456 results about "Wave height" patented technology

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention discloses a two-dimensional wave spectrum intelligent forecasting method based on a physical information neural network, and belongs to the technical field of ocean information prediction.The two-dimensional wave spectrum intelligent forecasting method comprises the steps that basic data are collected, and the basic data comprise high-temporal-spatial-resolution sea surface wind field data and sea wave height data within the research range; historical sea wave data are obtained, ocean and meteorological data closely related to sea wave changes are integrated and processed, and a sea wave forecasting database is constructed; physical constraints are determined according to the sea wave forecasting database; an ANN neural network model is adopted as a basic model, physical constraints are added, the neural network meets the control equation of the mode, and a PINN physical information neural network is constructed; and carrying out interpretability analysis on the model forecasting process and result. According to the method, the sea wave dynamic process and the deep learning method are fused, interpretability is added for deep learning, and the black box problem of the deep learning method is solved.

A whole ship strain field rapid reconstruction method using ship motion and discrete strain information can rapidly reconstruct a whole ship structure strain field through fusion analysis of ship motion information and finite discrete strain monitoring data, and provides a more reliable reference basis for ship structure safety assessment and auxiliary decision making. The method comprises the following steps: firstly, acquiring ship motion monitoring data, and establishing a wave time-frequency parameter inversion Kalman filtering model based on ship motion information; secondly, according to the wave height obtained through inversion, the wave pressure load on the surface of the ship body is solved by combining phase parameters and relative positions of the wave surface and the ship body; thirdly, establishing a whole ship structure strain field dynamic reconstruction artificial intelligence model fused with a physical information neural network-finite element method, and formulating a layout strategy of measuring points; and finally, performing network parameter optimization on the PINN-FEM strain field reconstruction model by using strain monitoring data of finite discrete measurement points, and improving the overall calculation precision of the model.

The invention discloses an extreme sea condition parameter identification system based on deep learning, and relates to the technical field of ship navigation auxiliary equipment, in particular to a self-adaptive sea condition identification device which is used for acquiring image data and inertial measurement data of a current sea condition; the wave field visual depth estimation module is used for extracting visible light image features and infrared image features of a wave area from image data of the current sea condition, fusing the extracted visible light image features and infrared image features, using an encoder-decoder architecture and fusing an energy function to obtain a pixel-level wave height field, and outputting the pixel-level wave height field. The three-dimensional reconstruction of the wave surface is realized; the multi-modal data fusion module uses a filter dynamic model and a cost function to eliminate space-time asynchronous errors between inertial measurement data and visual perception data, performs multi-modal data fusion, and outputs wave field real-time parameterization information. According to the invention, the sea condition parameter real-time high-precision identification capability of the autonomous unmanned ship or the offshore carrying platform can be improved.

The invention discloses a sea condition perception and path prediction navigation method and system for a high-speed unmanned ship, and relates to the technical field of multi-source path decision optimization, and the method comprises the steps: collecting storm real-time perception data through a sensing device, carrying out the preprocessing, outputting a standardized sea condition state vector, and providing an experience reference for real-time path planning through ship-borne historical data; constructing a multi-target navigation prediction algorithm model to predict and generate a path candidate scheme set on the basis of the storm real-time sensing data and the ship-borne historical data, and performing evaluation through a conditional random field in combination with path candidate schemes generated by the storm real-time sensing data, the ship-borne historical data and target navigation prediction; and carrying out multi-dimensional safety risk scoring on an evaluation result through a fuzzy analytic hierarchy process by combining the wave height and direction influence, the hull stability and the obstacle avoidance complexity, and determining an optimal sailing path. According to the invention, fusion of multi-source sea condition perception and dynamic path optimization is realized, and the navigation stability, the adaptability and the risk control capability of the high-speed unmanned ship are improved.

