1118 results about "Satellite data" patented technology

The invention provides a photovoltaic power station photovoltaic power prediction method and system. A cloud layer motion time sequence library is constructed based on a historical cloud picture, and a nonlinear model of cloud speed and output attenuation is established. The light sensor array monitors the cloud layer thickness and the aerosol density in real time, calculates the light intensity attenuation gradient of the shadow area in combination with the model, and predicts the remaining time of the cloud cluster reaching the core area. Dividing a power prediction confidence interval according to remaining time, inputting satellite data into a multi-scale meteorological fusion architecture, and adjusting a meteorological feature extraction path of an attention mechanism based on cloud density. And fusing the surface reflectance correction value calculated by the scattering data of the light sensor to generate a short-term power prediction sequence of the sudden change cloud layer scene. And inputting the sequence into an energy management system, and outputting an active and reactive double-target scheduling instruction in combination with a grid-connected point voltage deviation and a reactive compensation parameter constrained by a power grid rule. According to the technical scheme provided by the invention, the cooperative scheduling precision of the photovoltaic power station in the cloud abrupt change scene is improved.

The invention discloses a soft soil foundation deformation prediction method and system based on big data, and relates to the technical field of rock and soil monitoring. InSAR satellite data, Beidou GNSS displacement data, optical fiber strain data and meteorological and geological parameters are integrated through a multi-source sensing network. ERA5 reanalysis data is adopted to establish an atmospheric delay compensation function, and dynamic sliding window filtering and strain gradient constraint are combined to realize data space-time alignment and anomaly cleaning. Based on a generalized Kelvin creep constitutive model, environment coupling functions of temperature, humidity and pore water pressure are fused to dynamically correct model parameters, and the creep response characterization capability in a complex environment is enhanced. And performing distributed joint training on the regionalized geological data through a federated learning framework, fusing differential privacy encryption and a node credibility verification mechanism, realizing safety aggregation and migration optimization of cross-regional data, and generating a geological partition adaptive deformation prediction result. The method effectively improves the reliability of soft soil foundation deformation prediction.

The invention discloses a high-resolution radar echo extrapolation prediction method fused with satellite data, and the method specifically comprises the following steps: firstly, inputting historical radar echo sequence preprocessing at a previous T moment, including denoising, normalization processing and data set segmentation, and obtaining cleaned data; then, through a deterministic modeling method (SimVP), a fuzzy prediction sequence of a future T duration is obtained, then a variational auto-encoder (VAE) maps an original radar echo image and the fuzzy prediction sequence to a low-dimensional potential space, and two-stage diffusion modeling is carried out on the basis; in the first stage, a space-time converter (ST-Translator) is used to extract space-time evolution characteristics of radar echoes; in the second stage, satellite data at the corresponding time of the previous T moment is input, preprocessing including normalization processing, feature selection and data set segmentation is carried out, cleaned data is obtained, and the influence of the satellite data is dynamically adjusted in the diffusion process by adopting a multi-source fusion denoising network Fsrform so as to make full use of satellite information; and finally, inversely transforming output results of the two stages into a pixel space to obtain a high-resolution radar echo extrapolation prediction result of the future T duration. According to the invention, computing resource consumption can be effectively reduced, and the precision and detail fidelity of short temporary rainfall prediction are improved.

The invention provides a deep learning-driven coastline change dynamic monitoring method, and belongs to the technical field of image understanding and recognizing.The deep learning-driven coastline change dynamic monitoring method comprises the steps that a time sequence attention matrix is formed by constructing a multi-source satellite data fusion network, and coastline form change features are extracted by applying a fourth-order time sequence convolutional neural network to construct a first fluctuation matrix; and performing dimension reduction processing by using a space-time attention mechanism to generate a second fluctuation matrix, and converting the second fluctuation matrix into a quantitative index through a fluctuation conversion matrix. The method comprises the steps of inputting data into a pre-trained CoastDynNet model to analyze a physical mechanism, performing fine adjustment by applying a coastline evolution optimization function, performing multi-scale decomposition by using a WaveletTempNet network to separate long and short term changes, and finally constructing a multi-model integration framework by using a Bayesian model averaging method to generate a comprehensive coastline dynamic evaluation report.

