59 results about "Glacier" patented technology

The invention belongs to the technical field of glacier identification, and provides a system for monitoring glacier dynamics by an unmanned aerial vehicle low-altitude flight photogrammetry method, which comprises the following steps that: a glacier area unmanned aerial vehicle low-altitude photogrammetry module selects an unmanned aerial vehicle as required, and plans a route and parameters; the image preprocessing module is used for extracting key feature points from each image by using Pi * 4Dcap; fine three-dimensional information, an ortho-image and a glacier digital surface model DSM are obtained through point cloud generation and densification, and glacier dynamic information can be identified by utilizing the glacier digital surface model DSM after correction and interpolation processing are carried out on the three-dimensional information, the ortho-image and the glacier digital surface model DSM; calculating the elevation, volume, speed and mass change of the glacier based on a digital surface model (DSM) of the glacier; the glacier fissure is identified by utilizing the orthoimage and the glacier digital surface model DSM, and the system can comprehensively and accurately monitor the dynamic information of the glacier.

The invention discloses a glacier area surface water resource distribution image extraction method based on deep learning, and belongs to the field of surface water resource image extraction, and the method comprises the steps: obtaining a multispectral remote sensing image covering a glacier area and digital elevation model data, and carrying out the data preprocessing; building a YOLOv12 instance segmentation model comprising a backbone network, a neck network and a detection head, and constructing an improved YOLOv12 segmentation model; carrying out model training and optimization; segmenting and extracting surface water resources through the optimized segmentation model; processing is performed after segmentation is completed, and finally an optimized multi-class surface water resource distribution diagram is generated. According to the method, based on an improved YOLOv12 network architecture, the glacier area surface water resources in the remote sensing image are comprehensively and automatically extracted by fusing vision, a convolution block attention module and a space-to-depth convolution technology, the problems of high reflection of ice and snow, ice cracks, complex stone glacier textures and fuzzy edges are effectively solved, and the segmentation precision is improved.

The invention relates to the technical field of glacier thickness inversion, particularly discloses a glacier thickness inversion method fusing a laminar flow model and random unit interpolation, and aims to solve the problems that a traditional glacier thickness estimation method based on the laminar flow model excessively depends on a glacier center streamline, and the glacier center streamline in a complex area is difficult to accurately obtain. A random unit interpolation method is introduced, the dependence of glacier thickness estimation on a central streamline is avoided, and glacier terrain and flow velocity data are fully utilized. Besides, the difference of different glacier shape factors is considered in the model, specific kinetic parameters are distributed for the glacier in combination with temperature information, and manual parameter determination can be effectively avoided. Experimental results show that glacier thickness distribution which is more practical can be obtained through the improved method.

The invention provides a method for simulating runoff in a cold and cold mountainous area by combining LSTM (Long Short Term Memory) to improve a VI-glacier model. The method is used for solving the technical problems that runoff components in the cold and cold mountainous area are diverse and each runoff source cannot be directly distinguished. The method comprises the following steps: firstly, determining model input data including DEM data, vegetation data, soil data, meteorological data, hydrological data and glacier boundary data; a GPM rainfall product is adopted to drive meteorological data; secondly, introducing a degree-day factor model to quantify the glacier ablation process, coupling the degree-day factor model with the VIC model, and constructing a VI-glacier model; then, an SCE-UA automatic optimization algorithm and an artificial trial and error method are combined to calibrate the VICs-glacier model, and an optimal parameter value is obtained; and meanwhile, error correction is carried out on a simulation result of the VI-glacier model through the LSTM model, so that runoff correction is realized. The model provided by the invention can relieve the uncertainty of water resources caused by glacier changes, and the future runoff can be predicted more accurately.

