2473 results about "Ocean environment" patented technology

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

The invention relates to the technical field of marine anomaly detection, in particular to a marine ecological anomaly early warning method and system based on multimode sensing and spatio-temporal reasoning. Comprising the following steps: acquiring original multi-modal data including image data and time sequence data; performing global context coding on the acquired image data, performing dynamic evolution coding on the time series data, and performing multi-modal feature alignment through feature normalization; carrying out feature fusion on the multi-modal features based on a dynamic ecological knowledge graph fusion mechanism; predicting fusion features based on space-time atlas changes; and performing attribution and diagnosis based on a prediction result. According to the method, the model can automatically amplify key signals and suppress irrelevant noise, and the capturing capability of early weak abnormal signals is greatly improved in a complex marine environment.

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 relates to the technical field of marine environment monitoring, and discloses an intelligent collection method and system for marine multi-dimensional information and a storage medium. The method comprises the following steps: synchronously acquiring environmental parameters at a plurality of measuring points and a depth layer, and constructing a hydrological characteristic data set; calculating spatial correlation and dividing a high variation region and a low variation region; constructing an equipment spacing model based on an information density classification result, and generating a point distribution scheme by adopting an optimization algorithm; combining real-time monitoring data to judge significant changes and dynamically updating layout parameters; and issuing an instruction to the equipment through the main and standby wireless channels to complete deployment. According to the invention, the adaptability and layout efficiency of marine environment perception are improved, and the method is suitable for intelligent monitoring application of complex and changeable sea areas.

A drifting buoy trajectory prediction method based on a hybrid neural network prediction model, includes: S1, obtaining marine environmental data and historical trajectory data of a drifting buoy; S2, performing preprocessing on the marine environmental data and the historical trajectory data to obtain input data configured to predict northward and an eastward velocities of the drifting buoy; S3, inputting the input data into the hybrid neural network prediction model to obtain predicted values of the northward and eastward velocities; S4, calculating latitude and longitude coordinates of a trajectory point of the drifting buoy based on the predicted values of the eastward and northward velocities; and S5, predicting, by repeating the step S1-S4, latitude and longitude coordinates of trajectory points of the drifting buoy at multiple time points to obtain a sequence of trajectory point coordinates to thereby achieve trajectory prediction of the drifting buoy over a target future period.

The invention relates to the technical field of ocean data interpolation filling, in particular to a space-time deficiency filling method and system based on context association and physical guidance. The method comprises the following steps: acquiring seawater dissolved oxygen data and context data; multivariable space-time dependence extraction is carried out based on the obtained seawater dissolved oxygen data and context data; gaussian noise diffusion is carried out based on the obtained seawater dissolved oxygen data; noise prediction is carried out based on double-view space-time correlation; and the prediction error is constrained based on the joint loss function. According to the method, a physical consistency constraint mechanism based on a partial differential equation is introduced in a model training process, so that model output better conforms to a physical coupling rule among variables in a marine environment. The constraint effectively inhibits non-physical fluctuation possibly occurring in the interpolation result, enhances the physical credibility and interpretability of the result, and provides a more reliable data basis for subsequent scientific analysis and process modeling.

The invention discloses a gravity type submerging net cage parameter design optimization method, and relates to the field of mariculture equipment structure optimization, and the method comprises the steps: collecting the multi-period marine environment data of a target sea area, and constructing an environment load sequence with a time-varying characteristic; establishing a three-dimensional coupling finite element model, performing disturbance analysis on a plurality of structure control parameters, and identifying a key design parameter set based on a response sensitivity function; and constructing a multi-objective optimization model including floating and sinking time control, maximum stress minimization and attitude offset constraint, performing global optimization by adopting an adaptive genetic algorithm, obtaining an optimal parameter solution, substituting the optimal parameter solution into the simulation model for dynamic response verification, and if a response index is converged to a target interval, outputting the optimal parameter solution as final design configuration. According to the method, systematic optimization of structural parameters of the submerging and surfacing net cage can be achieved, the stability and safety of the submerging and surfacing net cage under complex sea conditions are improved, and the method has high engineering applicability.