The invention discloses a ship inclination measurement method and system based on Kalman filtering fusion, and relates to the technical field of ship stability measurement, and the method comprises the steps: multi-sensor synchronous data collection, state space modeling and Kalman filtering fusion, adaptive dynamic adjustment of process noise, drift compensation, and fault tolerance and output. When the method and the system provided by the invention are used for measuring inclination, inclination angle error accumulation caused by gyroscope zero offset integral error, accelerometer dynamic interference, sensor noise and temperature drift, GNSS dependency and the like can be effectively avoided, available precision can still be maintained through multi-sensor depth coupling and an intelligent filtering algorithm, and the accuracy of inclination measurement is improved. The method is simple in operation, independent of satellite data, higher in stability, capable of greatly reducing hardware cost compared with GNSS and IMU schemes, and especially has the advantages of being simple in cost and installation configuration in ship refitting; and the method is also suitable for GNSS rejection or cost sensitive scenes such as inland ships, unmanned ships, offshore buoys and the like.

The invention discloses a lake terrain prediction method based on machine learning fused multi-source satellite data, and relates to the technical field of machine learning, and the method comprises the steps: extracting the optical features of a target lake from an optical satellite image; extracting water features of each water body pixel in the target lake from the SWOT satellite data; performing space-time alignment and fusion on the optical features and the water features to obtain a fusion feature vector; the actually measured water depth point of the real water depth is matched to the geographic position of the corresponding pixel, and then the real water depth and the fusion feature vector of the corresponding pixel form a training sample; selecting a machine learning model and initializing the structure or parameters of the machine learning model; training the initialized machine learning model by using the training sample; utilizing the trained machine learning model to predict the water depth value of the corresponding pixel according to the fusion characteristic vector of each water body pixel; and determining the terrain of the target lake according to each water depth value. According to the method, the terrain of a large-scale lake can be efficiently predicted at low cost.

The invention provides a satellite-borne SAR ocean image geometric positioning precision improving method and system, and the method comprises the steps: collecting AIS data at the same imaging moment as an SAR satellite and in an imaging region range, correcting the orbit position of the satellite-borne SAR satellite according to the actual imaging moment of a ship, and fusing the AIS data with the SAR satellite data, thereby improving the geometric positioning precision of the satellite-borne SAR ocean image. The method comprises the following steps: optimizing a ship to obtain a matching point pair of an SAR image position and an AIS position of the ship, correcting an RD model error by taking the AIS position as a control point according to an optimized matching point pair result and an orbit position of a satellite-borne SAR satellite corresponding to an actual imaging moment of each ship, and performing iterative solution again to obtain new geographic coordinate information of each image pixel value, finally, a system geometric correction image product with improved positioning precision is obtained, and the problem that in the prior art, ship positioning precision is not high only depending on SAR images obtained by SAR satellites is solved.

The invention belongs to the technical field of ocean data processing, and discloses a GNSS-R and scatterometer combined same-orbit time sequence sea surface wind speed inversion method and system, and the method comprises the following steps: obtaining historical satellite data of ocean surface wind, carrying out the preprocessing of the historical satellite data, and obtaining the preprocessed data, constructing a training sample set in combination with the reference wind speed and the surge auxiliary data; constructing an initial wind speed inversion model by using a plurality of single-moment multi-frequency information fusion modules and multi-moment interactive attention enhancement modules, and training the initial wind speed inversion model by using the training sample set to obtain a same-orbit time sequence sea surface wind speed inversion model; and obtaining a time delay-Doppler correlation power diagram, GNSS-R observation parameters, scatterometer parameters and effective wave height of storm and surge, and completing sea surface wind speed inversion by using the same-orbit time sequence sea surface wind speed inversion model to obtain sea surface wind speed prediction data.

The embodiment of the invention discloses an agricultural vehicle positioning method and device, electronic equipment and a storage medium, and the method comprises the steps: determining the posture, position and speed of an agricultural vehicle under a geographic coordinate system according to inertial navigation data obtained by an inertial navigation system; establishing an initial observation equation according to a satellite observation equation corresponding to a global satellite positioning system and a visual observation equation corresponding to a visual sensor; when it is detected that satellite data collected by a global satellite positioning system is valid, the initial observation equation serves as a target observation equation; when it is detected that the satellite data collected by the global satellite positioning system is invalid, the initial observation equation with the satellite observation equations removed serves as a target observation equation; the current error state quantity of the inertial navigation system is corrected according to the target observation equation, and the attitude, position and speed of the agricultural vehicle are corrected according to the updated error state quantity, so that the actual positioning information of the agricultural vehicle is obtained, and the robustness of the positioning result of the agricultural vehicle is improved.

