322 results about "Wave height" patented technology

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 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 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 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 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 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 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 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 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.

The invention provides a sea wave height prediction method and system based on hybrid quadratic decomposition, and relates to the technical field of sea wave height prediction, and the method comprises the steps: obtaining to-be-predicted original wave height data; performing primary decomposition on the original wave height data to obtain an intrinsic mode function and a residual term; calculating a sample entropy of the intrinsic mode function; according to the size of the sample entropy, classifying the intrinsic mode function to obtain a reconstructed mode component; performing secondary decomposition on the high-frequency components through a VMD algorithm to generate sub-sequence components; establishing a periodic sensing time deep learning model; and inputting the subsequence component, the intermediate-frequency component and the low-frequency component into a periodic sensing time deep learning model, and outputting a sea wave height prediction result.

The invention relates to the technical field of sea wave height prediction, and discloses a multi-scale physical enhancement type irregular sea wave prediction method which comprises the following steps: (1) collecting historical data of sea waves, preprocessing the data, and dividing a training set, a verification set and a test set; (2) decomposing the wave data into a high-frequency component, a medium-frequency component and a low-frequency component through wavelet decomposition; (3) constructing a WaveFormer model; (4) the WaveFormer model is trained, and (5) the test set is sent to the WaveFormer model to be trained, and a wave height prediction value is obtained. According to the method, an original wave sequence is decomposed into a high-frequency component, an intermediate-frequency component and a low-frequency component by adopting two-layer stationary wavelet transformation, the model captures multi-scale characteristics of irregular waves explicitly, the reasonability of wave prediction is ensured, sequence-level prediction is realized on the premise of ensuring the prediction precision, the prediction efficiency is improved, and the prediction cost is reduced. And a data driving item is constructed based on a wave prediction result, so that the model prediction accuracy is further improved.

The invention relates to the technical field of ocean monitoring and data processing, in particular to a sea condition estimation method based on multi-source information fusion. The method comprises the following steps: emitting electromagnetic waves to a target sea area by using a radar, receiving a sea clutter reflection signal and converting the sea clutter reflection signal into a digital image signal to obtain a sea clutter image, and after preprocessing and sea wave edge feature extraction, calculating to obtain a first meaningful wave height parameter of sea waves in the sea area through a preset formula based on extracted sea wave edge discrete points; collecting the surging data of the ship in the target sea area in real time through RTK GPS equipment, performing preprocessing and spectral analysis to extract frequency components related to the sea waves, and calculating a second meaningful wave height parameter of the sea waves in the sea area based on the frequency components; and effectively fusing the two types of parameters, performing deviation correction on the first meaningful wave height parameter by using a fusion result, and outputting a sea condition estimation result. The method can improve the accuracy and reliability of sea condition estimation, can adapt to different marine environments and weather conditions, and is wide in application prospect.

The invention relates to a coastal zone sediment erosion pore scale flow field observation system and observation method. The observation system comprises a wave-flow water tank, a sediment accumulation body arranged in the wave-flow water tank and PIV equipment used for observing the internal flow field and the pore size of sediment. A wave making plate and a wave dissipating plate opposite to the wave making plate are arranged in the wave flow water tank, experimental fluid used for simulating waves is arranged between the wave making plate and the wave dissipating plate, and a wave height meter floats on the experimental fluid; the sediment accumulation body is formed by accumulation of transparent solid particles with the same refractive index as the experimental fluid; the PIV equipment comprises a laser light source and a camera, the laser light source is focused on the sediment accumulation body, and the camera directly faces the sediment accumulation body and is used for collecting images. Compared with the prior art, a clearer sediment pore internal flow field image is obtained through the combination of the refractive index matching technology and the image particle velocity measurement method, so that effective observation of the pore scale flow field eroded by the sediment in the coastal zone is realized.

The invention discloses a buoy attitude self-adaptive wind wave calibration method for a typhoon landing path sea area, and relates to the field of ocean monitoring, and the method comprises the steps: obtaining attitude information, original wave height information and wind speed and direction information of a target buoy through a six-degree-of-freedom IMU, a micropressure array wave height meter and a three-axis ultrasonic wind speed sensor carried by the target buoy; compensating the original wave height information based on the attitude information and the wind speed and direction information to obtain compensated wave height information; based on remote sensing inversion data, a storm surge numerical forecasting model or historical tide level data, obtaining a storm surge lifting height of the current area, and taking the storm surge lifting height as a zero drift error of the micropressure array wave height meter; and outputting target wave height data based on the zero drift error and the compensated wave height information. According to the calibration mechanism of fusion compensation and multi-source correction provided by the method, the adaptability and practicability of a traditional buoy system under extreme meteorological conditions are remarkably improved.

