333 results about "Sea ice" patented technology

A method for retrieving tropospheric wet delay and atmospheric water vapor content over polar sea ice with TDS-1 satellite grazing angle spaceborne GNSS-R is provided, including: Si, obtaining TDS-1 GNSS-R raw intermediate frequency signal data, VMF3 grid data, GPT3 grid data and ERA5 data; S2, correcting an error of tropospheric wet delay estimation of grazing angle spaceborne GNSS-R; S3, constructing a grazing angle spaceborne GNSS-R tropospheric wet delay estimation model; S4, calculating grazing angle spaceborne GNSS-R ZWD; S5, calculating a Tm value of a target point based on GPT3 model, substituting the Tm value into a conversion factor II, and combining calculated GNSS-R ZWD to obtain a GNSS-R IWV estimated value; and S6, verifying inversion performance of GNSS-R ZWD and integrated water vapor (IWV) by using reference data.

The invention discloses an euphausia superba fishery change prediction method based on an ocean circulation-sea ice-species distribution coupling model, and relates to the technical field of ocean science. Euphausia superba fishery historical observation data, satellite remote sensing data and reanalysis data are collected, euphausia superba resources and environmental data are integrated, and the euphausia superba fishery change prediction method based on the ocean circulation-sea ice-species distribution coupling model is obtained. Constructing a polar region resource environment database covering total elements of physics-biology-environment; and based on an FVCOM model, constructing an ocean circulation-sea ice coupling model of a surrounding sea area of the peninsula of the south pole, and simulating and verifying physical environment characteristics of a target sea area. By constructing the ocean circulation-sea ice-species distribution coupling model, integrating multi-source data and analyzing a cooperative driving mechanism of a multi-scale physical process to euphausia superba resource change, compared with a traditional method, the change of an euphausia superba fishery can be simulated and predicted more accurately, and the method has the advantages of being high in practicability and the like. And the problem of inaccurate prediction caused by limited space-time coverage rate of observation data is effectively solved.

The invention provides a north pole atmosphere coupling forecasting method capable of automatically fusing sea ice concentration and thickness, which belongs to the technical field of meteorology, and comprises the following steps: firstly, acquiring GFS background field data and correcting by applying an ice-sea heat exchange correction equation; and then sea ice concentration and thickness information is extracted, and physical consistency verification is carried out based on a thermodynamic equilibrium equation. Performing multi-element fusion through an Arctic ice sea assimilation model, calculating the sea ice surface temperature by applying a polar region sea ice heat flux equation, and establishing a sea ice concentration thickness boundary layer interaction equation set to describe the modulation effect of sea ice on a boundary layer; the method comprises the core steps of performing deep fusion on various sea ice features by applying a feature fusion model, adaptively adjusting the weight based on an ice-gas interface balance index, generating an optimized mode initial field and boundary conditions, and finally setting a parameter scheme to execute an integral program to obtain a forecasting result. The technical problem of low forecasting accuracy caused by insufficient physical consistency of sea ice information is solved.

The invention discloses a sea ice target detection method based on multi-dimensional feature extraction, and the method specifically comprises the following steps: S1, data preprocessing: carrying out the denoising of an original sea ice image through a Gaussian filter, carrying out the multi-scale analysis of the sea ice image through an image pyramid, and obtaining sea ice images under different scales; s2, local feature extraction: extracting texture features of each local region by using a gray-level co-occurrence matrix; s3, performing enhanced texture feature processing on the extracted texture features through a directional filter and an enhancement algorithm to obtain final texture feature data; and S4, performing feature fusion on the obtained multi-dimensional features and the texture features to form a comprehensive feature image, and obtaining a classification result of the sea ice target. According to the sea ice target detection method, through multi-scale analysis, image representation is obtained under different scales by using an image pyramid transformation method, changes and expressions of texture features under different scales are captured, and the stability of sea ice target detection is improved.

