13 results about "Sea ice thickness" patented technology

The invention provides a sea ice thickness extraction method and device based on unmanned aerial vehicle laser point cloud rapid classification. The method comprises the steps of obtaining laser point cloud data collected by an unmanned aerial vehicle platform; determining a local sea surface elevation reference based on the laser point cloud data; based on the local sea surface elevation reference, ship point cloud is identified through relative height screening and spatial clustering, and geometric feature verification is carried out on a clustering result; after sea water points and ship point clouds are excluded from the laser point cloud data, spatial clustering is carried out on the remaining point clouds to identify sea freezing point clouds; and based on the local sea surface elevation datum and the sea ice point cloud, extracting the sea ice thickness. According to the method, the advantages of high spatial resolution and high cost performance of the unmanned aerial vehicle are utilized, a refined point cloud processing technology is matched, classification is rapid and accurate, thickness extraction precision is high, the method adapts to a polar region complex observation environment, reliable data support can be provided for polar region navigation channel development, navigation safety investigation and climate change research in real time, and the method has remarkable practical value.

The invention relates to the field of sea ice thickness measurement, in particular to a sea ice thickness measuring device based on an icebreaker, which comprises a mounting part, an outward extending part is arranged in the mounting part, a micro moving part is arranged in the outward extending part, a lifting part is arranged in the micro moving part, a measuring part is arranged in the lifting part, and the measuring part is provided with a snow cleaning chassis. The bottom of the probe rod is fixedly connected with six scrapers which are circumferentially distributed, the scrapers are used for rapidly cleaning a snow accumulation layer and floating ice impurities in a to-be-measured area, the surface of the ice layer is completely exposed, when the probe rod makes contact with the ice layer, a signal is immediately fed back to an external control assembly, the downward moving cylinder body is controlled to stop descending, and it is ensured that the ice thickness measurer only conducts thickness measurement from the surface of the ice layer. And the problems that the ice layer and the accumulated snow layer are difficult to effectively distinguish and the accumulated snow thickness is easy to be included into the sea ice thickness measurement result by utilizing an intelligent sensor and a sound wave type measurement technology are effectively avoided.

This invention belongs to the field of marine remote sensing information processing technology and discloses a sea ice thickness inversion method based on multi-source heterogeneous fusion and physical constraints. The invention acquires multi-source heterogeneous GNSS-R data and performs hierarchical grouping and quality control; performs systematic bias and geometric correction on the data to generate a spatiotemporally continuous normalized reflectivity field; constructs a three-layer coherent reflectivity physical model of air-sea ice-seawater; constructs a two-stream physical sensing neural network, embedding the physical model as a differentiable constraint module into the network loss function, and uses the reflectivity field as input to invert sea ice thickness. This invention establishes a refined quality control system for the characteristics of semi-enclosed sea areas, overcoming the limitations of long revisit cycles and low coverage of single satellites. This invention also constructs a high spatiotemporal resolution sea ice monitoring system, solving the problem of instability in inversion of pure data-driven models in thin ice areas, and significantly improving the physical robustness of sea ice thickness inversion.

The invention discloses a sea ice thickness partition inversion method based on three-layer dynamic roughness of a wind cloud satellite, and the method comprises the steps: firstly obtaining GNSS-R remote sensing observation data, sea ice reference product data and marine environment parameter data, and obtaining a matching sample set through correlation matching; further calculating to obtain electromagnetic physical parameters and in-ice propagation attenuation related parameters of the sea ice and the seawater; constructing a dynamic roughness reflection model of an air-sea ice-seawater three-layer medium structure; dividing the specular reflection points in the matching sample set into normal area sea ice samples and rough area sea ice samples; performing physical method inversion on the sea ice sample in the normal area by adopting a dynamic roughness reflection model to obtain the sea ice thickness of the normal area; constructing a second-stage machine learning model for the rough region sea ice sample, and carrying out regression inversion to obtain the sea ice thickness of the rough region; and finally carrying out fusion processing on the sea ice thickness inversion results of the normal area and the rough area and outputting.

