361 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 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 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 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 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 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.

The invention belongs to the technical field of meteorological safety, and discloses a vertical gradient artificial intelligence recognition algorithm for low-altitude meteorological potential safety hazard airspace which is not encountered in n years in history. Comprising a multi-source data preprocessing module, an extreme vertical structure anomaly threshold extraction module, a small sample extreme recognition deep learning module, a vertical gradient risk level evaluation and interpretation module and a rolling retraining and real-time forecasting module. The low-altitude meteorological potential safety hazard airspace vertical gradient artificial intelligence recognition algorithm has the following advantages that (1) the spatial-temporal resolution is high, and real-time response is realized; (2) systematized vertical gradient sensing; (3) learning a priori threshold value and a small sample oriented to an'encountering once in n years' extreme event; (4) interpretable risk grading and trigger factor description; (5) performing dynamic self-adaption and online iterative updating; and (6) end-to-end closed-loop pushing is compatible with multiple platforms.

The invention discloses a point cloud-driven thoracic cavity whole organ dynamic reconstruction and respiration monitoring method and system, and the method comprises the steps: constructing a three-dimensional geometric model corresponding to each target structure in a thoracic cavity based on the thoracic medical image data of a target object, and converting the three-dimensional geometric model into static point cloud data; driving the static point cloud data to perform dynamic deformation simulation in a respiratory cycle based on a respiratory movement rule of the target object to obtain dynamic point cloud data synchronized with a respiratory time phase; performing time-space synchronization association on the dynamic point cloud data and the electrical characteristic parameters, and constructing a digital twin thoracic cavity model; deploying a virtual sensing assembly which dynamically deforms along with the thoracic cavity in the digital twin thoracic cavity model, simulating and monitoring the dynamic breathing process of the target object, and generating a virtual physiological signal; and performing signal processing on the virtual physiological signal to obtain a dynamic reconstruction image reflecting respiratory movement. Continuous simulation of the full-breathing movement process is achieved, and the temporal-spatial resolution and diversity of a virtual database are improved.

The invention discloses a near-infrared two-region activatable probe as well as a preparation method and application thereof. The near-infrared second-region activatable probe comprises a near-infrared second-region fluorescent light-emitting unit and an analyte specific response unit; the near-infrared second-region light-emitting unit comprises a hemicyanine fluorophore structure; when the analyte specific response unit is chemically coupled with the near-infrared second-region light-emitting unit, the intramolecular charge transfer process of the light-emitting unit can be inhibited, and fluorescence quenching is caused; the analyte specific response unit is separated from the light-emitting unit after being subjected to specific reaction with an analyte, so that fluorescence is activated. The near-infrared two-region hemicyanine fluorophore selected by the near-infrared two-region activatable probe has higher fluorescence quantum efficiency, and the near-infrared two-region probe which can be activated by a specific analyte and has a high on-off ratio can be constructed by introducing an analyte specific response unit, so that high-temporal-spatial-resolution fluorescence detection of deep tissues can be realized; the method has a wide application prospect in the field of biomedical detection.

The invention discloses a physical constraint fused deep learning gravity satellite groundwater vertical signal separation method, which comprises the following steps: S1, acquiring and preprocessing basic data: calculating to obtain total groundwater reserve abnormities needing vertical separation, unifying all data to a preset temporal-spatial resolution, and removing abnormal values; s2, constructing a deep learning model, which is configured to receive the preprocessed time sequence including the total groundwater reserve abnormality and meteorological data as input, and output shallow groundwater reserve abnormality and deep groundwater reserve abnormality at corresponding time; s3, the deep learning model is trained, optimization is carried out by adopting a composite loss function, and the composite loss function comprises a data-driven loss item Ldata and a physical constraint loss item Lwater based on water balance; and S4, after training is completed, inputting a total groundwater reserve abnormal signal into the deep learning model for processing, and outputting a separated shallow groundwater reserve abnormal time sequence and a separated deep groundwater reserve abnormal time sequence.

The invention belongs to the technical field of meteorological monitoring, and provides a sky-sea intelligent eye meteorological perception system for correcting airspace weather forecast in real time based on an unmanned aerial vehicle. An air-sea-land intelligent coupling mechanism is established, meteorological foundation observation, an ocean circulation field and unmanned aerial vehicle detection data are fused in real time based on an ensemble Kalman filtering algorithm, and an efficient three-dimensional wind field reconstruction engine is constructed; a physical enhancement AI correction strategy is adopted, an LSTM-Transform hybrid model is used for driving forecast updating, and meanwhile, a gradient constraint mechanism is introduced to effectively inhibit non-physical mutation of a meteorological field. The final value of the system is reflected in deep coordination of airspace management and control, a risk thermodynamic diagram can be automatically generated, an obstacle avoidance path can be planned, and an air traffic management system is linked to trigger a control instruction. The comprehensive application of the system breaks through the limitation of a traditional method in temporal-spatial resolution, and provides high temporal-spatial resolution early warning support for scenes such as port scheduling and unmanned aerial vehicle logistics.