The invention provides a space-time intelligent ocean satellite internet of things perception information screening method and device, and belongs to the technical field of data processing. Marine environment data space-time prediction is carried out based on a block time sequence converter, marine hot spot area mining is carried out based on a density clustering algorithm of mean shift, and marine environment data comprises sensing data of sea surface temperature, salinity, wave height and the like; marine environment risk assessment is carried out based on a BP neural network, information screening feedback control is carried out based on multi-agent reinforcement learning, the efficient information processing requirement of a complex marine environment detection scene is met, and the low-redundancy and high-precision data acquisition and transmission adaptive feedback control effect is achieved. The method can be directly applied to an ocean observation and detection facility networking system in the ocean Internet of Things, can provide powerful support in the aspects of military application and civil application, and has wide and important application prospects and value.

The invention discloses an intelligent optimization control method for a wave power generation device based on an improved MPC, and the method comprises the steps: collecting the wave height, period, flow velocity and device motion posture data in real time through a multi-source sensing module, constructing an exciting force prediction model based on a bidirectional long-short-term memory network, and achieving the precise prediction of the wave exciting force of n control periods in the future; prediction data and real-time feedback signals are input into an improved jellyfish search optimization algorithm, through a two-stage chaotic mapping perturbation mechanism, the convergence speed and the global optimization ability of the algorithm are remarkably improved, and finally optimal electric damping control parameters are output. Compared with a traditional MPC method, the method has the advantages that a feed-forward-feedback composite control framework is formed by fusing deep learning prediction and an intelligent optimization algorithm, so that the energy efficiency conversion efficiency of the power generation system is improved.

The invention discloses a near-shore wave-flow field-sediment coupling simulation method considering a wave breaking nonlinear effect, and belongs to the field of near-shore hydrodynamics, and the method comprises the steps: inputting WRF high-resolution wind field, tide boundary conditions and water depth information, and initializing a wave module, a flow field module and a sediment module; calculating local wave breaking dissipated energy by adopting an improved wave breaking energy dissipation formula to obtain a wave energy density spectrum and a breaking probability; a near-shore flow field is calculated under radiation stress nonlinear correction, and hydrodynamic boundary conditions are updated; calculating sediment suspension and deposition by using a coupling diffusion coefficient, and updating sediment concentration distribution; waves, flow fields and sediment are mutually coupled through loop iteration, and coastal wave-flow field-sediment simulation is achieved under extreme conditions such as typhoons, storm surge and strong storm waves. The method can improve the prediction precision of the wave height, the flow velocity and the sediment concentration under the condition of offshore strong storm waves, and is used for typhoon storm surge prediction, harbor basin deposition evaluation and offshore ecological environment management.

The invention discloses a risk monitoring and control method for offshore construction, and particularly relates to the technical field of collision control for offshore construction, and the method comprises the steps: obtaining the real-time position, the navigational speed, the course, the tonnage, the wind speed, the wind direction, the wave height, the wave direction, the flow speed, the ship type, the ship historical track and the ship behavior mode of a ship, and carrying out the preprocessing; and inputting the processed multi-source data into the constructed ship trajectory prediction model, outputting a plurality of possible trajectories and trajectory probabilities of a future time window, and then calculating a collision probability and a dynamic safety distance, thereby improving the degree of closeness between the collision probability and a real collision probability, improving the accuracy of collision probability analysis, and improving the accuracy of ship trajectory prediction. The safety boundary of the ship and the construction area is quantified in real time, the failure of a fixed threshold value in a complex environment is avoided, a dynamic judgment reference is provided for risk level classification, the risk identification precision is improved, a response instruction is matched more accurately, and the collision harm is further reduced.

