268 results about "Sea state" patented technology

The invention discloses an unmanned ship multi-agent collaborative obstacle avoidance method and system for complex sea conditions, and relates to the field of unmanned ship multi-agent collaborative obstacle avoidance, and the method comprises the steps: obtaining the real-time data of each unmanned ship and the surrounding environment; generating a candidate obstacle target point cloud cluster list based on a density clustering algorithm; on the basis of a Kalman filter, real-time absolute motion state estimation of the candidate obstacles is obtained, and an obstacle feature list is output; determining a safety radius compensation amount required by autonomous obstacle avoidance of each unmanned ship; obtaining the safe sailing space of each unmanned ship at the current moment; constructing a global synthetic potential field, and generating a group of optimal alternative paths of the unmanned ship from the current position to the target point; on the basis of adopting a consensus binding algorithm, an unmanned ship multi-agent collaborative obstacle avoidance path reaching a consensus is obtained. The method has the advantages that safe, efficient, cooperative and consistent intelligent obstacle avoidance of a multi-unmanned-ship cluster in a real complex marine environment is realized.

The invention discloses a sea condition perception and path prediction navigation method and system for a high-speed unmanned ship, and relates to the technical field of multi-source path decision optimization, and the method comprises the steps: collecting storm real-time perception data through a sensing device, carrying out the preprocessing, outputting a standardized sea condition state vector, and providing an experience reference for real-time path planning through ship-borne historical data; constructing a multi-target navigation prediction algorithm model to predict and generate a path candidate scheme set on the basis of the storm real-time sensing data and the ship-borne historical data, and performing evaluation through a conditional random field in combination with path candidate schemes generated by the storm real-time sensing data, the ship-borne historical data and target navigation prediction; and carrying out multi-dimensional safety risk scoring on an evaluation result through a fuzzy analytic hierarchy process by combining the wave height and direction influence, the hull stability and the obstacle avoidance complexity, and determining an optimal sailing path. According to the invention, fusion of multi-source sea condition perception and dynamic path optimization is realized, and the navigation stability, the adaptability and the risk control capability of the high-speed unmanned ship 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 buoy ship collision early warning method based on artificial intelligence, a medium and a system, and belongs to the technical field of buoy ship collision early warning. A ship trajectory prediction model is utilized to dynamically adjust neuron time constant parameters through an adaptive adjustment function to output a ship future position prediction sequence, and the prediction sequence is input into a greedy snake path verification algorithm to carry out gridding processing and continuity inspection to delete abnormal path segments. Based on the verified prediction sequence, calculating a collision probability value comprehensively considering a distance parameter relative speed parameter course angle difference value and a sea condition complexity coefficient, determining a collision risk level according to the collision probability value and a preset threshold value, and calculating estimated collision time; and the collision early warning information including the risk level estimation time and the emergency degree is output, so that the technical problems of low accuracy and poor timeliness of collision early warning of ships in a sea area are solved.

The invention discloses an intelligent fishing point dynamic prediction method and system based on multi-source marine environment data fusion. The method comprises the following steps: step 1, access, space-time alignment and pre-screening of multi-source heterogeneous marine environment data; step 2, priori knowledge base construction and suitability modeling based on target fish ecological habits; 3, constructing a fishing point prediction model fusing the multi-time-sequence environmental characteristics and deep learning; step 4, fusing two-channel prediction results under the Bayesian framework and quantifying uncertainty; 5, generating a dynamic mask of a real-time sea condition safety threshold value and fishery regulation space constraint; 6.1, constructing a comprehensive scoring function of the risk perception function. According to the method, multi-source heterogeneous data is constructed, a target fish ecological suitability model and a depth time sequence prediction model are combined, the fishing point posterior probability is generated through a Bayesian fusion mechanism, the prediction uncertainty is quantified, and dynamic fishing point recommendation with risk perception and compliance safety is realized.

