193 results about "Cloud detection" patented technology

The invention provides an urban green land coverage rate dynamic monitoring method and device. The method comprises the following steps: acquiring multispectral satellite remote sensing data, and preprocessing the multispectral satellite remote sensing data; carrying out cloud detection and shadow detection on the preprocessed remote sensing image, and calculating an effective pixel proportion index, a spectrum quality index, a time phase matching degree index and a spatial integrity index; based on the four indexes and the corresponding weights, constructing a fuzzy comprehensive evaluation matrix, and selecting an optimal data combination; calculating a normalized difference vegetation index (NDVI) based on the near infrared band and the red light band in the optimal data combination, and performing illumination angle correction and time sequence smoothing processing on the NDVI to obtain an optimized NDVI; performing green space identification on the optimized NDVI by adopting a first deep learning model, and outputting a green space distribution map; and calculating a green land area change rate, an NDVI difference value and connectivity index change based on the green land distribution map. According to the invention, high-precision dynamic monitoring of the green land coverage rate is realized, and the monitoring efficiency and accuracy are improved.

The invention relates to the technical field of remote sensing image processing, and discloses a remote sensing image adaptive identification method and system for territorial space planning, and the method comprises the steps: obtaining a multi-source remote sensing image data set of a research region, feature extraction, cloud detection, quality evaluation and adaptive preprocessing are carried out; carrying out prototype network coding, calculating a category prototype and probability, and supporting fine tuning of a set; carrying out multi-scale cavity convolution and category scale attention fusion; evaluating the adaptability score of the comprehensive fusion feature map set, and carrying out weighted fusion, classification and normalization; change detection is carried out, stable and change regions are segmented, and time sequence context features are extracted and constrained optimization is carried out; entropy is fused, a boundary is decided, uncertainty is estimated, and weighted fusion is carried out according to a change area; conditional random field optimization, confidence level grading and connected domain identification are carried out; the automation level, the adaptive capacity and the recognition reliability of remote sensing monitoring of territorial space planning are improved.

The invention provides a three-dimensional laser point cloud detection method and system for a tunnel water-endowed area lining wide and thick belt, and the method comprises the steps: detecting the tunnel water-endowed area lining wide and thick belt through a rolling ball method, scanning the tunnel point cloud through three-dimensional laser, and collecting the laser point cloud data of the tunnel water-endowed area lining wide and thick belt; establishing a high-fidelity tunnel twinborn model; simulating tunnel deformation conditions through finite elements, constructing a finite element simulation tunnel deformation analysis model, and outputting a tunnel deformation true value of the tunnel twinning model; building a tunnel simulation scanning virtual simulation platform according to the tunnel twinborn model and the laser scanning characteristic data; performing simulated three-dimensional laser scanning on the tunnel twin model in a virtual environment to obtain large sample detection data; training a deformation detection neural network model in an auxiliary manner according to the large sample detection data; performing tunnel point cloud registration through a neural network model; a tunnel section point cloud is obtained by fitting the central axis of the tunnel, and tunnel deformation analysis is carried out; and the tunnel deformation analysis processing accuracy is obviously improved.

The invention discloses a method and system for assimilating meteorological satellite hyperspectral error non-Gaussian distribution data based on quantum computing, and the method carries out the generalized quality control of meteorological satellite hyperspectral data in consideration of an outlier value on the observation brightness temperature and equivalent data of a satellite hyperspectral infrared detector channel. Comprising the steps of channel optimal selection considering quantum information entropy, deviation correction based on artificial intelligence generalized integration generation type deep learning and cloud detection based on a minimum residual method to remove cloud view field point data, so that quality control data is obtained. And performing quantum space solution on the quantum non-Gaussian increment four-dimensional variational assimilation model compatible with error Gaussian distribution and non-Gaussian distribution based on quality control data to obtain analysis field data, and solving the problem that the brightness temperature assimilation of the hyperspectral infrared channel with error non-Gaussian distribution (or obvious non-Gaussian characteristics) is limited in the prior art. Meanwhile, the method is high in solving speed, and the obtained analysis field data has good precision and calculation timeliness.

The invention discloses a ground engineering construction quality supervision digital delivery system, which relates to the technical field of engineering supervision, and comprises the steps of generation and multi-source data checking, activity record structuring and anti-verification before hiding, space-time reference unification and point cloud fusion playback, rule checking and differential rectification, and object-level marshalling delivery and asset authority management. According to the method, a unified data pool, activities, events and evidence chains are constructed through object codes to achieve time-space consistent association, image, point cloud and detection consistency check is conducted before hiding, blocking difference and unified reference fusion point cloud are generated, indexes support three-dimensional playback, rule base batch check is conducted, and issuing rectification is conducted. And the object-level delivery list and fingerprints support cross-platform identification and auditing to form a traceable link.