The invention discloses an underwater robot multi-target dynamic resource scheduling method, and relates to the technical field of robot resource scheduling. The method comprises the following steps: constructing an underwater robot resource scheduling problem model, and establishing a target optimization model which comprises the steps of minimizing task maximum execution time, minimizing total power consumption and balancing load; generating reference points and distributing the reference points in a target space; a population is coded and initialized, a scheduling scheme is represented by adopting multi-segment chromosome coding, the scheduling scheme comprises a task allocation sequence and tasks allocated by a robot, and an initial parent population is generated in a random mode; a non-dominated solution set is obtained through evolutionary iteration, and the non-dominated solution set is obtained through reference point niche selection and combined evolutionary iteration of crossover and mutation operation based on an improved NSGA-III algorithm; and obtaining an optimal scheduling scheme, and screening an optimal compromise solution from the non-dominated solution set as the optimal scheduling scheme. According to the invention, a data-driven intelligent scheduling scheme is provided for collaborative operation of underwater robots in a complex marine environment.

The invention discloses a submarine cable shape monitoring method based on an ultra-weak fiber bragg grating, which comprises the following steps of: obtaining curvatures # imgabs0 #, # imgabs1 # and # imgabs2 # in X-axis and Y-axis directions from curvatures in two orthogonal directions of curvatures calculated by three uniformly distributed optical fibers, representing angle offset from an ith sensing unit to an X-axis positive direction, and obtaining a bending curvature, an arc angle and a direction angle; and coordinate information in the space is further obtained by combining rotation and translation matrix iterative calculation of a shape reconstruction algorithm, namely, three-dimensional space coordinates of the measured curve can be determined point by point, and finally, complete shape reconstruction and other steps are realized. According to the invention, high-precision large-scale three-dimensional deformation monitoring can be realized. By optimizing the eccentric distance and the strain transmission efficiency of the optical fiber, the tiny deformation of the submarine cable is accurately captured, and by combining an innovative three-dimensional curve reconstruction algorithm, the bending, inclination and other deformation states of the cable can be accurately reflected in different environments. Therefore, the method has obvious advantages in a monitoring task with a high-precision requirement, especially in a complex marine environment.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention discloses a wave power generation efficiency optimization control method and device based on artificial intelligence. The method comprises the steps that S1, a marine environment data set is collected; s2, forming a preprocessed marine environment data set; s3, according to the preprocessed marine environment data set and the structure parameters of the wave energy power generation system, constructing an optimization objective function; s4, performing global search on the optimization objective function by using a bitter fish optimization algorithm, initializing an optimization population, and determining a plurality of candidate optimal parameter regions; s5, adopting an adaptive jellyfish search algorithm to perform local fine search on the area where the candidate optimal parameters are located, updating and optimizing the key control parameters, and obtaining the key control parameters for further improving the electric energy conversion efficiency; and S6, the key control parameters are applied to the wave energy power generation system, and dynamic closed-loop feedback control is formed. According to the invention, the system is always in an optimal working state, so that the electric energy conversion efficiency is further improved, and the energy loss is remarkably reduced.

The invention discloses a submarine pipeline state early warning method and system based on scour-vibration coupling sensing, and the method comprises the steps: obtaining the historical marine environment information of a target submarine pipeline region, laying a monitoring sensor array according to the historical marine environment information, collecting multi-source monitoring data, and carrying out the preprocessing of the multi-source monitoring data, thereby forming a multi-source monitoring data set. And constructing a multi-physical field coupling analysis model, inputting the data set into the model to analyze the response condition of the pipeline in the current seabed environment, and obtaining pipeline response simulation information. And analyzing resonance influence caused by environmental scouring in different sections, generating resonance derivative characteristics, and constructing pipeline multi-modal fusion characteristics in combination with monitoring data. Predicting the future response state of the pipeline through the submarine pipeline state analysis model, and carrying out risk early warning prompt; meanwhile, the fatigue life of each section is analyzed based on resonance derivative characteristics, fatigue-prone sections are identified, and maintenance suggestions are generated and pushed. Therefore, the accuracy and timeliness of sensing the state of the submarine pipeline are improved, and the early warning intelligence of the submarine pipeline is effectively improved.