The invention relates to the technical field of satellite altimetry, in particular to an altimetry method based on a residual network multi-mode deep learning model, which can remarkably improve the robustness of SSH inversion under sea condition noise interference, and is characterized in that a fusion self-attention mechanism and a residual network multi-mode deep learning model ViTResNetMDL is established, and the model ViTResNetMDL is applied to improve the robustness of the SSH inversion under sea condition noise interference. A satellite-borne GNSS-R sea surface height inversion geometric physical model is established, a ViTResNetMDL model is composed of ResNet50, ViT and MHL-NN, the first part adopts a ResNet50 convolution structure to extract effective features from an original DDM, the second part adopts a ViT layer which captures global features in an effective scattering area DDM based on a self-attention mechanism in a Transform module, the third part is composed of linear layers, and the fourth part is composed of linear layers. And using the MHL-NN to retrieve the sea surface height.

The invention provides a TMD lockout control method for motion suppression of a semi-submersible platform, and the method comprises the steps: carrying out the frequency domain decomposition of a random wave predicted by a target sea condition, obtaining the energy distribution of each frequency component, obtaining the equivalent amplitude of each frequency component according to the energy distribution, considering the amplitude response of the platform under the action of waves of different frequencies, and achieving the motion suppression of the semi-submersible platform. The method comprises the following steps of: calculating a weight ratio of platform motion response caused by each frequency component under the action of random waves by integrating three types of information, namely amplitude, amplitude response and motion suppression effect, and finally carrying out weighted combination on the locking time corresponding to each frequency component according to the weight ratio, so as to obtain the motion suppression effect of the platform. And the optimal locking control time suitable for the current random wave is obtained. According to the method, a high-complexity non-causal control command does not need to be deduced, control parameters can be updated in real time according to short-term sea condition changes, pitching response of a platform is effectively reduced, and comprehensive optimization control over multi-frequency waves is achieved.

The invention provides a dynamic sparse observation-oriented deep neural process ocean data assimilation method, and relates to the field of ocean data processing, and the method specifically comprises the following steps: constructing a training data set; simulating actually observed non-uniform and uncertain characteristics through Gaussian nuclear diffusion; building an ocean assimilation network oriented to sparse dynamic observation, outputting an analysis field and estimating uncertainty; and performing end-to-end training on the ocean assimilation network model by taking the reanalysis true value field as a supervision signal, and optimizing network parameters by combining a minimum error term and a structure constraint term. And after training is completed, inputting the background field in the test stage and sparse observation into the ocean assimilation network model for reasoning to obtain an ocean state reconstruction field conforming to the actual physical quantity scale. According to the technical scheme, the problem that in the prior art, calculation feasibility, cross-scale correlation modeling and credible uncertainty output cannot be considered under the real conditions of sparse observation and dynamic change of spatial-temporal distribution is solved.

The invention belongs to the technical field of ocean information engineering, and discloses a joint simulation method for a structure collision effect in a floating installation process of an offshore converter station. The joint simulation method comprises the following steps: S1, setting sea condition parameters of an operation sea area and geometric parameters and material parameters of a floating system; s2, initializing a hydrodynamic model and a multi-body model; s3, solving the barge motion equation under the current time step by using the hydrodynamic model to obtain barge motion data; s4, calculating an LMU contact force and a DSU contact force under the current time step according to the barge motion data in the step S3, feeding back the LMU contact force and the DSU contact force to the hydrodynamic model, and updating the external load of the barge; and S5, repeating the steps S3-S4 until simulation in the set time step is completed, and then outputting a simulation result. According to the method, precise calculation of dynamic movement of the floating body and precise simulation of complex contact-nonlinear restoring force of the LMU can be considered, and high-precision simulation analysis of the whole floating installation process of the offshore converter station is achieved.

The embodiment of the invention relates to a suspension collaborative steering and fault-tolerant method for a wave adaptive unmanned ship, and the method comprises the steps: collecting the environment perception information and attitude data of the unmanned ship in real time, and carrying out the fusion processing, and generating a multi-source data matrix; pre-judging the front sea condition based on the multi-source data matrix, and when the sea condition characteristic parameters meet preset dynamic adjustment conditions, adjusting the expansion and contraction amount of each group of electromagnetic suspensions in a pre-judgment time window so as to form a draught gradient adapting to the sea condition change; when a steering instruction is received, the draft difference of floating bodies on the two sides of the unmanned ship and the thrust difference of propelling systems on the two sides are synchronously adjusted, so that the steering track is controlled, and transverse inclination of the ship body is restrained; in the sailing process, the working states of the electromagnetic suspension and the propelling system are monitored in real time, faults are classified, when it is detected that the execution units are abnormal, a fault-tolerant reconstruction strategy is triggered according to the fault types, and stress distribution or propelling thrust is reconstructed by locking abnormal parts and utilizing the remaining execution units, so that the sailing capacity of the unmanned ship is maintained.

