221 results about "Sea ice" patented technology

The invention discloses a highly dense broken ice image segmentation method based on an iteration MGAC and an SAM model, and the method comprises the steps: carrying out the sea ice instance segmentation of a preprocessing image based on the SAM model, and obtaining a sea ice mask image corresponding to the preprocessing image; according to the binary image and the sea ice mask image, obtaining an initial sea ice residual region image which is not identified by the SAM model; obtaining initial sea ice residual region images under different gray threshold values to obtain an initial seed mask graph; taking the initial sea ice residual region image and the initial seed mask image as inputs of a preset MGAC contour model, and obtaining an MGAC sea ice recognition result based on a multi-round iteration partitioning mechanism; and performing union operation on the MGAC sea ice identification result and the sea ice mask image to obtain a crushed ice segmentation mask result. The method solves the problem that the existing method is insufficient in structure extraction precision and boundary integrity of the dense broken ice area.

The invention discloses a polar region sea area ice area route dynamic planning method based on multi-source data, and the method comprises the following steps: obtaining sea ice, ocean current and wind field historical data of a target channel sea area, and carrying out the preprocessing; inputting a preprocessing result into a constructed sea ice forecasting model to obtain a sea ice concentration and sea ice thickness distribution forecasting result of a planning day; combining the forecast result with ocean current and wind field forecast data, and utilizing a pre-established random forest navigational speed reasoning model to obtain target channel sea area planning daily navigational speed space distribution data; and carrying out route planning with minimum time cost through an improved A * algorithm to obtain a dynamic planning result of the target channel sea area route. The invention relates to the technical field of ship navigation in a sea ice area, and relates to the technical field of ship navigation in the sea ice area, a polar sea area navigation speed background field is deduced according to a fitting relationship through random forest modeling and fitting of the relationship between the navigation speed and sea ice, ocean current and a wind field, and dynamic route planning with minimum time cost is carried out.

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 discloses a ship route planning method and system in a polar region sea ice environment and a program product. The ship route planning method comprises the steps of preprocessing satellite remote sensing radiation intensity data, performing image reconstruction of inter-ice water channel detection through an improved Vision Transform model, planning and visualizing a ship route and the like. The deep features of the image blocks are extracted through a Transformer encoder which comprises a multi-head self-attention mechanism and is processed by a multi-layer perceptron; and the Transform encoder firstly performs layer normalization on the input, and then performs multi-head attention mechanism and multiple linear transformation and regularization processing. And realizing path planning in an inter-ice water channel environment by fusing a dynamic planning cost function through a fast marching method. Compared with a traditional algorithm, the method has the advantages that the calculation precision is higher in the same scene, the calculation efficiency is greatly improved, and the planning result obtained after multiple costs are comprehensively considered is given instead of pursuing the shortest distance.

The invention provides an SAR image polar region sea ice detection method and system based on deep learning and a medium, the land and the water area are segmented by using a water area detection algorithm based on a DeeplabV3 network, the segmentation precision of the DeeplabV3 network is improved by means of cavity convolution, a spatial pyramid pooling module and a decoder, and the calculation and memory overhead is reduced by using a lightweight MobileNet; then, a sea ice detection algorithm based on a short-term dense cascading STDC network is used for precisely segmenting sea ice and water in a water area, the STDC network reduces the calculation complexity through specific module design and keeps multi-scale information, and meanwhile, multiple optimization means are adopted in decoder design to improve the detection performance; in addition, data is prepared and augmented, and the generalization ability of the model is enhanced. The sea ice detection precision and efficiency are improved, and the method is suitable for resource-limited environments such as on-satellite environments and the like.

The invention discloses a low-frequency sound propagation loss forecasting method suitable for a polar region sea area, and the method comprises the steps: building a two-dimensional ice layer model of a forecast sea area according to remote sensing data based on the setting that sea ice of the polar region sea area is a uniform elastic medium; calculating hydrological conditions of the ocean sound channel of the polar sea area; establishing a forecast sea area two-dimensional seabed model according to the seabed topography sediment database; an under-ice sound field model is constructed based on a three-dimensional cylindrical coordinate system, sound pressure from unit intensity sound sources with different sizes and depths to a receiving point in a forecast sea area is calculated through a frequency domain finite element method in combination with a PML technology, and sound propagation loss of a needed position is obtained through calculation. According to the method, the application limitation of traditional ray and normal wave theories under complex boundary conditions is broken through through a finite element method, a synergistic effect mechanism of actual ice layer parameter space change, submarine topography fluctuation and sound velocity profile gradient characteristics is systematically analyzed, and sound field modeling is more accurately carried out on different ice layers and submarine topographies in the Chukong sea region all year round; the influence of sound wave propagation is simulated.

