435 results about "Spatiotemporal resolution" patented technology

The invention discloses a geological disaster early warning method and a precise early warning system based on multi-source data fusion, and relates to the technical field of geological disaster early warning. According to the method, multi-source heterogeneous data such as remote sensing, meteorological and geological monitoring are fused, a standardized protocol is utilized to unify a data format and temporal-spatial resolution, a standardized data set is formed, key features are extracted by adopting principal component analysis and a recursive feature elimination algorithm, and a long-short-term memory network and a convolutional neural network model are combined, so that the real-time performance of the system is improved. According to the method, the disaster risk is accurately predicted, the space risk distribution diagram is generated, in addition, through application of the real-time stream processing framework and the self-adaptive learning algorithm, rapid distribution of early warning signals and dynamic optimization of model parameters are achieved, the accuracy and timeliness of an early warning system are remarkably improved, and the geological disaster risk is effectively reduced.

The invention discloses a high-temporal-spatial-resolution refined flow field reconstruction method, device and equipment and a medium, and relates to the technical field of ocean current reconstruction, and the method comprises the steps: carrying out the normalization and temporal-spatial alignment of satellite remote sensing, buoy observation and numerical simulation data; based on the alignment data, performing rehearsal on the unstructured nested grid through an FVCOM model, and then dynamically encrypting the grid according to the flow field gradient and generating a background flow field; inputting the background flow field into a PINN-GAN combined framework, and outputting a refined flow field through physical constraint loss and double-discriminator adversarial training; and scheduling a calculation task by adopting a heterogeneous accelerator, verifying the reconstructed refined flow field in real time, and performing feedback optimization. Through generation of the background flow field and refinement reconstruction, the ocean current flow field with high temporal-spatial resolution can be reconstructed efficiently and accurately.

The invention discloses a multi-channel meteorological risk early warning information self-adaptive targeted publishing system and method based on low-altitude flight, and solves the problems that existing low-altitude meteorological early warning is low in temporal-spatial resolution, single in risk identification and insufficient in information pertinence. The system integrates air-based, space-based, foundation, social and topographic data, and generates a low-altitude meteorological dynamic database through fusion; micro-scale risks such as wind shear are extracted and graded through an AI model, a digital twin simulation response is constructed in combination with the state of the aircraft, and a risk grid model and coordinates are output; calculating a comprehensive risk index, generating evasion guidance, converting into multi-role early warning information, generating a three-dimensional thermodynamic diagram, and finally adaptively selecting a publishing channel according to user roles, terminals and network states. According to the invention, the accuracy and timeliness of low-altitude flight meteorological risk early warning are improved, and the safety of low-altitude flight is guaranteed.

The invention relates to a method, device and product for monitoring river flow in real time, and the method comprises the steps: obtaining tower footing video data of a target river reach which comprises a target section and a pier buoy; inputting the tower footing video data into a water level and flow velocity identification model, and obtaining the water level elevation and the surface flow velocity of at least part of the cross section in the target section through the water level and flow velocity identification model; inputting the water surface elevation into a three-dimensional digital river channel model to obtain a section water passing area of a corresponding cross section; and obtaining the cross section flow of the target cross section according to the cross section water passing area and the surface flow velocity. According to the embodiment of the invention, automatic flow estimation based on the tower footing video can be realized, the manual measurement cost is reduced, the spatial-temporal resolution of flow monitoring is improved, and the method can be used for large-range river monitoring, especially in areas lacking hydrometric stations, and can provide data support for drainage basin water resource management and flood prevention early warning.

