28 results about "The arctic" patented technology

To provide a device and method that can provide an environment in which participants in a "space" for thinking and discussing can consider things from a wide variety of perspectives. [Solution] According to this embodiment, the map classifies fields related to society in the first direction, classifies the level of each field in the second direction (social domain in the Arctic direction), and classifies words related to individuals in the third direction (personal domain in the Antarctic direction). Multiple nodes with semantic content are placed in the social domain and the personal domain according to the words (attribute information) at their placement location.

The application discloses a collaborative scheduling method based on an arctic sea parrot optimization algorithm and peak-valley time-of-use electricity price, belongs to the technical field of micro-grid intelligent scheduling and renewable energy management, and comprises the following steps: step S1, a micro-grid system model is established; step S2, operation constraint conditions are constructed, including the power balance and the upper and lower limit constraints of the power of each component of the micro-grid and the state constraints of the storage battery which need to be satisfied by the wind turbine, the photovoltaic, the gas turbine, the fuel cell, the storage battery and the power exchange with the power grid in the micro-grid; step S3, an objective optimization function of operation cost and pollutant emission is constructed; and step S4, the optimal scheduling scheme is output by applying the objective optimization function. The collaborative scheduling method based on the arctic sea parrot optimization algorithm and the peak-valley time-of-use electricity price can significantly improve the optimization precision and convergence efficiency of the system, meanwhile, the high reliability and robustness of the scheduling process are ensured, and finally, the intelligent collaborative scheduling of the micro-grid energy is realized.

This invention provides a green and low-carbon intelligent planning method and system for Arctic shipping routes. The method includes: constructing a gridded environmental model of the Arctic sea area; establishing a mapping relationship between ship navigation resistance and propulsion power based on a semi-empirical ice resistance formula to construct an ice resistance-power-carbon emission coupled model, and determining the carbon emissions per unit distance; constructing a multi-objective reward function, including a carbon emission penalty term based on the estimated carbon emissions and a time penalty term based on navigation time; and performing path training and strategy optimization based on the gridded environmental model and the multi-objective reward function to output the optimal route that satisfies the multi-objective constraints. This invention, by constructing an ice resistance-power-carbon emission coupled model and introducing an equivalent turning time mechanism, achieves synergistic optimization of navigation efficiency and carbon emission control in complex ice-covered environments, improving the engineering feasibility and green and low-carbon level of planned routes, and is suitable for intelligent decision-making in Arctic mixed ice and water navigation scenarios.

The application provides an Arctic sea wave prediction method based on automatic fusion of sea ice density, and belongs to the technical field of weather prediction. The wind field data automatic acquisition system is established to ensure the continuity of the wind field driving data. The sea ice wave interaction optimization model is applied to dynamically adjust the IC4M2 sea ice wave parameterization scheme. The open boundary line is set in the high latitude sea area of the North Atlantic, and the radiation boundary condition algorithm is combined with the sponge layer absorption technology to process the boundary wave propagation. The WAVEWATCH III sea wave numerical model is started, the fourth-order precision finite difference discrete format is combined with the adaptive artificial viscosity control algorithm to calculate the Arctic sea wave field, and the sea wave prediction product business processing system is established, so that the technical problem of low accuracy of the Arctic sea wave prediction caused by the insufficient parameterization precision of the sea ice wave interaction is solved.

The invention provides a synthetic aperture radar-based one-dimensional sea wave spectrum inversion method for an arctic sea area. The method comprises the following steps: acquiring a Sentinel-1 dual-polarized SAR image, sea ice concentration data and a WAVEWATCH-III simulated one-dimensional sea wave spectrum of the arctic sea area; estimating sea ice concentrations from the SAR image by using a K-means clustering algorithm, grouping the sea ice concentrations, and adopting different hydrodynamic modulation transfer function parameters for different groups of sea ice concentrations and polarization modes; an XGBoost machine learning model is adopted, and an SAR one-dimensional sea wave spectrum inversion algorithm in the north pole sea area is established; and the accuracy of the SAR one-dimensional sea wave spectrum inversion algorithm in the arctic sea area is verified. According to the method, the problems that traditional one-dimensional sea wave spectrum inversion depends on an initial guess spectrum, and sea ice influences modeling are complex are solved, and high-precision one-dimensional sea wave spectrum inversion of the arctic sea ice coverage area is achieved.

