Abrégé

A method for extracting a black-odorous water body based on a CART classification model includes: selecting a research region, designing sampling points within the region; monitoring relevant chemical indicators of the water body at various sampling points, extracting remote sensing reflectance data of the water body, determining a type of the water body according to a classification standard of relevant chemical indicators for an urban black-odorous water body; comparing and analyzing the remote sensing reflectance data to obtain spectral change features of the black-odorous water body and a general water body; constructing each node of a decision tree according to the spectral change features and based on Gini index, constructing a decision tree classification model to obtain classification results of the black-odorous water body and the general water body, calculating a classification accuracy; analyzing the classification results to obtain spatiotemporal distribution changes of black-odorous water bodies in the region.

Classes IPC  ?

• G01N 33/18 - Eau
• G01N 21/31 - CouleurPropriétés spectrales, c.-à-d. comparaison de l'effet du matériau sur la lumière pour plusieurs longueurs d'ondes ou plusieurs bandes de longueurs d'ondes différentes en recherchant l'effet relatif du matériau pour les longueurs d'ondes caractéristiques d'éléments ou de molécules spécifiques, p. ex. spectrométrie d'absorption atomique
• G01N 21/77 - Systèmes dans lesquels le matériau est soumis à une réaction chimique, le progrès ou le résultat de la réaction étant analysé en observant l'effet sur un réactif chimique

Abrégé

A method for observing a per unit yield of wheat based on computer vision and deep learning technology, including: acquiring wheat spike images and coordinate position data; calculating a camera parameter, and performing a distortion correction and a cropping on the images; performing a wheat spike recognition on the wheat spike image by using a deep learning target recognition model; performing a wheat spike recognition on the wheat spike image by using a trained deep learning target recognition model 1 and cropping a wheat spike from the wheat spike image; performing a wheat grain recognition on the wheat spike image by using a trained deep neural network target recognition model 2; calculating a number of spikes per unit area in a same wheat field by using a corrected wheat spike image and a recognized wheat spike; calculating a number of effective grains of spikes in same wheat field by using the corrected wheat spike image and a recognized wheat grain; predicting thousand-grain weight according to the number of spikes per unit area, climate condition and deep neural network model of thousand-grain weight; and calculating per mu yield according to predicted thousand-grain weight and the number of effective grains of spikes per unit area.

Classes IPC  ?

• G06Q 50/02 - AgriculturePêcheForesterieExploitation minière
• G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient

Abrégé

The present application belongs to the technical field of marine observation. Provided are a sea surface temperature (SST) prediction method and apparatus, and an electronic device and a storage medium. The method comprises: on the basis of first satellite image data, determining first observation data of each pixel in an image of a target sea area at a target spatial resolution, and on the basis of second satellite image data, determining second observation data of each pixel in the image; and performing fusion and analysis on the first observation data and the second observation data of each pixel, so as to determine an SST prediction result for the target sea area at the target spatial resolution and a target temporal resolution, wherein the target temporal resolution is determined on the basis of a temporal resolution of the second satellite image data. By means of the present invention, an SST fusion product with high precision, high temporal-spatial resolution and high coverage can be effectively acquired, thereby thoroughly satisfying the requirements of rapid development in the fields of marine resource development, etc., and greatly increasing the application value of an SST product.

Classes IPC  ?

• G01S 17/95 - Systèmes lidar, spécialement adaptés pour des applications spécifiques pour la météorologie
• G01S 17/89 - Systèmes lidar, spécialement adaptés pour des applications spécifiques pour la cartographie ou l'imagerie

Abrégé

A mode localization-based inductive miniature electric field sensor and a detection method thereof. The sensor comprises: a substrate (1); a resonator array (2) arranged on the substrate (1) by means of a support structure; sensing structures (3) that are arranged on two sides of the resonator array (2) and each comprise a movable structure (32) and a fixed structure (31), wherein the fixed structure (31) is fixed on the substrate (1) by means of the support structure, and the movable structure (32) is supported by an elastic beam (8); driving electrodes (4) arranged on one side of at least one resonator and used for driving the resonator array (2) to drive the movable structures (32) to vibrate; disturbance electrodes (7) arranged on one side of the elastic beams (8) and used for applying voltage disturbance to enable the resonator array (2) to generate a mode localization phenomenon; and a detection cover plate (5) arranged above the resonator array (2) and the sensing structures (3), wherein the side of the detection cover plate (5) close to the disturbance electrodes (7) is used for shielding an electric field to be detected above the sensing structures (3) close to the disturbance electrodes (7), and the other side is used for introducing said electric field above the sensing structures (3) distant from the disturbance electrodes (7).