The invention discloses a near-shore sea ice remote sensing extraction method based on FY-3 / MERSI satellite data, and the method comprises the following steps: S1, carrying out the preprocessing of an obtained MERSI image, and obtaining an MERSI image only containing sea ice and seawater; s2, establishing a coastal sea ice remote sensing recognition algorithm according to the difference of the spectral characteristics of sea ice and seawater in each channel of the MERSI image, and evaluating the precision and accuracy of the coastal sea ice remote sensing recognition algorithm; s3, according to a coastal sea ice remote sensing recognition algorithm, extracting coastal sea ice information from the MERSI image of each day; and carrying out 10-day averaging and monthly averaging on the daily sea ice data extracted in each month to generate a sea ice ten-day product and a monthly product. According to the invention, automatic identification of high-spatial-resolution sea ice information of a high-latitude offshore area in winter can be realized.

Example positioning methods and apparatus are described. One example positioning system includes a first global navigation satellite system (GNSS) antenna, an in-vehicle communication apparatus, and a controller. The first GNSS antenna is configured to obtain satellite data, and transmit the satellite data to the in-vehicle communication apparatus. The in-vehicle communication apparatus is configured to determine first positioning information based on at least one of the satellite data or first inertia measurement unit (IMU) data, and transmit the satellite data to the controller. The controller is configured to determine second positioning information based on the satellite data and second IMU data. Precision of the second positioning information is higher than precision of the first positioning information.

The invention relates to a remote sensing water depth inversion system and method based on image space constraint and residual compensation, and belongs to the technical field of earth remote sensing detection and space remote sensing. Comprising the following steps: preprocessing satellite data, including active satellite data preprocessing and passive satellite data preprocessing; a BP neural network water depth inversion model is constructed, the preprocessed satellite data is used for training, and a preliminary water depth estimation value is output; establishing a residual compensation model and introducing spatial consistency constraint, and in the residual compensation process, introducing a spatial smooth penalty term by calculating the difference of water depth values of adjacent pixels to suppress local mutation; and carrying out optimization training on the model by using a joint loss function which is formed by weighted combination of mean square error and space consistency constraint loss, and outputting a final water depth inversion result. According to the method, an image space constraint loss term and a space smoothing penalty term in residual compensation are introduced, so that the consistency and the smoothness of an inversion result in space are ensured.

The invention discloses an urban heat island effect decision-making method based on satellite data and artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining satellite remote sensing data and urban basic data of a target urban region, the urban basic data comprising population density distribution, historical intervention effects, infrastructure information and urban function partition data; preprocessing the satellite remote sensing data to generate a standardized surface temperature distribution image, and based on the surface temperature distribution image, identifying an urban heat island area and dividing the intensity grade of the urban heat island area; the method has the advantages that satellite remote sensing data and urban multi-dimensional basic data are fused, dynamic scoring and optimization algorithms are combined, the heat island area is accurately recognized, the optimal intervention strategy is generated, the problems that a traditional method is insufficient in data utilization and poor in decision-making scientificity are solved, and the method has the advantages of improving treatment accuracy and resource utilization efficiency.

The invention relates to the technical field of data fusion transmission, and provides a heterogeneous data fusion transmission method and system for airborne multimode satellite communication. Acquiring real-time communication parameters including a dynamic spectrum occupancy state, a polarization direction difference and a hidden transmission demand level; generating a dynamic frequency hopping control instruction based on the time-varying characteristic of the dynamic spectrum occupancy state, driving carrier switching and reserving a spectrum isolation area between adjacent carriers; performing orthogonal polarization diversity analysis on the carrier switching result based on the polarization direction difference, suppressing cross polarization interference and outputting multiple paths of baseband signals; calling a preset waveform parameter library according to the hidden demand level to determine a modulation parameter, and loading the baseband signal to the asymmetric phase subcarrier to generate a hidden modulation waveform; and fusing the frequency hopping instruction and the hidden modulation waveform to generate a cooperative modulation instruction, and controlling the radio frequency unit to realize anti-interference fusion transmission. According to the technical scheme provided by the invention, the anti-interference capability of fusion transmission of multi-mode satellite data in a dynamic spectrum environment is improved.

The invention discloses a multi-path routing dynamic scheduling method and device for a satellite communication system and a medium, and relates to the field of satellite data transmission, and the method comprises the steps: expanding a path between a source node and a target node in the satellite communication system into a plurality of paths, and obtaining communication quality index parameters of all satellites in the plurality of paths; for each of the plurality of paths, constructing a routing cost function through the communication quality index parameter; updating the graph data of the satellite communication system in real time according to the real-time path change during the operation of the satellite communication system; for each piece of graph data updated in real time, based on the communication quality index parameters, calculating the total routing cost of a plurality of paths between a source node and a target node in the graph data through a routing cost function, selecting a first preset number of paths from the total routing cost of the plurality of paths according to a sequence from low to high, and determining the first preset number of paths as an optimal path combination at the current moment. The method can reduce the transmission energy consumption of the satellite network.

