263results about "Measuring open water movement" patented technology

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention relates to the technical field of data analysis, in particular to an intelligent hydrological data analysis method and system, and the method comprises the following steps: collecting water level, flow velocity and precipitation data, calculating the change rate of adjacent moments, marking hydrological parameter abrupt change points, carrying out the statistics of time intervals, predicting the duration of extreme events, and obtaining the distribution characteristics of the hydrological abrupt change points. According to the method, by analyzing the time interval and the duration time of the data points, the distribution characteristics of hydrological abrupt change can be mastered in the early stage, response can be made in advance, the propagation sequence and the diffusion rate of the abrupt change points in different river reaches can be analyzed, and accurate division of the influence range and the risk level of hydrological events is enhanced; effective management of hydrological event space risks is achieved, understanding of hydrological key influence factors is deepened by screening key variables and evaluating the evolution relation of the variables along with time, accuracy of short-term and long-term hydrological risk prediction is enhanced, and a scientific basis is provided for dynamic early warning of hydrological abnormal events.

The invention relates to the technical field of ocean engineering and remote sensing, in particular to an ocean wave measurement method based on GNSS (global navigation satellite system) wave buoy and atmosphere data fusion, which comprises the following steps: S1, acquiring three-dimensional displacement data of a buoy in real time; s2, collecting atmospheric condition parameters over the target sea area; s3, performing time and space synchronization on the data acquired by the GNSS wave buoy and the data of the atmospheric sensor array; s4, mapping the GNSS wave buoy data and the atmosphere data to a unified feature space; s5, extracting wave characteristic parameters; and S6, establishing a coupling model for quantifying the dynamic influence of atmospheric factors on wave generation, development and propagation. According to the method, the GNSS wave buoy data and the atmospheric condition data are fused, the physical basic model and historical data optimization are combined, high-precision wave characteristic prediction can be achieved, and the defects of a traditional wave measurement method in the aspects of precision, real-time performance and coverage range are effectively overcome.

The present disclosure provides a method for remote sensing blue-green wave band ratio logarithmic water depth retrieval of wavelet spline instantaneous tidal height correction, and belongs to the field of remote sensing water depth retrieval. Aiming at water depth retrieval precision reduced by blue-green light and a tidal height in a remote sensing water depth retrieval process, the model fully considers a linear correlation between an attenuation ratio of the blue-green light in water and a depth, the smoothness of the tidal height, and the characteristics of consistent convergence, first-order continuous derivation and second-order continuous derivation of the tidal height with time change, and constructs the method for remote sensing blue-green wave band ratio logarithmic water depth retrieval of wavelet spline instantaneous tidal height correction, and according to Molokai experiment verification, compared with an early model, the invention improves the remote sensing water depth retrieval precision.

The present disclosure provides an ice floe motion prediction method, a device, a storage medium, and an electronic device. In the method, the electronic device is configured to acquire a historical motion information sequence of ice floe in a to-be-navigated region, wherein the historical motion information sequence includes multiple pieces of historical motion information of the ice floe at different time points; invoke a motion prediction model to analyze a spatiotemporal relationship of the multiple pieces of historical motion information; and obtain motion prediction information of the ice floe in the to-be-navigated region.

The invention relates to a sea area island remote sensing information change monitoring method and system, and relates to the technical field of remote sensing monitoring, and the method comprises the steps: obtaining a remote sensing image time sequence of a target sea area island, the remote sensing image time sequence comprises a plurality of remote sensing images, and each remote sensing image has an acquisition timestamp; collecting tide height information corresponding to the target sea area island at each collection timestamp, and water depth distribution information of the monitored sea area where the target sea area island is located; and performing correction processing on each remote sensing image according to the tidal height information and the water depth distribution information corresponding to each acquisition timestamp, and generating an island boundary contour change time sequence. The invention solves the problems that the traditional sea area island remote sensing monitoring adopts a single-moment image for analysis, the real coastline change and temporary boundary offset caused by tide cannot be distinguished, the boundary extraction precision is reduced due to shallow water area seabed reflection interference, the monitoring error is large, and the monitoring precision is low. The application precision requirements of sea area management, coastal zone protection and the like cannot be met.