Classes IPC  ?

• G01R 29/12 - Mesure du champ électrostatique

Abrégé

A pod performance test system for a near space aerostat includes: a HCI test control device, a motion simulation control device, a suspension device, and a motion simulation anti-twisting mechanism. The HCI test control device is electronically connected to the motion simulation control device, the motion simulation anti-twisting mechanism and a pod of the near space aerostat separately. The motion simulation control device is electronically connected to the motion simulation anti-twisting mechanism. The motion simulation anti-twisting mechanism is connected to the pod by a connector. The motion simulation control device controls the motion simulation anti-twisting mechanism to simulate various complex motion states according to a motion control parameter from the HCI test control device, and thus the pod is in the various complex motion states. When the pod is in the various complex motion states, performance test results of the pod under the various complex motion states can be obtained.

Classes IPC  ?

• G01M 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe

Abrégé

A method and a device for a predicting desert locust are provided. The method includes: acquiring environment factor data of a target area, where the environment factor data includes total precipitation data, soil temperature data, soil water data, and vegetation index data; extracting fluctuation features corresponding to the environment factor data through wavelet transform, where the fluctuation features include a precipitation fluctuation feature corresponding to the total precipitation data, a soil temperature fluctuation feature corresponding to the soil temperature data, a soil water fluctuation feature corresponding to the soil water data, and a vegetation fluctuation feature corresponding to the vegetation index data; and predicting time when the desert locust presents in the target area based on the fluctuation features.

Classes IPC  ?

• A01K 29/00 - Autres appareils pour l'élevage

Abrégé

An optical satellite remote sensing transfer calibration method based on near-space aerostat, including: performing a temporal-spatial matching between first observation image data of a target region collected by a benchmark radiometric payload and second observation image data of the target region collected by a satellite-borne payload, to obtain a plurality of matchups of observation image data (S1); obtaining an average radiance of the benchmark radiometric payload from a plurality of first observation image data (S2); obtaining an average DN captured by the satellite payload in the target region from a plurality of second observation image data (S3); obtaining a matching radiance for an observation band of the benchmark radiometric payload corresponding to the satellite payload based on the average radiance of the benchmark radiometric payload (S4); and obtaining a calibration coefficient of the satellite payload based on the matching radiance and the average DN (S5).

Classes IPC  ?

• G06V 20/13 - Images satellite
• G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c.-à-d. étalonnage de caméra
• G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
• G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques

Abrégé

The present invention discloses a CEEMDAN-based method for screening and monitoring soil moisture stress in farmland, characterised by the steps: preprocessing of remote sensing images, construction of NDVI long time series, CEEMDAN decomposition, calculation of statistical descriptors, screening of soil moisture stress sequences, ground data measurement, construction of soil moisture stress characteristic curves, fitting of soil moisture stress response characteristic curves and predicting the content of soil moisture stress. The invention adopts CEEMDAN decomposition, which solves the problems of noise residue and low reconstruction accuracy in the previous methods, and the high reconstruction accuracy of decomposed component data is more conducive to capturing the transient effects of soil moisture stress, and realizes the screening and extraction of soil moisture stress by combining with the ground measured data. The inverse model of soil moisture content is fitted by combining the effects of multiple indicators, and the CEEMDAN algorithm with remote sensing technology tools to achieve accurate monitoring of soil moisture in a large area of farmland.

Classes IPC  ?

• G01N 21/359 - CouleurPropriétés spectrales, c.-à-d. comparaison de l'effet du matériau sur la lumière pour plusieurs longueurs d'ondes ou plusieurs bandes de longueurs d'ondes différentes en recherchant l'effet relatif du matériau pour les longueurs d'ondes caractéristiques d'éléments ou de molécules spécifiques, p. ex. spectrométrie d'absorption atomique en utilisant la lumière infrarouge en utilisant la lumière de l'infrarouge proche
• G01N 21/3563 - CouleurPropriétés spectrales, c.-à-d. comparaison de l'effet du matériau sur la lumière pour plusieurs longueurs d'ondes ou plusieurs bandes de longueurs d'ondes différentes en recherchant l'effet relatif du matériau pour les longueurs d'ondes caractéristiques d'éléments ou de molécules spécifiques, p. ex. spectrométrie d'absorption atomique en utilisant la lumière infrarouge pour l'analyse de solidesPréparation des échantillons à cet effet
• G01N 33/00 - Recherche ou analyse des matériaux par des méthodes spécifiques non couvertes par les groupes
• G01N 33/24 - Matériaux de la terre

Abrégé

A system and a method are provided. The system includes an airborne detection system and a ground assistance system. The airborne detection system includes an unmanned aerial vehicle, a receiving device, a transmitting device, and a connecting device. The transmitting device includes a transmitting sub-device, a transmitting coil, and a bucking coil. The receiving device includes a receiving sub-device and a three-component magnetic sensor. The connecting device includes a signal and rope composite cable and is used to connect the UAV, the transmitting device, and the receiving device. The three-component magnetic sensor is used to acquire three-component electromagnetic data. The bucking coil is disposed on an inner side of the transmitting coil, and coil winding direction of the transmitting coil and the bucking coil causes current directions flowing through the transmitting coil and the bucking coil to be opposite to each other, to reduce an electromagnetic interference.

