194 results about "Sea state" patented technology

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 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 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 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 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 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.

The invention discloses an intelligent floating and sinking type net cage and a control method and system based on a backstepping method, and the control method comprises the steps: collecting the sea condition and net cage attitude data in real time through multi-sensor fusion, and achieving the nonlinear robust control of the depth motion of the net cage through a sliding mode control algorithm based on the backstepping method; the system can automatically adjust the floating and sinking strategy according to the wind speed and the wave height, dive to avoid wind when typhoon comes, and float to breed when the sea condition is stable; dynamic control over buoyancy-gravity balance is achieved through air inflation and deflation of the air bag and combined adjustment of the flow of the water inlet and outlet channel, and the device has the advantages of being rapid in response, high in stability and high in adaptability.

This utility model discloses a multi-point spraying system for marine engineering. It includes an installation frame with multiple spraying units arranged in an axial array on the inner side, a sensor module, and a control module. The installation frame consists of an upper ring, a lower ring, and vertical supports. Each spraying unit includes a nozzle, an angle adjustment mechanism, and a water pump. The lower ring has a sludge suction nozzle. The sensor module contains various sensors and also includes a remote monitoring module. The control method first collects sea state parameters to establish a digital model and determine the initial spraying values. During operation, parameters and modes are adjusted in real time, and a fault diagnosis program can be triggered. This application achieves the technical effects of adapting to different sea conditions, accurately adjusting spraying parameters, realizing efficient marine operations, and possessing fault diagnosis and remote monitoring functions.

A method of operating a floating wind turbine (1), FWT, is provided. The floating wind turbine (1) is exposed to waves during operation, the waves causing a wave induced motion of the floating wind turbine (1). The floating wind turbine (1) is configured to operate a protective function. The method comprises obtaining, during operation of the floating wind turbine (1), monitored wave data (70) indicative of a wave height of the waves the floating wind turbine (1) is exposed to during operation. It further comprises processing the monitored wave data (70) to obtain processed wave data, wherein the processing comprises at least a processing by descriptive statistical analysis, comparing the processed wave data to a threshold (140) that corresponds to a predetermined sea state and activating the protective function upon detecting that the processed wave data reaches or exceeds the threshold (140).

The invention relates to the technical field of hydrodynamic performance testing, and particularly discloses a hydrodynamic performance testing method based on a ship model test, which comprises the steps of hydrodynamic performance analysis in still water, ship model stability performance analysis, hydrodynamic performance analysis in a wave angle and ship comprehensive hydrodynamic performance evaluation. In the hydrodynamic performance test of the ship model, the hydrodynamic performance of the ship model in a still water state and at different wave angles is deeply analyzed, and the stability performance of the ship model at each wave angle is analyzed in combination with the corresponding test information of the ship model at each wave angle. The comprehensive evaluation of the hydrodynamic performance of the ship model is improved, the stability limit of the ship under different sea conditions is helped to be known, the overturning risk of the ship is reduced, the hydrodynamic performance of the ship under different sea conditions can be accurately evaluated, and the safety risk of the ship in navigation is reduced.

The invention relates to the technical field of ocean wave monitoring, in particular to an intelligent sea condition compensation method based on machine learning, which corrects an error generated by a buoy motion condition under a high sea condition, which cannot be simulated by a rotary wave simulation test board adopting sine waves as an excitation source, through a large-scale wave flume extreme working condition test. A buoy wave measurement data compensation method in a severe environment is researched, and the buoy measurement data precision is improved.

The application discloses a kind of short-term wave height distribution characterization methods based on spectral width, it is related to marine engineering technical field, and the method comprises: based on the statistical parameter of given measured short-term sea state calculation spectral width factor;Wherein, statistical parameter includes spectral frequency, spectral peak frequency, energy density spectrum and energy density spectrum maximum;According to local measured data situation determines characteristic parameter;Wherein, local measured data includes the significant wave height under multiple groups of short-term sea state, three-one wave height, spectral frequency, spectral peak frequency, energy density spectrum and energy density spectrum maximum;Distribution parameter is calculated according to characteristic parameter;Based on spectral width factor and distribution parameter determine the exceeding probability of short-term wave height distribution, provide more accurate input parameter for the design and construction of related structure of nearshore engineering, ocean engineering, near island reef engineering, make preparation for the characterization of short-term wave height distribution and short-term wave height prediction.

The application discloses a kind of ship compliance monitoring methods based on unmanned aerial vehicle and AIS data fusion.The method controls satellite positioning module, IMU and vision sensor of unmanned aerial vehicle to collect data;Satellite positioning antenna is compensated using IMU real-time attitude lever arm effect, and the corrected unmanned aerial vehicle coordinates are obtained;Based on vision SFM algorithm and unmanned aerial vehicle positioning displacement baseline, the relative position vector of ship is solved, and the absolute coordinate of ship is obtained by space projection;Through linear interpolation, the time and space alignment of measured data and AIS data is realized;Finally, coordinate deviation is calculated, and dynamic threshold based on flight height, sea state and other environmental parameters is combined to determine the compliance of ship.The application realizes independent high-precision solution and full-dimensional verification of ship position, heading and speed, effectively solves the problems of AIS data fraud identification difficulty, large environmental interference and other problems, and greatly improves the accuracy and intelligent level of maritime non-site law enforcement.

