterative optimization until a preset condition is met, outputting a navigational speed adjustment parameter combination, a generator set start-stop decision sequence and a power distribution scheme, and generating a ship power system control instruction set. According to the ship power system optimization scheduling method and system based on the genetic algorithm provided by the invention, the operation efficiency of the ship power system is improved, the energy consumption is reduced, and the carbon emission is reduced.

The invention provides a target submarine path prediction and tracking method for an unmanned ship, and belongs to the technical field of unmanned ship dynamic tracking. Track data are collected in various sea areas through a sonar radar array sensor of the underwater unmanned ship, and the comprehensiveness and robustness of the data are ensured. The core of the method is that a multi-head attention mechanism and a target submarine trajectory prediction model of an LSTM network are combined, trajectory features can be accurately captured, the moving trajectory of a future target submarine can be predicted, particularly, an environment sensing module is introduced, the influence of obstacles around the target submarine on target submarine trajectory prediction is considered, and the target submarine trajectory prediction accuracy is improved. And the prediction accuracy is improved. In the training process, a model interaction strategy is adopted to optimize the model performance and robustness. Finally, the system is successfully deployed in the unmanned ship, collects data in real time, predicts a target trajectory, guides the unmanned ship to accurately track, and provides powerful technical support for the fields of ocean exploration, resource development, safety maintenance and the like.

The invention discloses a ship power system optimization control method and system based on a simulated annealing algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining operation parameters and historical energy consumption data in real time, and constructing a multi-objective optimization function of dynamic weight distribution; generating an initial temperature parameter and a solution set in combination with the navigation state and the environment data; a neighborhood search strategy disturbance solution set is improved, a new solution is evaluated by using a dynamic acceptance probability function, temperature parameters are adaptively adjusted for iterative optimization, and an optimal control parameter combination is output; and an adjustment instruction set is generated after multi-dimensional efficiency verification, and a propulsion device, a generator set and an energy storage module are cooperatively controlled, so that global energy consumption optimization is realized. According to the method, through data driving and intelligent algorithm fusion, energy efficiency and environmental adaptability are improved, and stable operation under complex working conditions is guaranteed. According to the ship power system optimization control method and system based on the simulated annealing algorithm, the energy efficiency level and the environmental adaptability of the ship power system are improved.

The invention relates to the technical field of traffic infrastructure maintenance, and particularly discloses a method for evaluating the anti-collision toughness of a rusted steel bar bridge structure after ship collision, and the method comprises the following steps: firstly, building a rusted bridge pier finite element model based on a rusted steel bar concrete theoretical degradation model; simulating ship collision by adopting an equivalent static method, and determining dynamic damage factors of the ship; structural nonlinear static analysis and nonlinear dynamic analysis are conducted by considering sudden external force to obtain a bridge function loss function, and then a bridge function is calculated; constructing a response surface agent model to perform dynamic response prediction; determining a bridge structure damage state judgment criterion; the vulnerability of the rusted bridge is calculated through Monte Carlo simulation; and according to the bridge function loss function, combining the repair time and the recovery function to calculate a structure anti-collision toughness index. The method provided by the invention fills up the blank of ship collision resistance toughness evaluation of the coastal rusted bridge pier, provides a scientific basis for bridge safety evaluation and maintenance decision, and is of great significance for guaranteeing safe operation of the bridge.

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

The invention relates to the technical field of detection and identification, and discloses a mobile ship dynamic tracking and locking method and system based on target detection and identification, and the method comprises the steps: constructing a manifold embedded network, and mapping the multi-modal features of a ship to a Riemannian manifold space; constructing a continuous evolution modeler of the Shenchang differential equation model, and predicting a continuous evolution trajectory of the features under time, view angle and scale changes; a multi-scale bridging network is constructed, and bidirectional conversion between long and short distance feature representations is realized; constructing a differential geometric attitude encoder, and representing a ship attitude in an SO (3) Lie group space; a feature memory and reconstruction mechanism is realized, and the problem of short-time disappearance target recovery is solved; the components are integrated to construct a unified tracking and locking system. According to the method, the problems of discretization processing limitation, feature representation fragmentation, cross-condition consistency deficiency and the like in the prior art are solved, remarkable technical effects are achieved in the aspects of cross-condition recognition capability, continuous feature evolution capability, short-time disappearance target recovery capability and the like, and the method is suitable for the scenes of Yangtze River shipping monitoring, maritime affair safety supervision and the like.

