114 results about "Cloud detection" patented technology

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 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 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 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 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 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 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 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.

The application discloses a radar lens cleaning device and a cleaning method, and belongs to the technical field of cloud detection equipment. The device comprises a supporting assembly, a cleaning assembly and a displacement assembly. The supporting assembly is arranged on a scanning head and is distributed on both sides of the lens along a first straight line direction. The cleaning assembly comprises a cleaning soft tube and a driving mechanism. The cleaning soft tube is detachably connected with the supporting assembly, and a cleaning surface is formed on the cleaning soft tube. The cleaning surface faces the lens. The driving mechanism can control the cleaning surface to move along the radial direction of the cleaning soft tube, so that the cleaning surface can contact or move away from the lens. The displacement assembly can drive the cleaning surface to reciprocate along a direction perpendicular to the first straight line direction and rub against the lens. The radar lens cleaning method adopts the above radar lens cleaning device, has high cleaning efficiency, can achieve better cleaning effect, and can protect the lens and avoid damaging the lens during the cleaning process.

The application relates to the technical field of battery management, and discloses a lithium battery cloud detection system for high-altitude pure electric heavy truck testing, which comprises a vehicle-mounted data acquisition module, an edge computing gateway and a cloud intelligent analysis platform.The vehicle-mounted data acquisition module is used for collecting electric variable data, vehicle operation data and environmental data of a pure electric heavy truck lithium battery system in real time.The edge computing gateway is in communication connection with sensors of the vehicle-mounted data acquisition module at an input end, and is provided with a wireless communication unit at an output end, and is used for pre-processing, compressing and encrypting the collected data.The cloud intelligent analysis platform is in communication connection with the edge computing gateway through the wireless communication unit, is used for receiving and storing the processed data, and is used for analyzing the data to generate a battery health state evaluation, an energy consumption analysis report, a charging strategy suggestion and an infrastructure planning suggestion.The application significantly improves the testing depth, operation safety and economy of a pure electric heavy truck in an extreme environment.

The application discloses a kind of water body missing data reconstruction method and system for optical remote sensing image, belong to image processing technical field, wherein, the method includes: vectorization processing and buffer zone processing to GSW data set, obtain the water mask range of the region to be processed;Cloud detection algorithm and topographic shadow detection algorithm are marked to cloud and shadow in optical remote sensing image, obtain marked image;Water classification data in the water mask range of marked image is calculated using Otsu algorithm;Obtain GSW water frequency data, using 98% water frequency threshold to classify it, greater than threshold as reference permanent water, less than threshold as reference seasonal water;Respectively to the intersection of the mask to be reconstructed, reference permanent water, effective data and reference seasonal water are handled, and then reconstruct permanent water and seasonal water.The method makes full use of existing image, while ensuring the stability of algorithm accuracy, improves the reconstruction rate of time series image.

The invention discloses a portal crane multi-source point cloud fusion method based on grab bucket positioning and a grab bucket direct control method, and belongs to the technical field of portal crane automatic control, and multi-source point clouds comprise first laser point clouds used for modeling a grab bucket and materials and second laser point clouds used for modeling an operation area. The fusion method comprises the following steps: converting a first laser point cloud into a ground coordinate system through a ground point cloud detection algorithm; according to grab bucket position information collected in real time, the relation between a ground coordinate system and a portal crane grab bucket positioning system coordinate system is obtained, and the first laser point cloud under the ground coordinate system is converted into the portal crane grab bucket positioning system coordinate system; and performing registration and fusion on the second laser point cloud and the first laser point cloud converted to the coordinate system of the portal crane grab bucket positioning system. The point cloud coordinate system and the portal crane grab bucket positioning system coordinate system can be aligned in real time, and the problem of coordinate system deviation caused by dependence on static structure parameters in the prior art is solved.