This paper constructs a remote sensing sea ice dataset and, based on the characteristics of sea ice images, improves the YOLOv5 model by adding the SE attention mechanism, improving the SPP pooling pyramid structure, and replacing the SiLU activation function with the FReLU. The model's confidence is verified using evaluation metrics such as accuracy, recall, F1-score, and mean average precision, ultimately resulting in an optimal sea ice target detection model. The test set is input into the target detection model to obtain recognition results, extracting information such as the location and size of the sea ice. Based on this extracted information, a grid map is created. The starting and ending points are determined based on the ship's own position, and a path is planned using the Theta* algorithm. The generated path is evaluated using metrics such as route distance, average offset distance, number of ship turns, and number of sea ice avoidances. This paper implements sea ice recognition at a remote sensing scale, constructs simulated navigation scenarios, and plans the optimal navigation path. This paper can improve the safety of ships sailing in ice areas and provide technical support for collision avoidance and navigation decision-making assistance in ice areas.

An SAR sea ice remote sensing image segmentation method based on an MAE and a ViT model comprises the following steps that an unmarked AI4Arctic data set is used, a mask auto-encoder MAE is used for pre-training a visual Transform model, a picture is segmented into patches, then random masking is carried out, sine and cosine position embedding is adopted for carrying out position coding on visible patches, and a pre-trained ViT model is obtained; three segmentation labels of sea ice concentration SIC, sea ice development stage SOD and floating ice size FLOE are extracted from AI4Arctic original NetCDF ice map data, a single-channel grey-scale map is converted into a three-channel PNG image to serve as a fine tuning data set, fine tuning training of an encoder-decoder architecture is carried out in an end-to-end mode, any weight is not frozen in training, and the fine tuning data set serves as a fine tuning data set. And a multi-task ViT prediction model taking SIC, SOD and FLOE as segmentation targets is obtained. The ViT of the invention can better capture SAR image features, improve the accuracy of sea ice segmentation, significantly reduce the demand for training computing power and dependence on data annotation, and improve the speed of model training.

The invention discloses a fan site selection method and system based on different-mode nested grids, and belongs to the technical field of fan site selection, and the method comprises the steps: firstly constructing a large-region sea ice background field by using global data to preliminarily screen candidate regions, then constructing a small-region high-resolution background field through a grid nesting technology, and carrying out the dynamic simulation, according to the method, a sea ice evolution field is obtained, an initial field is corrected by fusing field and satellite data, errors are quantized, comprehensive scoring is carried out to screen target site selection, finally, simulation parameters are dynamically optimized according to actually measured data, and a final site selection is determined through iterative correction. According to the method, multi-scale evaluation from macroscopic preliminary screening to microcosmic fine screening is realized, and through data fusion and dynamic optimization, the precision and reliability of fan site selection in a complex sea ice environment are remarkably improved.

The invention provides an ice layer thickness identification and stress sensing cooperative navigation monitoring method and system, and relates to the technical field of ship navigation safety monitoring. The method comprises the following steps: acquiring and preprocessing sea ice images around a ship in real time; a self-built data set is made through labeling, a deep learning model is selected for segmentation, segmentation results of the snow accumulation layer and the ice layer are obtained, mask pixel values of the snow accumulation layer and the ice layer are obtained, and then the actual thickness is calculated; acquiring real-time stress data; using a timestamp synchronization mechanism to match the sea ice image of the time corresponding frame and the stress data; carrying out the normalization processing of the sea ice image and the stress data, carrying out the weight fusion of the normalized data, and calculating a comprehensive risk coefficient; a risk early warning level is output according to the interval where the coefficient is located; and when the coefficient exceeds a set threshold value, sound-light alarm and interface warning information are sent out. According to the invention, through collaborative analysis and risk assessment of multi-dimensional data, the sailing safety of the ship in the polar region and the sea ice region is improved.