Classes IPC  ?

• G01V 3/165 - Prospection ou détection électrique ou magnétiqueMesure des caractéristiques du champ magnétique de la terre, p. ex. de la déclinaison ou de la déviation spécialement adaptée à l'utilisation pendant le transport, p. ex. par une personne, un véhicule ou un bateau fonctionnant au moyen de champs magnétiques ou électriques produits ou modifiés par l'objet ou par le dispositif de détection

Abrégé

A CEEMDAN-based farmland soil water stress discrimination and monitoring method, characterized by comprising the steps of: pre-processing a remote sensing image, constructing an NDVI long time series, performing decomposition by using CEEMDAN, calculating statistical description indexes, discriminating a soil water stress sequence, actually measuring ground data, constructing a soil water stress characteristic curve, fitting a soil water stress response characteristic curve, and predicting the degree of soil water stress. The method performs decomposition by using CEEMDAN, so as to solve the problems of previous methods such as noise residue and low reconstruction precision, high reconstruction precision of decomposed component data better helping to capture the instantaneous impact of soil water stress, thus achieving discrimination and extraction of soil water stress in combination with actually measured data; and the method integrates the impact of multiple indexes on the soil water content to fit a function model of the multiple indexes and the water soil content, and combines the CEEMDAN algorithm and remote sensing technical means, thus achieving accurate monitoring of the soil water of large-area farmlands.

Classes IPC  ?

• G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
• G06V 20/10 - Scènes terrestres
• G06V 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects

Abrégé

The present application relates to the technical field of power sources. Provided are a combined-energy power supply circuit applied to an aerostat, and a power supply control method. The combined-energy power supply circuit comprises: a solar power generation circuit, a wind power generation circuit, a laser power generation circuit, a direct-current bus, an energy storage battery and a load circuit, wherein the solar power generation circuit, the wind power generation circuit and the laser power generation circuit are all connected to the energy storage battery and the load circuit by means of the direct-current bus, and are used for supplying power to the load circuit and charging the energy storage battery; and the energy storage battery is connected to the load circuit by means of the direct-current bus, and is used for supplying power to the load circuit. By means of the circuit, power supply control is realized on the basis of an actual power utilization requirement of an aerostat and the state-of-charge of an energy storage battery, thereby solving the technical problems of existing energy power supply systems being incapable of meeting the energy supply requirement of an aerostat when same executes a long-endurance station-keeping task and the requirement of same for carrying a higher-power load.

Classes IPC  ?

• H02J 1/10 - Fonctionnement de sources à courant continu en parallèle

Abrégé

The present application discloses a data compression method for a quantitative remote sensing application of an unmanned aerial vehicle, which relates to the technical field of compression techniques. The method comprises: preprocessing a multispectral image collected by the unmanned aerial vehicle; sequentially performing three-dimensional convolution and two-dimensional convolution on the multispectral image by using an encoder to obtain deep feature information; performing quantization and entropy coding on the deep feature information; achieving the optimal trade-off between the loss and the code rate of the image by means of end-to-end joint training to obtain an optimal compressed image; and reconstructing the optimal compressed image by using a decoder. The multi-spectrum graph is subjected to multiple convolutions to improve the image reconstruction quality and the compression ratio. The convoluted deep feature information is subjected to quantization and entropy coding to remove the redundancy in the feature image and improve the image reconstruction quality and the compression ratio. The end-to-end joint training is used to optimize the trade-off between the loss and the code rate of the image. Therefore, a high compression ratio can be achieved while the compression quality is improved and problems such as blockiness, blurring and artifacts can be avoided.

Classes IPC  ?