The invention provides a file detection processing method and system and a server, and relates to the technical field of network security, the method is specifically used for a malicious file detection process between a client and a cloud, in the detection process, the client transmits feature vector data of a to-be-detected file to the cloud instead of original data, and the detection efficiency is improved. Therefore, the privacy problem is effectively solved, and quick and timely file security detection can be realized in the client by combining a related model deployed at the cloud end; besides, the detection result of the cloud can be acquired by utilizing the edge terminal of the client, and the to-be-detected file can be processed under the control of the edge terminal, so that the local processing of the client is realized, the strong dependence on the cloud is reduced, and the consumption of network bandwidth resources is reduced; abnormity which cannot be judged by the edge terminal can be directly uploaded to the cloud detection and analysis module and is confirmed by the cloud and then fed back to the edge terminal for final treatment, so that the accuracy is improved, and misoperation is reduced.

The application discloses a multi-task cooperative iterative defogging method for remote sensing images, and belongs to the image restoration technical field based on computer vision. The method comprises the following steps: 1, acquiring a remote sensing image dataset; 2, inputting remote sensing foggy images of a training set into a defogging network; 3, inputting the remote sensing foggy images of the training set, a blue gradient prior feature map and a depth prior feature map into a cloud detection network; 4, calculating a cloud perception reconstruction loss by using remote sensing defogging images and a cloud probability map; 5, combining the defogging network and the cloud detection network obtained through iterative training to obtain a defogging network framework; and 6, selecting remote sensing foggy images in a test set in step 1 as inputs of the defogging network framework, evaluating and outputting test results. Experimental results show that the application is superior to existing similar methods in objective indexes and subjective visual evaluation.

This invention relates to the field of remote sensing technology, specifically to remote sensing image classification methods, systems, computer equipment, and storage media. The remote sensing image classification method includes the following steps: based on raw remote sensing data, using satellite cloud image analysis and surface temperature mapping techniques to perform comprehensive analysis of microclimate characteristics; using K-means clustering analysis algorithm to classify the data; and generating a comprehensive climate characteristic dataset. The beneficial effects of this invention are that by integrating K-means clustering, adaptive histogram equalization, convolutional neural networks, long short-term memory networks, and fully convolutional network algorithms, it can effectively identify geographic patterns and monitor abnormal climate events. Simultaneously, it optimizes surface classification, combines Gaussian filtering, MODIS cloud detection algorithms, and data assimilation techniques to deeply preprocess raw data, improve data quality, enrich information content, and effectively predict and respond to climate change using long short-term memory networks and time series analysis techniques.

The invention discloses a blockage detection method for a particulate filter, and relates to the field of vehicle tail gas treatment, and the method comprises the following steps: generating a pressure difference volume flow relation curve by using a preset curve fitting model constructed based on a weighted least square method, calculating the change trend similarity between the pressure difference volume flow relation curve and a curve in a non-blockage state, and determining the blockage of the particulate filter according to the change trend similarity. And based on a comparison result corresponding to the change trend similarity, sending a cloud detection confidence coefficient to the vehicle end controller, so that the vehicle end controller superposes the cloud detection confidence coefficient and the vehicle end detection confidence coefficient, and outputting a jam detection result. According to the method, the disturbance variable introduced by sensor drift in the pressure difference and the volume flow is suppressed based on the weighted least square method, the precision of the generated pressure difference volume flow relation curve is improved, the blockage detection is realized based on the change trend similarity, the cloud detection confidence coefficient and the vehicle end detection confidence coefficient are further superposed by the vehicle end controller, and the vehicle end detection accuracy is improved. Double verification is realized, and the blockage detection precision of the particle trap is improved.