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 relates to the technical field of ocean internal solitary wave detection, in particular to an internal solitary wave detection algorithm fusing multi-modal feature deep learning under a high sea condition, and the algorithm comprises the steps: collecting an internal solitary wave data set under the high sea condition, and extracting the spectrum features of internal solitary wave data; constructing an internal solitary wave detection model AspectNet under the high sea condition fused with adaptive two-dimensional spectrum analysis according to the spectrum features; a multi-modal internal solitary wave detection model VIT-Multi-ISWNet under the high sea condition is constructed through an internal solitary wave detection model AspectNet under the high sea condition in combination with a multi-modal space-time attention mechanism, and the multi-modal internal solitary wave detection model VIT-Multi-ISWNet under the high sea condition is used for detecting the position of an internal solitary wave under the high sea condition. The internal solitary wave detection method fusing multi-modal feature deep learning under the high sea condition is high in universality, and target detection of more ocean phenomena can be achieved by modifying different types of data set training.

The invention relates to the technical field of deep sea communication and ocean observation, in particular to a long-term stable real-time communication system based on domestic satellites under high sea conditions. Comprising a submerged buoy body, the submerged buoy body is provided with a communication floating body module, a multi-source domestic satellite communication module, a low-power-consumption control module and a data processing module which are electrically connected in sequence, and an intelligent sensing module and an underwater data access module which are respectively connected with the data processing module; the device further comprises a main floating body. The system can be widely applied to deep-sea subsurface buoy platforms, marine environment monitoring stations, deep-sea scientific investigation equipment and other scenes, realizes real-time transmission of hydrodynamic and biogeochemical multi-element synchronous observation data at the water depth of 0-6000 meters, and supports research on the dynamic process of isolated waves, near-inertia internal waves and the like in the ocean and safety guarantee of the marine environment.

The invention relates to a stabilization self-adaptive control method and device based on double gyroscopes, and the self-adaptive control method comprises the steps: constructing a real-time updated'sea wave-ship-double gyroscope 'coupling dynamic model, and introducing the online identification of ship rolling parameters; physical limit, damping, maximum torque and other constraints of the double-gyroscope executing mechanism are explicitly written into a control law; the two gyroscopes are symmetrically arranged and opposite in precession direction, so that yawing torques are mutually counteracted and rolling torques are superposed, and the stabilization efficiency is improved; adaptively selecting PID according to the rolling amplitude, the sea condition uncertainty and the working state of the actuator, and adaptively switching the fuzzy adaptive PID and the model prediction control strategy; calculating a control instruction based on the selected strategy and the updating model, and performing physical constraint processing such as torque saturation and angle amplitude limiting; and outputting a safety instruction to drive the gyroscope to generate stabilization torque. The method can adapt to different working conditions, and the efficient and stable active stabilization performance and stabilization effect are kept under the multiple working conditions.

The invention discloses a bay environment bearing capacity evaluation data management and prediction analysis system, and relates to the technical field of environment monitoring and data analysis. The system comprises a data acquisition and database construction module, an evaluation index and model construction module, an environmental bearing capacity evaluation module, an early warning and risk prediction module and a data integration and visualization module. According to the method, a multi-dimensional evaluation index system is constructed, multiple aspects such as human activity pressure, ocean state parameters and ecological response are covered, index weights are determined by adopting an entropy weight method, comprehensiveness and objectivity of evaluation results are ensured, and meanwhile, an environment capacity calculation model and a state space evaluation model are combined, so that the evaluation accuracy is improved. According to the bay environment bearing capacity evaluation method, a complete bearing capacity evaluation model is constructed, the bay environment bearing capacity is quantitatively evaluated, the current state of the bay environment can be accurately mastered through the comprehensive and scientific evaluation method, and a powerful basis is provided for formulating scientific and reasonable environment protection and management measures.