• H04N 21/234 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé

Abrégé

The present disclosure provides a data compression method for quantitative remote sensing with an unmanned aerial vehicle. The method performs preprocessing on a multispectral image acquired by an unmanned aerial vehicle, successively performs a three-dimensional convolution and a two-dimensional convolution on the multispectral image by an encoder to obtain deep feature information, performs quantizing and entropy encoding on the deep feature information, optimally distributes a loss and a code rate of the image through end-to-end joint training to obtain an optimal compressed image, and reconstructs the optimal compressed image by using a decoder. Image reconstruction quality and a compression ratio are improved by performing a plurality of convolutions on a multispectral pattern; quantizing and entropy encoding are performed on the convoluted deep feature information, to remove redundancy in a feature image, so as to improve the image reconstruction quality and the compression ratio.

Classes IPC  ?

• H04N 19/124 - Quantification
• H04N 19/91 - Codage entropique, p. ex. codage à longueur variable ou codage arithmétique
• H04N 19/147 - Débit ou quantité de données codées à la sortie du codeur selon des critères de débit-distorsion

Abrégé

An apparatus for inverting parameters of vegetation leaves based on remote sensing is provided. The apparatus is obtained by performing inverse processes of a PROSAIL model based on a deep neural network, achieving strong physical mechanism and high accuracy. A method for inverting parameters of vegetation leaves based on remote sensing is provided. In the method, the apparatus for inverting parameters of vegetation leaves based on remote sensing is used, and the parameters of the vegetation leaves are obtained by performing inversion based on remote sensing data of the vegetation leaves, achieving high reliability and high accuracy.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques

Abrégé

The present disclosure discloses an extraction method of raft culture area based on multi-temporal optical remote sensing images, including: constructing a raft culture marker sample library including culture types such as fish, shellfish, and algae; optimizing the deep learning model of the UNet network by using ASPP (Atrous Spatial Pyramid Pooling) and the shape constraint module; using the deep learning model to extract a corresponding multi-temporal raft culture area by using the multi-temporal optical remote sensing images with medium resolution in the target area; combining prior knowledge, fusing the extraction results of the raft culture area to obtain a final extraction results of the raft culture area.

Classes IPC  ?

• G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
• G06V 20/50 - Contexte ou environnement de l’image
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 10/772 - Détermination de motifs de référence représentatifs, p. ex. motifs de valeurs moyennes ou déformantsGénération de dictionnaires
• G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
• G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
• G06V 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques

Abrégé

An optical satellite remote sensing transmission calibration method based on a near-space aerostat. The method comprises: performing spatial-temporal matching on first observation image data of a target area that is collected by a radiation reference load and second observation image data of the target area that is collected by a satellite load carried by a satellite, so as to obtain a plurality of observation image data pairs (S1); obtaining the average radiance of the radiation reference load according to a plurality of pieces of first observation image data in the plurality of observation image data pairs (S2); according to a plurality of pieces of second observation image data in the plurality of observation image data pairs, obtaining an average pixel brightness value in the target area that is collected by the satellite load (S3); according to the average radiance of the radiation reference load, obtaining the matching radiance of the radiation reference load which corresponds to an observation wave band of the satellite load (S4); and obtaining a calibration coefficient of the satellite load according to the matching radiance and the average pixel brightness value (S5).

Classes IPC  ?

• G01J 3/28 - Étude du spectre
• G01M 11/02 - Test des propriétés optiques

Abrégé

The present invention relates to the field of environmental monitoring. Disclosed is a malodorous black water body extraction method based on a CART classification model, comprising the steps: selecting a research region, and designing a plurality of sampling points within the range of the research region; monitoring related chemical indicators of a water body at each sampling point, extracting remote sensing reflectance data of the water bodies, and determining the water body types according to classification standards for related chemical indicators of urban malodorous black water bodies; carrying out comparative analysis on the remote sensing reflectance data extracted at each sampling point, and selecting spectral change characteristics of malodorous black water bodies and general water bodies; constructing nodes of a decision tree according to the Gini index and according to the spectral change characteristics, constructing a decision tree classification model, obtaining classification results of malodorous black water bodies and general water bodies, and calculating the classification accuracy; and analyzing the classification results to obtain temporal and spatial distribution changes of the malodorous black water bodies in the research region. The present invention can be used for solving the problems of insufficient objectivity and selection of a few characteristics in current malodorous black water body threshold setting.

Classes IPC  ?

• G06V 20/13 - Images satellite

Abrégé

The present disclosure discloses a remote sensing-based extraction method for a type of a river channel, including: obtaining multi-source remote sensing data of a target area including a river channel; preprocessing the multi-source remote sensing data, and obtaining corresponding reflectance data; according to the reflectance data, analyzing a water index and a vegetation index of the target area; and according to the water index and the vegetation index, constructing first preset conditions for determining whether there is water in the river channel and second preset conditions for determining whether the river channel is a non-dry river channel to determine the type of the river channel. Types of river channels can be divided into four types: non-dry river channels, seasonal dry river channels, temporary water channels, and dry river channels.