The invention provides an intraday high-frequency automatic monitoring method and system for lake cyanobacterial bloom based on a GOCI-II satellite, and aims to solve the problems that an existing method is susceptible to interference of thin cloud, low in recognition precision, high in false positive rate, low in efficiency and the like, and the processing flow depends on manpower. The system is based on an improved AFAI index, fine cloud detection, cloud expansion processing and classification correction strategies are combined, misrecognition caused by thin clouds and shadows is effectively restrained, and the extraction accuracy and robustness are improved. The system has the whole-process unattended processing capacity from GOCI-II data automatic downloading, preprocessing, algal bloom recognition, thematic map making to report output, can generate a monitoring report within one hour after satellite imaging, and supports intra-day multi-temporal cyanobacterial bloom dynamic monitoring. The method has been successfully applied to lakes such as Taihu Lake, lakes, Chaohu Lake and Hongze Lake, is particularly suitable for monitoring and early warning cyanobacterial bloom in large and medium lakes, and has wide application prospects in the fields of water environment supervision, water quality risk control, ecological assessment and the like.

The present application relates to a power distribution Internet of Things terminal debugging and testing system. The system comprises a cloud test service center (100), a power distribution Internet of Things terminal debugging device (200) and an intelligent gateway (300); the intelligent gateway (300) is used for acquiring terminal information of power distribution Internet of Things terminals and uploading the terminal information to the cloud test service center (100); the cloud test service center (100) is used for issuing a test command to the power distribution Internet of Things terminal debugging device (200) on the basis of the terminal information; the power distribution Internet of Things terminal debugging device (200) is used for carrying out signal conversion on the test command, determining an analog signal corresponding to the test command, and applying the analog signal to the power distribution Internet of Things terminals; the intelligent gateway (300) is used for acquiring digital signals and transmitting the digital signals to the cloud test service center (100), wherein the digital signals are obtained by converting the analog signal by the power distribution Internet of Things terminals; the cloud test service center (100) is used for determining test results of the power distribution Internet of Things terminals on the basis of the digital signals. Fully-automatic closed-loop debugging can be implemented, and the power distribution efficiency is greatly improved.

The invention provides a cloud condition adaptive geostationary satellite sea surface temperature inversion method based on deep learning, and relates to the technical field of sea surface temperature inversion, and the method specifically comprises the steps: obtaining geostationary satellite observation data, geographic information data, polar orbit satellite sea surface temperature and cloud products, and analyzing the sea surface temperature and atmospheric background data; performing standardization preprocessing on the obtained stationary satellite observation data; constructing a cloud detection data set; a cloud detection model of deep learning is constructed and trained; constructing a clear sky data set; a clear sky inversion model of deep learning is constructed and trained; constructing a data set for under-cloud inversion model training; constructing an under-cloud inversion model of deep learning and training the under-cloud inversion model; and integrating the cloud detection model, the clear sky inversion model and the under-cloud inversion model, adaptively calling the corresponding model according to a real-time cloud detection result, and generating a final sea surface temperature product. According to the technical scheme, the problem that in the prior art, the accuracy of a reconstructed product on the spatial structure cannot be comprehensively reflected is solved.

The invention discloses a greenbelt cooling effect evaluation method and system based on a remote sensing image and a streetscape image, and the method comprises the steps: obtaining the data of the streetscape image, carrying out the denoising and cutting, recognizing, extracting and calculating the pixel coverage rate of arbors, grass trees and shrubs, and constructing the greening feature indexes of the streetscape image; acquiring an urban satellite remote sensing image, performing radiometric calibration, correction and identification, and constructing a greening characteristic index of the remote sensing image; acquiring a multispectral remote sensing image of the city, performing cloud detection, and obtaining the surface temperature of the city through surface temperature inversion; and constructing a random forest model and performing iterative training based on greening characteristic indexes of the surface temperature, the streetscape image and the remote sensing image of the city, and evaluating the cooling effect by using the fitted random forest model. The method gives full play to the advantages of wide coverage range of the remote sensing image and rich details of the street scene image, makes up for the limitation of a single data source, and achieves the comprehensive and accurate evaluation of the cooling effect of the green land.