The invention relates to the technical field of meteorological model optimization and polar region ocean data processing, in particular to a method and system for optimizing a COARE model based on sea ice density, and the method comprises the steps: obtaining the historical actual measurement data of the polar region ocean, calculating the real roughness and the model roughness outputted by the COARE model, and determining the roughness difference value; dividing a wind speed interval, and fitting and establishing a correction function of a difference value between the sea ice concentration and the roughness; and introducing the sea ice concentration into the COARE model, judging the interval according to the wind speed, calling a corresponding function to correct the roughness of the model, and outputting the ocean-atmosphere flux conforming to the polar region sea ice condition to complete the model optimization. According to the method, the sea ice concentration is introduced, and the wind speed partition correction relation is established, so that the roughness of the polar region under the sea ice coverage condition is dynamically corrected by the COARE model, the limitation of the original model in the aspects of wind speed applicability and polar region adaptability is broken through, and the accuracy and practicability of polar region ocean-atmosphere flux calculation are remarkably improved.

The embodiment of the invention provides a self-adaptive energy recovery system, device and method, and the system comprises an environment sensing module which is used for collecting external environment parameters and device operation state data; the data analysis module is connected with the environment sensing module and used for processing the collected data and obtaining sea ice height information, threat level and position information of the energy recovery device through analysis; the control execution module is connected with the data analysis module and used for generating a control instruction according to the sea ice height information, the threat level and the position information of the energy recovery device; the energy recovery device is connected with the control execution module and is used for executing the control instruction and carrying out position adjustment and / or energy recovery operation; and the energy storage module is connected with the energy recovery device and is used for storing the recovered electric energy. External information can be sensed in real time, self-adaptive adjustment can be made, damage to a wind power airport is reduced, and secondary utilization of sea ice mechanical energy can be achieved.

The invention relates to the technical field of intelligent transportation, in particular to a state space modeling method for ship ice region navigation decision model training, which comprises the following steps of: constructing a sea ice dynamic model, and estimating an unobservable sea ice state by using unscented Kalman filtering to obtain a hidden variable vector; inputting the hidden variable vector, the observable state information and the external force field code into a Transform encoder, and outputting future sea ice center position distribution; constructing a confidence collision avoidance domain of the time index according to future sea ice center position distribution, wherein the confidence collision domain is updated in a rolling manner along with time; taking observable state information, hidden variable vectors and transformation embedding of a confidence collision avoidance domain as reinforcement learning states, and adopting near-end strategy optimization to train a sea ice dynamic model; and obtaining a ship ice region navigation decision model based on a joint termination criterion of the verification set collision rate, the arrival rate and the strategy divergence. The method improves the strategy convergence stability and the ice region safety passing rate, and is suitable for autonomous navigation and simulation training of polar regions and high-latitude sea areas.

The invention discloses a polar region water surface unmanned ship path planning method based on an improved A * algorithm. Aiming at the problems that interference information exists in a radar map in a polar region environment, a sea ice area is difficult to accurately extract and the like, firstly, a radar induction area is extracted by utilizing a Hough circle detection technology, and a multi-threshold image segmentation method based on a Kapur information entropy and emperor penguin optimization algorithm is adopted to filter out the interference information such as latitude and longitude marks and radar echoes; and an accurate sea ice distribution diagram is obtained. And then, an improved cost evaluation function fusing credibility, threat information potential energy and pheromone concentration is constructed, a TI-PC-A * path planning algorithm is proposed, and safe path planning of the sea ice area is realized. The method improves the accuracy of polar region sea ice image processing and the intelligence of path planning, and is suitable for an autonomous navigation task in a polar region environment.

The invention discloses an ice water remote sensing classification method based on an LAU-Net model, and belongs to the technical field of remote sensing image intelligent processing. The method comprises the following steps: constructing an ice water classification sample set; based on a standard U-Net network architecture, constructing an improved model LAU-Net fused with a local attention module; training the LAU-Net model by using the ice water classification sample set, and dynamically adjusting a learning rate, a batch size, an optimizer weight attenuation factor and regularization strength through an automatic hyper-parameter optimization module; and adopting the trained LAU-Net model to carry out ice water classification on the input image. According to the method, the local attention mechanism and network structure optimization are introduced, so that the classification precision of the ice water boundary in the remote sensing image and the model operation efficiency are improved, and the technical requirements of dynamic sea ice monitoring are met.