Classes IPC  ?

• G06V 20/10 - Scènes terrestres

Abrégé

A stability control method and device based on particle active disturbance rejection are provided. The method includes: establishing an active disturbance rejection controller model based on a dynamic model and a speed loop control model of a tethered balloon system, where the speed loop control model is established through theoretical modeling of executive components of a control system of the tethered balloon system; and optimizing to-be-optimized parameters of the active disturbance rejection controller model using a particle swarm optimization algorithm, determining an optimal active disturbance rejection controller model, and using the optimal active disturbance rejection controller model to implement stability control of a photoelectric pod. An active disturbance rejection controller is optimized by using a particle swarm optimization algorithm, which can effectively isolate the internal and external disturbances of the photoelectric pod and improve the imaging stability of the photoelectric pod.

Classes IPC  ?

• G05D 1/08 - Commande de l'attitude, c. à d. élimination ou réduction des effets du roulis, du tangage ou des embardées
• B64C 39/02 - Aéronefs non prévus ailleurs caractérisés par un emploi spécial
• B64B 1/50 - Ballons captifs
• B64B 1/40 - Ballons

Abrégé

A pod (60) performance test system (1) and method of a near space aerostat. The system (1) comprises: a human-computer interaction test control device (10), a motion simulation control device (20), a suspension device (30), and a motion simulation countertwisting mechanism (40). The human-computer interaction test control device (10) is electrically connected to the motion simulation control device (20), the motion simulation countertwisting mechanism (40), and a pod (60) of the near space aerostat, respectively. The motion simulation control device (20) is electrically connected to the motion simulation countertwisting mechanism (40). The motion simulation countertwisting mechanism (40) is connected to the pod (60) by means of a connecting member (50). On the basis of motion control parameters sent by the human-computer interaction test control device (10), the motion simulation control device (20) can control the motion simulation countertwisting mechanism (40) to simulate multiple complex motion states, so that the pod (60) is in multiple complex motion states. When the pod (60) is in multiple complex motion states, performance test is performed on the pod (60) and performance test results corresponding to the multiple motion states are obtained.

Classes IPC  ?

• B64F 5/60 - Test ou inspection des composants ou des systèmes d'aéronefs

Abrégé

A wheat yield observation method based on computer vision and deep learning techniques, comprising: acquiring a wheat ear image and coordinate position data; calculating parameters of a camera and performing distortion correction and cropping on the image; performing wheat ear recognition on the wheat ear image by using a deep learning target recognition model; performing wheat ear recognition on the wheat ear image by using a trained deep learning target recognition model 1, and cropping same into wheat ears; performing wheat grain recognition on the wheat ear image by using a trained deep neural network target recognition model 2; calculating the number of ears per unit area in a same wheat field by using the corrected wheat ear image and the recognized wheat ears; calculating the number of effective grains on each ear in a same wheat field by using the corrected wheat ear image and the recognized wheat grains; predicting the thousand grain weight according to the number of wheat ears per unit area, the climate condition, and the thousand grain weight deep neural network model; and calculating the yield per mu according to the predicted thousand grain weight and the number of effective grains on each ear per unit area.

Classes IPC  ?

• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
• G06T 7/62 - Analyse des attributs géométriques de la superficie, du périmètre, du diamètre ou du volume
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

Abrégé

The present disclosure relates to the field of video data real-time transmission and processing. Disclosed are a dynamic processing method and apparatus based on an unmanned aerial vehicle video pyramid model, which are used for solving the problems of the rendering efficiency of an unmanned aerial vehicle video being low, the system operation not being smooth, etc. The method comprises: constructing an unmanned aerial vehicle video pyramid model; and by using the unmanned aerial vehicle video pyramid model and according to a video loading request, performing resampling and segmentation by means of a video server, and performing dynamic scheduling and rendering, loading and displaying on a processed unmanned aerial vehicle video by means of a client. By means of the dynamic processing method for an unmanned aerial vehicle video provided in the present disclosure, unmanned aerial vehicle video data similar to the current map resolution can be dynamically loaded by using an unmanned aerial vehicle video pyramid model and according to a viewpoint distance and a field-of-vision range of the current scene view camera, such that high-performance on-demand rendering of a multi-resolution unmanned aerial vehicle video is realized, thereby ensuring the high efficiency of scene displaying and the smoothness of system operation.

Classes IPC  ?