The invention discloses a silty silt seabed critical start calculation method considering seabed liquefaction influence, and belongs to the technical field of silt dynamic simulation and calculation. The method comprises the following steps: firstly, acquiring wave parameters such as wave height, period and water depth, and calculating a characteristic Reynolds number based on a Stokes wave theory; in combination with field observation or indoor test data, the liquefaction grade is calculated through an empirical function, and the bed state is divided; then according to the liquefaction grade, the sediment erosion coefficient and the critical starting stress are corrected in an index form; and finally, correcting empirical coefficients by using field and indoor data, and constructing a regional correction coefficient table. The method can accurately reflect the influence of liquefaction on sediment starting, the accuracy of the calculation result is improved compared with that of a traditional method, the method can adapt to different sea area environments, operation is easy and convenient, and a reliable basis is provided for offshore engineering design and sediment movement prediction.

The invention discloses a GIS-based offshore photovoltaic intelligent site selection method and equipment, and relates to the technical field of offshore photovoltaic site selection. Based on a multi-criterion decision model of an analytic hierarchy process, multiple factors (including solar irradiation intensity, water depth, wind speed, distance from a power grid and wave height) in offshore photovoltaic power station site selection are fully considered; according to the method, the overall life cycle key factors of the offshore photovoltaic project from construction, operation to safety are covered, the overall target of'selecting the optimal offshore photovoltaic site 'is disassembled into quantifiable criterion layers and then corresponds to specific alternative schemes, so that evaluation of each index serves the overall target, and coherence and pertinence of analysis logic are ensured.

The invention discloses an offshore wind power operation and maintenance method considering uncertainty and opportunity collaborative maintenance. The method is used for solving the technical problems that an offshore wind plant is high in operation and maintenance cost and large in uncertainty. The method comprises the following steps: firstly, acquiring wind speed, wave height, electricity price, wind speed and point prediction data thereof, and a prediction value of residual life of a wind turbine generator component; generating a group uncertain scene as the input of a subsequent optimization model through a space-time probability model; constructing a two-stage random mixed integer programming model as an optimization model, and setting decision variables and constraint conditions of the model; and finally, solving the two-stage random mixed integer programming model every day by adopting a rolling time domain optimization algorithm, and outputting a maintenance scheduling plan. The embodiment proves that compared with an existing advanced scheduling strategy, the offshore wind power operation and maintenance economy can be further improved, and the availability and robustness of the scheduling strategy are enhanced.

The invention relates to the technical field of simulation laboratories, and discloses a multifunctional laboratory capable of simulating different marine environments based on ship body carrying, which comprises a ship body capable of simulating different marine environments and manufacturing various controllable flow fields from laminar flow to turbulent flow in an experiment cabin through a controllable flow field generation unit; through the controllable wave generation unit, waves with specified wave heights and periods can be generated in the experiment module; the water body temperature and salinity regulation and control unit is used for quickly and accurately regulating the temperature and salinity of the water body in the experiment module; water bodies with different turbidity and eutrophication levels are simulated through the environmental biological simulation unit; through an outside-in test mode, a sealing door at the bottom of the experiment module is opened, the underwater robot and other equipment are allowed to freely enter and exit, the dynamic response and control algorithm of the underwater robot when entering a complex environment (external real ocean) from a calm environment (in the experiment module) can be tested, and in-situ storage of a sampled sample can be realized.

The invention relates to a pressure sensing inverse echo measurement LSTM short-time sea wave significant wave height prediction method, and belongs to the technical field of marine environment parameter inversion and marine dynamics observation. The method comprises the following steps: firstly, preprocessing multi-source observation data, and matching and integrating data from different sources; establishing a model, and improving the performance of the model by adjusting the structure and parameters of the model; and finally, evaluating the precision of the model, and verifying the precision and stability of the model in a complex environment. According to the method provided by the invention, a plurality of long and short-term memory network layers are combined with a plurality of full-connection layers to realize hierarchical fusion of time features, so that the prediction precision of the significant wave height of the short-term sea wave is improved, and important data support is provided for the directions of ocean dynamics, environmental evaluation and the like.