The invention relates to a method for constructing a high-resolution atmosphere carbon dioxide concentration data set based on XGBoost-BO, and belongs to the technical field of environment monitoring and artificial intelligence modeling. The method comprises the following steps: preprocessing OCO-2 satellite data and multi-source auxiliary data, and fusing the preprocessed OCO-2 satellite data and multi-source auxiliary data to obtain a new data set; a Bayesian optimization method is adopted to search for an optimal hyper-parameter, a target function is optimized through second-order Taylor expansion, a regular term is introduced to control the complexity of the model, and ten-fold cross validation is used to evaluate the performance of the model; quantizing the contribution degree of each feature to model prediction through a tree SHAP method, and analyzing global feature importance ranking and feature contribution distribution of individual samples; and performing model verification by using the test set and the site actual measurement data. According to the method, the problems that an existing model-based reconstruction method is insufficient in interpretation and prone to falling into local optimum are solved, and the temporal-spatial resolution of CO2 concentration monitoring can be improved.

The invention relates to a lightning nowcasting method based on multi-source satellite data, and belongs to the technical field of lightning nowcasting. The method comprises the steps of obtaining and preprocessing satellite data of a multi-source channel and corresponding lightning data, constructing a U-Net lightning prediction model, collecting a historical multi-source satellite image sequence, constructing a PredRNN satellite image prediction model and predicting the lightning occurrence probability. According to the method, the PredRNN and the U-Net model are combined, the lightning distribution can be predicted at the future moment only by using the satellite image, the dependence on historical lightning data is broken through, and the method has a wide application prospect in the field of lightning nowcasting.

The invention discloses a fire point self-adaption method based on multi-source synchronous satellite data, which relates to the technical field of remote sensing and geographic information, and comprises the following steps of: aiming at satellite remote sensing images with different resolutions, respectively selecting fire point detection algorithms matched with the resolution characteristics of the satellite remote sensing images to independently finish preliminary detection of fire points, extracting fire point space distribution and feature data corresponding to respective resolution scale; preprocessing the extracted multi-source fire point spatial distribution and feature data, and establishing a multi-source fire point data set; according to the method, the scale consistency and fusion precision of multi-source satellite fire point detection are improved, and the problems of fire point boundary breakage, false splitting and the like caused by resolution difference are solved. Through scale difference quantification and machine learning evaluation, in combination with scale correction of high-resolution images, adaptive dynamic adjustment of fire point boundaries is realized, and the reliability of fire monitoring and emergency decision making is improved.

The invention discloses a remote sensing recognition method and system for abandoned sloping farmland, and the system comprises a data preprocessing module which is responsible for collecting and processing multi-source remote sensing image data, including Landsat and Sentinel-2 satellite data; the deep learning model training module is used for carrying out classification training on the remote sensing images by utilizing a UNet + + network and other deep learning algorithms; and the comprehensive interpretation model construction module is used for realizing comprehensive interpretation of the abandoned land of the slope cropland in combination with a typical terrain accurate classification and fuzzy classification technology. Through a deep learning algorithm and a multi-source data fusion technology, the system realizes high-precision abandoned slope cropland recognition, and the detection efficiency and accuracy are improved; the system integrates various remote sensing data and machine learning algorithms, and can comprehensively and accurately identify different types of land cover changes, including spring crops, summer crops and the like.

The invention relates to the technical field of meteorological prediction, in particular to an extreme meteorological prediction method based on a typical meteorological year, which comprises the following steps: firstly, constructing a data set of the typical meteorological year, and generating high-quality meteorological data through interpolation and fusion technologies in combination with ground, reanalysis and satellite data; the generative adversarial network is used for enhancing data, the problem that extreme event samples are insufficient is solved, and key meteorological variables are extracted through super-resolution and feature dimension reduction; based on a convolutional neural network and a long-short-term memory network, constructing a deep learning model to perform multi-task prediction; a physical constraint rule is introduced, such as energy and mass conservation, and it is ensured that prediction conforms to a meteorological rule; and through an uncertainty quantification technology, generating a confidence interval of a prediction result, and providing risk assessment and decision support information. According to the method, the precision and credibility of extreme meteorological event prediction are improved, and disaster prevention and reduction, resource scheduling, agricultural production and the like are supported.