The invention discloses a large-scale regional sea wave rapid forecasting method and device based on data driving, and the method comprises the steps: obtaining multi-source marine physical environment data of a target sea area, and generating a standard physical field data flow with unified temporal-spatial resolution; constructing a multi-channel space-time input tensor containing wind field driving information, terrain boundary information and historical wave state information; the difference between the predicted wave height and the real wave height is minimized through a back propagation mechanism, so that a trained wave height prediction model is obtained; receiving latest wind speed field data output by a real-time observation or numerical forecasting mode, and outputting a sea wave significant wave height prediction field at a future target moment; the device is used for implementing the method. According to the technical scheme of the method provided by the invention, through a physical lag alignment mechanism, the time delay of transmitting wind energy to wave energy is accurately captured, and the modeling precision is improved; and meanwhile, rapid deduction of a large-scale sea wave field is realized based on deep learning, and high accuracy and high timeliness are achieved.

The invention discloses a spectral analysis-based sea surface wave model construction method and system, and the method comprises the steps: carrying out the grid division of a sea surface, calculating parameters for each grid of the sea surface, and building a gravity wave model, a surge model and a shockwave model for the whole sea surface; obtaining a gravity waveform height amplitude according to the gravity wave model, obtaining a surge waveform height amplitude according to the surge model, and obtaining a shock wave height amplitude according to the established shock wave model; superposing the gravity waveform height amplitude, the surge waveform height amplitude and the shockwave height amplitude, and carrying out inverse fast Fourier transform on the superposed amplitudes to calculate a sea surface waveform height spectrum which changes along with time in an airspace within a certain range; and constructing a sea surface wave geometric model in an STL format according to the sea surface waveform height spectrum changing along with time in the airspace in a certain range. According to the invention, the authenticity and accuracy of simulation are improved.

The invention provides a polar region multi-source remote sensing sea wave significant wave height optimization interpolation fusion method and device, and belongs to the field of remote sensing, and the method comprises the steps: carrying out the preprocessing of collected sea ice marginal region and open sea surface data; north-pole geometric adaptation correction is carried out, system deviation of multiple satellite sensors is eliminated, and consistency and accuracy of data are ensured. Real-time wave height forecast is used as a background field, a weighting function is constructed in combination with a satellite observation error covariance matrix, dynamic background field fusion is carried out, and the precision and reliability of a data sparse region are improved; according to different characteristics of a sea ice marginal region and an open sea surface, different weights are set, a transition weight function is introduced, partition fusion is carried out, boundary discontinuity is avoided, and therefore the final fusion wave height is obtained; the robustness of the fusion method is verified through K-fold cross validation, and meanwhile precision verification is conducted in combination with multi-dimensional data. According to the method, the problem of insufficient accuracy verification spatial representativeness caused by rare north pole in-situ observation quantity and concentrated distribution is solved.

The invention belongs to the technical field of ocean engineering and water electrolysis hydrogen production. In order to solve the problem that a traditional model is difficult to deal with dynamic sea conditions, a platform-environment coupling digital twin model is constructed, multi-source monitoring data is fused, and platform motion response is predicted through deep reinforcement learning. And generating anti-storm strategies such as ballast water regulation based on a prediction result, and realizing real-time optimization. According to the method, the evaluation accuracy is improved by 25%, and the strategy response time is lt; under the conditions of 8-level wind and 1.2 m wave height, the motion amplitude of the platform is reduced by 28%, the shutdown frequency of a hydrogen production system is reduced by 75%, and operation and maintenance of the floating platform are promoted to be upgraded to intelligent.

The invention provides an effective wave height real-time prediction method and device based on dynamic residual correction, and relates to the field of ocean engineering. The method comprises the following specific steps: acquiring wave height data and performing multi-dimensional feature screening; constructing an integrated filter fusing L1 trend filtering and variational mode decomposition, optimizing parameters by using a sea image optimization algorithm, introducing a causal sliding window to extract features so as to construct a time sequence input tensor, and inputting the time sequence input tensor into a stacked bidirectional long-short-term memory network based on an attention mechanism after noise addition standardization so as to obtain a basic predicted value; calculating a manifold coherent structure, PID dynamics and physical statistical characteristics, and cascading with the basic prediction characteristics to construct a comprehensive element characteristic vector; a LightGBM architecture is constructed, and a prediction residual error is fitted after optimization is carried out through a sea image optimization algorithm; and finally, executing linear reconstruction based on the dynamic safety threshold constraint, and outputting a real-time correction result. According to the method, the error evolution rule is deeply mined by using manifold geometric features, and the real-time precision and robustness of significant wave height prediction are remarkably improved.

