98 results about "Internal wave" patented technology

The invention discloses an internal wave characteristic field information rapid prediction method based on deep learning, and belongs to the technical field of ocean internal wave detection. According to the method, rapid prediction is carried out on the basis of traditional numerical simulation data by using deep learning, so that the efficiency of inner solitary wave prediction is remarkably improved; through multi-channel coupling learning and an iterative prediction strategy, the inner wave field prediction achieves the effect of high precision and high efficiency. Meanwhile, in combination with high-resolution numerical mode data, the consistency of a model prediction result and a physical process is ensured, and rapid extension period prediction of a solitary wave characteristic field in the ocean is realized while low error and high relevancy are kept. The method is suitable for application scenes of real-time ocean internal wave monitoring and early warning and the like, and can be widely applied to the technical fields of ocean environment monitoring and early warning, intelligent ocean observation, ocean engineering safety and the like.

The invention relates to the technical field of ocean engineering surveying and mapping, and particularly provides an ocean engineering surveying and mapping terrain correction method based on multi-beam sounding. The method comprises the following steps: constructing a tremor-sound velocity coupling model through multi-source data synchronous acquisition, and generating a sound velocity dynamic correction term; fusing the static sound velocity profile, the internal wave disturbance term and the sound velocity correction term, and establishing a time-shifting sound velocity field of spatio-temporal evolution; realizing dynamic space homing calculation through a double-shaft decoupling rotation matrix based on a hull attitude and a time shifting sound velocity field; tunnel effect noise is suppressed by adopting an airspace adaptive filtering technology; the terrain deformation rate is calculated in combination with continuous cycle data, and a deformation threshold value is dynamically set through soil mechanical parameters to trigger graded safety early warning; and outputting the high-precision three-dimensional terrain model without the sound velocity error and the noise interference. According to the method, the problems of geological activity interference compensation deficiency, tunnel effect noise suppression insufficiency, large dynamic environment homing error and the like in traditional surveying and mapping are solved, and the reliability of complex sea area topographic survey is 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.

The invention relates to an internal solitary wave identification and amplitude inversion method in mode data based on machine learning, and belongs to the technical field of ocean observation. In combination with a Mask-RCNN and a variational auto-encoder, firstly, sea surface height data is preprocessed and imaged, texture features are extracted through a local binary pattern algorithm, a labeled data set is constructed, model training is performed, and internal solitary wave stripes and positions thereof are identified from the labeled data set; through extraction and processing of temperature field data, the temperature field at the water depth is inversed by using a variational auto-encoder; and extracting the amplitude of the internal solitary wave in the temperature field based on the internal wave identification result. According to the method, large-scale and refined ocean mode data can be efficiently and accurately processed, and the efficiency and accuracy of data processing are remarkably improved. The method has a wide application prospect, and especially in marine disaster early warning, marine scientific research and internal wave mechanics research, the automatic identification and amplitude extraction capability of internal waves can be greatly improved.

The invention belongs to the field of near-inertia internal wave modeling, and particularly relates to a near-inertia internal wave modeling method based on a reverse echo observation array, which comprises the following steps: arranging a reverse echo measurement device array in an observation area, and acquiring echo signals from the seabed to the sea surface; performing data cleaning and band-pass filtering on the signals, and extracting near-inertia internal wave signals; calculating the depth and the relative vorticity of a mixed layer by combining historical temperature-salinity data; calculating a wind input energy flux by using the wind stress data; calculating a net level energy flux by adopting a boundary integration method; judging whether the boundary energy flux influence is smaller than a threshold value or not, and if not, adjusting array layout; and finally establishing a multivariable linear model containing wind energy input, mixed layer depth, relative vorticity and energy flux. According to the method, the temporal-spatial resolution and prediction precision of near-inertia internal wave modeling are effectively improved, the problems that in a traditional method, the boundary effect is remarkable, and multi-factor comprehensive modeling is insufficient are solved, and the method is suitable for long-term efficient monitoring of a large-range marine environment.