The invention belongs to the technical field of regional hydrological numerical simulation, and particularly relates to a runoff simulation method, system and application of a loose coupling hydrological model based on glacier and non-glacier zones.The method comprises the steps that firstly, a glacier area and a non-glacier area are divided based on glacier cataloguing data and accumulated snow covering products, gridding processing is carried out through a digital elevation model, and a runoff simulation result is obtained; and performing hydrological process simulation on the non-glacier area and the glacier area by adopting a VIC model and a DETIM model respectively to realize differential modeling, and obtaining a basin total runoff simulation process through result fusion. Through the loose coupling framework, the problems of poor model expansibility and high reuse difficulty in traditional integrated modeling are solved, the simulation precision and deployment efficiency are improved, and the method is suitable for water resource evaluation and climate response research in the cold and cold mountainous area.

The invention relates to a method for surveying and mapping glacier topography by using an unmanned aerial vehicle. The method comprises the following steps: acquiring multi-view image data of a target glacier by using the unmanned aerial vehicle; performing correction and de-noising processing on the multi-view image data to obtain high-quality image data; key topographic feature points are extracted according to the high-quality image data, and a matching point pair set is obtained; generating three-dimensional point cloud data according to the matching point pair set; acquiring a three-dimensional terrain model of the target glacier according to the three-dimensional point cloud data; and obtaining geometric parameters of the target glacier according to the three-dimensional terrain model of the target glacier. According to the method, high-precision three-dimensional reconstruction of the glacier in the complex terrain is realized, and important technical support is provided for monitoring and research of the glacier.

The invention provides a glacier dynamic early warning method and system, and the method comprises the steps: enabling a core agent to adjust a base sliding coefficient of a target model according to the melt water amount of a target glacier body in a past observation time period through employing the capability of an agent AI for learning a complex mode from data and the preciseness of an ice flow model in dynamics deduction, and achieving the dynamic early warning of the glacier. And the target model carries out deduction prediction based on climate forecast data on the basis of the adjusted base sliding coefficient so as to generate a prediction change result of the target glacier body in a prediction time period, and the accuracy and robustness of the prediction change result are guaranteed by accurately setting the base sliding coefficient under the condition that the two modes are combined. The risk assessment model obtains an instability index of the target glacier body in the prediction time period according to the prediction change result; and the risk assessment model triggers early warning when the instability index exceeds a corresponding dynamic threshold value, so that accurate implementation of early warning is ensured.

This invention relates to the field of glacier monitoring technology, specifically to a mountain glacier displacement monitoring system. The mountain glacier displacement monitoring system includes a drone body, a positioning module, a mounting module, a data control platform, and at least one monitoring probe. The drone body, positioning module, mounting module, and monitoring probe are all electrically connected to the data control platform. The positioning module is connected to the drone body. The mounting module is used for deploying or retrieving the monitoring probe. This mountain glacier displacement monitoring system has strong environmental adaptability and enables probe retrieval, thereby reducing deployment and usage costs and operational risks.

1. Name of the designed product: ceiling lamp (North Pole Glacier series C185). 2. Use of the designed product: for lighting. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view.

The invention discloses a self-adaptive ICESat-2 glacier photon denoising method, which comprises the following steps of: extracting a potential photon center point coordinate from ATL03 original photon data, fitting a curve by using the center point coordinate and constructing an envelope line; performing coarse denoising on the original photon data through the envelope line to obtain coarse screening photon point data; introducing an LOF strategy to construct a self-adaptive ellipse search window; calculating the slope of the curve to determine the search direction of the self-adaptive elliptical search window; a density clustering method is adopted, a grid adjustment mechanism is introduced to optimize density estimation, and photon space density is calculated; and based on the adaptive elliptical search window, the search direction and the photon space density, performing fine denoising on the coarse screening data to obtain signal photons. The method can accurately identify signal photons and reduce elevation calculation errors, so that the glacier surface height inversion precision is improved, meanwhile, the operation time can be greatly shortened, the algorithm operation efficiency is improved, and the actual requirement for large-scale data rapid processing is met.