The invention relates to a fishing boat meteorological route planning system, and belongs to the field of meteorological route planning system construction. The system takes a layered structure as a main framework and is sequentially divided into a data source layer, a data processing layer, a supporting layer, an interaction layer, a front-end UI layer and an access layer from bottom to top; in the design of a front-end page, a Vue framework is adopted, Jave and Python languages are adopted for back-end codes, and a PostgreSQL database is adopted for a database end. And finally, the sea condition meteorological data and the course planning process and result are displayed on a front-end page of the system, and the data can also be transmitted to an Internet of Things platform to be displayed.

The invention discloses a remote sensing and marine observation data fusion method and system based on two-dimensional variation, and relates to the technical field of marine sciences, and the remote sensing and marine observation data fusion method based on two-dimensional variation comprises the steps: obtaining marine satellite remote sensing data, extracting marine dynamic features, and obtaining marine satellite remote sensing data; constructing an ocean dynamic characteristic thermodynamic diagram as a two-dimensional observation field; the method comprises the following steps: acquiring temperature, salinity and ocean current data of different depth profiles in real time by using an ocean buoy observation network, generating a two-dimensional ocean state background field in combination with ocean historical data and a spatial interpolation method, and estimating a background error covariance matrix of the two-dimensional ocean state background field; taking the minimum difference between a two-dimensional ocean state background field and multi-source marine satellite remote sensing data and ocean buoy observation as a target, combining with a background error covariance, constructing a two-dimensional variational cost function, calculating an optimal ocean state analysis field, and obtaining spatial continuous two-dimensional ocean state optimal estimation. The method has the beneficial effect that the precision and practicability of marine environment monitoring and prediction are improved.

The invention discloses a marine traffic flow probability prediction method based on multi-source AIS-meteorological data fusion, and belongs to the field of marine traffic safety management, and the method comprises the following steps: S1, obtaining a standardized multi-source data set; s2, fusing the dynamic statistical characteristics of the AIS ship and the meteorological environment characteristics through a ship motion transfer function, and constructing a space-time patch considering a short-term ship motion trend and a long-term sea condition trend; s3, based on the space-time patch, fusing patch Transform time sequence modeling and adaptive dynamic graph space correlation learning to obtain a space-time fusion feature; and S4, outputting the traffic flow state prediction value and the probability distribution of the traffic flow key indexes. The marine traffic flow probability prediction method based on multi-source AIS-meteorological data fusion breaks through the limitation that a traditional method is single in data dimension, insufficient in space-time correlation capture and lack of probability confidence, and high-precision probability prediction of the marine traffic flow under the complex sea condition is achieved.

The invention provides an intelligent obstacle avoidance and path planning method based on a ship, and relates to the technical field of intelligent control of small leisure ships. S1, the state and surrounding environment information of the ship are obtained through a multi-sensor group deployed on the ship, and a distortion compensation algorithm combining Kalman filtering with a sea condition coefficient is adopted; outputting the compensated obstacle information, sea condition parameters and ship task types; s2, the local end uploads the compensated obstacle information and the sea condition parameters output in the step S1 to a server end, the server end introduces sea condition correction factors to construct a CPA model, collision risk indexes are calculated through the CPA model, and the path conflict probability between ship pairs in the water area is predicted according to a path conflict probability calculation formula. According to the invention, accurate risk judgment can be ensured through collision risk correction, and multi-ship ordered avoidance is realized through scenarized priority, so that the problems of path conflict, low decision-making efficiency and increased navigation risk caused by lack of collaboration in a multi-ship environment are solved.

A method includes converting, via a wavelet transform, (i) data associated with a prior ocean state forecast to wavelet space prior ocean state data and (ii) ocean observations to wavelet space observation data, and then filtering the wavelet space observation data. The method includes generating a correction value based on a difference between the wavelet space prior ocean state data and the filtered observation data, and determining a wavelet space increment value based on (i) the generated correction value, (ii) an error covariance associated with the prior ocean state forecast, and (iii) an error covariance associated with the ocean observations. The method includes converting, via an inverse of the wavelet transform, the wavelet space increment value to a physical space increment value, and generating a current ocean state forecast based on (i) the converted physical space increment value and (ii) a background state associated with the prior ocean state forecast.