The invention provides an ocean internal wave feature detection method based on AUV fixed-depth navigation, and relates to the technical field of ocean exploration, ocean background profile data are collected when an AUV sinks, and actually measured temperature and actually measured depth data are collected when the AUV is fixed depth; the method comprises the following steps: correcting actually measured temperature and actually measured depth data according to ocean background profile data, converting a water body temperature time sequence of an AUV depth-keeping navigation time period into a water body fluctuation time sequence, retaining a low-frequency signal in water body displacement fluctuation by using an LMD (Local Mean Decomposition) method, and determining the amplitude and wavelength of an internal wave.

The invention discloses a marine environment spring layer characteristic diagnosis data processing method, and aims at complex dynamic characteristics of a land frame edge sea spring layer to realize accurate diagnosis through multi-source data cooperation and an intelligent algorithm. The method comprises the following steps: acquiring and preprocessing field observation, remote sensing, sonar, terrain and tidal ocean current data; extracting spatio-temporal characteristics based on a CNN-LSTM hybrid network, and generating a dynamic spring layer threshold; fusing multi-source data by adopting an improved D-S evidence theory, and constructing a three-dimensional spring layer characteristic field; predicting an internal wave in combination with an attention mechanism model, and optimizing a preorder model by using sonar data. The method breaks through the limitation of a fixed threshold value, realizes spring layer dynamic identification, three-dimensional reconstruction and internal wave prediction, improves the diagnosis precision of a complex sea area, and provides key technical support for underwater detection, engineering safety and environmental research.

The invention discloses an internal wave electromagnetic response numerical simulation method based on a layered ocean model. The method comprises the following steps: acquiring geomagnetic field parameters; calculating distribution characteristics of seawater conductivity along with depth, and dispersing the distribution characteristics into a layered ocean conductivity model; an internal wave mechanical model is constructed, internal wave velocity field distribution is established through characteristic parameters of internal waves, and dynamic input is provided for electromagnetic field response calculation; substituting the internal wave velocity field and the geomagnetic field into a Maxwell equation set, and constructing an internal wave induction electromagnetic field control equation; and applying tangential electric field continuity and normal current continuity conditions on a layered interface, and performing discrete and iterative solution on an equation by adopting a finite difference method to obtain distribution characteristics of the electric field and the magnetic field at different depths and frequencies. According to the method, the problem of insufficient precision caused by a uniform conductivity hypothesis is solved, and application limitation caused by an ideal geomagnetic field hypothesis is avoided, so that accurate simulation of internal wave induction electromagnetic response under a complex ocean condition can be realized.

According to the method, a universe control equation set comprising a control equation, a free surface, upper and lower layer fluid interface continuity, structure-fluid interface continuity and fluid interface and seabed constraint conditions is constructed, and a vertical-horizontal decoupling modal analysis technology is combined, so that a multi-body radiation coupling model of the bottom-supported cylindrical array with relative motion between columns is provided; an intrinsic mode expansion technology is adopted to synchronously represent a joint radiation mechanism of a surface gravity wave and an internal wave, potential function space transformation and mode matching under a multi-column coordinate system are realized through a Graf addition theorem, and a radiation matrix coupling model considering a bottom-supported boundary constraint is constructed. According to the method, the wave-column coupling effect of the density layered interface can be accurately analyzed, a multi-column interference energy transfer path is quantified, and the modal truncation error is remarkably reduced.

The invention discloses an ocean internal wave prediction method based on satellite remote sensing data, and the method comprises the steps: firstly obtaining a multi-source satellite remote sensing image of a target sea area, and building a multi-source satellite remote sensing internal wave crest line data set; and then constructing an internal wave numerical model, performing internal wave numerical simulation calculation by adopting the internal wave numerical model in a computational domain range of each two-dimensional section, further obtaining an internal wave generation statistical data set, finally constructing an internal wave generation statistical model, and obtaining an initial internal wave crest line. On the basis, matching an internal wave crest line of the remote sensing image with an initial internal wave crest line to obtain corresponding initial crest line information, an initial arrival time field and generation moments of N two-dimensional sections at a generation source of the Lusong strait, and finally constructing an internal wave initial arrival time error statistical model. And correcting the initial arrival time field to obtain a corrected arrival time field, thereby constructing an internal wave propagation model and realizing prediction of an internal wave crest line.