The invention provides a spectrum self-attention and cloud mask guided remote sensing image cross-modal cloud detection method. According to the method, the spectral self-attention sub-network in the remote sensing image cross-modal cloud detection network is constructed, and the dependency relationship between different spectral bands of the remote sensing image is effectively mined and utilized, so that the recognition accuracy of cloud region features is improved. According to the method, cross-modal migration from the source domain image to the target domain image is realized by using the migration sub-network of the unsupervised image domain in the remote sensing image cross-modal cloud detection network, and the multi-spectral image with the target domain features is generated, so that the defect of the source domain image in spectral information is overcome, the false detection and omission ratio is effectively reduced, and the detection efficiency is improved. And the robustness of cloud detection of the model in a complex scene is enhanced.

The invention discloses a method and system for processing a circular weld joint based on point cloud data, and belongs to the technical field of machine identification of weld joints, and the method comprises the following steps: collecting the point cloud data of the surface of a workpiece through a visual sensor; preprocessing the collected point cloud data; calculating a normal vector of each point in the point cloud data; segmenting the workpiece surface point cloud and the reflective interference points based on a normal vector, and filtering the interference points deviating from a preset range in the normal direction to obtain a target point cloud and a reference point cloud; projecting the target point cloud to a reference plane obtained by fitting the reference point cloud, generating a target projection point cloud, detecting boundary points of the target projection point cloud, screening candidate weld joints based on distance distribution from the boundary points to the center of mass, and extracting weld information. According to the method, the workpiece made of the material with high light reflection is treated, the finally obtained result can still keep good precision, and the quality of the workpiece is improved.

The invention discloses a thick cloud-thin cloud integrated detection method and system based on spectral features and a multi-scale depth model, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: 1, training a plurality of scene-level models of different scales based on a cloud detection data set, generating a plurality of scene-level cloud probability graphs and scene-level binary cloud masks of different scales; step 2, based on a plurality of scene-level cloud probability graphs of different scales and dark pixel features of the image, pixel-level cloud probability graphs facing thick cloud and thin cloud are generated respectively; and step 3, combining the scene-level binary cloud mask, fusing the pixel-level cloud probability graphs facing the thick cloud and the thin cloud by adopting dark pixel gradient, and then combining an adaptive threshold and distance weighting to generate a pixel-level binary cloud mask so as to realize thick cloud-thin cloud integrated detection. According to the method, manual segmentation threshold setting is avoided, and fusion of the thick cloud probability graph and the thin cloud probability graph and full-automatic accurate detection of the thick cloud, the thin cloud and the cloud edge are realized.

The invention discloses a method for polar region all-day cloud detection by using FY-3D infrared channel brightness temperature, and relates to the technical field of cloud detection, and the method comprises the following steps: obtaining CALIOP data and FY-3D / MERS-II data, specifically CALIOP cloud layer product data and MERS-II observation data and positioning data, carrying out the preprocessing, and carrying out the preprocessing to obtain an FY-3D infrared channel brightness temperature; according to the method, space-time matching is carried out according to transit trajectories of CALIOP and FY-3D satellite MERS-II sensors, matched infrared channel radiance information is extracted, calculation is carried out based on the matched infrared channel radiance information, and the brightness temperature and the brightness temperature difference are calculated. CALIOP data are combined with FY-3D / MERS-II data, the CALIOP is used as a truth value standard of cloud detection, space-time matching is carried out with the FY-3D / MERS-II data, and the brightness temperature difference is calculated. The polar region all-time cloud detection is realized, the accuracy of the cloud detection is improved, and a high-quality data set is provided for the training of a subsequent machine learning model, so that the effects of being not limited by the dependence of visible light and stably working under an all-time condition are achieved.

The invention provides a hyperspectral remote sensing image cloud detection method based on a space-spectrum dual-coding U-shaped network, and relates to the technical field of remote sensing image processing, and the method comprises the steps: collecting a hyperspectral image, carrying out the preprocessing and manual marking, and obtaining a cloud detection data set; constructing a hyperspectral cloud detection model based on the space-spectrum dual-coding U-shaped network; training a hyperspectral cloud detection model through the marked cloud detection data set; and obtaining a to-be-detected hyperspectral image and inputting the to-be-detected hyperspectral image into the trained hyperspectral cloud detection model to obtain a cloud detection result. Through the hyperspectral cloud detection model, the recognition accuracy and efficiency of the hyperspectral image are improved, and the problem that a current cloud detection method only pays attention to spatial information features and ignores spectral discrimination information extraction is solved.