The invention provides a wind power prediction method based on marine meteorological characteristics and wake flow interference, and aims to solve the problem that the existing wind power prediction does not fully consider the thermal and dynamic characteristics of a marine boundary layer, the wake flow interference effect of a fan group and the attenuation of a salt mist environment. According to the method, power prediction is realized by constructing a multi-source marine meteorological characteristic field, a wake flow interference field and an environment attenuation compensation mechanism. Specifically, a meteorological characteristic matrix is generated by combining an integral norm of wave height change, a position and wind direction characteristic matrix is constructed based on a fan position and a prevailing wind direction, wake flow interference intensity is quantified through a Gaussian kernel function, and meanwhile, an attenuation compensation item containing an error function is designed to process a salt spray corrosion effect. Establishing a bimodal wind speed probability distribution model, and combining a space-time adaptive quantile regression output power prediction interval; the method effectively solves the problems of marine meteorological dynamic characterization, wake flow interference quantization and environmental attenuation compensation, and significantly improves the precision and reliability of offshore wind power prediction.

This invention relates to the field of fluid dynamics analysis technology, specifically to a wave impact boundary force simulation and analysis method based on SPH (Self-Proofing and Propagation). The invention obtains a divergence index based on the energy accumulation efficiency of each wave from initiation to breakup, the spatiotemporal distribution characteristics of energy release during breakup, and the wave height. All waves are divided into different clusters. The critical time step is adjusted according to the dispersion and divergence index of waves within each cluster to obtain an optimized critical duration. The interference-sensitive zone of each wave is identified, and the modal coupling coefficient is obtained based on the correlation strength and energy distribution of the pressure signal in the time-frequency domain within the interference-sensitive zone. The baseline artificial viscosity coefficient is adjusted based on the optimized critical duration and modal coupling coefficient to obtain an optimized artificial viscosity coefficient. The SPH algorithm is then used for wave impact boundary force simulation and analysis. This invention effectively improves the balance between accuracy and efficiency in multi-scale wave impact boundary force simulation.

The invention discloses an intelligent control type floating fan system for extreme sea conditions. The system is composed of a sensing layer, a risk identification layer, a control decision layer, an adaptive learning module and a structure response layer. The sensing layer obtains wind speed, wave height, attitude inclination angle and anchoring tension, and calculates disturbance indexes; the risk identification layer generates a risk index by using a Sigmoid function; the control decision-making layer outputs connecting rod compression, air bag release and thruster thrust instructions according to the control decision-making layer; and the adaptive learning updates the weight according to the action deviation. The structure response layer comprises a carbon fiber adjustable connecting rod, a multi-cavity air bag provided with an air storage tank and a high-speed valve, a multi-direction vector propeller and a modularized interlocking floating body. According to the system, rapid risk identification and linkage control are realized, and the steady state and risk avoiding capability of the platform are improved.

The invention provides a WaveWatch III and U-Net-based global wave numerical forecasting system and a significant wave height intelligent correction network construction method. The method comprises the steps of mode input, WaveWatch III source code downloading, model output, significant wave height intelligent correction network construction and the like. The method has the beneficial effects that the forecasting precision of the WaveWatch III model in all sea areas in the world is remarkably improved, particularly the forecasting capability under extreme sea wave conditions is improved, and more accurate and stable scientific basis and technical support can be provided for global marine environment monitoring, disaster early warning and coping strategy formulation.

The invention provides an intelligent driving long-tail risk scene training sample enhanced synthesis method, and relates to the technical field of data processing, and the method comprises the steps: splitting a wind speed component, a wave height component and a visibility component into independent sequences, and carrying out the time alignment, combining the wind speed component, the wave height component and the visibility component at the same time point into a slice unit, mapping the interaction relation between the wind speed direction and the wave height direction to a two-dimensional matrix, introducing the visibility component into the two-dimensional matrix to obtain coupling matrix data, and performing nonlinear change on numerical values in the coupling matrix data to obtain a data matrix; calculating the fluctuation intensity under the combined action of wind wave direction coupling and visibility shielding to obtain a coupling intensity factor, and calculating an energy accumulation value of multi-factor coupling under the extreme weather condition; and generating a synthetic sample by taking the coupling strength factor and the energy accumulation value as constraint conditions, and performing classified storage on the synthetic sample to obtain an enhanced training sample set. The method can synthesize and react samples under real extreme sea conditions.