The invention discloses a typhoon wave height prediction method based on a deep learning MOE-Transform model, and the method comprises the following specific steps: collecting historical typhoon data of a research region, and constructing a virtual typhoon data set in combination with a central pressure difference formula; processing the historical and virtual typhoon data sets through a Holland typhoon empirical model and a storm growth relation to obtain a wind field, an air pressure field and a significant wave height field; correcting the field data by using an error model on the basis of the ERA5 data set to form a typhoon space-time fusion database containing meteorological data, the significant wave height field and typhoon data; and training and testing the MOE-Transform model to obtain a typhoon wave significant wave height prediction model for typhoon wave height prediction. According to the method, the multi-task adaptability and generalization ability are improved, and the precision and timeliness of typhoon wave height prediction are improved.

The invention relates to the technical field of sea wave height prediction, and provides a sea wave significant wave height prediction method and system fused with multi-source data, and the method comprises the following steps: correcting obtained satellite observation data based on obtained buoy observation data; carrying out preliminary fusion on the obtained reanalysis data and the corrected satellite observation data by adopting an optimal interpolation method; adding a layer of data mask to mark the position of the satellite data in the fused data; and inputting the marked initial fusion data into a VQ-VAE model, compressing the fusion data into discrete potential variables, inputting the discrete potential variables into a GPT model, and generating a prediction result of the significant wave height of the sea wave. According to the method, artificial intelligence, data assimilation and fine tuning technologies are combined, the data assimilation technology is introduced to fuse the multi-source time-space sparse marine observation data and the model simulation result, and the fine tuning technology is used in the training process to amplify the adjustment effect of the observation data, so that the precision and timeliness of sea wave prediction are improved.

The invention discloses a non-contact wave measurement method and device based on machine vision. The method comprises the steps that a multi-view imaging system is used for synchronously shooting from multiple vision; the shot image is subjected to standardization processing, a wave surface image is obtained, the standardization processing comprises color adjustment, noise reduction processing, data enhancement, texture analysis and edge detection and frequency domain analysis, and the wave surface image comprises at least two complete wave periods; and performing image analysis on the wave surface image by using a pre-trained convolutional neural network to obtain a wave height prediction value. The problems that a traditional monitoring method needs to make contact with a measured object and the environment, deployment is complex, and the method is prone to being affected by the marine environment can be solved.

The invention belongs to the technical field of sea wave height prediction, and discloses a multi-scale sea wave height prediction method and system based on a large language model, and the method comprises the steps: S1, carrying out the decoupling interaction based on an amplitude phase, and obtaining enhanced sea wave height data; s2, extracting based on multi-scale semantic information: constructing a feature pyramid structure for the enhanced sea wave height data, outputting sea wave self-encoding features of different scales, performing self-encoding on a lexicon of a large language model, outputting word vector features, and obtaining the multi-scale semantic information by using a cross-modal attention mechanism and a dynamic fusion mechanism; and S3, a step of large-scale language model prediction based on sea wave height domain cue word driving: inputting the multi-scale semantic information and the domain specific semantic features into a large-scale language model, and outputting prediction features. According to the method, sea wave complex spatial-temporal change information is effectively mined, and accurate prediction of the sea wave height is realized.

The invention relates to an unsteady-state sea wave direction spectrum estimation method. The method comprises the steps of S1, signal decomposition; s2, direction distribution calculation; s3, adaptively adjusting the time domain analysis step length; s4, performing nonparametric kernel estimation on the transient wavelet distribution probability; and S5, direction spectrum time domain adaptive reconstruction is carried out. In the time domain analysis process of wavelet analysis, a two-dimensional plug-in non-uniform bandwidth algorithm is adopted to determine a data-adaptive two-dimensional non-uniform and non-parametric kernel function, the probability of different wavelets in a wave number-direction space is analyzed, the lateral propagation sea wave energy in the non-main wave direction is estimated, and the wave energy in the non-main wave direction is obtained. Therefore, the precision and the anti-noise capability of the direction spectrum estimation method are improved. In the time domain analysis process of wavelet analysis, a sea wave signal fluctuation intensity detection algorithm based on the frequency-direction-wave number three-dimensional change rate of wave energy distribution is introduced, the time duration of sea wave signal sections is divided according to the fluctuation intensity, the time domain precision is adjusted, the calculation efficiency is improved, and meanwhile, the sea wave signals which rapidly change in the time domain are accurately captured. Measurement is avoided.