The invention discloses a marine environment forecasting method based on combination of machine learning and numerical forecasting, and particularly relates to the field of marine environment forecasting, which comprises the following steps: extracting marginal region features based on real-time observation data, constructing a forecasting field through a double-loss function, extracting extreme event features through space-time decoupling, and constructing a forecasting field through a double-loss function; and outputting a forecasting result of the target sea area. According to the marine environment forecasting method based on the combination of machine learning and numerical forecasting, the problem of initial field optimization failure caused by covariance matrix estimation deviation when marginal sea area data are sparse is relieved by embedding vorticity conservation equation residual calculation in a hidden layer; the dependence on the number of extreme event samples is reduced by realizing the targeted extraction of the extreme ocean event features; through a physical hard constraint layer technology of a residual error correction network, an initial field error of a data sparse region is corrected in combination with sea surface height and a flow velocity field constraint residual error feature map, and the problem of a large prediction error of an edge sea area is relieved.

The invention provides an energy optimal path planning method and device for an unmanned surface vessel in a complex marine environment, and belongs to the technical field of path planning. The method provided by the invention comprises the following steps: constructing a comprehensive energy consumption model; constructing a hybrid enhanced particle swarm optimization algorithm; constructing a safety space set and an adjacent graph through a grid method, generating an initial optimal path by adopting an A * algorithm, and carrying out bounded random disturbance and safety correction on target particle waypoints; smoothing the path generated by iteration by adopting a B-spline technology, resampling the smoothed optimal solution, and then reinjecting the optimal solution into the population; adjusting an inertia weight and a learning factor based on the number of iterations, and introducing double guide factors to carry out secondary adjustment on a cognitive item of particle speed updating; and embedding the comprehensive energy consumption model as a fitness function into a hybrid enhanced particle swarm optimization algorithm, performing real-time energy consumption evaluation, individual and global optimal path updating and path smooth optimization on each candidate path in an algorithm iteration process, and outputting an optimal navigation path of the unmanned surface vessel.

The invention discloses an intelligent ship autonomous collision avoidance method based on COLREGs and a DDPG algorithm, and relates to the technical field of an intelligent ship technology and an autonomous collision avoidance algorithm, and the method comprises the steps: building an intelligent ship kinematics model based on the motion parameters of a ship in a north-east coordinate system; aIS, radar and visual information are fused, COLREGs rule constraints are embedded, an environment model is constructed, and a ship size safety threshold value is calculated. According to the method, COLREGs and a DDPG algorithm are combined, a high-precision environment model is constructed by utilizing multi-source sensing data, and an algorithm structure is optimized aiming at a typical collision avoidance scene, so that the intelligent ship can automatically identify the meeting situation type, determine the way-giving responsibility or the direct navigation obligation and generate an optimal collision avoidance path in a complex marine environment; compared with a traditional collision avoidance method depending on manual driving and fixed rules, the autonomous collision avoidance capability of the ship in dynamic and complex ocean traffic scenes is remarkably improved, and the collision risk caused by human factors is reduced.

The invention discloses a marine environment real-time monitoring and early warning system based on machine learning, and relates to the technical field of machine learning. Comprising the steps that an ocean multi-source sensing module collects ocean environment data in real time through a sensor and a combined collection scheme; the multi-source feature extraction module performs time domain, change rate and frequency domain feature analysis on the data to construct a unified multi-dimensional feature vector; the multi-model fusion prediction module outputs a marine environment state vector through dynamic weighting and deviation correction based on a parallel learning architecture of a deep neural network, a long-short-term memory network and a one-dimensional convolutional neural network; and the ocean risk identification and early warning module generates graded and classified early warning information through double study and judgment of a sea condition classifier and an abnormal event detector. According to the method, comprehensive acquisition, deep feature mining, high-precision prediction and accurate early warning of marine environment data are realized, the problems of low prediction precision, risk identification lag and the like in the prior art are effectively solved, and reliable guarantee is provided for marine operation safety.

The invention discloses a jacket platform real-time correction and state prediction method based on a reduced-order model, and relates to the technical field of ocean engineering structure health monitoring and safety early warning. Comprising the steps of establishing a jacket platform finite element model; obtaining a sample matrix; constructing a reduced order model; reducing the order of the reduced-order model until the calculation precision of the reduced-order model meets a preset precision requirement, and obtaining a global reduced-order basis matrix and the reduced-order model meeting the preset precision requirement; reconstructing a stiffness matrix and a mass matrix by using the global reduced-order basis matrix; obtaining a corrected finite element model; inverting the marine environment load based on the on-site platform structure strain response data, and deducing the marine environment load; inputting the marine environment load into a pre-trained LSTM model, and outputting time series data including future load through the LSTM model; and predicting the future operation state trend of the jacket platform. The operation safety and reliability of the jacket platform are improved.