The invention relates to the technical field of ocean resource development, in particular to a method for calculating vortex-induced vibration of a deep-sea mining riser under the action of internal waves. According to the method, velocity and acceleration distribution characteristics of a flow field are determined according to wave parameters such as amplitude and wavelength of ocean internal waves, and load input is provided for calculation of vortex-induced vibration of the deep-sea mining riser; the ocean internal wave flow field has remarkable particularity, is different from uniform flow and typical shear flow, and is unsteady non-uniform shear flow; based on the particularity of an ocean internal wave flow field, vortex-induced load distribution on a deep-sea mining riser under the action of a traditional wake flow vibrator model is obtained by improving the traditional wake flow vibrator model; according to the geometric nonlinear problem caused by the large slenderness ratio of the deep-sea mining super-long riser and the weak constraint characteristic of the deep-sea mining riser, the rigid body displacement and the elastic deformation of the riser are separately calculated by adopting a co-rotation coordinate method, so that the calculation efficiency is improved to meet the actual engineering requirements; the invention discloses a calculation method for vortex-induced vibration of a deep-sea mining riser under the action of ocean internal waves.

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 application belongs to the technical field of marine environment monitoring and early warning, and specifically relates to a multi-channel internal wave flow early warning system and an early warning measurement method, which comprises the following steps: a buoy body is fixedly arranged through a fixed anchor, and a marine profile comprehensive detection module synchronously collects multi-dimensional profile data such as flow velocity, temperature and salinity. A main control unit is embedded with a mixed driving early warning model, a phase velocity of an internal wave is solved based on a physical model, a nonlinear correction is performed through a machine learning residual correction module, a high-precision internal wave phase velocity is obtained, and then early warning time, shear flow and displacement amplitude are calculated and an alarm level is determined. Alarm information is redundantly sent to a shore station receiving system through a ground mobile communication link and a satellite communication link of a multi-channel communication module, and the shore station receiving system regularly updates the machine learning model by taking measured data as incremental samples. The application realizes high-precision and low-delay early warning of internal wave flow, has high communication reliability, and is suitable for marine engineering, underwater vehicles and safety guarantee of offshore operations.

This invention relates to the field of marine observation data processing technology, specifically a method for quality control and evaluation of real-time communication mooring data, comprising the following steps: data preprocessing to obtain preprocessed data; multi-dimensional anomaly identification of the preprocessed data, removing anomalies to obtain data to be verified; multi-node spatiotemporal synchronization verification of the data to be verified to obtain synchronized data; adaptive consistency verification of the synchronized data using an internal wave mode; checking the presence of internal wave data observation signals on the synchronized data after consistency verification; and data quality level evaluation. This invention solves the data deviation problem caused by resetting the coefficients of inductively coupled sensors, achieving dual quality control of real-time data and post-corrected data; it constructs a data quality evaluation system adapted to deep-sea real-time communication moorings, quantifies data quality levels, and provides a basis for data reliability in marine scientific research.

This invention discloses an intelligent identification and positioning method for ocean internal waves based on multi-source remote sensing imagery, belonging to the field of marine remote sensing technology. The key technical solutions include the following steps: determining the geographical location and time range of the target sea area; collecting multi-temporal high-resolution remote sensing image data from synthetic aperture radar (SAR) and optical satellite imagery; labeling the internal wave features of the preprocessed multi-temporal high-resolution remote sensing image data to generate ground truth images; using a trained deep convolutional neural network model to perform pixel-level analysis on the input remote sensing images to extract ocean internal wave features and generate binarized images of the internal wave features; and converting the extracted binarized images of the internal wave features into corresponding latitude and longitude locations based on the geographical location information of the original remote sensing images. This achieves high-precision geographic positioning of internal waves, is suitable for marine research and high-precision environmental monitoring, and has broad application prospects.