The invention provides a sea ice area extraction method and device based on remote sensing, which are applied to the field of seawater detection, and the method comprises the steps: obtaining a remote sensing image of a target area; pixels of the remote sensing image are partitioned based on the normalized vegetation index, the pixels with the normalized vegetation index smaller than a preset vegetation index threshold value are divided into first type regions, and the first type regions comprise at least one of a water body region and a sea ice region; determining a normalized deicing index of the first type region; performing normalized deicing index threshold segmentation on the first type region through a maximum between-class variance method to obtain a sea ice classification threshold; and filtering and post-processing the remote sensing image based on the sea ice classification threshold, and taking the first type region of which the normalized deicing index is greater than the sea ice classification threshold as a sea ice region. The method is suitable for sea ice monitoring in a large area, and sea ice information can be efficiently and accurately extracted.

The invention provides a similarity identification method for independent floating ice in an arctic sea ice marginal area and a related device, and relates to the field of meteorology. The method comprises the following steps: the electronic equipment acquires a first image and a second image which are sequentially shot for a target area; identifying a first independent floating ice in the first image; identifying at least one second independent floating ice in the second image; and determining a target floating ice most similar to the first independent floating ice from the at least one second independent floating ice according to the Hauschoff distance between the first independent floating ice and each second independent floating ice. Therefore, compared with a traditional algorithm which is easily influenced by shape change and position drift, the method has the advantages that the characteristic that the farthest point distance between the two sets can be effectively measured by using the Hauschoff distance is utilized, so that the similarity between the floating ice can be evaluated more accurately.

The invention discloses a near-shore sea ice remote sensing extraction method based on FY-3 / MERSI satellite data, and the method comprises the following steps: S1, carrying out the preprocessing of an obtained MERSI image, and obtaining an MERSI image only containing sea ice and seawater; s2, establishing a coastal sea ice remote sensing recognition algorithm according to the difference of the spectral characteristics of sea ice and seawater in each channel of the MERSI image, and evaluating the precision and accuracy of the coastal sea ice remote sensing recognition algorithm; s3, according to a coastal sea ice remote sensing recognition algorithm, extracting coastal sea ice information from the MERSI image of each day; and carrying out 10-day averaging and monthly averaging on the daily sea ice data extracted in each month to generate a sea ice ten-day product and a monthly product. According to the invention, automatic identification of high-spatial-resolution sea ice information of a high-latitude offshore area in winter can be realized.

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 discloses an arctic channel route planning system and method based on a visual sea ice risk, and the system comprises a future sea ice data obtaining module which is used for obtaining the sea ice data of an arctic region in the future N days; the sea ice risk grade determination module is used for calculating to obtain a sea ice risk grade; the sea ice risk display module is used for displaying sea ice risk levels corresponding to different areas of the north pole; the route preliminary planning and drawing module is used for drawing an initial global route; and the route prediction display module is used for obtaining the risk condition of the initial global route in the cross-day period. Aiming at the complex ice condition environment of the ice area, the ship route design is carried out by considering the influence of sea ice on ship navigation, so that the whole ship route design process is visually displayed, the ship route can be flexibly adjusted, a more efficient, more economical and safer ship route is provided for ship navigation in the north pole, and the ship route design efficiency is improved. And the sailing safety and efficiency of the ship in the Arctic ice region are improved.

The invention discloses a sea ice type identification method and device based on a synthetic aperture radar image, and belongs to the technical field of satellite remote sensing marine monitoring. The method comprises the steps of 1, preprocessing a dual-polarized SAR image, and extracting input feature data; step 2, constructing a finely labeled sea ice type data set covering a generic north pole scene by combining visual interpretation and external auxiliary data; step 3, inputting the constructed finely labeled sea ice type data set into a sea ice type identification model improved based on a DeepLabv3 + architecture, and performing iterative optimization through a multi-graphics card parallel training mode to generate a high-precision sea ice type identification model; and 4, performing sea ice type identification on the extracted input feature data by using a high-precision sea ice type identification model. According to the method, the incidence angle dependency related to the sea ice type in the original SAR data is more accurately reserved, and the precision of the model is improved.

