279 results about "Downscaling" patented technology

The invention provides a surface soil humidity multi-source data fusion and downscaling method and system. The surface soil humidity multi-source data fusion and downscaling method comprises the following steps: performing quality evaluation and standardization processing on multi-source soil humidity data; carrying out anomaly detection and restoration by adopting a hierarchical spatial-temporal feature extractor; carrying out regional partitioning based on an adaptive boundary generation algorithm and a soft boundary transition model; data fusion and error control are realized through a regional cascade fusion network and an error chain tracking algorithm; and carrying out downscaling processing by utilizing a terrain adaptive downscaling framework and a multi-scale residual learning model to obtain a soil humidity data product with the resolution of 1km. The soil humidity data of complex terrains and high vegetation coverage areas can be effectively processed, the data fusion precision and downscaling effect are remarkably improved, and the technical problems of a traditional method in the aspects of data quality evaluation, anomaly detection, partition boundary processing, error control and the like are solved.

The invention provides a meteorological downscaling method based on space-time fusion and physical constraint, and belongs to the technical field of meteorological downscaling, and the method comprises the steps: carrying out the preprocessing of multi-source meteorological related data and a high-resolution meteorological truth value, and constructing a training data set; an improved U-Net model is constructed, spatiotemporal features and multi-source auxiliary features are obtained through a multi-branch feature extraction unit, high-resolution information is recovered through fusion and decoding, and an attention enhancement module is embedded to highlight a key area; a model is trained through a training data set, parameters are optimized by adopting a loss function fusing topographic features and physical rules, and prediction error differentiation constraint on a complex area and violating the physical rules is achieved; and preprocessing target low-resolution data, inputting the preprocessed target low-resolution data into the model, and outputting high-resolution meteorological data and a physical attribution result. The problems that in the prior art, multi-source meteorological data fusion is insufficient, downscaling precision of a complex terrain area is insufficient, and prediction errors violating physical laws are lack of effective constraints are solved.

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention discloses a precipitation field space downscaling method, and relates to the technical field of meteorological data processing, and the method comprises the steps: obtaining high-resolution topographic information data and precipitation pairing data composed of high-resolution and low-resolution precipitation data, and carrying out the normalization processing of the high-resolution topographic information data and the precipitation pairing data; inputting the normalized precipitation pairing data and the high-resolution terrain information data into a pre-trained precipitation field space downscaling model, and outputting to obtain a high-resolution precipitation field; wherein the precipitation field space downscaling model comprises a convolutional neural network and a de-noising diffusion probability model, and the de-noising diffusion probability model is constructed based on residual calculation, forward noise addition, a multi-scale feature extraction fusion network and reverse de-noising. According to the method, the high-resolution precipitation field can be reconstructed from the low-resolution precipitation field.

A computer-implemented method for downscaling an ocean surface wind (OSW). The method comprises: obtaining an input image with a set of wind data, constructing an OSW downscaling model based on a TransUNet architecture; using the OSW downscaling model to generate an output image with a set of resulting wind data according to the input image, and before the OSW downscaling model is used to generate the output image, training the OSW downscaling model according to a plurality of sets of training samples, wherein the OSW downscaling model is trained by optimizing model parameters of the OSW downscaling model in a sense of minimizing a loss function for improving resolution.

The invention relates to the technical field of typhoon path prediction, and discloses a typhoon path prediction method, system, terminal and storage medium based on multi-modal data of sounding equipment and a sky big model, and the method comprises the steps: obtaining multi-source heterogeneous data collected by detection equipment related to typhoon prediction, carrying out fusion and adaptive enhancement on the multi-source heterogeneous data on the basis of a big Pantou meteorological model, and generating a high-resolution three-dimensional field; constructing a cascaded AI downscaling network, introducing a physical constraint layer, carrying out local refined modeling on a typhoon eye region according to the high-resolution three-dimensional field, and outputting a high-resolution three-dimensional meteorological field; and designing a typhoon-environment field interaction model based on a graph neural network, performing typhoon prediction according to the high-resolution three-dimensional meteorological field, and outputting a typhoon path ensemble forecast containing a confidence interval and a typhoon thermodynamic structure analysis report. The typhoon path prediction speed and precision are improved, the prediction time is shortened, the prediction error is reduced, and calculation resources are saved.