The application belongs to the technical field of geophysical exploration, and particularly relates to a method for detecting sea ice thickness based on a Cole-Cole model, which comprises the following steps: a central loop transient electromagnetic method exploration device is used; the device is placed on the sea water and the sea ice surface to observe magnetic field data; a Cole-Cole model is introduced on the basis of a pure resistivity model transient electromagnetic method theory to obtain a time-domain transient electromagnetic response of complex resistivity; a theoretical transient electromagnetic response is calculated according to a time-domain transient electromagnetic response of complex resistivity; a measured transient electromagnetic response is obtained; a nonlinear inversion method is used to perform a transient electromagnetic response inversion containing a polarization effect; Cole-Cole model parameters m 水 、τ 水 、c 水 、ρ 水 of sea water are extracted; and the ice layer thickness and the Cole-Cole model parameters of the ice layer are obtained, so that the method is suitable for the exploration of polar sea ice thickness, and the ice thickness is detected by the dielectric difference between ice and water to distinguish the sea ice medium and the sea water medium with similar resistivities, and the method solves the problem of the existing polar ice thickness detection method.

The invention belongs to the technical field of new-generation information, and particularly relates to a polar navigation channel navigability evaluation method based on a multi-source data strong synergistic effect, which comprises the steps of data preprocessing and grid division; construction of a comprehensive evaluation index system: constructing an index system covering five dimensions of sea ice, weather, ocean, navigation channel and policy management, the method specifically includes the following 17 indexes: sea ice concentration, sea ice thickness, air temperature, wind speed, low cloud amount, precipitation amount, snowfall amount, liquid water amount, ocean current flow velocity, sea surface temperature, sea wave height, sea surface air pressure, channel depth, channel width, channel intersection point, port facility and national territorial sea, and performing dimensionless processing on each index; carrying out weight calculation and an optimization algorithm; and constructing a navigability evaluation system and a navigability index. According to the method, the limitation of narrow evaluation dimension in the traditional technology is overcome, the comprehensive representation of the multi-factor coupling effect of the polar navigation environment is realized, and the comprehensiveness and objectivity of the evaluation result are effectively improved.

The invention discloses an ocean ice condition dynamic evaluation method and device, and relates to the technical field of ocean environment monitoring, and the method comprises the steps: obtaining the historical data of the sea ice thickness of a target sea area, analyzing the temporal and spatial variation trend, and building a sea ice thickness initial digital twin; dynamically correcting the initial digital twin; a space-time self-attention mechanism model is trained, sea ice thickness probabilistic prediction is achieved, multi-climate scenes and high-intensity human activities are set as external constraints, sea ice thickness initial digital twinborn antagonism deduction after dynamic correction is driven, and sea ice thickness extreme change and critical mutation risks under different external pressures are quantitatively evaluated; based on quantitative evaluation of extreme change and critical sudden change risks of sea ice thickness under different external pressures, a sea ice thickness time-varying risk field is dynamically generated, and a self-adaptive optimal route plan with a dynamic risk avoidance capability is provided for a ship. According to the invention, the safety of polar navigation is improved.

The present application relates to the field of sea ice detection, in particular to a sea ice thickness estimation method based on GNSS reflected signal phase. According to the oscillation frequency of the reflected signal phase with the satellite elevation angle, the thickness of the sea ice is estimated. The present application takes the navigation satellite as the signal source, receives and processes the GNSS direct signal and the signal reflected by the sea surface, extracts the frequency of the reflected signal phase oscillation with the satellite elevation angle, and estimates the thickness of the sea ice through the linear correspondence between the phase oscillation frequency and the sea ice thickness. The algorithm has low complexity, high detection accuracy, rich signal resources, global all-day, all-weather coverage, wide detection range, low cost, is not affected by the antenna pattern, is not limited by the receiver mode, does not need to select a specific satellite, has low algorithm complexity, high detection accuracy, and can effectively estimate the thickness of the sea ice.