The invention provides an optical complex water body suspended sediment monitoring method based on man-machine cooperation, and belongs to the technical field of remote sensing water environment monitoring. The method comprises the following steps: acquiring a multispectral remote sensing image of a target area, preprocessing the multispectral remote sensing image, and performing space-time fusion to obtain a spectral data set with high space-time resolution; carrying out manual labeling on the spectrum data set, constructing a suspended sediment concentration classification data set, and carrying out synthesis expansion on a minority class of samples to generate an equalization training set; training a suspended sediment concentration inversion model by using the equalized training set to obtain an initial suspended sediment concentration predicted value, and dynamically optimizing the spectral feature weight and the classification boundary of the suspended sediment concentration inversion model based on expert knowledge; and locally correcting the initial suspended sediment concentration predicted value and diffusing the initial suspended sediment concentration predicted value to a global range based on a hydrodynamic law and a space consistency constraint to obtain a global suspended sediment concentration predicted value. The method combines remote sensing image analysis, machine learning and expert knowledge to realize high-precision inversion of SSC.

The invention discloses a method for obtaining high-temporal-spatial-resolution port traffic observation data from AIS data, and the method comprises the following steps: S1, carrying out the preprocessing of the historical trajectory of a ship based on AIS data; s2, based on a stay index formula and a K-means algorithm, dividing the preprocessed historical ship trajectory into a stay section and a moving section; using an isolated forest algorithm to extract center point coordinates of the stay section to obtain a ship travel chain; marking a staying type and a port to which the staying type belongs for each staying section based on a space rule and a process logic; s3, key indexes reflecting the port traffic state are constructed through time-space statistics of mooring and anchoring behaviors; a directed shipping network with ports as nodes is constructed, and traffic relation and characteristics between the ports are mined in combination with a network analysis method. According to the method, the timeliness and accuracy of port traffic situation monitoring can be improved, and the problems of slow updating, coarse granularity and high heterogeneity of traditional statistical data are solved.

The invention discloses an LCZ temporal-spatial resolution control method based on multi-source remote sensing information, and relates to the technical field of image processing. The LCZ temporal-spatial resolution control method based on the multi-source remote sensing information comprises the following steps of scene weight evaluation, environment weight evaluation and temporal-spatial fusion feedback evaluation. According to the method, the initial weight is obtained through the multi-source remote sensing information identification scene, the remote sensing data and the scene characteristic data are obtained to obtain the scene weight evaluation coefficient, and the geographical environment data are obtained and combined with the remote sensing data to obtain the environment weight evaluation coefficient; then, whether dynamic weight redistribution is carried out or not is judged according to the scene weight evaluation coefficient and the environment weight evaluation coefficient, finally, a weight regulation and control evaluation coefficient is obtained according to the obtained space-time fusion feedback data to judge whether weight secondary distribution is carried out or not, and the effect of improving the space-time resolution control accuracy is achieved. The problem of inaccurate temporal-spatial resolution control in the prior art is solved.

The invention discloses a model training method and device, electronic equipment and a storage medium. Comprising the following steps: sampling an original video frame sequence based on a target training temporal-spatial resolution to obtain target training data; inputting the target training data into a teacher model, generating a target token set through dynamic token selection, and generating teacher training features through forward propagation; performing multi-scale cutting on the target token set according to a target self-attention weight of the teacher model to generate at least three student training masks with different token numbers; inputting the target training data and the different student training masks into a student model for forward propagation, and generating student training features; and performing alignment distillation on the student training features and the teacher training features to obtain a target student model. The defect of a video understanding model in downstream flexible reasoning is overcome, and dynamic token selection and multi-scale mask training under high temporal-spatial resolution are utilized, so that the model can obtain better performance under various downstream calculated amount limits.

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 discloses a geological disaster risk intelligent pre-judgment method based on deep learning, and relates to the technical field of geological disaster monitoring and early warning. According to the method, high-precision alignment and feature extraction can be automatically performed on monitoring data with different temporal-spatial resolutions and different physical meanings, such as optical remote sensing, radar measurement, laser point cloud and the like, uniform and information-rich representations are generated, a solid data foundation is laid for subsequent accurate prediction, and the limitation of data splitting application in a traditional method is overcome; a space-time diagram with slope units as nodes is constructed, an attention mechanism diagram convolutional network with hydrological directivity introduced is utilized, and the model can accurately describe the spatial propagation process that slope substances migrate and accumulate along with a confluence path and block a river channel under the rainfall condition; meanwhile, the time sequence module effectively learns the influence of the past hydrological state on the future evolution trend.