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

The invention 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 discloses a polar region sea ice semantic segmentation method based on a vision-language model, a sea ice segmentation region is specified through a language, and the method comprises the following steps: obtaining a visible light polar region sea ice data set; encoding the visible light polar region sea ice data set by using a vision-language model to obtain a text embedding vector and an image embedding vector; fusing the text embedding vector and the image embedding vector to obtain image semantic information and text semantic information; and based on the text semantic information, decoding the image semantic information to obtain a polar region sea ice segmentation mask. According to the method, the target area can be selected and segmented more flexibly, and different types of ice can be segmented more accurately and efficiently. Besides, through calculation of a mutual attention mechanism of the text and the image, the text and the image can be mutually fused, and the text description can be accurately utilized to select and segment the corresponding image area.

The invention provides an unmanned aerial vehicle for intelligent identification of a polar ice rink. The unmanned aerial vehicle comprises an aerial vehicle main body, a solar battery pack, rudder wings, a spiral propeller and an observation cabin, the aircraft is powered by solar energy, so that long endurance time can be achieved; the rudder wings are symmetrically installed on the two sides of the aircraft body to achieve lifting and steering of the aircraft body. The spiral propeller provides propulsive force and cooperates with the rudder wings to complete lifting steering motion. A plurality of functional modules are arranged in the observation cabin, and the functions of ice rink recognition, aircraft and transport ship positioning, data transmission, state monitoring and the like can be achieved. Illumination warning lamps are arranged on the front edges of the two wings of the aircraft, so that ice rink identification and aircraft landing under the extremely night condition are facilitated. The invention also provides a polar ice rink intelligent identification method of the unmanned aerial vehicle. The sea ice morphology around the navigation channel can be observed in detail and accurately, accurate sea ice data can be obtained, the polar transport ship is helped to sail, a captain is assisted to make a decision, and the risk of distress is reduced.

The invention provides a method for calculating fatigue by adopting near-field dynamics under a complex ice condition based on propeller icebreaking, which is used for processing excitation data of an ice-induced load on a propeller of an icebreaker, and comprises the following steps: firstly, constructing a sea ice environment, an elastic propeller constructed by a plurality of units is simulated by adopting a finite element method, and the continuous interaction between the elastic propeller and ice blocks in the rotating process is considered. The contact algorithm adopts an element surface finite element (FEM) method to process force transmission from a force bearing point to a force bearing surface so as to influence the force bearing state of each node of the propeller, the force is transmitted to element stress through the node effect, and the ice load borne by the propeller structure is obtained and verified.

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 construction method of an earth system coupling device and the earth system coupling device, and the construction method comprises the steps: a first component module construction step: constructing a plurality of first component modules of a global climate system based on full coupling, the plurality of first component modules comprise an atmospheric component module, a land component module, a sea ice component module, a ground ice component module, a river component module, a first ocean component module and a sea wave component module; a second component module construction step: constructing a second component module based on the ocean circulation simulation system, wherein the second component module is a second ocean component module; a replacement step: replacing the first ocean component module with the second ocean component module; and a coupling step: reconstructing a coupling application interface of the coupler of the global climate system based on full coupling, and carrying out data exchange and interaction among different component modules through the coupling application interface.

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.

The invention discloses a polar region ship navigation planning method based on sea ice prediction and multi-stage path optimization, and the method comprises the steps: obtaining a navigation risk map based on a to-be-navigated ship according to the updated single-day sea ice data in a time group; the method comprises the steps of obtaining a candidate path set based on a K shortest path algorithm, determining final U candidate paths according to path similarity of a result set of the candidate paths, further obtaining comprehensive scores of the paths in the result set, and finally determining a final driving path of a to-be-sailed ship according to the comprehensive score of each final candidate path. And completing the path planning of the polar region ship navigation. According to the method, the time, the risk and the path smoothness under the distance suboptimal constraint are taken as optimization targets, and similarity calculation is performed on the paths in the result set of the obtained candidate paths, so that diversified risk avoiding scheme paths can be provided. According to the method, the prediction time range is sliced, so that the high-frequency environment updating requirement in the prior art is met.