The invention discloses a satellite route management and control switching method and system, and belongs to the field of satellite communication, and the satellite route management and control switching method comprises the following steps: monitoring satellite signal quality, collecting satellite data, judging whether nodes need to be switched or not, and if yes, switching the nodes; the triggering condition comprises that the signal quality decline is lower than a threshold value, and the communication equipment is about to break away from the current satellite network coverage range; when nodes need to be switched, an optimal switching target is selected from the candidate nodes, the signal quality of the optimal switching target is judged, and if the signal quality meets the use requirement, compared with the prior art, the method has the beneficial effects that when the coverage edge overlapping area of the multiple satellite networks is too small and the signal quality is judged to be poor, the optimal switching target is selected from the candidate nodes; according to the method, the switching node is limited, the random switching of the node is not allowed in the set time, the set time can be adjusted according to the actual condition, different conditions are matched, and the problems of communication quality degradation, network resource waste, terminal energy consumption increase and the like are reduced.

The invention relates to a lightning potential forecasting method and system based on a random forest. The method comprises the following steps: (1) collecting and preprocessing multi-source meteorological data; (2) feature extraction: cloud picture texture and shape features are extracted from satellite data, and echo intensity, top height and vertical development speed features are extracted from radar data; (3) constructing and optimizing a model; and (4) early warning thunder and lightning. The method has the advantages that the random forest serves as a typical representation of the ensemble learning algorithm, and the core of the random forest is to integrate prediction results of a plurality of decision trees. According to the integration mode based on Bagging, multi-dimensional feature information can be fully integrated, and the overfitting problem which is likely to happen to a single decision tree is effectively restrained. In a thunder and lightning potential forecasting scene, the algorithm can accurately capture a complex nonlinear relationship between meteorological elements and thunder and lightning potential by adaptively learning association between a large amount of historical meteorological data and thunder and lightning events, so that the forecasting accuracy is remarkably improved.

The invention relates to the technical field of sea wave height prediction, and provides a sea wave significant wave height prediction method and system fused with multi-source data, and the method comprises the following steps: correcting obtained satellite observation data based on obtained buoy observation data; carrying out preliminary fusion on the obtained reanalysis data and the corrected satellite observation data by adopting an optimal interpolation method; adding a layer of data mask to mark the position of the satellite data in the fused data; and inputting the marked initial fusion data into a VQ-VAE model, compressing the fusion data into discrete potential variables, inputting the discrete potential variables into a GPT model, and generating a prediction result of the significant wave height of the sea wave. According to the method, artificial intelligence, data assimilation and fine tuning technologies are combined, the data assimilation technology is introduced to fuse the multi-source time-space sparse marine observation data and the model simulation result, and the fine tuning technology is used in the training process to amplify the adjustment effect of the observation data, so that the precision and timeliness of sea wave prediction are improved.

The invention provides a multi-satellite parallel data transmission resource scheduling method based on a genetic algorithm, and belongs to the technical field of ground task management and control of spaceflight earth observation satellites. The method comprises the steps that historical data transmission resource use rules of all satellites are mined, and data transmission resource use rule models of all the satellites are established; in a data transmission resource scheduling process, each satellite generates a corresponding resource demand set according to a data transmission timeliness requirement, a priority degree and a data transmission resource use rule model of an important task, conflict resolution is carried out on a conflicting resource use demand set by utilizing a genetic algorithm, and a data transmission resource scheduling result is obtained through a multi-round parallel resource matching and conflict resolution process. The multi-satellite data transmission resource planning scheme is obtained, large-scale satellite data transmission resource scheduling is achieved, the large-scale remote sensing satellite group resource use efficiency is effectively improved, and the implementation rate of reconnaissance tasks is improved. According to the method, the data transmission resource distribution income among multiple satellites can be comprehensively planned, and the intelligent scheduling capability level of large-scale remote sensing satellite group resources can be effectively improved.