The invention relates to the technical field of port engineering construction, in particular to a tide wave joint test method and system based on multi-source data in port engineering construction. At present, the problems of insufficient data utilization, low test precision and the like exist in tide and wave joint test in port engineering construction. The method comprises the following steps: constructing a nested unstructured grid and dynamically configuring discrete parameters of a wave direction spectrum; a variation assimilation algorithm is adopted to optimize hydrodynamic parameters; solving a wave flow coupling equation to generate a combined field data set; the pile foundation stress is calculated in combination with an improved Morison equation, and a safety evaluation report is output through basin resonance characteristic analysis; the system sets corresponding modules to realize various functions based on the method. The device is mainly used for precise combined testing of tidal waves in port engineering construction so as to guarantee the safety of an engineering structure.

Systems and methods to detect ferromagnetic objects, particularly submerged, non-naturally occurring ferromagnetic objects, account for contributions to the magnetic field arising from topography, such as magnetic refraction at bathymetric slopes. Removing this topography component of noise from detected total magnetic field enhances detection capability of, for example, naval surveillance systems.

The invention relates to a tide level obtaining method and system based on an OWC wave energy conversion device. The method comprises the following steps: constructing an energy loss prediction model in the air chamber; acquiring field incident wave energy, reflected wave energy, water potential energy increment, kinetic energy increment and pneumatic output energy at the current time period; inputting incident wave energy and reflected wave energy into the model to obtain predicted energy loss; and the tide level variation is solved based on the energy conservation relational expression, and the current tide level is calculated. The system comprises an energy loss prediction module, a wave energy acquisition module, a water body energy acquisition module, a pneumatic output acquisition module, an energy loss calculation module, a tide energy solving module and a tide level calculation module, which cooperatively realize the method. According to the scheme, an external tide gauge station is not needed, the local sea area tide level can be inversed in situ, at high frequency and with high precision, the method is suitable for an OWC-breakwater fusion scene, and cooperative operation of a port and ocean energy is supported.

The present invention provides a system for measuring real-time meteorological and oceanographic data using a buoyage network for smart ports and harbours. It consists of a moored floating station or moored buoy, or network of buoy-based measurement stations (A) moored at the port or harbour waters with the marine navigation and land-based measuring station (B) mounted at jetty or wharf or quay wall of port and harbours. The buoy-based system (A) is equipped with a cylindrical steel buoy structure featuring a mast (7) and is secured using a two-point mooring system. The present system comprises of an integrated monitoring and data transmission system that collects the real-time meteorological and oceanographic data from the sensors and processes the acquired data based on calibration, enabling accurate and reliable measurements. The processed data is then stored in the system's internal memory for analysis and transmitted to a remote-control station.

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 method, a device, equipment, a medium and a product for estimating a sea condition deviation value of a radar altimeter, and relates to the field of ocean observation, and the method comprises the steps: screening L2-level GDR data of the radar altimeter based on a data identification bit and a sea surface parameter editing criterion of the radar altimeter; based on a threshold screening condition, constructing an SNSSB model according to the self-intersection point data set; according to different sea surface parameter combinations, training the SNSSB model, and determining a trained SNSSB model; and estimating a sea condition deviation value according to the trained SNSSB model, and according to the method, the sea condition deviation value of the radar altimeter can be accurately evaluated.

The invention belongs to the technical field of multi-constellation satellite-borne GNSS-R data ocean significant wave height inversion, and discloses a satellite-borne GNSS-R ocean significant wave height inversion model construction method and system under a high sea condition, and the method comprises the steps: obtaining modeling data, auxiliary data and verification data; feature variable parameters and auxiliary variable parameters of the modeling data are extracted, and space-time matching is carried out on the extracted variable parameters; performing data quality control and data set division on the variable parameters after space-time matching; constructing and training a WaveMambaFormer model by using the divided data set, and calculating feature importance and global interpretation by using an SHAP interpreter, so as to enhance the interpretability of the WaveMambaFormer model; and the effective wave height data of the ERA5, the WaveWatch III and the Jason-3 are respectively used as reference data to evaluate the inversion performance of the WaveMambaFormer model.