The invention belongs to the technical field of fruit tree yield remote sensing intelligent estimation methods, and particularly relates to a fruit tree yield remote sensing intelligent estimation method fusing multiple biological time-space characteristics of a flowering phase. Performing atmospheric correction, cloud detection and masking, region cutting and time sequence registration on the image, calculating a plurality of vegetation indexes including vegetation indexes reflecting flowering phase characteristics, performing maximum value synthesis monthly, performing sample matching on monthly-scale multi-source characteristics obtained in the step 1 and historical fruit tree yield data, constructing a training set and a verification set, and obtaining a monthly-scale multi-source feature data set, a monthly-scale multi-source feature data set and a monthly-scale multi-source feature data set, a monthly-scale multi-source feature data set and a monthly-scale multi-source feature data set; an input layer receives multivariable time sequence features, an encoder adopts a three-layer stacked LSTM structure, a feature level attention mechanism module is inserted between an LSTM output layer and a regression layer, an Adam optimizer is adopted, and batch size and training turns are set.

A system and method improves cloud detection and response by generating a normalized event log from a plurality of cloud computing layers. The method includes receiving a plurality of events, wherein a first event is generated in a first cloud layer of a cloud computing environment provided by a cloud service provider (CSP) and a second event is generated in a second cloud layer of the cloud computing environment; extracting data from each event; generating a normalized event based on the extracted data and further based on a predefined data schema, the predefined schema including a plurality of data fields, at least a portion of which are related to cloud layers; storing the normalized event in a transactional database having stored therein a normalized event log; and applying a rule from a rule engine on the normalized event to detect a cybersecurity threat in the cloud computing environment.

The invention provides a defogging and destreaking advanced method and device for an optical remote sensing image, and provides an optimization processing strategy of firstly removing thin cloud and then removing stripes for the common problems of stripe noise and thin cloud shielding in the remote sensing image, and is matched with an improved algorithm. Designing a segmented adaptive window moment matching fringe removal method based on an automatic sliding window, automatically dividing regions through cloud detection, dynamically determining a gray segmentation threshold value and the size of the sliding window, and effectively eliminating non-periodic fringes; and on the other hand, a shallow U-Net structure thin cloud and mist removing network with multi-scale cloud and mist feature fusion is constructed, an on-chip color balance loss function is introduced, and image splicing chromatic aberration is inhibited. According to the method, the image definition and the visual consistency can be remarkably improved, the problem of cloud and mist residue caused by'firstly removing stripes' or the problem of noise amplification caused by'firstly removing cloud and mist 'is avoided, high-efficiency and high-quality remote sensing image restoration is realized while texture details are kept, and the method is suitable for automatic processing of large-size high-resolution remote sensing data.

Embodiments of the present application provide a key point recognition device and method based on wireless radar signals. The method comprises: performing feature extraction on point cloud data obtained within a period of time to obtain first spatial feature data, Doppler velocity feature data, reflection energy feature data, density distribution feature data and time distribution feature data of the reflection point cloud; using a neural network-based fusion feature extraction model to detect the cascaded feature data to obtain fusion feature information; and using a neural network-based key point detection model to detect the fusion feature information to output key point data of the object. Thus, the key points of the object (such as a human body) can be detected according to the radar point cloud without limiting the action category, the required computing resources are less, and the detection accuracy is high; in addition, it is easy to implement, simple to operate, has strong noise resistance, and high privacy protection.

The present application relates to the technical field of remote sensing image cloud detection, and discloses a remote sensing image cloud recognition model construction, a cloud recognition method, a device and a medium, wherein three types of complementary features of spectrum, time and space are extracted from multi-temporal and multi-spectral remote sensing images, then a special three-branch network is designed for deep feature extraction, and weighted fusion is performed through a deep feature fusion module; finally, a high-precision recognition result is output by a segmentation module, effectively overcoming the inherent defects of misjudgment of high-reflective ground objects, thin cloud missed detection and fuzzy boundary caused by the dependence of traditional methods on single features, and significantly improving the accuracy, boundary detail fidelity and overall robustness of cloud layer recognition under complex ground surface environment and variable lighting conditions. The present application comprehensively utilizes multi-source and multi-dimensional information such as spectrum, spatial texture and time to improve the accuracy and detail fidelity of cloud layer recognition, and meets the requirements of cloud monitoring and remote sensing data analysis under complex ground surface environment.