The application discloses an Arctic shipping route navigation safety evaluation method and system based on multi-source data fusion and ship adaptability, and relates to the cross technical field of marine navigation safety guarantee, meteorological and hydrological data analysis and intelligent navigation. The method comprises the following steps: collecting multi-source data of the Arctic shipping route, dynamically fusing weights in combination with real-time reliability calculation of the data source after preprocessing and space-time alignment, completing confidence dynamic weighted fusion to obtain comprehensive environmental data; constructing a coupled physical stress model based on the data and inherent parameters of the target ship, introducing ice type and sea current factors to quantify real-time navigation risk, and generating a voyage full-cycle dynamic risk corridor; planning a route based on the risk corridor by using a hybrid heuristic algorithm, and combining a multi-index judgment mechanism with a self-defined weight to realize navigation safety grade judgment and global optimal route output. The application improves evaluation accuracy and ship adaptability, prolongs the dynamic prediction period, realizes global optimal route planning, and has strong operability in decision support.

The invention discloses an improved ant colony algorithm-based arctic channel path planning method, which belongs to the technical field of navigation, and comprises the following steps of: (1) calculating a navigable area of an icebreaker in an arctic region by combining ice condition data, meteorological data and the level of the icebreaker; (2) initializing pheromone concentration for effective nodes in the navigable area; (3) finding out a next-step transferable node set naavi of the kth ant starting from the current node i at the moment t, and calculating the probability of the ant starting from the node i to the node j in the naavi according to the pheromone concentration; completing one iteration; and (4) updating the pheromone concentration on the path from the node i to the node j, and entering the next iteration until a termination condition is met. According to the north pole channel path planning method based on the improved ant colony algorithm, the initial pheromone concentration is optimized while the characteristics of the ant colony algorithm are reserved, the early blind search is remarkably reduced, the calculation amount is reduced, and the search efficiency is improved.

The present disclosure relates to methods, apparatuses, and systems for unmanned underwater vehicles capable of operation in the Arctic region. An example unmanned underwater vehicle includes: a hull; one or more guide rails extending from the hull and attached to the hull by one or more legs; a vertically-deployed mast, wherein the vertically-deployed mast is configured to penetrate through an ice sheet; and a communications antenna, wherein the communications antenna is deployed with the vertically-deployed mast and enables communication above the ice sheet. The dorsally-located guide rails of an example define an extended position and a retracted position, wherein the guide rails disposed in the extended position are disposed further from the hull than the guide rails disposed in the retracted position. Embodiments described herein include systems employing air-deployable buoys, water-deployed buoys and small UUVs, and ice-penetrating buoys to support communications in the Arctic region.

To provide a device and method that can provide an environment in which participants in a "space" for thinking and discussing can consider things from a wide variety of perspectives. [Solution] According to this embodiment, the map classifies fields related to society in the first direction, classifies the level of each field in the second direction (social domain in the Arctic direction), and classifies words related to individuals in the third direction (personal domain in the Antarctic direction). Multiple nodes with semantic content are placed in the social domain and the personal domain according to the words (attribute information) at their placement location.

The invention discloses a two-parameter approximate representation and rapid inversion method of a north pole double-track sound velocity profile, and relates to marine environment parameter inversion. Comprising a two-parameter approximate representation method of a dual-track sound velocity profile, a surface refraction type normal wave cross-type dispersion curve extraction method, a horizontal invariant dual-track sound velocity profile rapid inversion method and a horizontal variation dual-track sound velocity profile rapid inversion method. According to the overall change characteristics of the north pole dual-track sound velocity profile, linear approximation is carried out on the sound velocity profile, and the dual-track sound velocity profile is represented through two parameters influenced by width and strength. Because a sound signal frequency dispersion structure crossing phenomenon is caused by the north deep sea surface double-channel sound velocity profile, and the stronger the double-channel intensity is, the more serious the crossing phenomenon is, the north deep sea double-channel sound velocity profile can be inverted by comparing simulated and actually measured sound field cross-type frequency dispersion curves and combining a double-parameter representation method of the double-channel sound velocity profile. The method can be realized by using a single hydrophone.