The invention relates to the technical field of intelligent operation and maintenance of ship power equipment, in particular to a DLinear-Transform-based anomaly detection method for a marine power generation steam turbine system, and the method comprises the steps: S1, carrying out the collection and processing of non-stationary time series data of the marine power generation steam turbine system; s2, constructing a DLinear-Transform model framework, and carrying out the construction of the DLinear-Transform model framework; s3, carrying out anomaly detection on the marine power generation steam turbine system on the basis of DLinear-Transform; and S4, verifying the performance of the model public data set. According to the method, a DLinear-Transform fusion framework is constructed, and anomaly detection of the key parameters of the ship power generation steam turbine system under the non-stable working condition is realized based on the DLinear linear characteristic and the Transform context modeling capability. Through SMD, SWaT and actual marine power generation steam turbine data set verification, the method is superior to existing optimal models such as TransAD and the like. Compared with an existing ship intelligent operation and maintenance technology, the system equipment anomaly detection and fault prediction capability is improved, and a new technical architecture is provided for predictive maintenance of ship power equipment.

The invention relates to the technical field of equipment monitoring, in particular to an intelligent monitoring method and system for ship power equipment, and the method comprises the following steps: synchronously collecting the operation time sequence data of the equipment; based on the ship power transmission topological structure, generating a dynamic physical constraint model between devices; and inputting the collected equipment operation time sequence data into the dynamic physical constraint model, calculating a host-gear box torque transmission residual error and a gear box-shafting rotating speed matching residual error sequence, and when the zero-crossing rate sudden change frequency of any residual error sequence exceeds a preset sudden change frequency threshold value in a preset time window, triggering an early warning mechanism. Compared with the limitation that a traditional single-point vibration threshold method cannot cover a multi-node cooperative fault, the cross-equipment coupling fault recognition rate is increased, and the coverage range of the cross-equipment coupling fault is widened.

The invention discloses a hierarchical cooperative motion control method for a ship dynamic positioning and wave compensation embarkation system. The method comprises the following steps: acquiring historical motion data of a ship; the historical motion data of the ship are fused, and the current ship posture and speed are obtained; the prediction model generates a predicted value of the disturbance of the ship in the future preset time; extracting to obtain a low-frequency slow drift motion component; the total output force of the thruster is calculated, and the ship dynamic positioning system controls the thruster to work based on the total output force of the thruster so as to compensate the low-frequency slow drift motion component; a Stewart platform in the wave compensation embarkation system controls supporting legs to work based on the target lengths of the supporting legs so as to compensate low-frequency motion left after compensation of the ship dynamic positioning system and high-frequency motion which is not compensated; and residual disturbance after the Stewart platform is compensated is compensated through the gangway ladder. According to the invention, an energy distribution mode that low-frequency disturbance is compensated by a ship dynamic positioning system and high-frequency disturbance is compensated by a wave compensation embarkation system can be realized.

The invention discloses a 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 unmanned ship dynamic obstacle avoidance control system and method based on stochastic nonlinear model predictive control, and adopts a control structure of a planning layer and a control layer to solve the problem of dynamic obstacle avoidance of an unmanned ship in a complex obstacle environment. The planning layer is based on a random nonlinear model predictive control technology, and firstly, random modeling is carried out on the unmanned ship and dynamic obstacles encountered by the unmanned ship in the sailing process; designing obstacle avoidance switching conditions according to the whole actual obstacle avoidance process; in an obstacle avoidance random nonlinear model prediction planning control algorithm, uncertainty propagation of a dynamic obstacle and an unmanned ship in a whole prediction time domain is evaluated through unscented transformation, then obstacle avoidance constraint is described in a chance constraint mode, a chance constraint expectation operator transformation algorithm and a unit jump function approximation algorithm are designed, and a chance constraint expectation operator transformation algorithm and a unit jump function approximation algorithm are designed. Opportunity constraints are converted into deterministic constraints, and then a nonlinear optimization problem is constructed for planning. And the control layer adopts a PI D algorithm to track a planning value so as to realize dynamic obstacle avoidance of the unmanned ship.