The present invention discloses a hybrid power generation device with built-in pendulum wave energy in a floating wind turbine platform, belonging to the technical field of offshore wind power generation; it includes a semi-submersible platform, the semi-submersible platform includes a plurality of buoys, connecting rods and mounting rods, a base is provided at the bottom of the buoy, the buoys are divided into an upper buoy and a lower buoy, a wave energy device is provided inside the buoy, a wind turbine is provided on the top surface of the semi-submersible platform, the buoys are connected by an inclined connecting rod, and the mounting rod and the buoys are connected by an intermediate connecting rod and an inclined support rod. The present invention adopts the above structure, and the semi-submersible platform is set on the sea surface to float unstably with the sea surface. With the help of the up and down displacement caused by the unstable floating, the pendulum rod in the internal wave energy device drives the hydraulic cylinder to move up and down to generate electricity. The inertial device is provided to generate electricity, which can absorb the displacement of the floating semi-submersible platform, thereby solving the problem of low stability and safety of offshore wind turbine power generation.

The present application discloses a method for separating wake internal waves and volume internal waves of a vehicle, which relates to the technical field of ship and ocean engineering. The method includes: obtaining a time cloud map of the internal wave height along the target direction induced by the vehicle during navigation; extracting the propagation speed of the internal wave along the target direction at different frequencies; determining a first critical frequency point for dividing the propagation speeds of the internal waves at all frequencies into two groups; and using the first critical frequency point to separate the internal waves into volume internal waves and wake internal waves along the target direction, so as to further separately extract the fluctuation characteristic parameters of the wake internal waves and the volume internal waves subsequently. This method can distinguish the characteristics of volume internal waves and wake internal waves, realize the separation of volume internal waves and wake internal waves, and has important application value for the detection and tracking of the internal wave wake of the vehicle.

The application relates to a SAR image ocean internal wave stripe segmentation method, equipment and a storage medium, the method comprises the following steps: step S1, acquiring an ocean internal wave SAR image, and performing feature extraction on the ocean internal wave SAR image by adopting Gabor transformation to obtain a feature image; step S2, stripe segmentation is performed on the feature image by using a K-means clustering algorithm, the bright / dark stripes in the ocean internal wave are separated from the background, and the regions where the dark / bright stripes are located are automatically judged according to the inter-class difference; step S3, a minimum circumscribed rectangle of the bright / dark stripe contour is constructed, and the dark / bright stripe position is obtained by translating a set distance along the normal direction of the long side of the minimum circumscribed rectangle. Compared with the prior art, the method has better bright / dark stripe segmentation effect on the SAR image with strong ocean internal wave characteristics, can better identify the bright or dark stripe features with large differences from the background, and has the advantages of strong robustness and high accuracy.

This invention provides a porous wave-damping plate for aquaculture cages, specifically belonging to the field of wave-damping technology for aquaculture cages. It comprises a porous plate, wave-damping buoys, and a hollow cavity structure, forming a highly efficient energy dissipation system. The alternating openings on the surface of the porous plate allow waves to pass through. When waves impact the plate, seawater enters the hollow cavity and drives the internal wave-damping buoys to generate minute movements and collisions, thereby significantly reducing wave height and impact force. This directly protects the fish in the cage from the impact of violent water currents. The invention includes a plate body, which is detachably and fixedly installed on the outside of the cage. The plate body is a hollow plate-like structure with through holes on both the front and back sides, and the cavity of the hollow plate-like structure is filled with several wave-damping buoys for dissipating wave energy. It includes multiple plate bodies, which are spliced ​​together to form a wave-damping plate assembly. The upper end of the plate body is provided with a tenon, and the lower end of the plate body is provided with a mortise. Adjacent plates are interlocked and positioned by the tenon and mortise.

The present application belongs to the technical field of dynamic response prediction of marine engineering structures, and specifically discloses an intelligent prediction method for dynamic response of floating wind turbines under internal wave action. Firstly, the dynamic response prediction result given by the intelligent prediction model and the actual operating state of the wind turbine are obtained in real time, the error between the two is calculated and fed back to the control system, the control parameters such as the blade pitch angle are automatically adjusted according to the error and the size of the internal wave load to reduce the blade vibration. At the same time, the genetic algorithm is selected to optimize the control parameters, the objective function considering the power generation efficiency, structural safety and internal wave load is set, and the optimal parameter combination is found through selection, crossover, mutation and other operations. A real-time monitoring system is established to continuously collect internal wave and wind turbine operation data, and a trigger condition is set. When the condition is met, the control parameters are recalculated and adjusted according to the current data. The method focuses on real-time feedback and optimization control, effectively improving the operation stability and power generation efficiency of the floating wind turbine in complex marine environment.