The invention discloses a polar region daytime sea fog / low cloud detection method and device based on multi-source polar orbit satellites and a medium. The method comprises the steps that remote sensing image data collected by the multi-source polar orbit satellites are acquired and preprocessed; calculating a sea surface identification index by using data of two preset wave bands in the preprocessed remote sensing image data, and jointly removing areas related to seawater and sea ice / snow in the preprocessed remote sensing image data according to the sea surface identification index and near-infrared wave band reflectivity data in the remote sensing image data; for each pixel of a third preset wave band in the remote sensing image data from which the seawater, the sea ice and the snow are removed, calculating a gray-level co-occurrence matrix of the pixels, and counting a plurality of texture features based on the gray-level co-occurrence matrix; and inputting the statistical texture feature values corresponding to the remote sensing images corresponding to all polar orbit satellites into the trained random forest model, identifying and removing medium and high clouds in the random forest model, and obtaining sea fog / low clouds as the remainder. The influence of satellite difference and solar altitude angle change on the method is small.

The invention provides a ship navigation risk quantification method under polar sea ice three-dimensional-time-lag velocity obstacle, and the method comprises the steps: collecting the data of a local sensor of a ship, and forming a ship motion state and ice block motion set; a three-dimensional-time-delay velocity obstacle model TDIVOD is constructed to process the ship motion state and the ice block motion set, and two three-dimensional binary mask graphs are generated and comprise a velocity obstacle domain and a feasible velocity domain; mapping the speed obstacle domain and the feasible speed domain into continuous quantitative indexes sensitive to polar navigation risks; designing an objective function for avoiding risk or angle change minimization according to the speed obstacle domain, the feasible speed domain and continuous quantitative indexes, calculating the objective function, and selecting a speed vector corresponding to a minimum value as an optimal avoidance speed vector; and carrying out navigation risk avoidance on the ship based on the optimal avoidance speed vector. The method provided by the invention has outstanding practical value and technical frontier in the multi-ice, multi-ship and high-uncertainty environment of the polar region.

The invention provides an unmanned aerial vehicle (UAV) collaborative operation system and a method thereof, and relates to the technical field of UAV collaborative operation. The system comprises an unmanned aerial vehicle, an unmanned ship and a ground station, the unmanned aerial vehicle comprises paddles used for providing power for the unmanned aerial vehicle and fixed to unmanned aerial vehicle arms through screws; an unmanned aerial vehicle-mounted wireless communication module; the depth vision camera is used for collecting obstacle information in a scene; the unmanned surface vehicle comprises an unmanned surface vehicle wireless communication module; the unmanned ship propeller is used for providing power for the unmanned ship; the ground station comprises a ground station wireless communication module; and the display screen module is used for displaying the digital map transmitted by the unmanned aerial vehicle. Through cooperative control of the unmanned aerial vehicle, the unmanned surface vehicle and the ground station, in combination with deep learning and an intelligent algorithm, efficient cooperative operation of polar region sea ice monitoring and the unmanned platform is realized.

The invention relates to the technical field of remote sensing image processing, and provides a satellite remote sensing sea ice detection method and device. The method comprises the following steps: processing a multispectral remote sensing image of a target sea area to obtain image data; extracting multi-dimensional features including an edge feature, a first spectral feature and a second spectral feature from the image data; wherein the edge feature is obtained by performing adaptive gradient enhancement and edge probability smoothing processing on the image data, the first spectral feature comprises a sea ice index after correction of suspended sediment influence, and the second spectral feature comprises a water body index; and performing sea ice distribution judgment on each pixel in the image data according to a classification threshold value corresponding to each feature in the multi-dimensional features, and generating a sea ice distribution diagram. According to the satellite remote sensing sea ice detection method and device provided by the invention, the high-precision identification of the sea ice can be realized based on the multi-dimensional features including the edge features, the first spectral features and the second spectral features extracted from the image data.