• H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé

Abrégé

A satellite image sequence cloud region repairing method and apparatus. The method comprises: respectively identifying and expanding a cloud region and a shadow region under the cloud from multiple multispectral images of a target region, and generating multiple single-band images (S11); according to the similarities between the single-band images, calculating a reference image and a repair sequence that are required to repair the multispectral images (S12); determining repair regions of the multispectral images according to cloud mask images corresponding to the single-band images (S13); according to the repair sequence, replacing the pixel values of the repair regions of the multispectral images with the pixel values of a non-cloud region of the reference image to obtain replacement repair images (S14); recording the number value of each reference image corresponding to each multispectral image in the repair regions to obtain replacement reference mask images (S15); and performing cloud repairing on each replacement repair image and each replacement reference mask image in the target region by means of Poisson fusion (S16). The method improves the efficiency and accuracy of satellite image sequence cloud region repairing.

Classes IPC  ?

• G06T 5/00 - Amélioration ou restauration d'image
• G06V 20/13 - Images satellite
• G06T 5/50 - Amélioration ou restauration d'image utilisant plusieurs images, p. ex. moyenne ou soustraction
• G06T 7/187 - DécoupageDétection de bords impliquant des croissances de zonesDécoupageDétection de bords impliquant des fusions de zonesDécoupageDétection de bords impliquant un étiquetage de composantes connexes
• G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
• G06T 7/12 - Découpage basé sur les bords

Abrégé

A remote sensing image geometric normalization method and apparatus. The method comprises: constructing a pyramid tile structure for a reference image, and releasing reference tile data, wherein the reference tile data is data in the pyramid tile structure (S11); according to the resolution and geographic coordinates of an image to be subjected to geometric normalization, calculating the level of a tile to be downloaded and the name of the tile to be downloaded, and according to the level of the tile to be downloaded and the name of the tile to be downloaded, downloading corresponding data from the reference tile data to obtain a standard tile set (S12); performing first geometric correction on the image to be subjected to geometric normalization and tiles in the standard tile set to obtain a first image processing result (S13); matching the first image processing result with the tiles in the standard tile set to obtain a plurality of control points, and using the plurality of control points to calculate a result evaluation precision (S14); and according to the result evaluation precision, determining whether to perform second geometric correction on the first image processing result (S15). The method improves the efficiency of processing a remote sensing image.

Classes IPC  ?

• G06T 5/00 - Amélioration ou restauration d'image
• G06T 7/00 - Analyse d'image

Abrégé

Provided is a high-altitude calibration device for a spatial solar cell, the device comprising: a housing, which comprises at least two symmetrically arranged outer side surfaces, wherein an accommodating space is configured in the housing; a calibration plate control assembly, which comprises a pitch driving electric motor and a transmission mechanism, the transmission mechanism comprising a stressed end connected to the pitch driving electric motor and a driving end extending to the outer side surface of the housing, and the driving end being horizontally arranged; a calibration plate connected to the driving end, a battery mounting mechanism being provided on the calibration plate; a data acquisition circuit arranged in the housing and being electrically connected to the battery mounting mechanism; and a tracking control circuit electrically connected to the calibration plate control assembly and the data acquisition circuit. During measurement, the calibration plate may adjust an opening angle according to the position of the sun in order to be aligned with the sun. When the calibration device is recycled, the calibration plate is fastened to the outer side surface of the housing, so that a solar cell on the calibration plate may be prevented from being damaged by the outside environment, and the folding protection of the solar cell can be realized.

Classes IPC  ?

• H02S 50/10 - Tests de dispositifs PV, p. ex. de modules PV ou de cellules PV individuelles
• G01M 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe

Abrégé

A method for extracting an oil storage tank based on a high-spatial-resolution remote sensing image is provided, including: acquiring an oil storage tank sample, and randomly dividing the oil storage tank sample into a training set and a testing set; building an oil storage tank extraction model based on a Res2-Unet model structure, wherein the Res2-Unet is a deep learning network based on a UNet semantic segmentation structure, and a Res2Net convolution block is configured to change a feature interlayer learning to a granular learning and is arranged in a residual mode; and performing a precision verification on the testing set.

Classes IPC  ?

• G06V 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G06T 7/11 - Découpage basé sur les zones
• G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”

Abrégé

A method and an apparatus were disclosed for measuring wood density of live timber. The method comprising: measuring a diameter of a test part of the live timber at a first height from a ground surface; transmitting and receiving microwave in a preset frequency range in air at a first distance greater than the diameter, measuring a first ratio of a transmitted microwave signal to a first received microwave signal at different frequencies; transmitting microwave in the air at the first height and microwave penetrates the test part, receiving the microwave at the first distance from the microwave transmitting position, measuring a second ratio of a transmitted microwave signal to a second received microwave signal at different frequencies, calculating a dielectric constant and an attenuation constant; calculating the wood density according to a relationship between the wood density and the dielectric constant and the attenuation constant.