The invention discloses a sand and dust vertical flux high-precision inversion method based on a laser radar. The method comprises the following steps: acquiring multi-wavelength back scattering and polarization information by using a 355 nm, 532 nm and 1064 nm three-wavelength polarization laser radar system; constructing a five-dimensional optical feature vector space, and combining a support vector machine classifier to realize automatic identification of dust particles; a variational data assimilation technology is adopted to fuse radar observation and numerical forecasting information to invert a three-dimensional wind field; inverting sand and dust mass concentration vertical distribution based on the corrected particle spectrum distribution model; flux calculation and uncertainty quantization are realized through adaptive weighted fusion and a Monte Carlo method; the method has the characteristics of high temporal-spatial resolution, high precision, strong adaptability and the like, and can be widely applied to the fields of weather forecast, environment monitoring, climate research and the like.

The invention relates to the technical field of magnetic resonance imaging, in particular to an intelligent sparse low-rank non-Cartesian magnetic resonance dynamic imaging method, which comprises the following steps of: acquiring magnetic resonance k-space data which is subjected to non-Cartesian continuous sampling by adopting multiple coils, continuously sampling data, rearranging the continuously sampled data along a time dimension by using a framing operator to obtain under-sampled k-space data, and then inputting the framed non-Cartesian under-sampled k-space data to be reconstructed into a trained network for image reconstruction. And reconstructing a final dynamic image through depth space-time sparsity, time low-rank learning, data consistency verification and loss function constraint. Compared with the prior art, the non-Cartesian magnetic resonance dynamic image reconstruction method has the advantages that the image is reconstructed through multiple times of network feedback iteration, the reconstruction speed of the non-Cartesian magnetic resonance dynamic image is greatly increased, and the temporal-spatial resolution is improved.

The invention discloses a high-resolution solar radiation data correction method and system based on deep learning, and the method comprises the steps: obtaining meteorological data and digital elevation model data, and carrying out the preprocessing; dynamically optimizing parameter configuration of the WRF model, and simulating and outputting high-temporal-spatial-resolution meteorological data as training data; fusing solar radiation flux values in meteorological reanalysis data and near-real-time product meteorological data as label data of a correction model; inputting the high-temporal-spatial-resolution meteorological data and the label data into a pre-constructed multi-scale feature fused deep residual coding and decoding network model for training, and obtaining an optimal parameter model based on multiple loop iterations; and correcting solar radiation flux data output by the WRF mode by using the optimal parameter model. According to the method, the problem that the radiation variable value output by the WRF in the traditional numerical weather mode is consistent with the real data trend but deviates is effectively solved, and the reliability of meteorological data simulation is enhanced.

The invention provides a high-temporal-spatial-resolution full-profile real-sea accurate corrosion test method and system, and the method comprises the steps: dividing the sea level into three layers of monitoring regions, namely an atmospheric near-sea-level layer, a sea-gas boundary layer and a seawater mixing layer, deploying corresponding sensor arrays in each monitoring region, and synchronously collecting the environmental parameters of each layer in real time; a standard corrosion coupon and a corrosion monitoring sensor for monitoring the local corrosion state of the sample are arranged in each monitoring area; performing space-time alignment on the acquired data to form an environment-corrosion strong association data set; an atmosphere-ocean coupling forecasting system is constructed, simulation and prediction of future environmental parameters are achieved, simulation results serve as input variables of a corrosion prediction model, and a material corrosion life prediction model is established in combination with historical test data. According to the invention, synchronous monitoring of multi-dimensional environmental parameters and material corrosion data within a range of 30 meters above and below the sea level can be realized, and an accurate basis is provided for material selection and corrosion prediction of marine equipment.