The invention discloses an urban blue-green space microclimate prediction system and method based on AI fusion. The method comprises the following steps of: 1, acquiring field microclimate measured data through linkage of a portable weather station and a GPS (Global Positioning System); 2, utilizing a machine learning downscaling algorithm and a deep learning semantic segmentation technology to extract high-resolution land surface temperature and urban land surface coverage classification data from the remote sensing image; 3, constructing a time-space aligned multi-modal GIS data set, and constructing a high-precision microclimate prediction model by using an ensemble learning algorithm; and 4, introducing an SHAP interpretability framework, carrying out deep analysis on the model, and quantifying the contribution degree of various environmental elements to a microclimate prediction result and the complex nonlinear influence of the contribution degree. According to the method, unprecedented high-precision and explainable scientific decision support can be provided for formulating urban planning, landscape design and thermal environment mitigation strategies, and development of healthy, sustainable and climate-flexible cities is powerfully promoted.

The invention relates to the technical field of time-space sequence prediction, in particular to a downscaling prediction method for a sea wave significant wave height time sequence, which can simulate a downscaling process from low-resolution sea wave data to local high-resolution sea wave data. The method comprises the following steps of 1, generating large-range low-resolution effective wave height data based on the SWAN; step 2, generating local high-resolution effective wave height data based on SWAN-ADCIRC; and step 3, performing downscaling prediction on the significant wave height based on the time sequence network model. On one hand, the problem of low resolution of numerical model prediction data can be efficiently solved, and on the other hand, the prediction precision of the significant wave height value can be improved. The method is especially suitable for scenes with higher requirements for spatial resolution and significant wave high prediction precision, and significant wave high-precision prediction is carried out on coastal sea areas with complex geometrical shapes.

The invention provides a wide-area multi-level soil attribute fine mapping method based on spatial downscaling. According to the method, long-time-sequence optical data information and ground elevation information are collected through a satellite sensor, long-time-sequence temperature and rainfall data are collected through a meteorological station, and spatial position auxiliary data extracted from the long-time-sequence temperature and rainfall data are combined with existing national-level soil grid database data to construct a high-quality data set; the method is used for training a random forest model constructed based on an integrated learning technology so as to realize high-precision and high-reliability refined soil attribute mapping under a wide-area scale. The method is reasonable in conception, high-precision and high-reliability refined soil attribute mapping under a wide-area scale is realized, a specific region multi-level soil attribute database with higher resolution can be constructed, and the method is suitable for popularization and application.

The invention belongs to the field of building performance simulation, and provides a monthly meteorological prediction data hourly downscaling method based on distribution adjustment time mapping. Comprising the steps of representative city determination, typical meteorological year data collection, global climate mode and monthly average resolution data set determination, main variable data set acquisition, candidate statistical distribution determination, maximum likelihood estimation, optimal distribution screening, empirical cumulative distribution function construction, distribution correction, quantile calculation, quantile mapping, distribution adjustment and model prediction. According to the method, hourly downscaling of monthly prediction data of different weather variables in the atmospheric circulation model is realized through a specific distribution adjustment and quantile mapping method, so that the physical characteristics of the data and the statistical characteristics of historical data are kept, and the long-term trend and extreme weather conditions of GCM prediction can be reflected; and reliable future meteorological data is provided for building performance simulation.

The invention provides a rainfall data processing method and device based on multi-source data fusion, and the method comprises the steps: inputting multi-satellite fusion remote sensing rainfall observation data, terrain elevation data and latitude and longitude data of a target region at a target time interval into a rainfall data downscaling model, carrying out feature fusion, channel attention enhancement and space attention enhancement processing on the model, and outputting downscaling image data of which the resolution is higher than that of remote sensing rainfall observation data; and correcting the downscaling image data according to the site rainfall observation data corresponding to the plurality of observation sites in the target area at the target time interval to obtain target rainfall observation data of the target area at the target time interval. According to the method, the capturing capability and the multi-modal fusion capability of the model for rainfall distribution information in remote sensing data can be improved, the capturing capability of the model for complex terrains and rainfall characteristics can be improved, and then the expression precision and the application reliability of the rainfall distribution information in the generated high-resolution image can be improved.