The invention discloses a cloud detection method based on double-branch fusion feature enhancement, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: obtaining a cloud detection data set, and carrying out the preprocessing of the data set; constructing a cloud detection network model based on double-branch fusion feature enhancement; the preprocessed data set is used for training a cloud detection model, and model parameters are saved; and predicting the images of the test set from the model parameters stored in the step 3, and quantifying the model performance through evaluation indexes. The cloud boundary detail extraction and thin cloud positioning capability can be improved, the analysis capability of the model on a cloud layer complex structure is effectively improved, the advantages of convolution operation and an attention mechanism are ingeniously fused, the feature expression of a key region can be dynamically highlighted and strengthened while the local spatial features of the image are extracted, and the method is suitable for the cloud boundary feature extraction and thin cloud positioning. Therefore, the detail features and the spatial dependency relationship of the cloud layer are more accurately captured, and it is ensured that image details are finely reconstructed in the gradual recovery process of the image spatial resolution.

The invention provides a large mountain photovoltaic environment fire danger identification method based on a meteorological satellite, and the method comprises the steps: carrying out the radiometric calibration processing based on the Himawari-8 data of a detection region, obtaining the spectral reflectivity or spectral radiation brightness of each wave band of the detection region, and obtaining a cloud shielding pixel through a cloud detection method; a normalized brightness temperature time sequence change function is established based on the time sequence change of the background brightness temperature, a brightness temperature estimation value is estimated, the maximum assumed threshold value of each stage is determined according to the brightness temperature time sequence change function, and then a time sequence method is used for distinguishing fire point pixels. And finally, carrying out credibility grading on the extracted fire point pixels to obtain a fire point grading graph, and sending out corresponding early warning according to fire point grades.

The invention provides a target detection credibility estimation method fusing data and model uncertainty, and belongs to the field of data processing, and the method comprises the steps: obtaining an image quality score through an image quality evaluation model; outputting a target frame by using a two-dimensional detection model, and performing Bayesian correction of confidence in combination with an image quality score and a weather perception model result; meanwhile, outputting a three-dimensional target frame by using a laser radar point cloud detection model, and correcting three-dimensional detection confidence in combination with a target distance and weather conditions; according to the statistical relationship between the number of point clouds in the detection frame and the detection accuracy, constructing the point number confidence coefficient based on Bayesian reasoning; and performing structural consistency analysis according to the spatial size of the detection frame and the prior size distribution of the target category, and outputting structural matching confidence. And finally, the five indexes are fused, and the overall credibility of the sensing result is calculated. According to the method, the credible judgment capability of a sensing system in a complex environment can be improved, and the method has good practicability and engineering value.

The invention discloses a deep learning inversion AOD-assisted atmospheric correction-based method and device and a medium, and relates to the field of atmospheric remote sensing and deep learning cross technologies, and the method comprises the steps: constructing a multi-type training sample set fusing actual measurement and physical simulation data, and carrying out mass screening and layering processing to obtain a multi-type training sample set; obtaining a pre-training sample, a fine tuning sample and an extreme scene sample matched with the satellite remote sensing data; establishing a deep learning network model taking data loss and inverse operator constraint loss weighted fusion as a total loss function, and adopting a cross-satellite transfer learning strategy to sequentially complete pre-training, satellite exclusive fine tuning and extreme scene enhancement training to obtain a trained aerosol optical thickness inversion model; radiometric calibration, cloud detection, geometric correction preprocessing and input model reasoning calculation are carried out on satellite remote sensing original data, post-processing and quality grading are carried out on an output result, and an aerosol optical thickness inversion result used for assisting atmospheric correction is obtained.

The invention discloses an offshore floating type low-altitude waveguide all-weather detection method and system, and relates to the technical field of marine environment monitoring and electronic information engineering.The method comprises the steps that the current weather condition is judged according to a rain intensity sensor, a miniature weather station, a visibility sensor and an infrared cloud measurement sensor; obtaining a temperature and humidity value range according to the current weather condition, and adding liquid water for correction according to the weather condition; a simulated brightness temperature is obtained according to a MonoRTM model by combining an air pressure profile constructed by sea surface air pressure and a pressure height formula; designing a multi-objective genetic inversion algorithm, constructing an objective function, and when the objective function obtains a minimum value, obtaining an inverted atmospheric parameter profile group; and according to the atmospheric parameter profile group, calculating an atmospheric refractive index profile, judging whether a low-altitude waveguide occurs or not, and uploading a result to a shore station data management center. According to the invention, the atmospheric absorption coefficient is corrected according to different weathers, and all-weather and high-precision monitoring of the low-altitude waveguide on the offshore floating platform is realized.