The application provides a ship structure fatigue strength analysis method and system, which calculates the main engine vibration load and the wave load received by the ship structure based on the ship data, the characteristic data of the ship structure and the global sea state data, then calculates the stress response process under the combined action of the wave load and the main engine vibration load, the spectral density function of the stress response process, each order spectral moment of the stress response process and the zero-crossing rate of the stress response process, then calculates the stress response and the stress response peak of the ship structure under the action of the sinusoidal vibration load, calculates the stress cycle number of the ship structure based on the stress response and in combination with the fatigue life S-N curve, further calculates the fatigue damage under the action of the main engine vibration load, and finally calculates the total coupling fatigue damage under the long-term sea state, and further calculates the fatigue damage of the ship structure under the combined action of the high and low frequency loads, which effectively improves the accuracy and reliability of the ship structure fatigue strength calculation.

The invention relates to a light-weight ship buffer seat suitable for high sea conditions and high navigational speed and a using method, the light-weight ship buffer seat comprises a lower mounting plate, an upper mounting plate and an X-shaped adjusting structure connected between the lower mounting plate and the upper mounting plate, and the X-shaped adjusting structure is composed of a spring damper and an elastic deformation guide frame. The installation angle of the spring damper is changed through the lead screw mechanism, and the vertical equivalent stiffness and damping of the spring damper can be dynamically adjusted. Meanwhile, the guiding and limiting connecting rod assembly is made of high polymer materials, and on the premise that the guiding and limiting functions of the structure are achieved, the weight is obviously reduced. The technical problem that under the broadband high-amplitude continuous impact of the ship, the comfort of passengers, the structural stability and the light weight of equipment are difficult to consider at the same time is effectively solved.

The application discloses a marine buoy multi-source fusion positioning method and system based on a factor graph optimization, which acquires GNSS observation data, IMU measurement data and marine environment auxiliary data; a factor graph containing state nodes, GNSS position factors, IMU pre-integration factors and marine dynamics constraint factors is constructed, and wave and current theories are used to constrain buoy movement; in view of multipath effects, marine surface reflection geometry and marine root mean square wave height are combined to calculate a multipath weighting factor, and a GNSS covariance matrix is adaptively adjusted; according to IMU data, a sea state level is discriminated, and an edge window length and a trigger interval of incremental smoothing solving are adaptively linked and adjusted; through adjacent buoy ranging information, collaborative constraints are constructed, and based on Mahalanobis distance and chi-square distribution threshold value detection, abnormalities are detected and local reconstruction is performed. The application effectively suppresses marine surface multipath interference, slows down the accumulation of calculation errors during signal interruption, and realizes high-availability continuous positioning under limited computing power.

The invention discloses a forward modeling simulation method for a multidirectional sea wave induction electromagnetic field. The method comprises the following steps: expressing energy distribution of different propagation directions and frequency components through a spectral function; setting external environment input required by forward modeling according to the seawater depth and geomagnetic field parameters of the research area; after parameter setting is completed, electromagnetic response calculation is carried out on each frequency-direction sampling point; component response is called on a preset frequency set and a preset direction set, linear superposition is carried out by adopting a frequency direction correspondence method, and a total electromagnetic field is obtained; and outputting time sequences and frequency spectrum results of the wind wave induction electromagnetic field, the surge induction electromagnetic field and the mixed wave induction electromagnetic field, thereby providing a reference basis for noise analysis, background interference suppression and target signal identification in electromagnetic detection. According to the method, the limitation of a traditional model is broken through, the electromagnetic interference characteristics under the complex sea condition are represented more truly, and reliable support is provided for noise suppression and target recognition in electromagnetic detection.

The invention discloses a sea surface target tracking method based on a visual guidance radar under a high sea condition. The method comprises the following steps: synchronously collecting millimeter wave radar point cloud, image, positioning, direction and speed data when an unmanned ship sails; detecting an image target by using a YOLOv8 model subjected to slideloss weighted training and model distillation optimization to obtain a target detection frame; preprocessing the radar point cloud; point cloud proposal and classification are carried out based on an image detection frame, clustering clusters are generated through DBSCAN clustering, and visual angle information is fused in proposal clusters; and the clustering cluster is converted to a world coordinate system, dual matching with a historical target is realized through a Hungary algorithm, the unmatched target is initialized, and the state of the matched target is updated by using extended Kalman filtering and the future position is predicted. According to the method, a close-range sensing blind area is filled, the precision and stability of sea surface target tracking under the high sea condition are remarkably improved, and reliable guarantee is provided for safe navigation of the unmanned ship.