The invention provides a high-resolution soil moisture inversion method and device, electronic equipment and a storage medium, and relates to the technical field of remote sensing monitoring. Target soil moisture inversion data under a first resolution and TVDI data under a second resolution corresponding to a target area can be acquired; wherein the target soil moisture inversion data comprises MWRI data, NDVI data and albedo data; the second resolution is greater than the first resolution; performing soil moisture inversion on the target soil moisture inversion data to obtain initial soil moisture data under the first resolution; and downscaling the initial soil moisture data according to the TVDI data to obtain target soil moisture data under the second resolution. In this way, the initial soil moisture data is downscaled according to the high-resolution TVDI data, the drought characterization capability of TVDI can be fully utilized, and the resolution and stability of soil moisture inversion are improved.

The invention relates to the technical field of weather forecast, in particular to a weather downscaling method fusing numerical weather forecast and AI weather forecast, which comprises the following steps: acquiring numerical weather forecast data and satellite remote sensing observation data, and performing unified preprocessing, interpolation and mapping to generate refined forecast data and observation reference data. Then, local compensation based on a deep neural network and model-independent element learning are adopted to carry out regional adaptive compensation, a joint objective function is constructed to implement variational assimilation and joint optimization, and meanwhile, a generative adversarial network and a graph neural network are combined with reinforcement learning to realize error compensation and node-level adaptive correction; and finally, continuously updating parameters through closed-loop feedback iteration to generate updated forecast data. According to the method, the problems of insufficient local details and poor model adaptability in the downscaling process in the prior art are effectively solved, and the forecasting precision and robustness are improved.

The invention provides a novel method for improving the spatial resolution of the vegetation optical thickness (VOD) by using a random forest method. According to the method, dynamic and static variables related to VOD, such as optical vegetation indexes, terrain parameters, soil humidity and rainfall, are comprehensively considered, and the memory effect (lag VOD) of vegetation optical thickness is introduced. By means of the method, researchers can downscale 0.1 degree VOD data based on the multi-frequency radiometer to the VOD data with the resolution of 0.01 degree. The research shows that the accuracy of the model is remarkably improved by adding the lag VOD variable. Besides, a normalized vegetation index (NDVI), an enhanced vegetation index (EVI) and a leaf area index (LAI) are used for evaluating the downscaled 0.01-degree VOD, a result shows that the downscaled 0.01-degree VOD is highly consistent with the spatial-temporal pattern of the optical vegetation indexes, and spatial-temporal dynamic characteristics of vegetation can be better captured.

The invention discloses a summer rainfall forecast correction downscaling method and system based on deep learning, and belongs to the technical field of meteorological prediction and climate simulation. In order to solve the technical problems of systematic deviation and spatial detail missing in forecasting, a deep learning downscaling model is constructed for summer multi-mode climate forecasting data published six months ahead of time in combination with high-resolution observation data and topographic data, deviation correction and spatial super-resolution reconstruction are performed on a summer rainfall forecasting result, and a deep learning downscaling model is constructed. And outputting the kilometer-level summer precipitation field of the target area. The model improves the spatial feature capture capability of multi-scale climate elements through an attention mechanism, and realizes the adaptive fusion of meteorological elements and topographic factors through a topographic perception module, thereby enhancing the medium and long term prediction precision of summer rainfall in a complex topographic region. According to the method, global climate information and local topographic features can be effectively integrated, and high-precision technical support is provided for medium-and-long-term prediction of regional summer rainfall, disaster prevention and reduction in flood seasons and water resource scheduling.

The invention discloses a multi-drive process multi-factor agricultural non-point source pollution prediction method based on coupling meteorological numerical forecasting. The method comprises the following steps: firstly, introducing numerical weather forecast data, and constructing a high-precision weather driving field with kilometer-level space and hour-level time resolution by combining WRF dynamic downscaling, DEM terrain correction and conservation resampling; secondly, establishing a multi-drive process coupling model system which comprises a meteorological drive layer, a hydrological response layer, a pollutant migration layer and a crop feedback layer and is used for simulating runoff production, sediment erosion, nitrogen and phosphorus migration and transformation and crop transpiration and root nutrient absorption processes; thirdly, performing precision verification on a simulation result by using observation data, and identifying key meteorological and hydrological factors through an error transfer matrix and a sensitivity analysis method; and finally, realizing parameter adaptive correction by adopting a long short-term memory network, finishing parameter optimization in combination with a multi-target genetic algorithm, packaging the model chain through a containerization technology, and realizing cross-platform deployment and visual output of a pollution load result. The method can be used for agricultural non-point source pollution prediction and management.