The invention discloses a glacier surface accumulated snow identification method using time sequence optics and SAR data. The method comprises the following steps: generating a monthly-scale cloudless NDSI composite image; constructing a wet snow-non-wet snow binary training sample set of spatial-temporal distribution, and constructing a monthly-scale wet snow distribution prediction model based on a convolutional neural network to perform spatial-temporal feature learning; obtaining a month-by-month wet snow distribution diagram of a corresponding year, and calculating and generating a glacier surface annual wet snow coverage frequency distribution diagram; in combination with backscattering coefficient characteristics of the time sequence SAR image, establishing an SAR image wet snow discrimination threshold dynamic calculation model by counting backscattering coefficient distribution of the time sequence SAR image, and extracting wet snow distribution of the multi-temporal SAR image; and for the obtained residual area outside the wet snow distribution area, terrain slope direction parameters are extracted through a digital elevation model, classification constraints are constructed in combination with accumulated snow seasonal characteristics, and fine distinguishing of dry snow and glacier ice is realized. According to the invention, the automation degree and classification precision of high mountain area accumulated snow classification are improved.

The invention discloses a self-adaptive homogeneous pixel offset tracking glacier time sequence monitoring method and system, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a plurality of groups of dual-time-phase SAR image pairs of a glacier; performing image recognition on the dual-time-phase SAR image pair, and outputting a binary segmentation result of a glacier change boundary; a binary segmentation result is used as mask input of pixel offset tracking, and a self-adaptive homogeneous pixel matching window is constructed by distinguishing a changing area and a non-changing area in a pixel offset tracking window, so that matching windows of master and slave images of a dual-time-phase SAR image pair only contain homogeneous pixels with the same changing characteristics as a central pixel, and the matching windows of the master and slave images of the dual-time-phase SAR image pair only contain homogeneous pixels with the same changing characteristics as the central pixel. Normalization matching is carried out; and when the local SNR of the homogeneous pixel matching window is smaller than a preset signal-to-noise ratio threshold value, performing offset calculation by adaptively adjusting the size of the window in the row and column directions, and outputting LOS-direction and azimuth-direction two-dimensional timing sequence offset results. According to the method, the glacier change detection accuracy and the deformation calculation precision can be comprehensively improved.

This application provides a system, method, equipment, and medium for optimizing glacier protection during highway construction and operation. The assessment and avoidance module acquires geographic environmental data and generates glacier protection zoning, highway alignment schemes, and optimized engineering schemes through glacier change models and spatial analysis algorithms. Pollution control, heat mitigation, and ecological restoration modules generate corresponding prevention, mitigation, and restoration instructions based on the glacier protection zoning or the aforementioned schemes. The monitoring and control module integrates multi-source monitoring data and instruction execution data to generate implementation effectiveness evaluation results and optimization instructions, which are then distributed to the corresponding modules. Each execution module adjusts subsequent instructions according to the optimization instructions to achieve dynamic optimization of glacier protection during highway construction and operation. Using this method, various disturbances to glaciers caused by highway construction and operation can be accurately and efficiently reduced, continuously improving glacier protection effectiveness and providing a reference for ecological protection and sustainable development of major infrastructure construction in glacier areas.

The invention discloses a plateau environment intelligent switching drying pipe system and a control method thereof. The first to fourth three-way valves are communicated with the first and second drying units through pipelines, air respectively flows through the first and second drying units to be dried, and the first and second drying units are respectively regenerated by an oil-water separator and an air compressor; the humidity sensors monitor the humidity of the inlet and the outlet in real time, the computer controls the first three-way valve to the fourth three-way valve to automatically switch working modes according to the humidity, one drying unit works while the other drying unit regenerates, continuous operation can be conducted under the unattended condition, and continuous moisture absorption and regeneration circulation is achieved. The device is suitable for Qinghai-Tibet Plateau weather stations, glacier scientific investigation equipment and unmanned aerial vehicle-mounted atmospheric sampling platforms, and the technical problem that a traditional drying pipe is difficult to regenerate and frequently replaced under the complex atmospheric condition is solved.

