20 results about "Solar flare" patented technology

The embodiment of the invention provides a remote sensing image atmospheric correction method and system for an ocean near-shore scene, and relates to the technical field of ocean remote sensing image atmospheric correction. According to the method, after remote sensing data is obtained, solar flare correction is executed by extracting a target mask and using a secondary simulation model of a satellite signal in a solar spectrum. And then a multi-band darkness index is calculated, and an optimal aerosol optical thickness value is inverted through a radiation transmission model. And calculating the atmospheric correction coefficient of each wave band according to the optimal aerosol optical thickness value so as to generate correction result data. According to the method, on the basis of robust flare correction of the SWIR proxy, scene-adaptive flare proxy is constructed by utilizing water body reflection and path radiation physical characteristics of the SWIR under a deepwater condition, and flare deduction is realized. Inversion is carried out through multiband combined darkness indexes, and the spectral characteristics of the deep water body reflected in the visible spectrum are utilized, so that single-band random noise is smoothed, and the accuracy of an AOT estimation result 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 flare occurrence position determination method and device, electronic equipment and computer medium. The method comprises the following steps: when it is determined that a flare occurs, a flare peak time, a full-disk image corresponding to the flare peak time and initial disk positions of each solar active region are obtained; according to the flare peak time and the initial disk positions of each solar active region, first disk positions of each solar active region corresponding to the flare peak time are determined; the first disk positions of each solar active region are subjected to coordinate system conversion, and pixel positions of each solar active region in the full-disk image are determined; according to the pixel positions of each solar active region and the full-disk image, first pixel values of regions in which each solar active region is located in the full-disk image are respectively calculated; and the first disk position of a solar active region corresponding to a maximum value is determined as the occurrence position of the flare. The application can accurately determine the occurrence position of the flare when the flare occurs.

A system for forecasting when solar flares will occur and identifying their possible locations creates a mask to analyze an image of a solar disk. The system also creates an intensity histogram using the solar disk. Additionally, the system stores a resulting full-disk image and a sub-image identifying possible flare signature sites. Further, the system applies a method to filter out false positive detections by using an equation that enhances true positive pre-flare signatures and minimizes false positive signatures.

This invention relates to the field of image processing technology, providing a method for removing nighttime flares based on the law of illumination and dynamic masking. The method includes: using a pre-trained depth estimation model to process a background image and a flare image based on the law of illumination to obtain a baseline image for the deep learning model; processing the baseline image of the deep learning model through a dynamic masking module to obtain an input image for the deep learning model; inputting the input image of the deep learning model into a neural network model; concatenating the output of the neural network model with the baseline image of the deep learning model; and finally, passing the convolutional layer to obtain an image with the flares removed. This invention constrains the strength relationships between multiple flares through the law of illumination and depth estimation, enabling the synthesis of a flare dataset that better conforms to the physical laws of the real world. Furthermore, the dynamic masking module allows the model to focus more on processing the flare region, achieving excellent image restoration results.

The invention provides a solar flare whole-process observation and analysis method and system, and the method comprises the steps: inputting an obtained coding grayscale image and an interference coding mask into a target spectrum neural network model, and obtaining a plurality of groups of reconstructed three-dimensional hyperspectral data; stacking a plurality of groups of three-dimensional hyperspectral data under the same optical filter wavelength according to the polarization dimension to obtain a plurality of groups of hyperspectral polarization data; performing multi-frame superposition noise reduction processing and space-time registration on the multiple groups of hyperspectral polarization data to obtain processed hyperspectral polarization data; performing dynamic inversion based on the processed hyperspectral polarization data to obtain a first physical parameter and a second physical parameter; and generating early warning information based on an evolution stage division result and an abnormal feature recognition result obtained through the first physical parameter and the second physical parameter. According to the method, the leak detection rate and the whole process delay of the key stage of flare observation are reduced, the analysis accuracy of the core physical parameters of the flare is improved, and the observation integrity, the real-time performance, the precision and the robustness are comprehensively improved.

The application discloses a kind of solar flare X-ray full-link simulation method and system based on Geant4, belong to space satellite detection and numerical simulation technical field.The method includes: build high-performance hybrid parallel computing platform based on Geant4;Based on the platform, construct layered magnetized solar atmosphere model, configure physical process list and generate high-energy electron particle source;Through light tracing algorithm and self-defined step action module, the transmission, scattering and polarization evolution of X-ray in layered magnetized solar atmosphere are tracked and simulated;Through pixelated imaging and data output module, photon information is mapped to multiple virtual detector imaging planes, and X-ray imaging, energy spectrum and polarization degree data of different observation angles are output.The application solves the problems of existing model and multi-satellite stereoscopic observation data mismatch, Compton scattering details missing, polarization information deficiency and low computing efficiency, and provides an effective tool for analysis and simulation of multi-satellite joint observation data.