The invention provides a significant wave height prediction method and device and electronic equipment, and relates to the technical field of sea wave data processing. The method comprises the following steps: acquiring an effective wave height data set for wave height prediction; determining model input data based on the correlation; carrying out two-layer decomposition on the effective wave height; wherein the primary decomposition comprises empirical mode decomposition based on a self-adaptive noise complete set and K-means clustering of mode components; the secondary decomposition comprises the step of optimizing variational mode decomposition of parameters based on a crown porcupine optimization algorithm; inputting model input data into a bidirectional long-short-term memory network based on an attention mechanism for training; the method comprises the steps of firstly screening data with relatively high correlation with the significant wave height as input data to reduce the calculation amount, then performing two-layer decomposition on the significant wave height to reduce the data complexity, and finally fully extracting data features through a bidirectional long-short-term memory network based on an attention mechanism to comprehensively predict the data so as to improve the prediction accuracy of the significant wave height. And the accuracy of predicting the significant wave height is improved.

The invention discloses a wave height short-term prediction system and method based on variational mode decomposition and wind and wave time correlation, and belongs to the technical field of ocean engineering.The method comprises the steps that firstly, original storm time series data are decomposed into IMF components with different center frequencies through variational mode decomposition; secondly, performing data cleaning on the decomposed IMF component based on a Pearson's correlation coefficient; then combining two wind speed IMF components with the strongest correlation with the wave height IMF component based on correlation analysis to form a wind speed wave height component combination, and determining an input time length through an autocorrelation coefficient ACF of wave height data and a cross correlation coefficient CCF of the wind speed IMF components and the wave height IMF components; and finally, according to the formed wind speed wave height component combination and the determined input time length, carrying out short-term prediction on the wave height based on a long short-term memory neural network. According to the system and the method provided by the invention, the influence of noise on prediction is reduced, and the prediction precision is improved while the prediction efficiency is improved.

The invention belongs to the field of ship navigation safety evaluation, and discloses a second-generation complete stability forecasting system and method for real sea navigation of a ship. The method comprises the following steps: acquiring actually measured marine environment parameters for sea wave forecasting, and acquiring ship navigation parameters for second-generation complete stability forecasting of a ship; sea wave forecasting is carried out, wherein regional meaningful wave height and average zero-crossing period forecasting of sea wave data is carried out based on an HP-VIT model; a pure stability loss sensitivity index forecasting model is constructed, and pure stability loss sensitivity index forecasting is completed; and a parameter rolling sensitive index forecasting model is constructed, and regional parameter rolling forecasting is completed. According to the method, rapid forecasting of the second-generation stability of the ship in the real sea environment can be achieved, the research result can provide auxiliary decision-making information for sailing safety guarantee, route planning correction and the like of the container ship, and the method has great significance in guaranteeing the safety of the ship and goods.

The invention discloses a delta-beach bar composite sand body prediction method, which comprises the following steps of S1, acquiring geological background data of a target work area, and dividing delta and beach bar deposition to obtain a beach bar deposition thickness; s2, carrying out source and sink system analysis, establishing a sediment transport channel path, and obtaining a sand body transport path plane distribution diagram; s3, the ancient landform, ancient wind field features and ancient wave field features of the target work area in the deposition period are restored, the ancient landform height and the distance to the source area of the underwater ancient protrusion are obtained according to the ancient landform, and the features include the ancient wind direction, the ancient wind path, the ancient water depth and the ancient wave height; s4, determining a favorable development zone, a favorable development layer section and a favorable development target zone of the delta-beach bar composite sand body; and S5, obtaining distribution characteristics of the delta-beach bar composite sand body of the target work area according to the favorable development zone, the favorable development layer section and the favorable development target area. According to the method, the delta-beach bar composite sand body can be accurately predicted, and technical support is provided for oil exploration and development.