Described here are systems and methods that utilize visual imagery and an optical flow-based computer vision algorithm to measure river velocity in streams or other flowing bodies of water. The systems and methods described in the present disclosure overcome the barriers of conventional flow measurement techniques by providing a fast, non-intrusive, remote method to measure peak flows.

The invention discloses a hydrological flow and speed measurement method, which relates to the technical field of hydrological measurement and comprises the following steps: selecting a dynamic anchor point in a target speed measurement area, and carrying out anchor point hovering on an unmanned aerial vehicle according to the dynamic anchor point; the unmanned aerial vehicle collects surrounding images of the unmanned aerial vehicle to obtain obstacle data, and a motion route is generated in real time; the unmanned aerial vehicle performs video acquisition on the target velocity measurement area to generate a three-waveband water surface video, and analyzes the three-waveband water surface video to generate an initial velocity distribution diagram; the unmanned aerial vehicle collects riverbed data and water surface fluctuation data of the target flow measurement area, and according to the riverbed data, a riverbed terrain is obtained, and water depth data and a section average flow velocity are obtained; and constructing a three-dimensional water flow model, collecting a movement track of the buoy group and gyroscope data, correcting the three-dimensional water flow model, and outputting a hydrological flow and velocity measurement result. The method has the effects of improving the measurement precision of flow measurement and speed measurement and improving the flexibility and universality when facing a complex water area.

The invention relates to the field of internal solitary wave prediction, and discloses an ocean internal solitary wave propagation prediction method based on an improved TimeXer model, and the method comprises the following steps: constructing the improved TimeXer model; remote sensing image data of a geostationary orbit satellite GK-2A and a polar orbit satellite are collected and preprocessed; extracting internal solitary wave parameters based on remote sensing image data, constructing an internal solitary wave prediction data set in combination with hydrological environment parameters, and training a model; and acquiring remote sensing image data of a geostationary orbit satellite GK-2A and a polar orbit satellite of a target sea area, preprocessing the remote sensing image data, inputting the remote sensing image data into the model which is tested to be qualified, and performing internal solitary wave prediction. According to the method disclosed by the invention, the precision and stability of propagation prediction of the solitary waves in the ocean can be effectively improved; by introducing an innovative module, the adaptability of the model to a complex marine environment is enhanced, and a reliable scheme is provided for accurate prediction of solitary waves in the ocean.

The invention relates to a multi-signal fusion real-time deduction method for three-dimensional wave parameters along with a ship, and a deduction device consists of a state sensor, a one-way stress sensor, a vertical acceleration sensor, a shunt, a terminal junction box, a signal acquisition instrument, a host, analysis software, a signal transmission cable and the like. The deduction method comprises the processes of system state matrix discretization, transfer function calculation, Kalman filtering deduction model construction, convergence analysis, result comparison and the like, and three-dimensional time-frequency parameters of waves are deduced in real time through fusion analysis of ship motion and actual measurement data of a key position stress response sensor. Real-time information of surrounding waves can be provided for more sailing ships, more comprehensive reference basis can be provided for ship sailing and ship type optimization, and the method has practical significance and market value.

Flood extent mapping and prediction methods and systems are disclosed. A computer-implemented flood extent mapping and prediction method comprises: obtaining elevation data associated with a waterway and defining a line through the waterway in an upstream to downstream direction; obtaining water gauge data comprising one or more locations of one or more water gauges along the waterway, respectively, and water level data at each of the one or more water gauges; correlating the one or more water gauges to one or more respective points along the line through the waterway based on the one or more locations; determining a water depth value at each of the one or more respective points using the water level data from correlated water gauges and the elevation data; and generating a flood map based on the water depth value at each of the one or more respective points along the line through the waterway.