The invention discloses a flow field measurement system and method fusing event data and a blurred image, and the method comprises the steps: turning on a sheet-shaped light source, and enabling the sheet-shaped light source to illuminate tracer particles in a gas flow field; starting a gas flow field to enable the gas flow field to carry the tracer particles to flow through the transparent box body; capturing the motion change of the tracer particles in real time through an event camera, and collecting event data; blurred images of the tracer particles are synchronously collected through an industrial camera; based on the event data and the blurred image, reconstructing a clear moving image at any moment by using an image deblurring technology EDI; and carrying out time synchronization and space alignment on the clear moving image and the high-resolution static image, and extracting flow field information in the gas flow field in combination with the movement track and brightness change of the particles. According to the method, the high-speed dynamic capture capability of the event camera and the high spatial resolution of the industrial camera are combined, the bottleneck of the spatial-temporal resolution in the traditional flow field measurement technology can be effectively overcome, and meanwhile, the method has a relatively good effect in a complex flow field and a high-speed dynamic scene.

The invention relates to the technical field of computers, and discloses a real-time rendering and interaction method and system for an immersive virtual reality scene. The method comprises the following steps: fusing tuner inertial data and eyeball tracking data, and constructing a prospective state prediction model; generating a predictive focus field in combination with scene visual saliency; synthesizing an anisotropic temporal-spatial resolution graph according to the predicted head angular velocity; gPU variable-rate coloring is driven to realize non-uniform rendering; and re-projection or dynamic fuzzy correction is executed in a self-adaptive manner according to the attitude prediction error before display. According to the technical scheme, the perception delay and the rendering load are remarkably reduced, and the frame rate stability and the visual immersion in a high-dynamic scene are improved.

The invention discloses a water vapor chromatography modeling method and device based on GNSS-MET, and belongs to the technical field of atmospheric water vapor information inversion. According to the method, satellite signals and meteorological parameters are obtained through a GNSS-MET receiver, and the oblique path water vapor content (SWV) is calculated to serve as chromatography input; densely and uniformly distributed observation stations are selected to construct a chromatography area, and voxel grids are divided; a horizontal constraint is constructed by using Gaussian distance weighting, a vertical constraint is constructed in combination with a water vapor vertical index distribution characteristic, and ERA5 reanalysis data is introduced as a prior constraint, so that the limitation of a traditional sounding data constraint is solved; and resolving the tomographic equation through a singular value decomposition (SVD) method to obtain three-dimensional water vapor density distribution. According to the method, the high temporal-spatial resolution characteristic of ERA5 data is utilized, the precision and reliability of water vapor chromatography are remarkably improved, and the method is suitable for the fields of extreme weather prediction, meteorological monitoring and the like.

The invention discloses a thunder approaching prediction method and device based on multi-source meteorological data, and particularly relates to the technical field of thunder disaster prediction.The method comprises the steps that standardization processing of temporal-spatial resolution unification is conducted on meteorological satellite data, radar data and lightning positioning data, and standardized temporal-spatial input data is generated; then spatial-temporal features are extracted through a depth separable 3D convolution module, and a compressed spatial-temporal feature graph is generated; then, a channel-space double attention mechanism (CPCA) is applied to the compressed feature map for feature optimization; and finally, processing the optimized feature map through a coding-decoding structure, and outputting a thunder and lightning probability distribution map. According to the method, the problem of spatial-temporal resolution difference and physical feature mismatching in multi-source data fusion is innovatively solved, the calculation efficiency is remarkably improved through a lightweight network architecture, the feature expression ability is enhanced by using an attention mechanism, and high-precision prediction of sudden thunder and lightning events is realized.