The invention discloses a point cloud detection method, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring a first point cloud data feature at a current moment; obtaining an initial query feature group; wherein the initial query feature group comprises at least one initial query feature; fusing context information in the first point cloud data features into the initial query feature group by using a cross attention mechanism to obtain a first fused query feature group; performing self-attention processing on the first fusion query feature group to obtain a target query feature group corresponding to the current moment; and acquiring a target query feature group at the previous moment as a historical query feature group, and performing target detection by using the target query feature group and the historical query feature group to obtain a target detection result at the current moment. In this way, the target detection speed can be increased.

The invention belongs to the technical field of remote sensing image processing and target inversion, and particularly relates to a hyperspectral image aerial aircraft height and spectral reflectivity inversion method based on wake clouds and shadows of the wake clouds. In order to solve the technical problems of low aerial aircraft height inversion precision and inaccurate spectral reflectivity inversion in the prior art, firstly, wake cloud and shadow detection, matching and geometric modeling are performed on a hyperspectral image, and the aircraft flight height is inverted by using a stable geometric relationship between the wake cloud and the shadow; high-precision height extraction is realized; and then combining flight height information to establish a radiation transmission model for coupling the aerial aircraft and the background environment, and inverting the spectral reflectivity of the model. Through integrated processing of wake cloud detection, shadow matching, height inversion and spectral reflectivity inversion, the accuracy and applicability of aerial aircraft characteristic inversion are significantly improved, and an effective technical support is provided for application of hyperspectral remote sensing in aerial aircraft monitoring and identification.

The invention discloses a video intelligent identification system for abnormal and illegal behaviors on a customs supervision operation site, and belongs to the technical field of intelligent identification and detection. The system comprises an input module, an edge cloud cooperation module and an output module. The input module comprises a video access module and a video preprocessing module; the edge cloud cooperation module comprises an edge end calculation module, a cloud calculation module and a communication optimization module; the edge end calculation module comprises a task complexity evaluation module, a dynamic task scheduling module, a task segmentation module and a dual-channel detection module; the cloud computing module comprises a cloud detection module and a fusion analysis module; and the output module comprises a report generation unit and an abnormity notification unit. According to the invention, the customs monitoring video is intelligently identified and analyzed based on edge cloud collaboration, the dynamic scheduling of tasks, the optimization of computing resources and the improvement of data transmission efficiency are realized, and the real-time performance and accuracy of identification and detection are obviously improved.

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

The invention relates to a cloud detection method based on combination of a laser radar and a microwave radar, and belongs to the field of radio. The detection method comprises the steps that a coordinate system where a laser radar radiation antenna is located is used as a reference, and system pointing angle deviation caused by the installation position of a microwave radar extension set is corrected in the mode that angle tracking parameters are calculated according to laser-microwave radar extension set antenna installation differences, and scanning space angle synchronization is guaranteed; time synchronization is guaranteed by controlling the time of a radar terminal issuing a control signal to a laser radar extension set and a microwave radar extension set, so that radar beams arrive at meteorological particle echo information to be consistent; after the radar terminal collects data, characteristic parameter extraction and data fusion are carried out on the composite observation data of the laser radar and the cloud radar. According to the method, the problem of target angle deviation caused by spatial distribution difference of the laser radar and the microwave radar during composite observation of the laser-microwave radar system is solved, and the consistency of observed targets is improved.

The invention belongs to the field of satellite remote sensing fire monitoring, and discloses a satellite remote sensing forest fire initial fire point monitoring method, which comprises the following steps: acquiring satellite data as input data; reading the zenith angle of the sun in the satellite data, and judging the time period as a day time period, a night time period and a morning and night junction time period; the solar zenith angle, the sensor zenith angle and the like need to be read in daytime period data so as to filter solar flares; carrying out simple cloud detection on data in all time periods by utilizing 11-micron channel brightness temperature and 11-4-micron channel brightness temperature, expanding an adaptive window by taking a suspected initial fire point pixel as a center, and calculating 4-micron channel brightness temperature difference, 11-micron channel brightness temperature difference and 4-11-micron channel brightness temperature difference of a background pixel by utilizing a time sequence method; and counting the mean value and the standard deviation of the background pixels, and confirming and extracting a forecast initial fire point by using a normal distribution starting point algorithm. According to the method, the timeliness and accuracy of early warning of the fire disaster are improved, and important technical support is provided for forest fire prevention decision making.