The application provides a method and device for detecting oceanic internal waves in a marine SAR image and electronic equipment, wherein the marine SAR image sample is used to label the oceanic internal waves in the marine SAR image by using a rotating box, the rotating box is more fitted to the arc-shaped oceanic internal waves, and the rotating box contains less redundant information; when the oceanic internal wave detection model is trained, less marine SAR image samples are required, and the training time is short; in addition, the updated training data set is used to iteratively train the oceanic internal wave detection model in the training process, so that the training time is further shortened; and since the rotating box contains less redundant information, the accuracy of the finally trained oceanic internal wave detection model is good.

This invention discloses a semantic segmentation method for ocean internal wave stripes in SAR images, relating to the field of semantic segmentation of remote sensing images. The method model consists of an encoder and a decoder. The encoder comprises four Transformer modules, each containing a self-attention layer, a feedforward neural network, and an overlap patch merging module. Within each module, the input image is processed N times through a multi-head self-attention mechanism, and then the merging module generates feature maps at four scales. The decoder consists of three modules: a serpentine convolution, an EVC module, and an expectation-maximization attention network. The advantages of this invention are: the model fully utilizes the multi-scale fusion module, improving performance and robustness; the use of serpentine convolution can better extract features of linear shapes; and the use of the expectation-maximization attention network improves model accuracy while reducing computational complexity.

The application 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 following steps: a three-dimensional explicit dynamic coupling model is established according to the typhoon frequency characteristics of a target sea area and steel casing structure parameters; according to the three-dimensional explicit dynamic coupling model, wave load is decomposed, and the vibration hammer, ocean current and internal wave flow load effects are integrated to determine the steel casing comprehensive dynamic load set. Through the establishment of an accurate model, the integration of multiple source loads, the identification of risk areas, the real-time monitoring of stress and the optimization of design parameters based on data, the application realizes the adaptive matching of the steel casing design and the complex marine environment, effectively improves the safety of the deep-sea temporary platform steel casing structure and reduces the cost.

The invention provides an ocean internal wave parameter inversion method and system based on temperature chain buoy data. The method comprises the following steps: preprocessing an acquired multi-depth temperature sequence and then constructing a temperature-time profile; detrending and self-adaptive smoothing are carried out through empirical mode decomposition, and a temperature background field and a pulsating field are separated; extracting a vertical gradient of a temperature background profile by using LOESS local regression, and introducing Tikhonov regularization and threshold gating to guarantee gradient robustness; mapping temperature pulsation into vertical displacement based on isothermal surface conservation, and screening effective data in combination with a credibility index; finally, the amplitude, the period and the wave height of an internal wave are inversed through multi-window wavelet spectrum and Hilbert envelope conjoint analysis, and the reliability of a result is improved through vertical modal consistency verification. According to the method, consistent inversion of ocean internal wave parameters can be completed only by means of a single temperature chain buoy, and the inversion accuracy under the background of weak gradient, weak signal and noise is remarkably improved.

The invention relates to an ocean internal wave three-dimensional visualization method based on a high-temporal-spatial-resolution unstable flow field, and belongs to the technical field of computer vision and ocean fluid mechanics. According to the method, a GPU texture visualization technology is utilized, an internal wave data processing and feature extraction method is provided for vertical flow characteristics of ocean internal waves, and complex dynamic behaviors of the ocean internal waves in an unstable flow field are accurately displayed by highlighting a vertical velocity component W. Compared with a traditional method, the method has the advantages that the spatial and temporal change of high-resolution internal wave data can be reflected more truly, and the vertical flowing characteristics of ocean internal waves in a three-dimensional space can be clearly visualized. The technology is suitable for influence analysis of marine ecological and physical processes and artificial object motion trails, and helps researchers to deeply understand the influence of internal waves on marine fluid motion.