A multi-source remote sensing data-based method for monitoring the concentration of suspended matter in an ocean dumping area, belonging to the technical field of remote sensing and monitoring of the concentration of suspended matter in ocean dredging and dumping areas. The method comprises: 1) selecting a certain sea area comprising a dumping area as a research area to acquire actually measured suspended matter concentration data; 2) separately acquiring the actually measured suspended matter data, and preprocessing remote sensing images; 3) matching the acquired data to form a data set, constructing a suspended matter concentration inversion model, and obtaining an image of the spatiotemporal distribution of the concentration of suspended matter in a body of water of the research area; and 4) constructing a multi-source remote sensing spatiotemporal data fusion model to obtain a high-spatiotemporal-resolution diagram of the spatiotemporal distribution of the concentration of suspended matter in the dumping area, and monitoring the conditions of a change in the concentration of suspended matter in the ocean dumping area. The present invention has advantages such as a short monitoring period, high spatiotemporal resolution, and a wide observation range, and can synchronously monitor a sea area having a large range. The present invention can also provide data support for operations such as supervision and management of ocean waste dumping, monitoring and management of an ocean waste dumping area, selection and division of a waste dumping area, supervision of use, and monitoring for illegal dumping.

The invention provides a three-dimensional triple configuration potential evapotranspiration fusion method and system considering neighborhood space-time non-stationary errors, and relates to the technical field of potential evapotranspiration estimation. The method comprises the following steps: obtaining a potential evapotranspiration observation value of a target watershed meteorological station and three sets of independent potential evapotranspiration data sets, and unifying temporal-spatial resolution; calculating a Gaussian kernel function matched with the predetermined space-time window, and performing data standardization by using the kernel function; constructing a variance calculation model of the three-dimensional triple configuration error, and obtaining a variance of an error of neighborhood dimension spatio-temporal information contained in each data set under a predetermined spatio-temporal window; and constructing a data fusion model based on a least square framework, determining an optimal fusion weight and realizing fusion of the multi-source potential evapotranspiration data. According to the method, the problems that local space-time non-stationary errors exist during fusion of the potential evapotranspiration data and the fusion precision is influenced by the scale effect of the space-time window are solved, and the precision of the potential evapotranspiration fusion data is effectively improved.

The invention provides a high-resolution emission flux inversion system and method. The method comprises the following steps: collecting atmosphere multi-source data; determining a distance weighting function, and calculating the correlation between the grid points in the simulation area and the observation value; generating a set sample meeting physical constraints of the assimilation object; performing singular value decomposition and dimension reduction on the set samples; replacing a tangent line and an adjoint mode of a regional air quality mode with a mixed assimilation method, and obtaining a simulated regional space grid point analysis increment; obtaining corrected concentration and flux distribution data; the data is used for carrying out emission flux inversion, and the unit time emission flux of different positions is estimated. According to the method, a traditional four-dimensional variation mode is replaced with a mixed assimilation method, and the calculation and programming difficulty is reduced; generating a set sample by using a four-dimensional sliding sampling algorithm, reducing dimensions, and calculating resource consumption; a joint assimilation algorithm is introduced to optimize the concentration and flux field, and accurate inversion of the high-temporal-spatial-resolution emission flux is achieved.

The invention discloses a large-scale regional sea wave rapid forecasting method and device based on data driving, and the method comprises the steps: obtaining multi-source marine physical environment data of a target sea area, and generating a standard physical field data flow with unified temporal-spatial resolution; constructing a multi-channel space-time input tensor containing wind field driving information, terrain boundary information and historical wave state information; the difference between the predicted wave height and the real wave height is minimized through a back propagation mechanism, so that a trained wave height prediction model is obtained; receiving latest wind speed field data output by a real-time observation or numerical forecasting mode, and outputting a sea wave significant wave height prediction field at a future target moment; the device is used for implementing the method. According to the technical scheme of the method provided by the invention, through a physical lag alignment mechanism, the time delay of transmitting wind energy to wave energy is accurately captured, and the modeling precision is improved; and meanwhile, rapid deduction of a large-scale sea wave field is realized based on deep learning, and high accuracy and high timeliness are achieved.