The invention discloses a ship carbon emission on-line monitoring method, device and equipment, and the method comprises the steps: building a comprehensive emission feature library through collecting infrared absorption spectrum, gas flow and other multi-source emission signals in a ship operation process; eliminating gas cross interference by adopting a spectrum separation analysis technology, and establishing a concentration inversion mechanism to generate an independent monitoring domain; high and low emission working condition modes are identified through working condition relevance evaluation, and intelligent allocation of monitoring resources is achieved; marine environment compensation nodes are established, layered temperature and humidity correction control is implemented, and environment-adaptive ship environment parameters are generated; the multi-stage early warning trigger time is determined by adopting an early warning threshold backward deduction analysis technology, a ship emission monitoring time sequence table and a graded monitoring instruction set are constructed, full-working-condition, all-weather and high-precision intelligent monitoring of ship emission is realized, and reliable technical guarantee is provided for ship environmental protection compliance and marine environmental protection.

The invention discloses a power generation efficiency optimization method for a floating photovoltaic power generation system. The method comprises the following steps: analyzing the motion response of a floating body in a marine environment, and determining the boundary condition of the floating body swinging under the action of waves; based on hydrodynamic analysis software AQWA, a hydrodynamic analysis model of the floating body is built, and the pitching angle of the floating body is verified; in combination with an inclined surface irradiance calculation model, an improved particle swarm algorithm is adopted for optimization, and the initial inclination angle of the photovoltaic panel when the photovoltaic panel receives solar radiation energy to the maximum extent is solved; in combination with different wave conditions of a certain sea area, simulation software is used for performing dynamic simulation on the floating body and the assembly, the influence of different environmental conditions on the power generation efficiency is analyzed, and the effectiveness of the scheme is verified. According to the method, the optimal initial angle is determined by optimizing the initial mounting inclination angle of the assembly, fully considering the movement condition of the floating body under the action of waves and combining the influence of different wave conditions, the radiation quantity received by the assembly is maximized, and the defects in the aspects of laying design, dynamic adaptability and power generation efficiency in the prior art are overcome.

The invention provides a water surface target grabbing system and a control method thereof, and relates to the technical field of marine operation equipment. The system comprises an unmanned ship and a cooperative mechanical arm, the unmanned ship and the cooperative mechanical arm share a processor; the unmanned ship supplies power to the cooperative mechanical arm; the unmanned ship is positioned through an inertial navigation system on the unmanned ship and a scene camera and a depth camera on the cooperative mechanical arm; a scene camera, a depth camera and an integrated sensor on the cooperative mechanical arm transmit data to a processor on the unmanned ship through a local area network, the processor calculates and analyzes the data transmitted by the scene camera and the depth camera, and an action instruction is sent to the cooperative mechanical arm through a CAN bus. According to the water surface target grabbing system, automatic grabbing is achieved, and the grabbing precision, stability and adaptability of the system in the dynamic marine environment are improved.

The invention discloses an AUV (Autonomous Underwater Vehicle) path planning and control integrated method for a complex marine environment, and provides an intelligent path control scheme integrating reinforcement learning and local obstacle avoidance mainly aiming at underwater navigation problems such as submarine topography fluctuation, frequent ocean current disturbance and uncertain dynamic obstacle distribution. According to the method, a realistic three-dimensional ocean grid environment is constructed, an improved Q-learning algorithm is utilized to complete global path planning, a group of optimal path point sequences are generated, and it is ensured that the AUV has the macroscopic navigation capability. Local obstacle avoidance is performed in combination with a three-dimensional dynamic window method (3D DWA), and sudden obstacles are dynamically avoided through speed sampling and trajectory prediction. A DDPG control strategy based on deep reinforcement learning is further introduced, end-to-end training of path planning and control execution is realized, and the system can directly output a control instruction according to a real-time sensing state. A cosine attenuation learning rate, a self-adaptive exploration mechanism, an energy consumption penalty function and a dynamic obstacle prediction and control action filtering technology are fused in the method, so that the intelligence of path planning, the stability of control output and the robustness of obstacle avoidance response are effectively improved. According to the invention, the autonomous navigation capability, the environmental adaptability and the safety task completion efficiency of the AUV in a complex marine environment can be obviously enhanced, and reliable intelligent control technical support is provided for underwater operation.