This invention provides a tolerance-based distance estimation method and system for dual-channel acoustic imaging in Arctic ice regions based on normal mode-constrained modes. The method includes: using a normal mode model to simulate and predict the frequency domain sound pressure, phase velocity, group velocity, and mode covariance matrix of the experimental sea area in the Arctic ice region; using Fast Fourier Transform (FFT) to transform the time-domain signal received by the vertical array in the ice region into frequency-domain data; obtaining the sound velocity and depth corresponding to the upper boundary, channel axis, and lower boundary of the Beaufort waveguide based on the dual-channel sound velocity profile of the experimental sea area; obtaining the order index of the Beaufort waveguide modes based on the intersection of the phase velocity dispersion curve with the sound velocity at the upper boundary and channel axis of the Beaufort waveguide; performing mode separation using a matched filtering mode filtering method to extract the modes confined in the Beaufort waveguide; and constructing a tolerance-based distance estimation operator for ice and seabed based on the extracted modes in the Beaufort waveguide to achieve tolerance-based estimation of underwater target distances.

The application provides a sea ice extraction method based on multi-feature superpixel segmentation, comprising the following steps: step 1, obtaining a dual-polarization SAR image of a target sea area and performing image preprocessing to obtain a preprocessed image; step 2, extracting a backscattering coefficient feature, a normalized feature, a morphological feature set, an OTSU feature and a texture feature from the preprocessed image and fusing them to obtain a multi-dimensional data feature set; step 3, using an SNIC superpixel segmentation method to segment the multi-dimensional data feature set to obtain a superpixel block set; and step 4, inputting the superpixel block set into a supervised training machine learning classifier to classify sea water and sea ice to extract sea ice. The application can effectively improve the sea ice classification precision and robustness, can generate a sea ice distribution feature set with a spatial resolution of 40 meters, and can effectively support the monitoring of the interannual and intermonthly changes of the Arctic sea route sea ice.

To provide a device and method that can provide an environment in which participants in a "space" for thinking and discussing can consider things from a wide variety of perspectives. [Solution] According to this embodiment, the map classifies fields related to society in the first direction, classifies the level of each field in the second direction (social domain in the Arctic direction), and classifies words related to individuals in the third direction (personal domain in the Antarctic direction). Multiple nodes with semantic content are placed in the social domain and the personal domain according to the words (attribute information) at their placement location.

The present invention provides an Arctic waterway fleet scheduling optimization method based on BeiDou III, comprising: designing an Arctic waterway multi-base station fleet information service framework based on BeiDou III, reshaping the information interaction process between ships and between ships and shores under BeiDou service; constructing an Arctic waterway fleet scheduling optimization model based on BeiDou III based on the designed Arctic waterway multi-base station fleet information service framework; and improving the differential evolution algorithm to solve the non-deterministic polynomial problem in the fleet scheduling process. The present invention designs a BeiDou-based multi-base station fleet information service framework to provide support for the dynamic scheduling of Arctic waterway fleets, solving the problem of difficulty in real-time information interaction on Arctic routes. With the support of the BeiDou multi-base station fleet information service framework, the information obtained by the framework is used to solve the dynamic scheduling optimization model of the Arctic waterway fleet. The model enables the fleet to quickly modify the freight scheduling plan after the navigation environment information is distorted, thereby reducing economic losses.