A high temporal-spatial resolution vegetation index fusion method based on a multi-source optical satellite image comprises the following steps: firstly, performing radiometric calibration, atmospheric correction and geometric fine correction on Landsat, Sentinel-2 and MOD09A1 data, and unifying temporal-spatial resolution to 10m / 8 days; pixel-level fusion is carried out by adopting an improved continuous correction method, a correction coefficient K is introduced to compensate Sentinel-2 critical period data defect influence, and fusion precision is improved through dynamic weight adjustment; and finally, a continuous and smooth EVI time sequence is constructed by using cubic spline interpolation and Savitzky-Golay filtering. According to the method, single-source data space-time limitation is broken through, after fusion, the vegetation index spatial resolution reaches 10 m, the time resolution reaches 8 days, the key phenological period extraction error is smaller than or equal to 3 days, the crop classification precision is larger than or equal to 90%, the accuracy and continuity of farmland-scale vegetation monitoring can be remarkably improved, high-precision data support is provided for agricultural application such as crop growth assessment and water resource management, and the method is suitable for popularization and application. The method is suitable for cloudy and rainy areas and various crop types.

The invention relates to the technical field of satellite navigation and positioning, in particular to a zenith troposphere wet delay calculation method based on deep learning, and the basic steps of segmented ZWD multi-source data fusion based on an improved Transform model are as follows: establishing a ZTD model, establishing a TCA-Transform model, and fusing data. The task of establishing the ZTD model is to calculate the atmospheric refractive index according to meteorological data so as to obtain ZTDPP; the TCA-Transform model establishment comprises the following steps of: improving a Transform model by using a domain self-adaptive method; according to the data fusion, the ZWDPP and the ZWDMODIS are fused by using a TCA-Transform model. And finally, a ZWD product with high temporal-spatial resolution and high precision can be obtained. The generalization ability of an original Transform model is improved, so that the troposphere delay correction ability in a specific area is improved, and a zenith troposphere delay (ZWD) product with high resolution and high precision is obtained.

The invention relates to a weather radar beam shielding rate calculation method based on temporal-spatial resolution improvement, belongs to the field of radio, realizes adaptive precise temporal-spatial matching of meteorological unmanned aerial vehicle sounding data and ECMWF forecast data under different weather echo types, realizes calculation of spatial distance in grids in an expansion field, and improves the weather radar beam shielding rate. The method realizes preliminary extraction of spatial-temporal characteristics, constructs a self-attention mechanism model fused with physical perception, successfully generates a high-spatial-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set, realizes generation of the high-spatial-temporal-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set, and improves the spatial-temporal-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set. Accurate calculation of the beam ray propagation path of the high-temporal-spatial-resolution radar is achieved, calculation of the shielding orientation and the maximum elevation angle of a shielding object in each orientation ray beam block is achieved, finally, a beam shielding power integral calculation model is introduced, and accurate calculation of the weather radar beam shielding rate is successfully achieved.

The invention belongs to the field of remote sensing technology and environment monitoring, and particularly relates to a three gorges reservoir area ground surface settlement monitoring method, system and device based on multi-source remote sensing data, and the method comprises the steps: obtaining a plurality of single-view complex images and corresponding image surface coverage data in a period of time, and carrying out the preprocessing of the image data; carrying out SBAS-InSAR processing on the preprocessed image data, wherein the SBAS-InSAR processing comprises carrying out interference, inversion and geocoding processing on the image; fusing the coded data by adopting a multi-source remote sensing data fusion technology; inputting the fused image into a pre-trained machine learning model to obtain a three gorges reservoir area ground surface settlement monitoring result; by introducing multi-source remote sensing data fusion and an advanced machine learning model, the problems of single data source, difference in temporal-spatial resolution, poor model adaptability and the like in the prior art are solved, and the precision, the stability and the robustness of ground surface settlement monitoring and evaluation are remarkably improved.