The invention provides a multi-source rainfall data precision correction and downscaling method based on a causal relationship constraint, and relates to the field of meteorology and hydrology, and the method comprises the steps: building a feature causal relationship structure tree for multi-source rainfall data, and adding the constraint of a feature causal relationship on the basis of an LSTM deep learning correction model; a CLSTM rainfall data precision correction model under the causal relationship constraint is established, a GRASPS rainfall data downscaling method is fused, a rainfall correction and downscaling model Casual-GRASPS of the causal relationship constraint is generated, and a high-precision, high-resolution and high-time-space continuous fusion rainfall data set conforming to the statistical causal relationship is generated. The method has the advantages that a reliable method is provided for downscaling of meteorological data such as rainfall, and a reliable data basis is provided for fine-scale rainfall research of drainage basins and urban areas.

The invention provides an interpretable random forest surface temperature downscaling method coupled with meteorological factors, and relates to the technical field of space downscaling. The method comprises the following steps: acquiring low-resolution surface temperature remote sensing data and multi-source feature data of a target area, and performing radiometric calibration, unified projection and normalization to form a preprocessed data set; dividing earth surface type subareas according to the earth surface classification map, solving a radiation correction coefficient by combining historical meteorological statistics, and performing subarea-level correction to obtain a subarea correction data set; training a random forest regression model by using the partition correction data set, and establishing a nonlinear mapping relationship between the surface temperature and the multi-source features; inputting high-resolution feature data to deduce a preliminary high-resolution surface temperature, and performing residual interpolation correction to obtain a final result; and the SHAP algorithm is adopted to explain feature contributions, so that the result precision, stability and interpretability are improved.

The invention relates to the technical field of satellite hydrological data processing, and particularly discloses a GRACE data super-resolution network space downscaling method fusing geographic information and environmental variables, which comprises the following steps: S1, acquiring original resolution GRACE data and original GLDAS data of a research area, and preprocessing the data; s2, dividing the data obtained by preprocessing in the step S1 into a training set and a test set, and training the GRACE data space downscaling model by using the training set to obtain a trained discriminator and a trained generator; and S3, inputting the GRACE low-resolution data in the test set and the high-resolution environment variable at the moment corresponding to the data into the generator trained in the step S2, and finally obtaining a downscaled high-resolution GRACE image. The method not only can be used for dynamic monitoring of regional scale underground water reserves and flood risk assessment, but also can be expanded and applied to scenes such as agricultural drought monitoring and ecological hydrological process simulation.

The invention discloses a climate mode pre-estimation downscaling correction method based on generative deep learning, and the method comprises the steps: recognizing the global climate mode hydro meteorological variable pre-estimation deviation through an unsupervised generative adversarial network, continuously reducing the adversarial loss through the adversarial training of a generator and a discriminator, and improving the accuracy of the adversarial training. Therefore, the trained generative deep learning network has the capability of processing a complex non-linear relationship so as to achieve the comprehensive optimum of downscaling and deviation correction. The method can be used for correcting the estimated average deviation of a multi-mode variable (rainfall, temperature and the like) set and improving the resolution ratio of the multi-mode variable set. The embodiment of the invention shows that the method can effectively estimate and downscale a multi-model set, and has a better downscaling effect than common quantile mapping and a convolutional neural network.

The invention relates to the technical field of remote sensing image intelligent processing, in particular to a multi-scale progressive surface temperature fusion downscaling method and device, and the method comprises the steps: preprocessing a surface temperature image in a multi-scale image library; constructing a multi-scale training data set containing the surface temperature and at least one auxiliary parameter; constructing a surface temperature downscaling network based on multi-parameter fusion, and constructing a domain transformation network based on heterogeneous high-frequency information guidance; training a surface temperature downscaling network and a domain transformation network based on a progressive dual-network joint training strategy; finely adjusting the downscaling model meeting a preset low-medium resolution condition; and performing low-medium-high progressive downscaling on the low-resolution surface temperature image to be processed to generate a multi-scale progressive surface temperature meeting a target high-resolution condition. The method fully considers the problem of descending scale difference of different resolutions, achieves the maintenance of the physical characteristics of the surface thermal field, and improves the processing efficiency of large-area remote sensing data.