This invention relates to the field of lidar control and data processing technology, and provides an underwater lidar control and data processing system based on a host computer program. The system includes a host computer (PC), an Ethernet / UDP communication link, a lidar host, and a software system running on the host computer. The lidar host has a built-in FPGA / main control unit, as well as a laser, photodetector, AD sampling unit, DA control unit, and SSD storage unit electrically connected to the FPGA / main control unit. The software system integrates 12 functional modules, constructing an integrated control architecture that coordinates the entire system. This invention achieves a fully closed-loop operation of equipment control, data acquisition, waveform analysis, data storage, and offline playback, solving the problems of existing underwater lidar host computer functions being scattered, poor parameter coordination, lack of engineering functions, and insufficient stability in harsh environments. It can be widely applied to scenarios such as sea ice thickness monitoring, underwater target detection, and shallow sea topographic measurement.

This disclosure relates to the field of sea ice monitoring technology, specifically to a sea ice thickness estimation method, apparatus, storage medium, and electronic device. The sea ice thickness estimation method includes: acquiring image data, GNSS signals, pile foundation data, and environmental parameters; segmenting the pile foundation area and ice layer area based on the image data to obtain the pile foundation area and ice layer area, and shielding the pile foundation area to extract image features of the ice layer area; calculating the satellite elevation angle based on the direct signal in the GNSS signal, and calculating the observations based on the reflected signal in the GNSS signal, to calculate signal features based on the satellite elevation angle and the observations; inputting the image features, signal features, pile foundation data, and environmental parameters into a pre-trained sea ice thickness estimation model to obtain the sea ice thickness value output by the sea ice thickness estimation model. The sea ice thickness estimation method provided by this disclosure can improve the accuracy of sea ice thickness estimation under marine photovoltaic background.

The invention relates to the technical field of sea ice monitoring, in particular to a sea ice thickness estimation method and device, a storage medium and electronic equipment. The sea ice thickness estimation method comprises the steps that image data, GNSS signals, pile foundation data and environment parameters are collected; based on the image data, carrying out image segmentation on the pile foundation area and the ice layer area to obtain the pile foundation area and the ice layer area, and carrying out shielding processing on the pile foundation area to extract image features of the ice layer area; calculating a satellite elevation angle based on a direct signal in the GNSS signal, and calculating an observed quantity based on a reflected signal in the GNSS signal, so as to calculate a signal feature according to the satellite elevation angle and the observed quantity; and inputting the image features, the signal features, the pile foundation data and the environmental parameters into a pre-trained sea ice thickness estimation model to obtain a sea ice thickness value output by the sea ice thickness estimation model. According to the sea ice thickness estimation method provided by the invention, the accuracy of sea ice thickness estimation under the offshore photovoltaic background can be improved.

The invention discloses a sea ice thickness calculation method based on image data, and the method comprises the following steps: obtaining the construction information and basic parameters of an icebreaker, and determining a camera installation point on the icebreaker and a sonar installation point on the side wall of the icebreaker; image data of sea ice on the ship side during icebreaking of the icebreaker are collected through a camera, sound waves are emitted to the underwater part of the sea ice through a sonar, echo data are received, and space-time alignment is conducted on the image data and the echo data; inputting the image data into a pre-trained neural network, and screening out image data of sea ice perpendicular to the sea surface; determining the height of the sea ice above the sea surface according to the echo data of the sea ice perpendicular to the sea surface, and determining the spacing by combining the camera mounting points; the sea ice imaging thickness is obtained according to the image data of the sea ice perpendicular to the sea surface, the actual sea ice thickness is calculated in combination with the sea ice imaging thickness, the distance and the camera focal length, the sea ice thickness can be rapidly determined through the image data, and the safety of polar region activity and icebreaker shipping is guaranteed.

The present disclosure provides a method and a device for evaluating a shipping risk caused by a sea ice disaster, and a computing device. The method includes: obtaining a historical daily-scale sea ice thickness data set of a monitored region; evaluating a risk level of sea ice disaster-inducing factors, vulnerability of a disaster-affected body and an exposure level in accordance with the historical daily-scale sea ice thickness data set; determining a disaster-bearing capability and a risk level of the disaster-affected body in the sea ice disaster in accordance with the risk level of the sea ice disaster-inducing factors, the vulnerability of the disaster-affected body and the exposure level; and evaluating shipping risks for different types of disaster-affected bodies within different return periods in accordance with the disaster-bearing capability and the risk level of the disaster-affected body in the sea ice disaster.