The invention relates to the technical field of sea wave height prediction, and discloses a significant wave height prediction method and system based on spatio-temporal evolution multi-scale feature extraction. The method comprises the following steps: performing significant wave height prediction on input significant wave height grid data of a sea area to be predicted by applying a trained hybrid heterogeneous parallel double-convolution dynamic network; the method further comprises the steps of obtaining the effective wave height grid data, inputting the effective wave height grid data into the hybrid heterogeneous parallel double-convolution dynamic network for effective wave height prediction, and then outputting an effective wave height prediction sequence of a future time step. According to the method, multiple features of SWH spatio-temporal dynamic evolution can be extracted, and hierarchical spatio-temporal features from a local scale to a global scale and local spatio-temporal dynamic crossing from a coarse scale to a fine scale can be mined at the same time; by constructing a heterogeneous parallel double-convolution framework, the internal irregularity of the SWH field and the irregular relation between SWH field data are overcome, and meanwhile the capacity of capturing the invariant relation in the SWH field is kept.

The invention relates to a super-large L-shaped high-sea-condition high-precision absorption type wave maker system and a wave simulation method.The wave maker system comprises a central control layer, an equipment control layer and an execution mechanism layer which are sequentially connected, and the central control layer is characterized in that a main control computer operates wave making control software; a target wave time calendar curve is generated, theoretical motion instructions needing to be executed by the wave making plates are synchronously deduced, the main PLC conducts format conversion and time sequence synchronization processing on the theoretical motion instructions, and wave making plate displacement curve instructions are generated and issued; the equipment control layer is used for calculating active absorption compensation from the PLC and generating a motor motion instruction; the executing mechanism layer comprises a super-large L-shaped wave making unit, a fan-shaped driving arm, a plurality of motors and other assemblies to execute wave making motion and feed back wave height information, and the motors drive the fan-shaped driving arm to drive a wave making plate to swing around a hinge assembly and drive the wave making plate to execute compensation action opposite to the phase of incident waves; high-sea-condition wave simulation and high-precision wave active absorption are realized, and a full-wave-direction test with navigational speed is supported.

The invention discloses a disastrous big wave occurrence probability and wave height combined prediction method, device and equipment, a medium and a product, and relates to the technical field of meteorological ocean forecasting. The method comprises the following steps: acquiring an effective wave height measured value and environmental factor data of a to-be-predicted region in a historical time period; key environment factors are screened through spatial correlation analysis and linear fitting verification, a first training data set and a second training data set are constructed, and a multiple linear regression model and a logic regression model are trained respectively to obtain a wave height prediction model and a big wave occurrence probability prediction model; and inputting the key environmental factors of the current time period of the to-be-predicted region into the model to obtain the predicted occurrence probability of the disastrous waves and the predicted value of the significant wave height in the predicted time period. According to the method, the key environmental factors are screened and the two models are used for respective prediction, the influence of the various environmental factors on the big waves is considered, the occurrence probability and the wave height of the disastrous big waves can be predicted more accurately, and the ocean safety is effectively guaranteed.

The invention discloses a reflooding cooling process analysis method and system. The method comprises the following steps: step 1, obtaining reflooding process data; 2, according to the re-submerging process data, obtaining a liquid core shear stress relation and a constitutive relation, and obtaining a disturbance wave height; 3, according to local shock cooling in the cooling process, a liquid film dynamic equilibrium relation is constructed, and the average vapor film thickness is obtained; 4, axial distribution of the minimum film state boiling temperature is obtained according to the disturbance wave height and the average steam film thickness; 5, shock cooling moments at different axial heights are obtained according to the axial distribution of the minimum film state boiling temperature, the relationship among the shock cooling speed, the shock cooling moments and the heights is obtained, the shock cooling speed of all the shock cooling moments is solved, and then analysis of the re-submerging cooling process is completed; according to the method, the problem of strong correlation of time-physical behaviors of two-phase evolution in the whole re-submerging process is solved, and accurate prediction of the whole strong transient re-submerging cooling process is realized.