Classes IPC  ?

• G01N 9/24 - Recherche du poids spécifique ou de la densité des matériauxAnalyse des matériaux en déterminant le poids spécifique ou la densité en observant la propagation de l'onde ou de la radiation des particules à travers le matériau
• G01N 9/36 - Analyse des matériaux en mesurant le poids spécifique ou la densité, p. ex. détermination de la quantité d'humidité
• G01N 33/00 - Recherche ou analyse des matériaux par des méthodes spécifiques non couvertes par les groupes
• H04B 1/04 - Circuits

Abrégé

Provided are a main cable guiding device for a mooring facility of a captive balloon, and the captive balloon, which relate to the field of aircraft equipment. The main cable (1) guiding device comprises a support base (2) and a main cable guiding mechanism. The main cable guiding mechanism comprises a support, a main cable pressing device and a main guiding wheel (7), wherein the support is rotatably connected to the support base; the main guiding wheel is rotatably connected to the support by means of a first rotating shaft; a main cable is wound around a guiding groove of the main guiding wheel; and the main cable pressing device applies pressure to the main cable in the radially inward direction of the main guiding wheel, so as to allow the main cable to fit with the main guiding wheel. The main cable guiding device is rotatably connected to the support base by means of the support, such that the support can swing at all angles along with the end of the main cable connected to the captive balloon, the angle remaining unchanged between the support base and the end of the main cable connected to a main cable winding and unwinding device; and the main guiding wheel rotates along with the main cable during the movement process of the main cable and will slide relative to the main cable, so as to prevent damage from occurring to the main cable.

Classes IPC  ?

• B64B 1/50 - Ballons captifs
• B64B 1/66 - Fixations d'amarrage

Abrégé

A ballast valve structure for use in an aerostat, comprising a ballast valve core. The ballast valve core comprises: a valve core framework, the valve core framework comprising two metal plates (4) arranged opposite to each other, and two magnetic elements (1) arranged opposite to each other; a sand discharging pipe, the sand discharging pipe being provided in the valve core framework in a penetrating mode; and a coil (3), the coil (3) being provided in the valve core framework. The valve core framework and the sand discharging pipe constitute a magnetic circuit. The electromagnetic principle is used for a whole valve, and the valve is a normally-closed valve. When the valve is closed, the magnetic elements (1) are used at an outlet to attract iron sand, and the outlet of the valve is in an open state.

Classes IPC  ?

• F16K 31/06 - Moyens de fonctionnementDispositifs de retour à la position de repos électriquesMoyens de fonctionnementDispositifs de retour à la position de repos magnétiques utilisant un aimant
• B64B 1/22 - Disposition des cabines ou nacelles

Abrégé

An aerostat ballast structure, comprising a hanging bracket (100), a hopper outer frame (200) and a hopper (300), wherein the hanging bracket (100) is mounted on an envelope of an aerostat; the hopper outer frame (200) is rotatably connected to the hanging bracket (100); the hopper (300) is mounted in the hopper outer frame (200); and a lower end face of the hopper (300) is provided with a discharge port. When the aerostat is pitching at a small angle, the hopper outer frame (200) can stay in a vertical state.

Classes IPC  ?

• B64B 1/70 - Dispositions pour le lestage
• B64B 1/22 - Disposition des cabines ou nacelles

Abrégé

This invention discloses a practical method for landslide detection in large space, which comprises the following steps: image synthesis, ice and snow detection, removal of non-potential landslide area, detection of potential landslide area, feature calculation, landslide detection model construction and precision validation; this invention avoids radiometric correction and outlier by detecting landslide from synthetic image. That guarantees practical applicability of the proposal. Firstly, detecting potential landslides can avoid the imbalanced sample distribution issue between background objects and landslides when training the landslide detection model. The landslide is further detected by building a random forest model based on the spectral features and textural features of potential landslide pixels in different neighboring time domains. It fully considers the changes of objects in different time domains, and lays a foundation for efficient landslide extraction. This model is relatively reliable and practical for automatically detecting landslide from large-scale images.

Classes IPC  ?

• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06V 20/13 - Images satellite
• G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
• G06V 10/60 - Extraction de caractéristiques d’images ou de vidéos relative aux propriétés luminescentes, p. ex. utilisant un modèle de réflectance ou d’éclairage
• G06V 10/34 - Lissage ou élagage de la formeOpérations morphologiquesSquelettisation

Abrégé

A method for soil moisture retrieval using multi-channel collaboration algorithm and passive microwave radiometry including: establishing mathematical relationship formula between brightness temperatures at any two channels according to microwave radiative transfer equation; collecting actual brightness temperatures of core channel and collaborative channels; selecting parameters to be retrieved including soil moisture value; giving a series of estimated values of parameters to be retrieved, calculating a series of predicted brightness temperatures of collaborative channels according to actual brightness temperature of core channel, microwave radiative transfer equation and mathematical relationship formula, comparing predicted brightness temperatures with actual brightness temperature, and determining soil moisture value.