The invention belongs to the technical field of microwave radar image processing, and relates to a wind wave significant wave height inversion method based on a wind wave period, which comprises the following steps: firstly, establishing a significant wave height calculation model taking the wind wave period as input; then, acquiring a sea surface microwave remote sensing image, and performing coordinate transformation, such as preprocessing of a shore-based sea observation radar image, a shipborne navigation radar image, noise suppression and the like; thirdly, inverting a wind wave period from the preprocessed image through a spectrum analysis technology; and finally, substituting the wind wave period obtained by inversion into a pre-established model, and calculating to obtain the significant wave height. According to the method, the wind wave period is introduced to serve as a key intermediate variable, the image features and the sea wave physical parameters are closely associated, the defect that an existing direct inversion method is insufficient in precision and stability is overcome, and an effective wave height obtaining means which is clear in physical significance, high in inversion precision and easy to popularize in a business mode is provided.

The utility model discloses a multifunctional deep sea environment simulation system, and relates to the technical field of deep sea engineering experiment devices. Comprising a wave flow experiment water tank which is a containing cavity capable of bearing water, and the top of the water tank is of an open structure; a deepwater experiment area, a transition experiment area and a shallow water experiment area are sequentially arranged in the water tank according to the wind direction; the fan array is used for providing a wind field with a preset condition required by an experiment above the water body; the flow guide array is used for guiding and controlling air generated by the fan array; the current generation array is used for generating current and completing circulation of the water body in the wave current experiment water tank through a backflow channel; the wave making array is used for generating waves with different periods and wave heights for the water body; and the wave absorbing plate is used for dissipating energy of the generated waves. According to the system, multi-factor combined action experiment simulation can be synchronously carried out, and the design precision is remarkably improved; the wind wave coupling effect on the structure is achieved, and the real sea wind wave environment is simulated.

The invention relates to a multi-channel multi-mode space-time 3D convolution and Transform fused satellite-borne GNSS-R wave height inversion model, and aims to solve the problems that a traditional empirical model is poor in adaptability to complex oceans and existing deep learning cannot fully excavate space-time correlation. According to the method, three modules are innovatively fused for cooperative processing: firstly, a 3D CNN-ConvLSTM is utilized to extract local spatial-temporal characteristics of multichannel GNSS-R data and capture a dynamic time sequence; secondly, performing deep coding on the sea surface environment parameters through a multi-head attention mechanism of Transform, and establishing global dependence between features; and finally, realizing cross-modal fusion by adopting a weighted summation and feature splicing strategy. And through training of an Adam optimizer and control of an early stop strategy, optimization is carried out by taking a mean square error as a loss function. Experiments show that compared with a traditional model and a machine learning model, the method has the advantages that the significant wave height estimation error is reduced by 40-53%, the correlation coefficient reaches 0.84-0.91, the precision and generalization ability under the complex sea condition are remarkably improved, and reliable support is provided for ocean remote sensing monitoring and disaster early warning.

The invention belongs to the technical field of computer vision and behavior recognition, and particularly discloses and provides a multi-dimensional drowning risk trajectory backtracking visualization system, which is characterized in that through time-space correlation analysis of swimmer stroke frequency variation coefficient, heart rate fluctuation trend and water flow data, traditional single data source monitoring limitation is broken through, and early warning mode upgrading is realized; based on spatial relationship detection of dynamic visual range rendering and risk key nodes, responsibility boundaries lacking environmental objective limitation and human intervention are distinguished, and wave height and illumination intensity are introduced to correct transparency of a mask layer in real time, so that the view of a lifeguard is highly consistent with an actual shielding state; a risk weight distributor and a three-dimensional space-time path are utilized to convert chain reactions of action abnormity, water flow environment sudden change and view shielding into a space-time evidence chain capable of being quantitatively traced, multiple data sources are integrated to generate a visual report, and the proof effect of drowning accident liability traceability and the accurate optimization effect of safety design are achieved.

The invention relates to a marine ship carbon emission accounting method and device based on interpretable residual learning, and a medium, and the method comprises the steps: complementing the missing of static parameters through ship IMO numbers, carrying out the unified coding and unit standardization of marine log dynamic data, combining the host power and oil consumption dual-path verification of the data validity, and carrying out the calculation of the marine ship carbon emission. After abnormity is cleaned, dynamic, static and meteorological data are fused to construct a carbon emission database; calculating the comprehensive action of wind resistance, still water resistance and wave resistance by applying a fluid mechanics law, generating a physically-driven carbon emission reference value, and synchronously extracting key characteristics such as navigational speed, wave height and ship length; with reference values and features as samples, uncovered dynamic features are learned through a multi-layer residual block chain structure, and a carbon emission accounting model is trained in combination with a cross validation and regularization mechanism; and finally realizing high-precision carbon emission prediction based on the training model. Compared with the prior art, the method has the advantages that the residual network is comprehensively used, interpretability analysis is achieved, and high-precision prediction of ship carbon emission is achieved.