The invention discloses a dynamic feature and physical gating fused wave height prediction system and method, belongs to the technical field of artificial intelligence and machine learning, is used for marine environment monitoring, and comprises a data acquisition and preprocessing module, a feature screening module and a model prediction module. The method comprises the steps of obtaining and preprocessing marine meteorological variable data, constructing a Shapley additive interpretation lightweight gradient elevator regression model to screen a core variable set, constructing a physical guidance gating feature perception Transform prediction model, constructing a mixed loss function constraint prediction model, and outputting a sea wave significant wave height prediction value after prediction model training is completed. According to the method, through a feature screening and dynamic weight distribution mechanism, the utilization efficiency and scene adaptability of multi-source marine meteorological features are remarkably improved, the method can be flexibly expanded to sea wave prediction tasks of different sea areas and spatial-temporal scales, and high-precision and high-reliability technical support is provided for ocean engineering, shipping safety and disaster early warning.

The invention relates to a wave height parameter correction method and system for low-angle laser radar observation data, and relates to the technical field of ocean observation data processing, and the method comprises the steps: carrying out the smoothing and data cleaning processing of original wave height data, so as to construct a fitting model of polynomial and Gaussian global trend analysis; and then, through double-model dynamic fusion, polynomial short-term fluctuation capture and Gaussian long-term trend capture are deeply combined, so that the generalization ability of the model is improved. And finally, intelligent reference interval positioning is introduced to adapt to wave height distribution changes in different periods and positions, excessive correction is avoided in combination with introduced correction factors, accurate correction data are obtained, the problem that the wave height is attenuated along with the distance is solved, and the efficiency and reliability of correction processing are remarkably improved.

The invention relates to the technical field of wave simulation generation, in particular to a high-gravity field wave simulation method. The high-gravity field wave simulation method comprises the following steps: in a high-gravity field, performing similarity rate verification on a high-gravity field wave making similarity relationship between a predetermined wave height and a frequency dimensionless quantity; under the condition that the similarity rate verification is consistent, calibrating parameters of the super-gravity field wave making similarity relationship; and according to the super-gravity field wave making similarity relationship of the calibration parameters, performing super-gravity field wave simulation. According to the invention, waves in different environments in a super-gravity field can be accurately simulated, a transfer function can be accurately constructed, and the influence of physical quantities such as different motion amplitudes, different frequencies and different gravitational accelerations on wave making can be accurately evaluated.

The invention provides a sea wave disaster defensive area demarcation method based on AIS data, a medium and a system, and belongs to the technical field of sea wave disaster defensive area demarcation The method comprises the following steps: firstly, establishing an initial matrix through grid division, carrying out statistics on ship traffic conditions of each grid for many years of AIS data, and constructing a multi-dimensional traffic frequency tensor and a first change matrix; meanwhile, collecting sea wave reanalysis data to calculate occurrence frequencies of different levels of wave heights, and establishing a sea wave risk degree matrix; constructing a composite disaster index model based on ship response characteristics, calculating a grid aggregation degree and dispersion, and generating a second change matrix by applying a risk propagation function; fusing ship and sea wave data to construct a risk assessment hypergraph, and performing region marking through a multi-criterion threshold and a graph coloring algorithm to form a third change matrix; and finally, optimizing the boundary of the defensive area by adopting an improved particle swarm algorithm, and outputting a final demarcation result, thereby realizing accurate and efficient demarcation of the ocean wave disaster defensive area.