The application discloses an unmanned underwater ice-breaking vehicle, which comprises a vehicle body, a pop-up device, front and rear battery cabin devices, a folding wing device, a water tank device, an electronic cabin device, a motor device and a tail device; the water tank device, the pop-up device and the front and rear battery cabin devices are arranged at the middle part of the vehicle body, the pop-up device is arranged above the water tank device, the front and rear battery cabin devices are arranged in front of and behind the water tank device; the folding wing device is arranged at the two side parts of the vehicle body, the electronic cabin device is arranged at the front end of the vehicle body, and the tail device is arranged at the tail end of the vehicle body; and the motor device is arranged at the rear part of the vehicle body and in front of the tail device. The application has the advantages of low cost, small noise, strong concealment, autonomous work, high safety, complete functions, simple maintenance and the like. The application can effectively break ice and provide support for civil and military tasks in the Arctic Ocean and other ice-covered sea areas, and has high practical value.

This invention discloses a method and system for predicting Arctic ship navigation risks based on a GA-BP neural network model, belonging to the field of risk prediction technology. Step 1: Identify key risk factors affecting Arctic ship navigation, determine the input and output of the GA-BP neural network model, and construct a dataset; Step 2: Normalize the dataset obtained in Step 1; Step 3: Establish a BP neural network model and determine its structure; Step 4: Optimize the weights and thresholds of the BP neural network using a genetic algorithm (GA) to obtain a GA-BP prediction model; Step 5: Train and test the GA-BP prediction model obtained in Step 4 on the normalized dataset from Step 2 to predict the risks of ice entrapment and ship-ice collision. This invention addresses the problem of cumbersome and inefficient steps in existing technologies for assessing and analyzing navigation risks in the Arctic.

The application discloses a sea ice classification method based on multispectral and SAR fusion remote sensing data and belongs to the technical field of satellite remote sensing image processing.The sea ice classification method based on multispectral and SAR fusion remote sensing data provided by the application solves the technical problems that most of the existing sea ice classification research based on remote sensing adopts a single data source, the classification precision is limited, how to comprehensively use the rich optical characteristics of multispectrum and the advantages of SAR in detecting ground features and being less affected by weather to improve the sea ice classification precision, and the like, and adopts Sentinel-1 SAR and Sentinel-2 multispectral data to realize the target of quickly and accurately obtaining large-range sea ice classification results based on multispectral and SAR fusion remote sensing data. The method has strong applicability, can be used for sea ice monitoring, and is also helpful for climate change research, polar resource development, polar scientific exploration and navigation of the Arctic Channel.

The invention relates to a north pole inter-ice water channel extraction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining FY-3D satellite MERS-II sensor data; performing brightness temperature conversion on the thermal infrared band data to obtain brightness temperature data; removing the bow tie effect of the brightness temperature data to obtain processed data; cutting the processed data to obtain model input data, inputting the model input data into a deep learning pre-training model, and extracting an inter-ice water channel of a north pole region; the land area in the inter-ice water channel is filtered out through mask data in the sensor data, and an actual inter-ice water channel is obtained; and enabling the actual inter-ice water channel to correspond to the pixel longitude and latitude information data in the sensor data to obtain an inter-ice water channel distribution diagram of the north pole region. Therefore, the technical problem that the precision of the extracted inter-ice water channel is poor due to the fact that an inter-ice water channel extraction method based on a thermal infrared image easily generates identification errors due to factors such as cloud interference, background complexity and water channel scale diversity in the related technology is solved.

The invention relates to the technical field of sea ice monitoring and forecasting, and particularly discloses a north pole inter-ice lake range extraction method, which comprises the following steps: acquiring original sea ice concentration data, and supplementing missing data to obtain sea ice concentration data; performing threshold segmentation and connected domain analysis on the sea ice concentration data to obtain sea ice concentration partitions; determining initial growth nuclei of the inter-ice lake growth region and the edge ice-water mixed growth region; taking pixels meeting a preset rule in the neighborhoods of the initial growth nucleuses of the inter-ice lake growth region and the edge ice-water mixed growth region as corresponding growth points, taking the growth points as new growth nucleuses for iteration, and stopping iteration until the range is stable to obtain a middle inter-ice lake region and a middle ice-water mixed region; and performing connectivity analysis on the central discrete low-density ice area, and outputting an inter-ice lake range, an edge ice range and an open water area range of the whole north pole. According to the method, unlimited automatic extraction of the real shape and range of the inter-ice lake is realized.