The invention discloses a passive microwave and SAR data fused sea ice concentration inversion method and device, and relates to the field of remote sensing image processing, and the method comprises the steps: carrying out the geographic projection and resampling of preprocessed SAR data; performing calculation to obtain sea ice concentration inversion parameters, aligning the re-sampled AMSR2 data with the re-sampled SAR data, and cutting an image overlapping region to obtain an image pair; training a Pix2PixGAN network, and inputting a part of sea ice concentration inversion parameters to the Pix2PixGAN network to obtain an ice water classification result; and removing a seawater region in the remaining sea ice concentration inversion parameters according to the obtained ice water classification result, inputting the remaining sea ice concentration inversion parameters after the removal operation into the Pix2PixGAN network, and calculating an output result by adopting an ASI algorithm to obtain the sea ice concentration. According to the invention, high-precision sea ice concentration inversion can be realized.

The invention provides a device for ice load measurement and ice-induced vibration suppression and an ocean structure. The device for ice load measurement and ice-induced vibration suppression is characterized by comprising a fixed strut beam structure (2) which is arranged on the outer surface of an upright or nearly upright ocean structure and is axially parallel to the ocean structure, and a steel plate (1) covering the range of a tidal range zone is arranged on the outer side of the strut beam structure (2) in a sleeving mode. And a stress measuring device (3) is mounted on the inner side of the strut beam structure (2). The fixed strut beams and the steel plates are installed on the outer surface of the ocean structure, the contact form of sea ice and the structure is changed, the crushing mechanism of the sea ice is disturbed, the energy of the sea ice is absorbed, the vibration amplitude is reduced, meanwhile, real-time monitoring of the ice load is achieved, frequency locking vibration is eliminated, and the safety and stability of the structure are ensured.

The invention provides a high-resolution sea ice intelligent detection method and device, electronic equipment and a storage medium, and belongs to the technical field of image processing.In the method, sea ice detection cue words generated according to an initial sea ice detection result obtained according to an optimization threshold algorithm guide an SAM model to conduct sea ice detection on a preprocessed visible light remote sensing image, and the sea ice detection cue words generated according to the initial sea ice detection result are obtained. According to the above process, the number of labeled samples is greatly reduced, and sea ice detection of the pre-processed visible light remote sensing image can be realized by the SAM model under the condition of zero sample.

The invention discloses a double-floating-pipe truss floating body structure suitable for an offshore floating type photovoltaic platform, and relates to the technical field of offshore photovoltaic power generation, the double-floating-pipe truss floating body structure comprises two double-floating-pipe truss assemblies, each double-floating-pipe truss assembly comprises two bottom round steel pipes, a plurality of vertical face inclined web round steel pipes are fixed to the outer surfaces of the two bottom round steel pipes, and the vertical face inclined web round steel pipes are fixed to the outer surfaces of the two bottom round steel pipes. Every two adjacent facade inclined web round steel pipes form a group, and facade straight web round steel pipes are fixedly welded between the outer surfaces of the facade inclined web round steel pipes in each group; according to the double-floating-pipe truss floating body structure suitable for the offshore floating type photovoltaic platform, the steel consumption is reduced, and meanwhile, the peripheral floating pipes have enough thickness to cope with the impact of offshore floating ice on the whole floating type photovoltaic platform in winter; therefore, the effect of protecting the internal floating body structure is achieved.

This invention provides an unmanned aerial vehicle (UAV) for intelligent identification of polar ice rinks, comprising a main body, a solar panel, control wings, a propeller, and an observation cabin. The UAV is powered by solar energy, enabling a long endurance. The control wings are symmetrically mounted on both sides of the main body, enabling ascent, descent, and steering. The propeller provides propulsion, working in conjunction with the control wings to complete ascent, descent, and steering movements. The observation cabin incorporates multiple functional modules for ice rink identification, UAV and transport ship positioning, data transmission, and status monitoring. Illumination warning lights are installed on the leading edges of the wings to facilitate ice rink identification and landing during polar night conditions. A method for intelligent identification of polar ice rinks using this UAV is also provided. This invention enables detailed and accurate observation of sea ice morphology around shipping lanes, obtaining precise sea ice data to aid polar transport ships in navigation, assist captains in decision-making, and reduce the risk of distress.