The present invention relates to a method for estimating glacier ice reserves based on a flying ice radar, comprising: acquiring a large-scale glacier topography; transmitting microwaves to measure ice thickness within the glacier boundary; receiving echoes to measure ice thickness within the glacier boundary; real-time positioning of the collected microwave signals; ice thickness data processing; and glacier reserve estimation. To improve the accuracy of glacier detection, the core device of the present invention, the glacier thickness radar, has an ice layer resolution capability of the order of centimeters and an ice thickness penetration capability of the order of 500 meters. It is lightweight, compact, and highly reliable, adapting to high-altitude and cold conditions and suitable for installation on drone platforms. Ultra-wideband, high-stability, low-phase noise signal generation technology, weak echo signal reception technology, and ultra-wideband antenna technology make the thickness radar system lightweight, compact, and highly reliable. The use of drones is suitable for climatic conditions such as high glacier altitudes and thin air, overcoming the shortcomings of relying on manpower to reach or towed measurements, thereby improving test efficiency.

The application is suitable for the technical field of remote sensing image processing, and provides a glacier change remote sensing detection method based on a deep twin neural network. Mainly comprising the following steps: step A, pre-processing of double-time-phase glacier remote sensing images to obtain training data for glacier change detection; step B, inputting the double-time-phase training data into the deep twin neural network for training to obtain the trained deep twin neural network; step C, inputting double-time-phase glacier remote sensing image data into the trained deep twin neural network to obtain a glacier change detection binary prediction image, vectorizing the obtained glacier change detection binary prediction image to obtain a glacier change area vector diagram. In the application, the spatial features of the subtle change area can be effectively enhanced, the glacier remote sensing image change detection map is obtained, and the glacier change detection precision is improved.

The application relates to the field of tunnel underpass glacier evaluation, a tunnel underpass glacier stability state evaluation system, a monitoring period of a subzone is set, frost heaving pressure analysis is carried out on each monitoring subzone divided by a tunnel structure, key monitoring subzones and non-key monitoring subzones are screened out, the spatial positioning of each key monitoring subzone and non-key monitoring subzone in the tunnel structure is extracted, adjacent non-key monitoring subzones are screened out, the frost heaving pressure interference of the key monitoring subzone on the adjacent non-key monitoring subzone is analyzed, the probability that the adjacent non-key monitoring subzone evolves into a key monitoring subzone is evaluated, and high-probability evolution non-key monitoring subzones are determined. Limited monitoring resources can be concentrated on the high-probability evolution non-key monitoring subzones, blind dispersion and waste of resources in low-risk areas are avoided, early risk warning signals can be sent, relevant personnel are reminded to pay attention to the frost heaving pressure change conditions of the areas, and construction personnel are timely informed to take corresponding measures.

This invention discloses a method for identifying stony glaciers based on machine learning. The method comprises the following steps: S1: Interpreting different geomorphic features on stony glacier optical images to obtain identification results; S2: Enhancing the reliability of different types of stony glaciers using the InSAR method to obtain vector data of residual and complete stony glaciers; S3: Slicing images based on the vector data, masking non-stony glacier areas to obtain images with only stony glacier features, and cleaning noisy data; S4: Importing the InSAR-enhanced dataset into RF, SVM, LR, DT, KNN, and ResNet models for training, obtaining the accuracy of different machine learning models in identifying stony glaciers, and obtaining the optimal machine learning model. The method proposed in this invention overcomes the problems of low efficiency and high subjectivity in existing stony glacier state classification, thus achieving automatic classification of complete and residual states of stony glaciers. Furthermore, this method can compensate for the lack of complete and residual state attributes in many existing stony glacier catalogs.

The invention relates to the technical field of radar detection, in particular to a glacier detection system and method. The system comprises a signal generation module used for alternately repeating complementary code elements with a preset time width to generate a pseudo-random coded signal with a preset code length, and performing square wave modulation on the pseudo-random coded signal according to a carrier square wave period to obtain a radio frequency signal, the preset time width being an integer multiple of the carrier square wave period, and the preset time width being an integer multiple of the carrier square wave period; the preset code length is integer power of two; the signal transmitting module is used for processing the radio frequency signal to obtain a transmitting signal and transmitting the transmitting signal to the glacier; the signal receiving module is used for receiving an echo signal of the glacier to the transmitted signal and carrying out digital signal processing on the echo signal to obtain a digital echo signal; the signal analysis module is used for analyzing the digital echo signal to obtain a glacier structure, and the glacier structure comprises at least one of the glacier thickness, the glacier terrain and the ice layer structure.