A lidar system and an inversion method for simultaneously detecting carbon sequestration efficiency of the oceanic biological pump and ocean environmental dynamic parameters. The information of Mie scattering and Brillouin scattering intensity and the information of frequency shift and linewidth of Brillouin scattering spectra are combined, and for the detection requirements of the current ocean carbon cycle and dynamic environmental parameters, the vertical profile distribution of the carbon cycle mechanism and environmental dynamic parameters in the euphotic layer is mainly detected; at the same time, the present disclosure constructs an inversion model of the carbon sequestration efficiency of the oceanic biological pump and the temperature and salinity of the environmental dynamic parameters and realizes the synchronous detection of the carbon sequestration efficiency of the oceanic biological pump and the vertical profile distribution of the ocean environmental dynamic parameters in the euphotic layer.

The invention relates to a self-adaptive task allocation and cooperative control method for a distributed unmanned ship cluster, and the method comprises the following steps: enabling an unmanned ship to collect the state information and environment information of the unmanned ship, constructing a state perception model with integrated cognition, and evaluating the navigation safety level and cruising ability of the unmanned ship; the unmanned surface vehicle determines an evaluation index according to a task type according to a state sensing model of the unmanned surface vehicle and information interaction between the unmanned surface vehicle and an adjacent unmanned surface vehicle, calculates fitness to different tasks, and dynamically negotiates and distributes the tasks based on a game theory and a negotiation mechanism; a reinforcement learning algorithm is introduced, the unmanned surface vehicles interact with the environment to adjust an action strategy according to reward feedback, meanwhile, learning experience is shared among the unmanned surface vehicles, and a cooperative control strategy is optimized. Compared with the prior art, the method fully considers the individual difference of the unmanned surface vehicles and the dynamic change of the marine environment, realizes the adaptive distribution and efficient cooperative control of tasks, and improves the operation efficiency, reliability and safety of the unmanned surface vehicle cluster in the complex marine environment.

The invention discloses an ocean in-situ radioactivity monitoring method, which is characterized in that feature extraction is carried out based on a maximum likelihood expected value maximization (MLEM) algorithm, and spectrum unfolding operation is carried out by adopting a multi-layer perceptron. The method comprises the following steps: firstly, measuring FWHM related parameters of a CeBr3 detector by adopting a standard source, simulating the response of the detector to gamma rays in a marine environment based on a Monte Carlo method, and establishing a full-spectrum response matrix H in a range of 0-2048keV; based on the response matrix H, reconstructing a gamma energy spectrum y obtained by the detector by using an MLEM iterative algorithm; and the reconstructed spectrum is used as the input of a multi-layer perceptron (MLP), and qualitative and quantitative analysis of radionuclides in the energy spectrum is realized. According to the method, the discrete characteristic peak signal is convolved into the single path address corresponding to the characteristic energy, so that the accuracy of the MLP model in the spectrum unfolding process is effectively improved.

The invention relates to the technical field of vehicle navigation and path planning, and provides a marine inertial navigation path planning method and system, and the method comprises the steps: obtaining environment risk information of a path corresponding to the driving direction of an autonomous underwater vehicle; adjusting a deviation tolerance threshold value corresponding to an inertial navigation system on the autonomous underwater vehicle according to the environmental risk information; acquiring a position calculation deviation of the inertial navigation system; the position calculation deviation is compared with a deviation tolerance threshold value, and triggering of a communication correction action is decided according to the backlog condition of the emergency event data; and when the communication correction action is triggered, planning and executing a communication correction track to realize position correction of the autonomous underwater vehicle. The method has the advantages that the navigation strategy is dynamically adjusted according to the environment risk and the data backlog condition, the communication correction opportunity is intelligently decided, and the autonomous navigation capability and the task adaptability of the autonomous underwater vehicle in the complex marine environment are improved.