The application relates to the technical field of satellite communication and discloses a federal learning cooperative prediction method for an arctic satellite communication channel, which comprises the following steps: determining an input feature vector of local monitoring data to establish a mapping relationship between the local monitoring data and a preset ITU-R physical model; calculating a baseline output of the ITU-R physical model, constructing an arctic channel residual correction term for an arctic propagation gap, and establishing a joint prediction framework of a multi-dimensional channel state parameter superimposed by a physical residual; outputting a local residual prediction result of a current round of a ship node; performing quantization compression processing on a model parameter increment to generate a federal parameter package to be uploaded; receiving the federal parameter packages uploaded by all participating ship nodes, performing global parameter iterative updating processing on the joint prediction framework until a preset convergence condition is reached, and outputting a converged arctic channel multi-step prediction result. The application can improve the accuracy of cooperative prediction for the arctic satellite communication channel.

The invention provides a normal wave limiting mode-based tolerance distance estimation method and system under two sound tracks of an arctic ice region, and the method comprises the steps: carrying out the simulation prediction of the frequency domain sound pressure, phase velocity, group velocity and modal covariance matrix of a test sea area of the arctic ice region through a normal wave model; the method comprises the following steps: converting a time domain signal received by a vertical array of an ice sea area into frequency domain data by adopting fast Fourier transform; obtaining sound velocities and depths corresponding to the upper boundary, the sound channel axis and the lower boundary of the Port waveguide according to the double-sound-channel sound velocity profile of the test sea area; according to the phase velocity dispersion curve and the intersection point of the upper boundary of the Port waveguide and the axial sound velocity of the sound channel, obtaining an order index of a Port waveguide mode; carrying out modal separation by utilizing a matched filtering modal filtering method, and extracting a modal limited in the muffle waveguide; and constructing a distance estimation operator of the tolerance of the ice layer and the seabed based on the extracted modality in the muffle waveguide, and realizing tolerance estimation of the underwater target distance.

The present invention relates to the field of marine engineering technology, and in particular to a high-resolution sea ice detection method based on a rotating fan-beam scatterometer. The sea ice monitoring method can distinguish between ice and water within a 10km grid in the Arctic and Antarctic regions. By utilizing scatterometer level 1 data and a polar stereographic projection conversion method, the spatial resolution of existing scatterometer sea ice monitoring methods is improved. In addition, a sliding time window is used to collect training data and dynamically train and update the model, which to a certain extent solves the problem that the ice-water characteristics of the radar backscatter coefficient vary greatly with the seasons.

The present disclosure relates to methods, apparatuses, and systems for unmanned underwater vehicles capable of operation in the Arctic region. An example unmanned underwater vehicle includes: a hull; one or more guide rails extending from the hull and attached to the hull by one or more legs; a vertically-deployed mast, wherein the vertically-deployed mast is configured to penetrate through an ice sheet; and a communications antenna, wherein the communications antenna is deployed with the vertically-deployed mast and enables communication above the ice sheet. The dorsally-located guide rails of an example define an extended position and a retracted position, wherein the guide rails disposed in the extended position are disposed further from the hull than the guide rails disposed in the retracted position. Embodiments described herein include systems employing air-deployable buoys, water-deployed buoys and small UUVs, and ice-penetrating buoys to support communications in the Arctic region.