The invention discloses a sea ice risk prediction method and device, computer equipment, a storage medium and a product. The method comprises the following steps: acquiring historical environment data and historical sea ice monitoring data; constructing a linear regression relation model between the target environment factor and the sea ice data and a time sequence analysis model of the sea ice data according to the historical environment data and the historical sea ice monitoring data; fusing the linear regression relation model and the time sequence analysis model to obtain a hybrid model; and acquiring real-time environment data, and generating sea ice prediction data by using the hybrid model. The sea ice generation and change rule can be better described by combining the explanation capability of linear regression on influence factors and the dynamic grasp of a time sequence analysis model on historical data, the advantages of different models can be effectively integrated by introducing a hybrid model, the prediction precision of sea ice risks and the capture capability of trends are improved, and the prediction efficiency is improved. Therefore, more accurate sea ice risk prediction under a complex environment condition is realized.

The invention discloses a north pole sea ice concentration prediction method based on Swin Transform, and belongs to the technical field of remote sensing data analysis. According to the method, the land mask image is generated to shield the land area, the spatial-temporal feature processing is fused, and the Swin Transform network structure is utilized to reduce the model calculation amount and enhance the perception ability of the model to the spatial-temporal features, so that the model has relatively high robustness. According to the invention, through analyzing the temporal and spatial change rule of sea ice coverage, accurate prediction and monitoring of the sea ice concentration in a period of time in the future are realized, and a reliable decision basis is provided for the fields of shipping safety, resource development, ecological environment protection, marine resource management and the like of the arctic sea area.

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 relates to the technical field of sea ice stress detection, and discloses a polar region sea ice stress buoy which comprises a floating unit, a positioning detection unit and a floating ice cleaning unit. Through the arrangement of the positioning detection unit, when positioning operation needs to be carried out, an operator starts a driving motor to drive a rotating disc to rotate through the driving motor, the position of an extrusion groove in the rotating disc changes, the extrusion groove pushes a synchronous rod to integrally move, and the synchronous rod can drive a top plate at the tail end to move towards the side wall of an ice hole; when the synchronous rod slides, the position of the descending groove in the synchronous rod changes, the descending groove drives the limiting block to slide downwards, and then the vibrating wire type strain gauge shell can be driven to move downwards so as to move to a stress detection area. In this way, the operation of positioning and moving the vibrating wire strain gauge to the detection position can be completed synchronously, so that the operation is simpler.

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 embodiment of the invention relates to an offshore photovoltaic sea ice identification method and system, a program product and equipment, and relates to the technical field of offshore photovoltaic, and the method comprises the steps: obtaining an unmanned plane multispectral image, a satellite remote sensing image, photovoltaic panel power generation data and underwater sonar data of an offshore photovoltaic site; global image features and multi-scale features are extracted from the unmanned aerial vehicle multispectral image and fused, first image features are determined, second image features are extracted from the satellite remote sensing image, time sequence features are extracted from photovoltaic panel power generation data, and ice anchor features are extracted from underwater sonar data; fusing the first image feature, the second image feature, the time sequence feature and the ice anchor feature through a plurality of image convolutional layers in an image neural network to obtain a fused feature; and the sea ice probability of each pixel point in the unmanned aerial vehicle multispectral image and the satellite remote sensing image is determined according to the fusion features and the external environment parameters, and a sea ice recognition result is determined. The sea ice can be accurately identified.

The invention belongs to the technical field of prediction of the thickness of sea ice in the arctic region, and particularly relates to an intelligent prediction method for the thickness of sea ice in a polar region based on multi-layer stacking space-time Transform. The method comprises the following steps: step 1, based on polar region sea ice thickness reanalysis data information of a target sea area, extracting a feature matrix in ocean SIT reanalysis data and constructing a space-time sequence forecast supervision data set; 2, preprocessing data of a background field and a target field; 3, an HSS-Transform model is constructed, and the HSS-Transform model is constructed; 4, inputting the sea ice thickness background field space-time sequence into an HSS-Transform model to obtain a sea ice thickness prediction space-time sequence in the next 15 days; step 5, carrying out reverse normalization operation on the obtained prediction space-time sequence, and restoring the land point; according to the method, the spatial-temporal characteristics of the sea ice thickness data can be extracted and processed at the same time on multiple time scales and spatial scales, the extension period intelligent prediction of the polar region sea ice thickness spatial-temporal sequence is realized, and the prediction level of the sea ice thickness is 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.