The invention discloses a high-resolution internal wave sound velocity field inversion method based on propagation time delay, relates to the technical field of ocean observation, and aims to solve the problem that fine changes of a sound velocity field under internal wave driving cannot be effectively captured in the prior art. And cumulative observation results at different times are equivalent to multiple groups of virtual nodes and virtual observation networks at the same moment, so that high-resolution inversion of an internal wave sound velocity field is realized, and fine changes of the sound velocity field driven by internal waves are effectively captured. The defect that satellite observation lacks deep information is overcome, and full-water-depth sound velocity field observation can be achieved.

The present invention discloses a method for rapid prediction of internal wave characteristic field information based on deep learning, which belongs to the technical field of ocean internal wave detection. The present invention uses deep learning to perform rapid predictions based on traditional numerical simulation data, significantly improving the efficiency of internal solitary wave predictions; through multi-channel coupled learning and iterative prediction strategies, the internal wave field prediction achieves both high precision and high efficiency. At the same time, combined with high-resolution numerical model data, the consistency of the model prediction results and the physical process is guaranteed, while maintaining low error and high correlation, achieving rapid extended-period prediction of the characteristic field of ocean internal solitary waves. The present invention is suitable for application scenarios such as real-time ocean internal wave monitoring and early warning, and can be widely used in technical fields such as marine environmental monitoring and early warning, intelligent ocean observation, and marine engineering safety.

The application discloses a kind of reconstruction method and system of marine internal wave three-dimensional structure, which comprises the following steps: obtaining multi-modal original remote sensing data and carrying out data perception to obtain multi-modal feature map;Obtain the physical characteristic vector of temperature-salinity profile and combine multi-modal feature map, carry out physical feature extraction and multi-scale modeling, obtain preliminary three-dimensional temperature-salinity profile and stratification parameter distribution;Three-dimensional structure reconstruction and uncertainty quantification are carried out on multi-modal feature map and preliminary three-dimensional temperature-salinity profile and stratification parameter distribution, to obtain the final full-water-depth three-dimensional grid field;Based on the final full-water-depth three-dimensional grid field, digital twin visualization and interaction are carried out, to realize internal wave three-dimensional structure reconstruction.The application can map two-dimensional sea surface observation signal to full-water-depth three-dimensional internal wave field with physical consistency.The application can be widely applied to the field of internal wave three-dimensional structure reconstruction as a kind of reconstruction method and system of marine internal wave three-dimensional structure.

The invention discloses a wave reduction type offshore photovoltaic array based on a wave reflection principle and a design method, and belongs to the technical field of renewable energy source development. Establishing a cylindrical buoy array frequency domain hydrodynamic model, calculating wave transmission indexes of different parameter combinations in the target wave period range, and determining candidate parameter combinations according to the wave transmission indexes; establishing a structural finite element model of the reflection unit group based on the candidate parameter combination, and performing strength check; a coupling dynamic model of the array structure and the mooring system is established, motion response and mooring stress are calculated, and judgment is carried out based on a preset criterion; outputting a reflecting unit group buoy array parameter combination meeting the requirement; the problem that the engineering construction and operation and maintenance cost is high due to the fact that an existing offshore floating photovoltaic array depends on external wave prevention / wave dissipation facilities in order to reduce the wave action is solved, and reduction of wave loads in the array and improvement of structural economy are achieved on the basis.

The invention belongs to the technical field of synthetic aperture radar (SAR) image processing, and discloses an underwater vehicle wake SAR image data generation method and system, and the method comprises the steps: respectively constructing an underwater vehicle wake model based on a computational fluid dynamics (CFD) method and a random sea surface model based on a sea wave spectrum model; performing linear superposition on the underwater vehicle wake model and the random sea surface model by adopting a VOF-spectrum coupling interpolation superposition method to obtain a composite sea surface simulation model; based on a composite sea surface simulation model, a dual-scale electromagnetic scattering model is adopted to calculate a backscattering coefficient, tilt modulation, fluid modulation and speed bunching modulation are introduced in sequence, and finally speckle noise is added to generate a wake SAR image. According to the method, the physical characteristics of the wave wake in the underwater vehicle can be simulated more accurately, and the physical authenticity and accuracy of wake generation are remarkably improved; a simulation result which is high in accuracy and has real SAR image statistical characteristics can be obtained, and the engineering application value is remarkably improved.