Classes IPC  ?

• A01C 14/00 - Procédés ou appareils pour la plantation non prévus dans les autres groupes de la présente sous-classe
• G05B 19/4155 - Commande numérique [CN], c.-à-d. machines fonctionnant automatiquement, en particulier machines-outils, p. ex. dans un milieu de fabrication industriel, afin d'effectuer un positionnement, un mouvement ou des actions coordonnées au moyen de données d'un programme sous forme numérique caractérisée par le déroulement du programme, c.-à-d. le déroulement d'un programme de pièce ou le déroulement d'une fonction machine, p. ex. choix d'un programme

Abrégé

A tropical cyclone intensity forecast information generation method and system. The method comprises: obtaining data to be processed according to different data dimensions (S101); preprocessing said data to obtain initial data (S102); and predicting the initial data by using a preset forecast model to obtain tropical cyclone intensity forecast information (S103). Because the initial data comprises a climate persistence characteristic factor, an environment characteristic factor, and a brainstorming characteristic factor, factors affecting tropical cyclones can be fully utilized to predict the tropical cyclone intensity forecast information in combination with the preset forecast model, so that prediction processing is more intelligent and objective, a tropical cyclone forecast system is perfected, and the forecast accuracy is improved.

Classes IPC  ?

• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G06Q 50/26 - Services gouvernementaux ou services publics

Abrégé

A system for numerical simulation and test verification of icing characteristics of an aerostat includes an aerostat icing characteristic calculation model and an aerostat icing characteristic test system. The aerostat icing characteristic calculation model is configured to obtain icing data of the aerostat through numerical simulation, and the aerostat icing characteristic test system is configured to obtain icing characteristic data of the aerostat through a physical simulation test. The calculation result obtained through the numerical simulation and the test result obtained through the physical simulation test are mutually verified and improved, so as to facilitate the in-depth research and accurate analysis of the icing characteristics of the aerostat.

Classes IPC  ?

• G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
• B64F 5/60 - Test ou inspection des composants ou des systèmes d'aéronefs
• G06F 111/10 - Modélisation numérique

Abrégé

The present disclosure provides an LTCC high-voltage transformer, comprising: an upper side column, a magnetic core middle column, a lower side column, a primary coil, a secondary coil, first mediums and second mediums, the upper side column, the magnetic core middle column and the lower side column being sequentially provided from top to bottom, the primary coil and the secondary coil being provided on the magnetic core middle column, the plurality of first mediums being uniformly distributed on the magnetic core middle column in a longitudinal direction, and the plurality of second mediums being respectively provided between two adjacent turns of coils. The present disclosure can effectively reduce magnetic flux leakage of a transformer in a horizontal direction and a vertical direction, and achieve the effect of low leakage inductance.

Classes IPC  ?

• H01F 27/24 - Noyaux magnétiques
• H01F 27/28 - BobinesEnroulementsConnexions conductrices

Abrégé

A magnetic compensation method based on an aeromagnetic compensation error model includes: acquiring an upper limit of an error of a magnetic noise caused by both an environmental background field in an exploration area and an aeromagnetic flight platform, by using the aeromagnetic compensation error model, before an actual flight; determining, according to the upper limit, whether the environmental background field and the aeromagnetic flight platform are suitable for the actual flight, and if so, performing a calibration flight to acquire a compensation coefficient; and acquiring data of an attitude term by performing the actual flight, calculating an interference magnetic field by the data of the attitude term and the compensation coefficient, and performing magnetic compensation.

Classes IPC  ?

• G01V 3/38 - Traitement de données, p. ex. pour l'analyse, pour l'interprétation ou pour la correction
• G01V 3/16 - Prospection ou détection électrique ou magnétiqueMesure des caractéristiques du champ magnétique de la terre, p. ex. de la déclinaison ou de la déviation spécialement adaptée à l'utilisation pendant le transport, p. ex. par une personne, un véhicule ou un bateau spécialement adaptée à la prospection aérienne
• G01V 3/40 - Prospection ou détection électrique ou magnétiqueMesure des caractéristiques du champ magnétique de la terre, p. ex. de la déclinaison ou de la déviation spécialement adaptée à la mesure des caractéristiques du champ magnétique terrestre