The invention belongs to the technical field of remote sensing image processing and computer vision, and discloses a marine oil spill segmentation method and system based on a spatial spectrum frequency constraint diffusion model. Preprocessing the multispectral remote sensing image to obtain reflectivity data after Rayleigh correction; inputting the reflectivity data into a trained spatial-spectral frequency constraint diffusion model, and generating an oil spill segmentation mask through iterative denoising; wherein the spatial spectrum frequency constraint diffusion model is integrated with a double-branch frequency analyzer (DF-P) and a spatial spectrum attention condition coding module (SS-A). Through innovative architecture design, end-to-end oil spill accurate segmentation under the condition of cross-sun flare is realized, the segmentation performance is remarkably improved, and the core design thought also has important reference value for similar optical remote sensing ground feature segmentation tasks; and meanwhile, more powerful technical support can be provided for marine environment protection and oil spill emergency response.

An X-ray solar imaging energy disperse spectroscopy covering Fe characteristic peaks belongs to the technical field of space X-ray detection, and comprises a nested X-ray focusing optical system, an X-ray imaging detection unit and a support shielding structure. The nested X-ray focusing optical system comprises an X-ray collimator assembly and an X-ray grazing incidence lens assembly. The effective observation area of the solar X-rays is enlarged through the multi-layer nesting of the X-ray lenses, and the observation of the solar X-rays with higher energy can be supported. The front end of the X-ray collimator assembly is covered with a shading film, so that solar heat entering the instrument is reduced, and the X-ray transmittance is guaranteed. The X-ray imaging detection unit comprises a shading film, an imaging detector and an imaging reading circuit. X-ray high-resolution imaging of solar flares is realized by adopting the X-ray imaging detector. The dynamic detection range of the instrument is improved through local high-speed reading of the solar flare imaging area.

The invention discloses a solar flare prediction model construction method based on intelligent survival analysis support, and the method comprises the steps: 1, defining main parameters in survival analysis in combination with a solar flare prediction scene; step 2, carrying out data preprocessing on the solar flare observation data; step 3, constructing a hybrid model combining LSTM (Long Short Term Memory) and DeepSurv (DeepSurv); step 4, training of a hybrid model combining LSTM and DeepSurv is carried out; and step 5, performing prediction and visualization after model training. By using the method of the invention, a time sequence characteristic and survival analysis fused extensible network can be constructed, solar flare prediction performance can be evaluated, and outbreak time prediction understanding can be assisted.

The invention discloses a Geant4-based solar flare X-ray full-link simulation method and system, and belongs to the technical field of space satellite detection and numerical simulation. The method comprises the following steps: building a Geant4-based high-performance hybrid parallel computing platform; a layered magnetization solar atmosphere model is constructed based on the platform, a physical process list is configured, and a high-energy electron particle source is generated; through a ray tracing algorithm and a user-defined stepping action module, tracking simulation is carried out on transmission, scattering and polarization evolution of X-rays in the layered magnetized solar atmosphere; and through a pixelated imaging and data output module, the photon information is mapped to imaging planes of a plurality of virtual detectors, and X-ray imaging pictures, energy spectrums and polarization degree data of different observation visual angles are output. The problems that an existing model is not matched with multi-satellite stereoscopic observation data, Compton scattering details are missing, polarization information is insufficient and calculation efficiency is low are solved, and an effective tool is provided for analysis and simulation of multi-satellite combined observation data.

Automated systems and methods are provided for continuous monitoring of the flaring of waste gas at an industrial facility, which employ an RGB camera operably coupled to a gateway device by a data communication interface. The RGB camera is configured to capture time-series color image frames of a flare and communicate the time-series color image frames to the gateway device. The gateway device includes an image processing module and a flare optimization module executing on the gateway device. The image processing module is configured to process the time-series color image frames to determine at least one flare parameter that provides a qualitative measurement of the combustion efficiency of the flare over time. The flare optimization module is configured to adjust relative amount of waste gas to at least one assist gas for the flare based on the at least one flare parameter to continuously optimize the combustion efficiency of the flare.

The problem of the presence of excessive flare in maskless photolithography systems is addressed by a system and method that utilizes an aerial imaging system to monitor flare associated with the maskless photolithography system.

This invention relates to a wide-spectrum, high dynamic range solar X-ray flare monitor, comprising an X-ray probe unit and a processing circuit unit. The X-ray probe unit receives X-rays emitted by the sun, converts the optical signal of the X-rays into an electrical signal, and sends the electrical signal to the processing circuit unit. The processing circuit unit processes the received electrical signal, converting it into X-ray information including energy and intensity, thereby achieving wide-spectrum, high dynamic range solar X-ray flare monitoring. This invention employs a dual-channel design, enabling high dynamic range detection of solar X-ray radiation flux exceeding five orders of magnitude and fine detection of solar spectral information.