The invention discloses an ocean structure design method based on bidirectional fluid-solid coupling. The ocean structure design method comprises the steps that S1, sea conditions are determined based on a sea area where an ocean structure is located; s2, establishing a fluid domain calculation fluid dynamic model and a structural domain finite element model of the ocean structure; s3, on the basis of the established fluid domain calculation fluid dynamic model and structural domain finite element model of the ocean structure, executing iterative bidirectional fluid-solid coupling simulation calculation for each sea condition to obtain a simulation result; s4, analyzing the simulation result, and identifying the maximum value and the spatial position of the key load parameter on the ocean structure; and S5, modifying and optimizing the design of the ocean structure based on the maximum value and the spatial position of the key load parameter to obtain the ocean structure meeting the requirements. According to the method, the structural performance of the ocean platform can be systematically evaluated with high precision, so that a designer is guided to carry out more scientific and reasonable design modification and optimization.

The application discloses a kind of floating platform for offshore wind power generation and its design method, comprising: 1) theoretical numerical simulation analysis, establish floating platform model;2) manufacture platform structure and supporting assembly by three-dimensional printing technology;3) build test device in experimental pool;4) after each component is built, carry out equipment safety inspection;5) set initial environmental parameter, determine sampling frequency and sampling time;6) start experiment, adjust control system to obtain set sea state;7) carry out platform performance measurement, adjust parameter to obtain optimal state;8) collect the response data of platform under different environments;9) using orthogonal experimental design method, carry out multiple experiments to optimize platform design;10) collect and process all data.The application starts from platform structure design, and the optimization step is simple and easy to operate, suitable for floating platform design and optimization under different sea conditions, and provides reliable verification for offshore wind power generation.

Techniques for wireless signaling are disclosed. In an aspect, a wireless sensing node receives a radio frequency (RF-S) configuration for sensing, performs one or more RF-S programs based on the RF-S configuration, acquires sea condition information based on the one or more RF-S programs, and sends the sea condition information to a network component.

The application discloses a complex sea state-oriented unmanned ship multi-agent cooperative obstacle avoidance method and system, relates to the unmanned ship multi-agent cooperative obstacle avoidance field, and comprises the following steps: acquiring real-time data of each unmanned ship and the surrounding environment; generating a candidate obstacle target point cloud cluster list based on a density clustering algorithm; acquiring real-time absolute motion state estimation of the candidate obstacle based on a Kalman filter, and outputting an obstacle feature list; determining a safety radius compensation required for autonomous obstacle avoidance of each unmanned ship; acquiring a safe navigable space of each unmanned ship at the current moment; constructing a global synthetic potential field, and generating a set of optimal candidate paths of the unmanned ship from the current position to a target point; and acquiring a consensus-reached unmanned ship multi-agent cooperative obstacle avoidance path based on a consensus binding algorithm. The application has the advantages that the multi-unmanned ship cluster can realize safe, efficient and cooperative intelligent obstacle avoidance in a real complex marine environment.

The invention discloses an FPV system strength evaluation method based on sea condition mapping, and belongs to the technical field of floating photovoltaic system design, and the method comprises the following steps: 1, inputting initial parameters established by an FPV system, and establishing an initial design scheme of the FPV system, 2, establishing a local strength model of the FPV system by using the initial parameters, 3, inputting sea condition parameters, and 4, establishing a local strength model of the FPV system by using the initial design scheme. The method comprises the following steps of (1) calculating a time domain dynamic response result by using sea condition parameters, and generalizing the time domain dynamic response result, (2) determining an environment boundary condition according to a generalization result, (5) carrying out strength evaluation on an FPV system by combining the boundary condition, and (6) generating optimization design decision guidance according to a strength evaluation result. According to the method, a rigid body connected hydrodynamic overall model and a rigid body connected hydrodynamic local strength model are solved respectively, full-flexible coupling dynamics analysis is replaced, therefore, the calculation process is simplified, meanwhile, high-precision evaluation capacity is kept in a key stress area, and the problems that in the prior art, multi-flexible-body coupling dynamics simulation is complex, and the calculation amount is huge are solved.