1. Name of the product in this design: Shoe Sole (Glacier Lava). 2. Purpose of this design: This design is used to make shoe soles. 3. The key design features of this product are the combination of shape and color. 4. The image or photograph that best illustrates the design's key points: a 3D model. 5. The design for which protection is sought includes color.

The invention discloses a lake-entering glacier terminal collapse monitoring and early warning system, which comprises a data monitoring unit, a transmission unit, an analysis unit, a data judgment unit and an early warning unit, and is characterized in that the data monitoring unit comprises an equipment box, a wind driven generator, a monitoring probe and an overwater detector, the wind driven generator is arranged at the top of the equipment box, and the monitoring probe is arranged at the top of the equipment box; the monitoring probe is arranged on the equipment box and used for acquiring glacier surface image information, the overwater detector is arranged in a water area around a target glacier, the data judgment unit and the data transmission unit are arranged in the equipment box, and the detection unit is arranged on the water. According to the method, ice crack information is accurately extracted through the deep learning model, the glacier surface movement speed is accurately detected in combination with an optical image cross-correlation method, and a scientific basis is provided for glacier protection and disaster prevention and control.

This invention discloses a method for extracting GNSS vertical deformation signals based on multi-source load correction and CME filtering. It constructs a load correction model for common-mode error filtering and applies non-tidal atmospheric, oceanic, and hydrological load effects. Through a five-step collaborative process—constructing a standardized GNSS time-series dataset, performing multi-source load hierarchical correction, optimizing CME filtering, dynamically adapting a noise model, and extracting and estimating vertical velocity—the method quantifies the spatial heterogeneity of GNSS vertical time-series nonlinear signals, background noise, and vertical velocity. By combining the correction derived from GRACE with CME filtering, this invention enables the quantification of the spatial heterogeneity of GNSS vertical time-series nonlinear signals, background noise, and vertical velocity, accurately depicting tectonic signals within complex suture zones of glaciers and plateaus.

The application provides a kind of glacier mud-rock flow dynamic risk early warning method and system based on seismic signal, belongs to natural disaster warning technical field, first constructs the response chain of seismic signal glacier, obtains the information such as the propagation path of seismic signal, the response order of each circle of glacier and the response characteristic transmission relationship, then analyzes the risk evolution node in response chain information, accurately determines the key nodes such as response trigger, energy accumulation and diffusion start, forms the glacier mud-rock flow risk evolution node set, then links the glacier mud-rock flow risk evolution node set with the topographic features of glacier area, generates the dynamic risk evolution path that can reflect the dynamic development process of glacier mud-rock flow, generates the dynamic risk warning content containing detailed warning information based on this dynamic risk evolution path, finally reversely associates the on-site response data to the response chain construction process, realizes the optimization update of the response chain information of seismic signal glacier, effectively improves the early warning ability to glacier mud-rock flow disaster.

1. Name of the designed product: ice bucket (glacier). 2. Use of the designed product: for storing ice cubes. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view.

The application belongs to the technical field of regional hydrological numerical simulation, and particularly relates to a runoff simulation method, system and application of a loose coupling hydrological model based on glacier and non-glacier partitioning. The method firstly divides the glacier area and the non-glacier area based on the glacier inventory data and the snow cover product, performs grid processing by using a digital elevation model, then respectively uses a VIC model and a DETIM model to simulate the hydrological process of the non-glacier area and the glacier area, realizes differential modeling, and obtains the total runoff simulation process of the basin by fusing the results. The application solves the problems of poor model expansibility and high reuse difficulty in traditional integrated modeling through a loose coupling framework, improves the simulation accuracy and deployment efficiency, and is suitable for water resource assessment and climate response research in alpine mountainous areas.