The invention relates to the technical field of meteorological prediction, and discloses a meteorological dynamic prediction multi-scale smooth transition method, device and equipment, and the method comprises the steps: building a focusing grid topological structure according to the regional focusing mode power frame characteristics for a target region; applying the dynamic equation and the physical process parameterization matched with the dynamic equation to the focusing grid topological structure to obtain a power framework parameter and a physical process parameter corresponding to each grid in the focusing grid topological structure; and performing focusing smooth nesting processing on the power frame parameters and the physical process parameters of each grid to obtain forecast values of meteorological elements under different resolutions. By implementing the method, large-scale coarse-resolution signals can be transmitted to high-resolution and small-scale high-fidelity under the focusing variable grid condition, the boundary effect is reduced while the temporal-spatial resolution of meteorological elements in a target area is improved, the maximum fidelity of prediction signals is realized, the prediction time efficiency is prolonged, and the prediction precision is improved.

The invention provides a wind cloud satellite observation data assimilation method, a wind cloud satellite observation data assimilation device, electronic equipment and a storage medium, and relates to the technical field of meteorological data processing. Real-time land surface temperature and space lattice points of a background field are subjected to temporal-spatial resolution alignment; if so, replacing the land surface temperature in the background field at the corresponding moment with the real-time land surface temperature corresponding to the non-missing measurement area; if the actual measurement area is a missing measurement area, correcting the land surface temperature at the corresponding moment in the background field based on a land surface temperature correction model to obtain an optimized background field; combining the observed radiance with the optimized background field, constructing an objective function, and calculating to obtain an analysis field; and carrying out iterative optimization on the analysis field to minimize the target function, and outputting an optimal analysis field. According to the method, background field errors can be reduced, so that the system has better satellite data assimilation capability, and meanwhile, the assimilation effect of the satellite data under the high-altitude complex terrain is improved.

In order to solve the problem that the snow depth predicted by a sentinel No.1 data set is difficult to meet high spatial resolution and high temporal resolution at the same time, the invention provides a snow depth prediction method, a storage medium and electronic equipment, and the method comprises the steps: obtaining sentinel No.1 data, auxiliary data and station data about the snow depth, constructing a geographic time weighted snow depth parameter based on the site data; carrying out resampling and space-time matching on the data to obtain a first data set; inputting the first data set into an integrated deep learning model to obtain a preliminary snow depth; obtaining time sequence climate data and geographical time data of the research area, performing resampling, and converting the preliminary snow depth data into time sequence data; the resampled time sequence climate data and geographical time data and the time sequence data of the initial snow depth form a second data set; and inputting the second data set into the ResNet-BiLSTM time sequence model to obtain snow depth data. According to the invention, snow depth prediction data with high temporal-spatial resolution can be generated.

The invention relates to a geological disaster early warning algorithm based on hyperspectral and Internet of Things data fusion analysis, and relates to the technical field of geological disaster monitoring and early warning, the geological disaster early warning algorithm comprises the following steps: S1, preprocessing hyperspectral data and Internet of Things data, and respectively extracting spectral-spatial features and dynamic topology time sequence features; s2, fusing the spectrum-space features and the time sequence features to generate cross-modal joint features; s3, performing parameter optimization on the fusion features through a quantum-classical hybrid optimization algorithm, and calculating a disaster risk probability; and S4, based on the optimization result and the risk probability, executing an edge-cloud collaborative early warning decision. According to the method, through non-negative tensor ring decomposition of the hyperspectral data and dynamic topology modeling of the Internet of Things, the limitation of a traditional single data source in temporal-spatial resolution and physical relevance is solved, multi-dimensional joint extraction of spectrum-space-mechanical characteristics can be realized, and the characterization precision of a rock-soil body deformation evolution law is remarkably enhanced.