The invention provides an urban rainfall area IDF curve establishment constraint method and system. The method comprises the following steps: collecting actually measured rainfall data and GCM simulated rainfall data; establishing a spatial downscaling model, and performing spatial downscaling on the grid monthly rainfall estimated by the GCM in the future period; calculating the condition transition probability of rainfall occurrence in each calendar month in the future period; generating a parameter set for driving a random rainfall generator, and inputting the parameter set into the random rainfall generator to obtain a daily rainfall sequence in a future period; performing time discretization from the daily scale to the hour scale on the daily rainfall sequence in the future time period; extracting an annual maximum value sequence from the hour drop sequence in the future time period to carry out regional frequency analysis, and constructing an urban rainfall region IDF curve; constructing a random forest model, and deducing alternative factors of IDF curve migration change; and an emergence constraint model is constructed, and the uncertainty of urban rainfall risk prediction under future climate change is reduced. The accuracy of rainfall frequency analysis is improved.

The invention discloses a multi-source information interaction enhanced soil humidity downscaling method and system, and belongs to the field of remote sensing image processing and computer vision. Comprising the following steps: constructing a single-multi-element interactive attention sub-module, a multi-source information interactive attention module, a residual space channel attention sub-module, a multi-scale feature fusion module, an adaptive double-domain joint loss function and a joint supervision type and degeneration type training mechanism, and obtaining a training convergence soil humidity downscaling network. And carrying out downscaling processing on the to-be-processed high-resolution auxiliary geoscience parameter data through training a convergence network to obtain a high-resolution soil humidity downscaling product. According to the method, the internal relevance between earth surface parameters is deeply mined, the multi-source information interaction attention module is constructed, the mapping relation between geoscience parameters is mined through information interaction between geoscience parameter characteristics, efficient fusion of multi-source data is achieved, and effective extraction and accurate calibration of multi-scale characteristics are achieved.

The RIME-RF spatial downscaling-based photovoltaic output prediction method comprises the steps of collecting photovoltaic power data and local meteorological observation LMD data of a photovoltaic power station in a target area, extracting common data of numerical weather forecast NWP data and the local meteorological observation LMD data, and constructing an input feature set; the method comprises the following steps: optimizing hyper-parameters of a random forest (RF) algorithm based on a frost ice optimization (RIME) algorithm, constructing an RIME-RF model, and performing spatial downscaling on numerical weather forecast NWP data; a VMD-CNN-GRU-SE attention mechanism photovoltaic power prediction model optimized based on BKA is adopted, original numerical weather forecast NWP data is combined with photovoltaic power data to train the prediction model, and numerical weather forecast NWP data after spatial downscaling is combined with the photovoltaic power data to train the prediction model. According to the prediction method, changes of fine meteorological factors influencing the photovoltaic power can be more accurately captured, downscaling errors are remarkably reduced, and short-term power prediction precision is improved.

The invention relates to the technical field of supplementing missing measurement data of an anemometer tower, in particular to a method for supplementing missing measurement data of an anemometer tower. In the data preparation stage, historical observation data of a target anemometer tower needs to be collected, and missing time periods and missing features are determined, so that subsequent method selection is linked; meanwhile, data of surrounding meteorological stations in the same period are obtained, the spatial correlation between the data and the anemometer tower is verified, and a basis is provided for probability distribution mapping and quantile matching in subsequent CDF-t and QDM methods. The nonlinear correction method based on cumulative distribution function transformation and quantile increment mapping shows unique advantages in the field of climate downscaling, the CDF-t can effectively eliminate system deviation by establishing a probability distribution mapping relation between observation data and mode output, and the system performance is improved. The QDM maintains statistical characteristics of historical sequences while retaining climatic change signals through quantile matching, and new possibility is provided for long-period data reconstruction through combination of the two methods.