The invention relates to the technical field of anti-collision systems, in particular to an offshore wind power construction safety pre-warning system based on AIS (automatic identification system) data, which comprises a ship dynamic uncertainty modeling module for acquiring AIS signal updating frequency and positioning precision marks of a target ship and generating a ship future position probability ellipse. According to the invention, the position coordinate, the speed value and the course angle of the ship are obtained in real time based on the AIS data, the reliability and the motion trend of ship position prediction are determined by combining the AIS signal updating frequency and the positioning precision mark, and the future position probability ellipse of the ship is accurately generated; a protection area is dynamically constructed, comprehensive calculation is carried out in combination with the spatial relation between a ship future position probability ellipse and the positions of surrounding fan pile foundations, and a hourly dynamic collision risk index sequence is obtained; and further according to the statistical deviation between the current motion state of the ship and the standard parameter of the function partition, combining with path difference analysis to obtain a navigation abnormity comprehensive index.

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

The invention relates to a distributed unmanned ship cluster collaborative awareness and control integration method and system in a high sea condition environment. The method comprises the steps of equipment installation and distributed communication network construction; task target and environment information is collected and subjected to preprocessing, space-time calibration and data fusion, fused data is transmitted and shared through a distributed communication network, and a distributed Kalman filtering algorithm is used for joint data processing; on the basis of an unmanned ship dynamics model containing interference factors, a model prediction control algorithm is adopted to carry out rolling optimization control input, hierarchical path planning is carried out through an improved A * algorithm and a dynamic window method, cooperation among unmanned ships is kept through a cooperative control protocol, and control parameters are automatically adjusted according to sea conditions; and constructing a collaborative optimization objective function, adjusting collaborative perception and collaborative control strategies according to the minimum value of the objective function, and performing closed-loop feedback optimization on the collaborative perception process. Compared with the prior art, the sensing accuracy, the control stability and the collaborative operation efficiency of the unmanned ship cluster in a high sea condition environment can be effectively improved, and the ability of the unmanned ship cluster to cope with a severe sea environment is remarkably enhanced.

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

The invention discloses a fault detection method for a ship power condenser system based on multi-scale feature fusion, relates to the fault detection method for the ship power condenser system based on the multi-scale feature fusion, and belongs to the field of fault diagnosis and safety monitoring of ship power systems. The objective of the invention is to solve the problems of insufficient detection precision and high false alarm rate of a traditional fault diagnosis method. The method comprises the following steps: step 1, acquiring input and output information of a condenser system; 2, constructing a multi-scale feature extraction model; 3, obtaining a trained multi-scale feature extraction model and parameters; 4, inputting online data of the condenser system into the trained multi-scale feature extraction model, and outputting predicted values of the liquid level and the vacuum degree of the condenser by the trained multi-scale feature extraction model; the residual generator obtains an online prediction error signal based on the prediction value; and step 5, performing fault diagnosis based on the online prediction error.

The invention provides a false AIS ship trajectory detection method and system based on time slot conflict detection and a storage medium. The method comprises the following steps: processing AIS data to be analyzed, filtering out AIS data related to a ship to be analyzed, and storing the AIS data in a database; arranging AIS data related to the to-be-analyzed ship according to the corresponding ship; calculating time slot switching times of the to-be-analyzed ship in the to-be-analyzed time period and specific time slots with time slot conflicts; a specific environment is simulated, and correction parameters are trained; and counting the time slot conflict condition of the AIS dynamic message actually broadcasted by the target ship, and analyzing the probability that the ship trajectory is a false trajectory. In order to solve the problem that a false broadcasting station cannot master other ship dynamic message information which can be received by a ship during actual navigation, that is, correct SOTDMA time slot access cannot be carried out, some false AIS ship trajectories which are disguised perfectly are effectively detected through a false AIS ship trajectory detection method based on time slot conflict detection, and the false AIS ship trajectory detection method based on time slot conflict detection is improved. And the purpose of accurately identifying the illegal ship is achieved.