The application discloses a kind of ice channel sea ice resistance calculation methods for layer ice first ice breaking, including collecting and obtaining the ice distribution video image in ice channel, to obtain the ice area feature map;And according to the ice area feature map, obtain the fitting probability density function for obtaining the ice size probability density distribution histogram, the ice shape feature distribution and the ice density of the ice distribution in the ice channel, and then the ice channel sea ice resistance calculation optimization model about the ice channel sea ice parameter can be constructed;Based on the ice channel sea ice resistance calculation optimization model, realize the ice breaking pilot guidance of ship in ice channel;Solve the problem that existing method cannot accurately obtain the ice channel sea ice parameter of layer ice first ice breaking, and then cannot effectively guide the ice breaking pilot of ship in ice channel.

This application discloses a multi-scale forecasting method and system for polar sea ice, relating to the field of climate forecasting technology. The method includes: acquiring raw sea ice concentration observation data; preprocessing the raw sea ice concentration observation data to obtain preprocessed data; extracting multi-scale spatial features from the preprocessed data to obtain feature tokens at multiple scales; aligning the feature tokens at multiple scales by feature dimensions to obtain aligned features; performing cross-scale attention fusion on the aligned features to obtain cross-scale fused features; performing intra-scale modeling optimization on the cross-scale fused features to obtain intra-scale optimized features; and performing linear mapping restoration and spatial decoding on the intra-scale optimized features to obtain sea ice concentration forecast results. The sea ice concentration forecast results include future daily, weekly, and monthly sea ice concentration forecasts. This application can solve the problems of single-scale modeling and information silos in existing technologies.

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.

This invention belongs to the field of marine photovoltaic anti-icing technology, and provides a detachable anti-icing structure and system for pile-based marine photovoltaic systems. It solves the technical problems existing in current anti-icing cone structures. The technical solution includes two semi-circular hoop assembly structures connected to the pile foundation by clamps. Each semi-circular hoop assembly structure includes an upper semi-circular hoop, a middle semi-circular hoop, and a lower semi-circular hoop. The upper, middle, and lower semi-circular hoops of each assembly structure are sequentially connected from top to bottom and fixed to the pile foundation. Each layer of semi-circular hoop is circumferentially spaced with several support plates. One end of each support plate is fixed to the surface of the semi-circular hoop, and the other end is fixed to an anti-icing vertical plate. All anti-icing vertical plates are fixedly connected by anti-icing horizontal plates. It utilizes the thickness and cross-section of the anti-icing vertical plates to contact the incoming ice, generating contact pressure through a small contact area that exceeds the compressive strength of the sea ice, crushing the sea ice at the contact point and causing brittle splitting.

A sea ice habitat restoration platform (10) and method (400) for extracting pack ice (216) from oncoming pack ice (208) has a marine platform (12) supporting a symmetrical cantilevering double inclined-plane ice ramp (28) applying a hogging moment, M, to the extracted pack ice exceeding the flexural strength, or, of the pack ice forming a slab of pack ice as the extracted pack ice longitudinally traverses a forward ice-ramp portion (42) at a ramp velocity about equal to a drift speed v of the pack ice (208) and laterally depositing on passing pack ice (210) the pack ice slab as rafted pack ice (226, 228, 230) setback from a free edge (220) of the passing pack ice (210) forming an augmented habitat for native ice-obligate and ice-associated species. The rafting process (400) over the course of a winter produces a meandering disturbance corridor (235) potentially hundreds of kilometers long.

The application provides an ice area navigation dynamic sea ice risk early warning method and system, the method first acquires the navigation information of a target ship, the first navigation radar image information of a first time point and the second navigation radar image information of a second time point, and calculates the time interval of the two time points, then respectively extracts the interested region of the sea ice echo through a mask matrix, fills the pixels of the interested region of the sea ice echo and the pixels of other regions except the interested region of the sea ice echo with different colors to obtain the binary image of the sea ice echo of the two time points, then calculates the distance, azimuth angle of each piece of sea ice and the target ship, and the latitude and longitude coordinates of the sea ice centroid of the two time points by using a specific calculation method, calculates the sea ice collision probability and the sea ice collision consequence, further calculates the sea ice collision risk index, and performs corresponding different level sea ice risk early warning on the sea ice according to the comparison result of the sea ice collision risk index and the preset risk threshold of different levels.