The invention discloses a thin cloud atmosphere top reflectivity estimation method and system based on deep learning. The method comprises the following steps: firstly, performing radiometric calibration on obtained remote sensing data to obtain a reflectivity image; secondly, 1 / 2 of a wave band is input, end-to-end feature extraction and cloud TOA reflectivity inversion are achieved through a convolutional neural network, and a preliminary inversion result is output; and finally, sampling and generating a denoised cloud TOA reflectivity image by using the trained denoising diffusion model according to the preliminary inversion result. According to the method, thin cloud reflectivity estimation is modeled as an image recovery problem, and preliminary inversion of thin cloud radiation characteristics is realized through an empirical relationship between wavebands. The advantages of the diffusion model in the aspects of image feature expression and reconstruction are combined, the radiation contribution of the cloud is accurately separated from complex earth surface and atmosphere mixed signals, effective estimation of the TOA reflectivity of thin cloud areas of different types and concentrations is achieved, and therefore a more robust physical basis is provided for downstream cloud detection and cloudless image recovery.

The application discloses a data fusion method, device and equipment, and the method comprises the following steps: receiving detection results of a camera and a laser radar; when the received detection results simultaneously comprise an image detection frame and a point cloud detection frame, calculating a center point corresponding to the image detection frame and a center point corresponding to the point cloud detection frame; traversing an image sub-region and a point cloud sub-region which are pre-coordinated; determining an image sub-region where the image detection frame is located and a point cloud sub-region where the point cloud detection frame is located according to the center point corresponding to the image detection frame and the center point corresponding to the point cloud detection frame; and further outputting the point cloud detection frame as a data fusion result when the image sub-region where the image detection frame is located and the point cloud sub-region where the point cloud detection frame is located are a pair of regions which are mutually coordinate-paired. Through the above method, data fusion can be realized, and the problem that the prior art cannot perform a data fusion operation when an internal parameter of a camera is unknown is solved.

The invention discloses a cloud detection method and system for guiding fusion of two encoders through optical characteristics, and mainly solves the problems of poor feature fusion effect and inaccurate detection in the prior art. The implementation scheme comprises the steps of obtaining a remote sensing image data set, and dividing a training set and a test set; a remote sensing image cloud detection network comprising a multi-scale optical feature guide unit, a feature convergence and divergence screening fusion unit and a multi-order interaction local space attention mixing channel attention unit is constructed, and a remote sensing spectrum segment is divided into an RGB visible spectrum segment and an infrared and atmospheric correction spectrum segment by designing a two-way coding structure in the remote sensing image cloud detection network. Multi-scale features of a dynamic HOT index and a full-band Cloud index are set at the same time; iteratively training the network by using the training set to realize adaptive detection of test remote sensing images with clouds in different data sources and scenes. According to the method, the optical characteristic and the high-brightness characteristic of the cloud are utilized, false detection and missing detection of detection are reduced in complex scenes such as cloud edges and thin clouds, the feature fusion effect is improved, and the method can be used for remote sensing image processing.

The application discloses a method and system for assimilating meteorological satellite hyperspectral error non-Gaussian distribution data based on quantum computing, which performs generalized quality control of meteorological satellite hyperspectral data considering outliers on the observed brightness temperature of a satellite hyperspectral infrared detector channel and equivalent data thereof, including optimal selection of the channel considering quantum information entropy, deviation correction based on artificial intelligence generalized integrated generative deep learning, and cloud detection and removal of cloud view point data based on the minimum residual method, thereby obtaining quality control data. Quantum non-Gaussian increment four-dimensional variation assimilation models compatible with error Gaussian distribution and non-Gaussian distribution are solved based on the quality control data, and analysis field data are obtained. The method solves the problem that the existing technology is limited in the assimilation of hyperspectral infrared channel brightness temperature with error non-Gaussian distribution (or non-Gaussian characteristics are obvious), and meanwhile, the solving speed of the method is fast, and the obtained analysis field data have good precision and calculation timeliness.