The invention relates to the technical field of ship engineering, and particularly discloses a dredger rake pipe space angle measurement method, which comprises the following steps of: collecting motion data of a dredger body and a rake pipe in real time, removing high-frequency noise by adopting median filtering, extracting features, constructing a comprehensive feature vector, and separating nonlinear influence of ship body shaking and rake pipe attitude by utilizing a fuzzy reasoning system, so as to measure the rake pipe space angle of the dredger. The attitude estimation result is corrected in combination with real-time error feedback, the estimation result is dynamically optimized and the system deviation is corrected through Bayesian estimation and a particle filter algorithm, so that the measurement precision is improved, and the system also evaluates the overall measurement accuracy in combination with a machine learning model, automatically adjusts the measurement parameters and flow, and improves the measurement accuracy. The adaptability and robustness of the system are improved, and for an inaccurate measurement result, the system can analyze reasons and re-measure the space angle so as to ensure long-term stable operation.

A method, device, computing equipment, and storage medium for predicting the drift of the floating object are provided. A method for predicting drift velocity includes: obtaining environmental characteristic parameters at the location of the floating object to be predicted, wherein the environmental characteristic parameters include wave characteristic parameters; inputting the wave characteristic parameters into a deep learning model for wave-induced drift velocity to obtain the wave-induced drift velocity; wherein the deep learning model for wave-induced drift velocity is trained based on first sample drift data, wherein the first sample drift data includes observed drift velocity of sample floating objects, corresponding sample water surface flow characteristic parameters, sample wind characteristic parameters, and sample wave characteristic parameters.

The invention provides a riverway safety monitoring method and system, and relates to the technical field of riverway flow monitoring, and the method comprises the steps: obtaining riverbed elevation distribution data of a target monitoring section area, dividing the target monitoring section area into a plurality of flow measurement subareas, obtaining the section parameters of each flow measurement subarea, and determining the flow measurement subareas; a radar flow velocity sensor for collecting river water flow velocity data is arranged in each flow measurement subarea, and a radar water level sensor for measuring first river water level data is arranged in any flow measurement subarea; constructing a hydrodynamic model, and inputting river water flow velocity data to obtain average flow velocity data of the corresponding flow measurement partitions; calculating second river water level data of the remaining flow measurement subareas according to the first river water level data and the riverbed elevation distribution data, calculating instantaneous flow and accumulated flow according to the section parameters, the first river water level data, the second river water level data and the average flow velocity data, and sending out an alarm signal when the accumulated flow exceeds a preset flow threshold. The accuracy of the monitoring result can be improved.

The embodiment of the invention provides a sea wave height determination method and device, a storage medium and electronic equipment, and the method comprises the steps: segmenting target point cloud data into a plurality of time sequence frames, and determining a wave crest region and a wave trough region of sea waves according to a local extreme point in the target point cloud data, each time sequence frame is used for indicating the sea surface shape at each moment; according to the wave crest area, the wave trough area and the multiple time sequence frames, a target sea wave contour model is fitted, and the target sea wave contour model is used for indicating the sea wave height and the three-dimensional shape of the sea wave at each time point; and determining the sea wave height at any time point based on the target sea wave contour model. According to the method, the problem that high-precision sea wave height data cannot be provided under complex sea conditions such as high storm waves and changeable tides in the prior art can be solved.

A method for measuring channel flow based on bionic eagle-eye vision and an apparatus thereof are provided. The method includes: acquiring a channel flowing video, and carrying out image frame interval extraction on the channel flowing video to obtain a target observation image; calculating an optical flow value of the target observation image, and calculating a surface flow velocity value of the target observation image in a world coordinate system based on position change information of the optical flow value of the target observation image between image frames and a spatial resolution of the target observation image; acquiring an average cross-section flow velocity based on a pre-trained surface cross-section flow velocity coupling model, and calculating the channel flow based on the average cross-section flow velocity and a cross-section water level value.

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.

A new GNSS-based real-time high-precision wave measurement method, wherein the GNSS phase, pseudo-range, Doppler frequency shift observations and broadcast ephemerides are collected by a GNSS receiver, and antenna carried by a sea surface carrier; the three-dimensional speeds of a carrier are acquired using an epoch difference of phase observations; and wave element information are then solved. The wave element information can also be obtained by integrating the speeds for a certain duration and removing a linear trend term to obtain a time-dependent displacement variations. No additional precise differential correction is needed, thereby saving on service costs and communication costs of precise differential corrections. High-precision wave element information is obtained in real time, and locally stored in a buoy or periodically returned by communication, thereby expanding the working range of GNSS-based ocean wave measurement.