The invention relates to the technical field of meteorological data processing, in particular to a progressive wind speed field depth downscaling method and device fusing multi-source data and space-time perception, electronic equipment and a storage medium. Multi-scale topographic features are introduced and self-adaptive fusion is performed, so that the wind field reconstruction precision in a complex topographic region is improved; a progressive multi-stage downscaling network architecture is adopted, large-multiple stable downscaling from the kilometer level to the hectometer level is effectively achieved, and error accumulation is controlled; reconstruction is carried out by utilizing a network trained through physical constraint loss, it is ensured that the divergence and the vorticity of an output wind field conform to the aerodynamics law, the physical rationality and the scientific credibility of a result are enhanced, and therefore an efficient solution is provided for obtaining a near-ground wind field with high resolution and high physical credibility.

The invention discloses an SMAP soil hydrodynamic downscaling method based on a TOPMODEL theory, and the method comprises the steps: enabling original SMAP data to be matched with DEM data in spatial attributes through standardization processing, and obtaining standardized SMAP data; on the basis of DEM data, a confluence area and a gradient are calculated, and then a terrain index is calculated according to a TOPMODEL model; constructing a time sequence through the standardized SMAP data, and executing space-time stabilization processing to obtain a basic soil moisture field; based on the terrain index and the basic soil moisture field, preliminary soil moisture data is obtained through calculation by means of a downscaling model, and target soil water grating data with high resolution are obtained by optimizing the quality of the preliminary soil moisture data; according to the downscaling method, the structure is simple and clear, batch operation is facilitated, and low-resolution SMAP soil water data can be upgraded into high-resolution soil water data with the same resolution as DEM data in complex terrain areas such as mountainous areas, hills and river valleys.

The invention discloses a method and a system for generating surface soil humidity based on time-space fusion of remote sensing data, and relates to the technical field of soil humidity processing, and the method comprises the following steps: obtaining a vegetation parameter image, an active microwave remote sensing image and a passive microwave remote sensing image of a required date, and superposing the vegetation parameter image and the active microwave remote sensing image to obtain a vegetation parameter image; obtaining a high-resolution parameter image; resampling the parameter image to low resolution, and performing spatial downscaling on the passive microwave remote sensing image through the low-resolution parameter image; the method comprises the following steps: taking a time sequence of each pixel in a passive microwave remote sensing image as a signal, obtaining phase raster data and high-frequency amplitude raster data of different frequencies of the signal, carrying out spatial interpolation, and fitting a spatial interpolation result and a spatial downscaling result in a time dimension to obtain a daily scale surface soil humidity image. According to the method, fitting of the spatial resolution and the temporal resolution is achieved, the soil humidity image with the fine temporal-spatial resolution is obtained, and the method has high application value.

The invention discloses a wind field correction method based on multi-stage hybrid physics-data driving, and relates to the technical field of wind field data processing and climate mode application. Executing dynamic downscaling simulation by using a regional climate simulation tool to obtain a multi-dimensional candidate variable field; screening target candidate climate variables according to ERA5 wind speed data, and forming a multi-dimensional key variable field; constructing a to-be-trained model comprising a multi-scale spatial feature extraction module, a time dynamic feature extraction module, a multi-scale spatio-temporal feature fusion module and an output module, and training by using the multi-dimensional key variable field to obtain a hierarchical multi-scale spatio-temporal fusion network for correcting wind field data; and by taking the wind field correction field as a boundary condition, generating a high-resolution wind field correction field by using a complex terrain micro-scale flow field refinement solver. The method not only improves the accuracy of wind speed prediction, but also ensures the physical authenticity and consistency of a high-resolution wind field under the influence of complex terrains through physical constraints.

The invention discloses an earth surface downlink short-wave radiation space downscaling method based on a super-resolution reconstruction technology, belongs to the technical field of meteorological data analysis, and solves the problems of insufficient solar radiation data space resolution and weak earth surface heterogeneity characterization capability in the prior art. Carrying out super-resolution reconstruction on the radiation data set by adopting a super-resolution combination model, and carrying out weighted fusion on a reconstructed data set output by the super-resolution combination model; according to the method, the output results of the multiple models are subjected to weighted fusion, weight optimization of the super-resolution sub-models is performed in combination with the high-resolution terrain factors, spatial downscaling from 5km to 1km resolution is finally realized, the spatial precision of the solar radiation data is remarkably improved on the basis of keeping the hour-level time resolution of the original data, and the accuracy of the solar radiation data is improved. And more earth surface heterogeneity information can be captured, and meanwhile, the high-time-resolution application requirement is met.