This invention discloses a method for estimating sea state levels based on visual information. The method includes acquiring image and video data of a target sea area through an image acquisition module; using a combination of median filtering and Gaussian filtering for noise reduction, and histogram equalization to enhance data contrast; performing image geometric correction using radial distortion correction and tangential distortion correction models; extracting wave texture features, motion features, and wave height features from the preprocessed data; inputting the extracted features into a trained sea state level classification model for training, and outputting the sea state level estimation result. During training, multi-model fusion and dynamic model updating methods are used to improve the accuracy and reliability of the estimation. This invention uses a camera to collect wave information within a certain distance and range, and automatically estimates the sea state level based on this information, thus solving the problems of high cost, limited measurement range, significant environmental influence, and low accuracy in existing sea state level estimation methods.

This invention relates to a method for predicting ship maneuvering motions under high sea states, comprising the following steps: establishing a three-dimensional numerical model of ship-wave coupling under high sea states based on computational fluid dynamics; installing sensors on the ship to collect real-time motion data of the ship under high sea states, and preprocessing the collected data; extracting feature parameters related to the ship's six degrees of freedom motion based on the preprocessed data; using the feature parameters as input, constructing a ship maneuvering motion prediction model using a neural network model, the output of which is the prediction result of the ship's six degrees of freedom motion over a future period; training and validating the prediction model using historical data; inputting the real-time collected data into the trained prediction model to perform real-time prediction of the ship's six degrees of freedom motion; generating ship maneuvering suggestions based on the prediction results; and providing real-time feedback and correction to the model based on the comparison between actual operating data and the prediction results. This invention has the effects of improving prediction accuracy, ensuring ship safety, and improving prediction precision.

The invention discloses a sea surface weak target detection method based on improved PatchTST, and relates to the technical field of radar signal processing, and the method comprises the following steps: obtaining a to-be-detected signal which comprises a sea clutter signal and a target-containing echo signal, carrying out the normalization operation of the to-be-detected signal, carrying out the phase-space reconstruction, and carrying out the phase-space reconstruction; dividing the reconstructed signal into a training set and a test set; a two-way difference module is used for improving the PatchTST model, and an improved IPatchTST model is obtained; the method comprises the following steps: firstly, performing phase-space reconstruction on original sea clutter time sequence data, constructing an IPatchTST network by using two-way differential input, establishing a sea clutter amplitude single-step prediction model, optimizing hyper-parameters of the model through a PSO algorithm, and realizing weak target signal detection by using peaks of prediction errors in a time domain and a frequency domain. Experiments on IPIX and SDRDSP data sets show that the method has good detection performance in different sea areas and sea conditions, and the detection rate and the calculation efficiency of the method are remarkably improved.

The present application relates to the technical field of structural response simulation, in particular to a ship container wave pressure intensity simulation method and system, in the present application, based on Fourier transform, frequency value, direction angle, energy coefficient, pressure amplitude and phase quantity are composed into a unified frequency domain vector, and the surface element pressure history is reconstructed by time step by time, so that the wave load is uniformly distributed in the spectrum domain phase, the frequency component phase superposition and the speed and heading offset are synchronously transmitted to each time point, the surface element pressure history and the sea state spectrum parameters are one-to-one corresponding, through the penalty function method, the displacement difference value and the allowable gap limit value are read at the node layer, the penalty coefficient is selected according to the constraint type to generate the penalty quantity, and the penalty quantity is superimposed to the node pressure record with the time step, the supporting, limiting and allowable movement state is quantified as the penalty term and is updated with the iteration increment, the constraint strength is uniformly controlled by the penalty coefficient grade table, and the working condition switching can reuse the grid and material input by replacing the matrix and penalty coefficient library.

A complex sea condition sea target positioning and tracking method constrained by dependent variables belongs to the technical field of intelligent maritime affair monitoring and ship navigation, and comprises the following steps: S1, constructing a sea target positioning and tracking state model including a state equation based on uniform linear motion and two observation equations adapted to AIS / GNSS and radar; s2, establishing a limited dependent variable observation model, and quantitatively observing nonlinear correlation between loss / truncation and the real state of the target; s3, performing prior prediction of the current target state and the covariance based on the historical state; s4, fusing the prediction state and actual observation (including censored data) to complete state and error covariance updating; and S5, introducing a Bernoulli random variable to calculate an observation state adaptive weight. Through combination of the limited dependent variable model and the filtering method, the problems of trajectory breakage and target missing detection of a traditional tracking method caused by incomplete observation data under complex sea conditions are solved, and the method is directly integrated to a shipborne radar monitoring system and a maritime affair supervision platform.

The utility model discloses a wind power energy storage windproof device for a mobile ocean platform, which is characterized in that an impeller is arranged in a windproof area of the ocean platform, and wind power data of the windproof area of the ocean platform is detected by a wind power detection component; the controller judges and outputs a current operation mode according to the wind power data, a pre-stored wind power operation rule and an obtained operation instruction, when the external wind power is small, the impeller is in a power generation mode, the controller controls the impeller to sequentially drive the reduction gearbox and the direct-current generator to generate power, and then the power is stored in the storage battery; the impeller is in a windproof mode, the controller controls the storage battery and drives the impeller to rotate through the impeller motor, and hedging and collision are carried out on natural wind, so that the wind load acting on the windproof area of the ocean platform is reduced, the force of wind power on the platform under the severe sea condition is effectively reduced, and the safety coefficient is improved.