The invention discloses a special-shaped steel pipe concrete pipe section hoisting dynamic pose control method, relates to the technical field of ocean engineering underwater pipeline installation, and mainly solves the problems of insufficient dynamic response and low space pose control precision when a special-shaped pipe section is hoisted in a complex water flow environment. The method comprises the steps that pipe section space pose design parameters are obtained, the pitch angle and the rolling angle of a pipe section are monitored in real time through an inclination angle sensor set, disturbance torque generated by real-time water flow force is calculated, sling length compensation amount is generated, and the take-up and pay-off speed of a chain block at each lifting point is independently adjusted; and meanwhile, adjusting mooring ropes moored on the two sides are pulled to control the horizontal rotation angle of the pipe section, the height of a lifting hook and tension of the mooring ropes are fed back and adjusted in cooperation with a floating crane positioning system, and finally accurate underwater in-position of the pipe section to the preset position of the seabed is achieved. The device is mainly used for hoisting and sinking operation of the special-shaped concrete filled steel tube section in the marine environment, water flow interference can be effectively resisted, and accurate butt joint and installation quality of a pipe section connector are guaranteed.

The invention discloses an ocean wind power underwater noise monitoring method and system, and belongs to the technical field of ocean environment monitoring, artificial intelligence and autonomous systems.The ocean wind power underwater noise monitoring method comprises the steps that a digital twinborn model fusing multi-mode perception is constructed; deducing future states of the system and the environment based on a digital twinborn model to generate a predictive state sequence; solving and generating a cooperative control strategy according to a joint optimization target of data quality and energy consumption; and analyzing the cooperative control strategy to synchronously regulate and control the physical system. According to the method, a digital twinborn driven cognitive decision and autonomous control architecture is adopted, a high-fidelity digital twinborn body is constructed to carry out prospective and uncertainty deduction on a physical system and a marine environment, and a closed-loop decision is carried out by taking risk-income optimization as a principle; the monitoring system can have long-term, autonomous and online self-improvement and risk avoidance capabilities, and the reliability of monitoring data in a complex marine environment and the full-life-cycle autonomous operation level of the system are remarkably improved.

The invention discloses a noise reduction and identification integrated method and system for ship target classification under marine environmental noise interference, and the method comprises the steps: carrying out the short-time Fourier transform of a radiation noise signal of a to-be-identified ship interfered by the marine environmental noise, and obtaining a corresponding time-frequency spectrum; inputting the trained ship target noise reduction and classification integrated model to obtain ship classification; the model comprises a deep noise reduction network module, an MP-SENet parallel decoder structure is integrated in DBSA-Net, and the deep noise reduction network module is used for independently reconstructing an amplitude spectrum and a phase spectrum of a ship signal in a time-frequency domain; the inverse Fourier transform module is used for performing inverse Fourier transform on the amplitude spectrum and the phase spectrum of the ship signal to obtain a denoised ship signal to be identified in a time domain form; and the multi-scale identification module is used for carrying out multi-scale feature extraction, extrusion-excitation of channel perception and hierarchical feature fusion on the to-be-identified ship signal after noise reduction to realize ship classification.

The invention belongs to the technical field of marine environment monitoring, and discloses a multi-mode deep learning fused marine remote sensing sea wave parameter inversion method and system, and the method comprises the steps: obtaining three-degree-of-freedom motion time calendar data of a ship in a target region and spaceborne synthetic aperture radar (SAR) image data; preprocessing ship motion time calendar data and the SAR image data, and constructing time-space aligned multi-modal input data; and inputting the preprocessed multi-modal input data into a pre-trained sea wave parameter inversion model, and calculating to obtain an inversion value of the sea wave parameter. According to the method, an attention mechanism is innovatively used to realize adaptive alignment and complementation of heterogeneous information in two dimensions of space and time, and an attention module automatically allocates weights according to task correlation; through time-space collaborative feature enhancement, the model can stably extract characterization strongly related to target parameters even under complex sea conditions, so that the inversion precision and robustness of sea wave parameters are remarkably improved.

The invention provides a deep sea temporary platform large-diameter steel casing dynamic response analysis method, and relates to the technical field of deep sea engineering, and the method comprises the steps: building a three-dimensional explicit dynamic coupling model according to the typhoon frequent generation characteristics of a target sea area and steel casing structure parameters; according to the three-dimensional explicit dynamic coupling model, the wave load is decomposed, the vibration hammer load effect, the ocean current load effect and the internal wave current load effect are integrated, and a comprehensive dynamic load set of the steel casing is determined. By establishing a precise model, integrating multi-source loads, identifying a risk area, monitoring stress in real time and optimizing design parameters based on data, self-adaptive matching of steel casing design and a complex marine environment is achieved, the structural safety of the deep sea temporary platform steel casing is effectively improved, and the cost is reduced.