The invention provides a regional ecological environment quality evaluation method and system based on remote sensing data, and relates to the technical field of environment remote sensing and ecological monitoring, and the method comprises the steps: extracting red light and near-infrared reflectivity at the peak of a growing season by using Landsat8 satellite data, and calculating a mixed vegetation index; synchronously measuring vegetation indexes of pure vegetation and bare soil on site to obtain a vegetation coverage rate, and obtaining water transparency, suspended solid concentration and chlorophyll a concentration through on-site measurement on the basis of green light, red light, blue light and near-infrared reflectivity in a heavy rainfall period and a dry season; the method comprises the following steps: constructing a linear regression model of reflectivity and water quality parameters through a least square method, calculating a water quality index, constructing an extreme weather influence factor based on ten-year extreme weather data, and finally fusing a vegetation coverage rate, water quality, the extreme weather influence factor, annual precipitation, annual average temperature, optimal regional vegetation temperature and historical rainfall extremum. And constructing an ecological quality index, and dividing ecological environment quality grades.

The invention relates to the technical field of landslide geological disaster hidden danger point identification, in particular to a landslide hidden danger point identification method based on an air-space remote sensing fusion neural network, which comprises the following steps: acquiring satellite SAR data, a digital elevation model, a vector boundary map and a precision orbit ephemeris of a research area; carrying out interference processing on the InSAR data, and carrying out deformation rate inversion, terrain residual error correction and atmospheric delay phase modeling and correction; preprocessing an original high-resolution remote sensing image, interpreting a high-resolution image, and outputting spatial distribution of suspected landslide hidden danger points in a research area; planning an aerial survey route of the unmanned aerial vehicle, and performing distortion correction, feature matching, dense point cloud generation and orthoimage generation processing; performing multi-dimensional feature analysis, and performing comprehensive interpretation in combination with an expert knowledge base; according to the method, the key effect of the multi-source remote sensing fusion technology in landslide hidden danger monitoring is displayed from data acquisition to hidden danger recognition.

The invention is suitable for the technical field of forest fire disaster early warning, and provides a forest fire disaster risk early warning method considering multi-source satellite information fusion, and the method comprises the steps: obtaining multi-source satellite information data which comprise stationary meteorological satellite data, polar orbit meteorological satellite data, high-resolution satellite data and auxiliary data; performing data preprocessing on the obtained multi-source satellite information data, and then performing multi-scale space-time fusion to obtain a fused target feature vector; performing fire point identification on the target feature vector by using a eucalyptus forest fire point enhancement detection algorithm, and judging whether a risk fire point exists or not; if yes, performing multi-dimensional coupling analysis on the risk fire point; according to the multi-dimensional coupling analysis result, a grading early warning graph is generated, decision data including fire point coordinates, spreading speed and emergency paths are output, an accurate and timely data basis is provided for disaster prevention and reduction, and power grid and economic losses caused by mountain fire disasters are effectively reduced.

The invention relates to the technical field of environmental monitoring, and discloses a method for reconstructing carbon dioxide concentration enhancement based on multi-source satellite data, which comprises the following operation steps: collecting environmental information such as carbon dioxide dry air column concentration, nitrogen dioxide total column concentration, population density, land utilization, weather and the like to construct an environmental database; pre-processing the collected multi-source data to ensure time-space consistency; according to the population density of the region, the total column concentration of nitrogen dioxide and the land utilization type, setting a threshold value, and dividing the target region into a discharge region and a background region; constructing a background concentration reconstruction model, inverting the carbon dioxide background concentration, and calculating a residual error with actually measured carbon dioxide to obtain a carbon dioxide concentration enhanced sample; based on the carbon dioxide concentration enhanced sample, a concentration enhanced reconstruction model is constructed, and high-resolution carbon dioxide concentration enhanced spatial-temporal distribution is obtained. According to the method, high-frequency monitoring of the carbon source in the region can be realized spatially, and powerful support is provided for dynamic tracking of carbon emission.

The invention discloses a radar real-time monitoring and early warning method and device for a weather target in a three-dimensional system, and relates to the technical field of radar monitoring and early warning, and the method comprises the following steps: S1, extracting early warning features; s2, a multi-elevation automatic identification method; s3, identifying a strong convective cloud cluster; s4, analyzing infrared channel parameters; and S5, weather target early warning. According to the method, space-time matching is carried out on satellite data and radar data in a built three-dimensional scene, so that the severe convection contours are recognized and extracted, statistical analysis is carried out on various parameters of cloud clusters and echoes within the overlapping range of the recognized convection contours, the severe convection echoes are extracted through a multi-elevation automatic recognition method, and the recognition accuracy of the severe convection contours is improved. And then radar parameter feature recognition is carried out, a foundation is laid for subsequent severe convective weather early warning, a cloud cluster main body can be effectively approached by recognizing the severe convective cloud cluster and analyzing infrared channel parameters, the feature parameters of the severe convective cloud cluster are efficiently extracted, and the weather target recognition precision is improved.