The invention discloses a glacier monitoring device based on RTK positioning and LoRa communication and a control method thereof, and relates to the technical field of polar region environment monitoring. A sensing module, a positioning module, a communication module, a friction power generation module, a hybrid energy storage module and a main control module are integrated in a shell of the glacier monitoring device; the sensing module defaults to sleep with low power consumption, and awakens the collected data only when the change of the monitored data exceeds a threshold value; the positioning module is used for regularly acquiring centimeter-level glacier position information based on an RTK technology; the main control module coordinates the work of each module and compresses and encrypts monitoring data and position information; and the communication module transmits the data back to a base station through a LoRa protocol. The friction power generation module generates power through friction energy generated when the device vibrates along with glacier. The hybrid energy storage module adopts a main and auxiliary energy combination mode, the main energy maintains the basic power consumption of the device, the auxiliary energy satisfies the power consumption demand of each module in a priority manner, and the device can fully adapt to the extreme environment of the glacier area, and can realize long-term unattended dynamic monitoring of the glacier.

The application discloses a time-varying glacier degree-day factor calculation method suitable for plateau areas, and the method comprises the following steps: step 1, multi-source data collection and processing; step 2, key driving factor screening; step 3, dynamic relationship model establishment; and step 4, time-varying glacier degree-day factor calculation. The method couples albedo and solar radiation, establishes the physical connection between the degree-day factor and the net radiation, realizes the dynamic calculation of the glacier degree-day factor, significantly improves the precision of glacier ablation simulation and prediction, effectively solves the applicability problem in the complex underlying surface area, reduces the data acquisition threshold and cost of large-scale application, and provides more accurate data support for water resource management and disaster warning.

The invention discloses an automatic glacier extraction method suitable for a multi-modal remote sensing image. The method comprises the steps of obtaining multi-modal remote sensing image data and performing data processing; calculating normalized index data, and fusing the normalized index data with the multiband data and the DEM data; carrying out labeling, carrying out cutting and data enhancement processing on a fused image and label data of the multi-channel multi-mode remote sensing image data, and carrying out data division; a multi-mode semantic segmentation framework of a four-branch encoder-decoder structure is constructed; and training and verifying the deep semantic segmentation model to obtain an automatic glacier extraction model. According to the embodiment of the invention, a multi-modal feature collaborative expression and hierarchical fusion mechanism is constructed, the complementary advantages of different remote sensing data are brought into full play, meanwhile, information interference among modals is effectively inhibited, the precision, robustness and generalization ability of glacier extraction in the complex cold and cold mountainous area are improved, and high-precision automatic identification and boundary extraction of glacier targets are realized.

The application relates to the technical field of geographic information processing, and discloses a glacier flow line extraction method based on terrain intervention. A digital elevation model and glacier polygon contour data of a target glacier are acquired and subjected to mask processing, a boundary grid node set is screened to determine a potential glacier source point set and a glacier terminal point; contour line distribution and an auxiliary gradient scalar are calculated, a high-value overlapping area is taken to generate a limited terrain intervention mask patch; a basic cost tracking grid is generated based on the shortest distance from a grid point of an elevation grid in the glacier boundary to the glacier boundary and relative elevation; the patch is used to replace a corresponding position of the tracking grid to generate a composite constraint cost grid; the potential glacier source point set is used as a starting point, the glacier terminal point is used as an end point, and an initial flow line network is extracted in the composite constraint cost grid through optimization; after being subjected to smoothing and dimension reduction and redundant branches are removed, glacier flow line data are output. The application can avoid flow line penetration and remove redundant branches, and the extraction precision is improved.

The invention relates to the field of image data processing, in particular to an ice fissure recognition method based on multi-source remote sensing data, which comprises the following steps: performing edge detection on the multi-source remote sensing data to obtain an ice fissure bottom image; preprocessing the ice fissure bottom image, and distinguishing a glacier fissure development area and a non-glacier fissure area; carrying out edge detection, tubular flow field identification and dynamic threshold optimization on the glacier fissure development area, and extracting to obtain glacier fissure data; and performing precision verification on the glacier fracture data to obtain final glacier fracture distribution information. By improving the valley bottom edge detection algorithm and the dynamic threshold optimization strategy and combining the high-resolution image, accurate identification of the ice crack is realized, the advantages of remote sensing data are effectively exerted, low-cost identification is realized, and a more reliable data result is obtained.