The invention discloses an intelligent early-warning and wave-absorbing system and method for navigation tunnel ship traveling waves, and belongs to the field of navigation tunnel wave absorbing.The intelligent early-warning and wave-absorbing method for navigation tunnel ship traveling waves comprises the steps that when a ship arrives at an entrance of a navigation tunnel, a lifting rotating assembly controls a wave-absorbing plate to rotate till the included angle between the wave-absorbing plate and the inner wall of the navigation tunnel is a preset angle; when a ship drives into the navigation tunnel, the wave height measuring assembly monitors the water level change in the navigation tunnel and the ship traveling wave height in real time and transmits monitoring data to the control terminal for processing, the control terminal judges the monitoring data and controls the early warning assembly to send out early warning information, and the lifting rotating assembly controls the wave absorbing plate to ascend and descend to absorb waves. And after the ship is driven out of the navigation tunnel, wave height monitoring is stopped, and the lifting rotating assembly drives the wave absorbing plate to ascend and descend to the initial position. According to the embodiment of the invention, the angle and height of the wave absorbing plate can be dynamically adjusted to adapt to complex wave changes in a tunnel, meanwhile, an early warning signal is sent out, and the navigation safety and efficiency of a ship are improved.

The invention relates to an open boundary circulating flow and reconfigurable terrain collaborative maritime work scouring intelligent test system and test method, a maritime work structure test model is installed at the center position of a main body structure inner cavity test section, and a set area with the maritime work structure test model as the center is used as a main test section; the open-boundary circulating flow generation system realizes back supplement and closed-loop operation of circulating water channels of the left bank and the right bank; a multi-beam three-dimensional terrain scanner is installed on the side portion of the large-span electric measuring bridge system, and sand bed terrain and scour pit three-dimensional shape data are obtained; a three-dimensional acoustic Doppler current meter and a capacitive wave height meter are installed on the track type instrument bridge measuring system, and multi-dimensional dynamic monitoring is conducted on the sand bed terrain and the scouring pit; the multidirectional irregular wave generation system is used for simulating regular waves and irregular waves in an offshore environment. According to the invention, the aspects of structure scale, terrain coupling and measurement and control feedback intelligence are comprehensively upgraded, and a more real and reliable test basis with strong engineering adaptability is provided for research on scouring stability of major ocean engineering.

The invention discloses a physical simulation method and device for the transportation effect of submarine organisms in a nuclear power plant, and the method comprises the steps: obtaining the motion parameters of target submarine organisms in the nuclear power plant, the motion parameters comprise one or more of individual mass, volume, body length, settling velocity, critical starting friction flow velocity, critical starting vertical line average flow velocity, starting wave height and critical starting flow velocity of target benthos of the nuclear power plant; establishing a physical model for simulating the transportation effect of the submarine organisms of the nuclear power plant; determining a simulant of the target benthos based on the motion parameters of the target benthos and a physical model; and simulating the amount of motion of the target benthos of the nuclear power plant by using the simulant and the physical model. In this way, it can be guaranteed that the motion process of the simulant should be kept similar to the actual motion process of the target benthic organism, the scale effect is avoided, and the premise is provided for accurately evaluating the entrainment and entrainment risk of the subbottom organism.

The invention discloses a real-time rendering method and system for physical simulation water surface waves, and the method comprises the following steps: constructing a generative adversarial network, obtaining a wave vector and an environment parameter variable of the waves, inputting the environment parameter variable into the pre-constructed generative adversarial network, and calculating the height of the waves through a Tessendorf method; calculating wave displacement through Fourier transform and inverse transform in a frequency domain based on the wave vector; calculating the velocity complex amplitude of the corresponding wave based on the wave vector, introducing a micro-scale vortex velocity field based on a particle system, and calculating the wave velocity based on the velocity complex amplitude and the micro-scale vortex field; and based on the wave height, the wave displacement and the wave speed, performing water surface wave rendering by adopting multi-cascade dynamic. According to the method, the physical rationality and visual authenticity of water surface wave simulation are improved, and water body physical simulation is promoted to develop from visual driving to the fusion direction of physical driving and data driving.