The application discloses a remote sensing image cloud area reconstruction method based on SAR prior knowledge guidance. The method firstly inputs the obtained optical remote sensing image into a cloud detection module to obtain a preliminary cloud detection result, then constructs a SAR feature extraction module, inputs the obtained adjacent time phase SAR image into the SAR feature extraction module, constructs a polarization covariance matrix of the SAR image, extracts relevant polarization scattering features, finally constructs a cloud area image reconstruction module, and sequentially inputs the obtained optical remote sensing image, the cloud detection result and the polarization scattering features into the cloud area image reconstruction module, so that the spatial structure feature reconstruction and the global consistency feature repair of the cloud-shielded area of the optical remote sensing image are realized, and finally a high-fidelity cloud-free image is generated. The application effectively improves the feature recovery capability of the cloud-shielded area of the optical remote sensing image by fusing the SAR image texture and polarization features, and solves the problems of optical remote sensing image information blurring and partial information loss caused by serious cloud and fog interference.

The application discloses a kind of for satellite in-orbit real-time cloud detection fast cloud determination method, belong to optical remote sensing image processing field, the method utilizes the principle of cloud top reflection sunlight, by establishing look-up table in ground platform, the method is realized in-orbit fast cloud determination by storing look-up table information in cloud determination load computing platform.The application according to the reflectivity of cloud layer in visible light compared to ground object in remote sensing image is higher, the thicker cloud layer is, the stronger reflection ability is, the greater albedo is, by establishing the relationship between the luminance information represented by observation instrument response DN value and cloud optical thickness, the cloud optical thickness is inverted by response DN value, to judge the cloud coverage of observation target area according to cloud optical thickness, in the case where guaranteeing identification accuracy, the speed of satellite in-orbit cloud detection is effectively improved.

The present invention discloses a remote sensing cloud repair method based on time-spectrum domain fusion and temporal self-attention enhancement, and relates to the technical field of remote sensing cloud image processing. The present invention includes: receiving a remote sensing missing image as a source image; constructing a generator, and inputting the remote sensing missing image into the generator for preliminary processing, wherein the generator includes a feature extractor, a ConformerPlus module, and a cloud detection module, and a new multi-head self-attention mechanism is embedded in the ConformerPlus module. The present invention can extract and fuse the time-spectrum domain information of the data, thereby enhancing the expression ability of the features, and improving the model's ability to capture edge information, thereby guiding the model to achieve progressive cloud removal from the outside to the inside, while also improving detail performance and suppressing noise interference, effectively improving the detail clarity and texture fidelity of the image.

The invention discloses a coaxial dual-wavelength laser radar for simultaneously measuring wind field and cloud height. The radar comprises a cloud measuring module, a wind measuring module and a transmitting and receiving telescope. The wind measuring module comprises a first laser, a first optical fiber coupling mirror, a second optical fiber coupling mirror, a wind measuring polarization isolation module, a polarization-maintaining optical fiber coupler and a balanced detector; the cloud measuring module comprises a second laser, a cloud measuring polarization isolation module, a third optical fiber coupling mirror, a fourth optical fiber coupling mirror and a photoelectric detector; wherein the wind measuring detection laser generated by the first laser forms a first optical signal after being emitted, and the cloud measuring detection laser generated by the second laser forms a second optical signal after being emitted, and the first optical signal and the second optical signal are beam-combined by a dichroic mirror and then transmitted to the transmitting and receiving telescope. Through the above arrangement, wind field information and cloud height information in a low-altitude environment are simultaneously obtained, and the emission optical paths of the wind measuring module and the cloud measuring module overlap, so that the volume and weight of the laser radar can be reduced to the maximum extent.

The invention discloses an E-mail processing method and a storage medium. The method comprises the following steps: acquiring a target E-mail; extracting target email features of the target email, determining a matching result of the target email features based on a preset abnormal email feature library, and determining a first category identifier of the target email according to the matching result; if the first category identifier represents that the target E-mail is a normal E-mail, sending an E-mail category detection request to a cloud server; receiving a second category identifier sent by the cloud server; the second category identifier is obtained by performing mail category detection on each target attribute feature by the cloud server based on a preset cloud detection strategy corresponding to each target attribute feature; if the second category identifier represents that the target E-mail is a normal E-mail, the target E-mail is sent to the target mail receiving account, the abnormal E-mail can be detected comprehensively, accurately and rapidly, and the probability that the user receives the abnormal E-mail is reduced.