The invention discloses a solar flare forecasting method and system based on fusion of physical parameters and geometric features of an active area. The method comprises the following steps: acquiring magnetic field observation data and optical image data of a target solar active area; extracting magnetic field physical parameters from the magnetic field observation data, and extracting geometric features of an active area from the optical image data; acquiring magnetic field observation data and optical image data of the target solar active area in a historical time window, and constructing a time sequence of magnetic field physical parameters and geometric features; extracting dynamic evolution characteristics of physical parameters and geometric characteristics of the magnetic field from the time sequence; fusing the physical parameters and the geometric features of the current magnetic field with the dynamic evolution features, and constructing an enhanced fusion feature vector; inputting the enhanced fusion feature vector into a pre-trained neural network model, and obtaining a prediction probability of generating a specific-level flare in the target solar activity area in the future; and generating and issuing flare forecast information according to a comparison result of the forecast probability and a preset threshold value.

The application discloses a kind of X-ray solar flare automatic detection method and system based on dual-mode trigger and nested interruption, which comprises: obtaining satellite X-ray flux data, and pre-processing to obtain standard flux sequence;Standard flux sequence is simultaneously monitored by dynamic parallel dual-mode trigger of ultrafast mode and conventional mode;When any mode meets trigger criterion, parallel double end determination mechanism of conventional attenuation determination and nested interruption truncation is used, event end time is obtained, and flare event closed loop interval is determined;For each flare event closed loop interval, the maximum flux value and the corresponding peak time are extracted, the peak time is reversed backtracking, the start time is corrected, and the flare event and the corresponding start and end time are output.The present application greatly improves the detection rate of strong flare, effectively controls the error of time parameter determination, and eliminates the uncertainty of subjective research and judgment;Lightweight algorithm and high physical interpretability are suitable for engineering low-power deployment.

The invention discloses a TROPOMI on-orbit calibration and performance tracking method based on background value statistics, which comprises the following steps: constructing a pure ocean background value data set, eliminating interference pixels by utilizing multiple physical thresholds such as cloud screening, solar flare screening and absorptive aerosol screening, and reducing errors in combination with geometric constraint conditions to obtain a TROPOMI background value; an empirical model of background values and observation geometry is established, angle dependence of the background values is eliminated through geometric normalization processing, then a long-term background value sequence is established, and a time sequence decomposition technology (STL) is adopted to separate seasonal fluctuation of a time sequence and a long-term attenuation trend of an instrument; finally, dynamic prediction and correction of a full-view-field background value of the sensor can be realized based on the constructed integrated space-time model, and a time consistency correction coefficient is calculated so as to eliminate long-term deviation introduced by instrument performance attenuation. According to the invention, the long-term consistency and reliability of atmospheric component products such as absorptive aerosol index and the like are obviously improved.

The invention relates to the technical field of remote sensing data acquisition, and discloses an unmanned aerial vehicle-mounted hyperspectrometer large-water-surface amphibious plant pattern spot acquisition device and method, and the device comprises a front light splitting and polarization decoupling module, a dual-channel synchronous hyperspectral imaging module, a self-adaptive spatial light field modulation illumination module, and an on-satellite real-time processing and control module. The method comprises the following steps: synchronously acquiring spectral images of an orthogonal polarization channel and carrying out decoupling calculation to separate solar flares on the water surface in real time so as to obtain flare-free diffuse reflection spectral data; then, a signal-to-noise ratio zoning graph is generated based on the data, and a lighting module is driven to carry out selective pulse light supplement on the recognized low-signal-to-noise-ratio area according to the signal-to-noise-ratio zoning graph; and finally, collecting and extracting an active reflection signal through time gating, fusing the active reflection signal with the flare-free data, and outputting the fused active reflection signal. According to the method, flare interference can be effectively removed, dark and weak target signals can be adaptively enhanced, and the quality and the automation level of hyperspectral data acquisition in a complex water area environment are improved.

The invention discloses a sunspot prediction method based on a double-sequence data input neural network, and relates to the technical field of electromagnetic environment influence analysis, and the method comprises the following steps: 1, obtaining historical data; step 2, sliding window input; step 3, carrying out data normalization; 4, establishing a neural network model; 5, measuring indexes; 6, training is completed, sunspot historical monthly average data is selected from a space environment forecasting center website of the national space science center of Chinese Academy of Sciences, and a GOES satellite observation sun X-ray solar flare event list issued by the national space science data center is selected as a solar flare original data set. According to the sunspot prediction method based on the double-sequence data input neural network, the descending trend of a future prediction result can be well fitted through double-feature input, and compared with single-sunspot data feature input, a prediction curve obtained through fitting has higher stability and robustness.

The application discloses a kind of solar flare prediction methods based on multi-feature fusion, comprising the following steps: S1: obtaining solar observation data within a certain time, and extracting prediction factor and flare burst data from it, different prediction factors are marked with a, b, c……, different flare burst data are marked with alpha, beta, gamma……;S2: different prediction factors and flare burst data are used to construct data set, and each prediction factor needs to be correspondingly constructed with each flare burst data, different data sets are marked with (a, alpha), (a, beta), (a, gamma)……, then each data set is divided into training set and test set.The application predicts the occurrence of solar flare by multiple prediction factors and calculation model, and summarizes and corrects the result set obtained by the calculation model through the result model, so as to obtain a result with higher confidence, effectively improving the accuracy of the model in predicting solar flare.