The application discloses an ice series stay profile node self-adaptive task scheduling method and system, and belongs to the technical field of underwater observation, which comprises the following steps: collecting the state information of the current ice series stay profile node; acquiring the observation coverage information of the cloud platform or the edge node to construct the observation redundancy distribution; calculating the observation priority of the node in different depth intervals and time periods based on the node state vector, the observation redundancy distribution and the business demand; optimizing and solving to obtain the observation task plan with the target of maximizing the total observation value or the unit energy observation value within the remaining life of the node; executing the observation task plan, and returning the profile observation data and the updated state information to the cloud platform or the edge node; performing dynamic feedback update to realize continuous optimization. The application can maximize the observation value under the condition of limited energy, and supports multi-node cooperation and cloud closed-loop control, and is especially suitable for long-term, unattended and self-adaptive underwater environment observation in ice-covered sea areas such as the Arctic and the Antarctic.

This paper discloses a method for predicting Arctic sea ice based on a multi-scale graph neural network and conservation laws. The method selects surface, ocean, and atmospheric variable data, grids the Arctic region at different spatial resolutions, and constructs a multi-scale graph. The method updates the features of each node in the graph and integrates the spatial information of the different-scale graphs to obtain a sea ice density prediction model. The method adds humidity conservation terms and potential vorticity conservation terms to the loss function of the sea ice density prediction model, constructing a composite loss function to ensure that the model adheres to the physical conservation laws of humidity and potential vorticity during the prediction process. The sea ice density prediction model is trained using historical data and the composite loss function, and the future sea ice state is predicted using the daily updated data and the trained model. This method significantly improves the accuracy and efficiency of sea ice density prediction, providing a scientific basis for climate change research, resource development, and shipping route planning.

The invention discloses an emergency situation evolution simulation method based on a multi-source data-driven dynamic Bayesian network, and belongs to the technical field of maritime emergency emergencies. According to the invention, a general accident emergency scene evolution framework is constructed based on a public safety triangle theory; identifying ship accident emergency scene elements and establishing an emergency scene evolution path; constructing a DBN model based on the path; the method comprises the following steps: determining DBN parameters by combining statistical analysis, a Markov transfer model and a Leaky Noisy-OR model; and simulating the natural environment change by using an evidence reasoning method, and evaluating the influence of the ship-end emergency, the shore-based emergency and the ship-shore cooperative emergency on the scene state evolution. According to the method, the dynamic evolution rule of different types of accident emergency situations on the north pole sea is disclosed, a scientific basis is provided for emergency management and decision making, and the emergency response capability of emergencies on the north pole sea is improved.

The application discloses a method and system for predicting arctic sea ice concentration and ice edge, and relates to the field of intelligent marine prediction. When the prediction time span of the sea ice concentration is less than or equal to half a year, a first sea ice concentration prediction model is used to obtain the monthly sea ice concentration of each spatial grid within the prediction time span; or a second sea ice concentration prediction model is used to obtain the monthly sea ice concentration of each spatial grid within the prediction time span; when the prediction time span of the sea ice concentration is greater than half a year, a third sea ice concentration prediction model is used to obtain the monthly sea ice concentration of each spatial grid within the prediction time span, so as to determine the monthly sea ice range and ice edge position of the Arctic within the prediction time span. The first sea ice concentration prediction model comprises an ice edge perception module, the second sea ice concentration prediction model comprises an edge perception modulation module, and the third sea ice concentration prediction model comprises an adaptive physics information gate expert hybrid mechanism. The application can improve the prediction accuracy of the sea ice concentration and the ice edge.

The invention relates to the technical field of north region ship navigation environment risk prediction application, and provides a north region ship navigation environment risk space-time prediction method and system based on deep learning, and the method comprises the steps: obtaining time sequence sea ice concentration data and sea ice thickness data in a recent T-day region, and processing the data into a T * X * W form; generating navigation risk index result distribution data based on the sea ice concentration data and the sea ice thickness data; performing parameter iteration on the initial space-time prediction model to obtain a space-time prediction model; inputting the navigation risk index result distribution data into the space-time prediction model, and outputting a grid map for risk prediction; according to the method, the deep neural network of the ViTs-CNN-CNN architecture is provided and is applied to the space-time prediction task, the calculation complexity of the Swin Transformer is remarkably reduced through a hierarchical structure and a local window self-attention mechanism, the efficiency is improved, and an excellent effect is achieved.