The invention discloses a background schlieren method flow field flow velocity measurement method based on pseudo-schlieren images. The method comprises the following steps: constructing a background schlieren experiment measurement system; obtaining a speckle plate image sequence without a flow field background; obtaining a background distortion image sequence; obtaining a pseudo schlieren image sequence; and obtaining the final flow velocity of the flow field to be measured. The method has the advantages that a background plate distortion image sequence under the action of a flow field is collected through a high-speed camera, and pseudo-schlieren transformation processing is carried out through an improved mixed gradient operator and a multi-scale feature fusion technology; and a flow velocity field is solved in combination with a PIV-optical flow fusion algorithm, and real-time processing is realized through GPU parallel computing. The method breaks through the limitation that the traditional PIV technology depends on tracer particles, has the advantages of non-contact, full-field measurement, high temporal-spatial resolution and the like, and is particularly suitable for compressible flow field and turbulent flow field measurement. Experiments show that the method can realize high-resolution and high-frame-rate image processing, the uncertainty of flow velocity measurement is less than 3.2%, and a new technical means is provided for complex flow field diagnosis.

The invention discloses a method and a device for observing an internal flow field of a vortex spinning transparent nozzle based on a particle image velocity measurement system. The geometric similarity error is less than or equal to 0.5% and the light transmittance is greater than or equal to 92%. According to the device, a transparent nozzle is integrated in a vortex spinning system, and the device is composed of a laser light source module, a high-speed image acquisition module and a tracer particle injection module. According to the method, a PIV (particle image velocimetry) technology is utilized, hollow glass bead tracer particles are uniformly put into air flow, light illumination of a double-pulse laser sheet and synchronous image acquisition of a high-speed camera are combined, and non-contact dynamic observation of flow fields of a vortex cavity, an air inlet channel and a fiber outlet area in the nozzle is realized. PIV algorithm processing is carried out on the image sequence, and high-temporal-spatial-resolution flow field parameters such as a velocity vector field, turbulent flow kinetic energy and pressure distribution can be obtained. The problems of disturbance defects and observation blind areas of traditional intrusive measurement are solved, and accurate experimental data support is provided for vortex spinning nozzle structure optimization and flow field regulation and control.

The invention provides a remote sensing estimation method for high spatial-temporal resolution crop evapotranspiration in an arid and semi-arid region, which comprises the following steps of: acquiring cloudless or less-cloudless high spatial resolution optical remote sensing data of a crop growing season in a research region, and calculating or inverting remote sensing earth surface parameters required by high spatial resolution evapotranspiration; comprising a vegetation index NDVI, a leaf area index LAI, a vegetation coverage FVC, a surface water index LSWI and a surface albedo; the method comprises the following steps: acquiring meteorological reanalysis data of daily scales ERA5-Land and GLADAS of a research area, wherein the meteorological reanalysis data comprises relative humidity RH, average temperature Ta, air pressure Pa, solar short-wave radiation # imgabs0 #, downward long-wave radiation # imgabs1 # and upward long-wave radiation # imgabs2 #; according to the technical scheme, multi-source remote sensing data, an evapotranspiration physical model and a space-time fusion technology based on evapotranspiration characteristics are utilized, and estimation of high space-time resolution evapotranspiration of the arid and semi-arid agricultural areas is achieved.

The invention relates to a multi-source meteorological data fusion technology, and discloses an air temperature reconstruction method fusing data of a micro-meteorological device, which improves the temporal-spatial resolution and precision of ground temperature under a complex terrain. The method comprises the following steps: densely deploying micrometeorological devices in a complex terrain area to obtain high-frequency observation data, carrying out quality control, and carrying out hierarchical processing in two dimensions of space and time by taking pattern forecast grid point data as an initial background field: in the spatial dimension, dynamically updating a fusion weight for grid points with observation stations by using geographical weighted regression, and carrying out data fusion; a residual machine learning model is combined with multi-topographic feature correction for grid points without observation stations, and a high-precision space fusion background field is generated; in the time dimension, errors after space fusion are decoupled into a trend term and a periodic term, an autoregressive integral moving average model is used for predicting a trend, a Fourier algorithm is used for correcting a periodic phase, and then time dimension machine learning correction is carried out on a grid point of an observation-free station. And finally, complete-process automatic, high-temporal-spatial-resolution and low-error complex terrain area air temperature reconstruction is realized.

The invention belongs to the field of land surface temperature reconstruction, and relates to a high-spatial-temporal-resolution land surface temperature reconstruction method used in a cloud environment, which comprises the following steps: calculating a first reference land surface temperature and a first land surface temperature residual error of a clear sky pixel under a low spatial resolution according to a first land surface temperature annual change model; seamless surface reflectance data under high spatial resolution are obtained; defining a relative solar radiation index based on the digital elevation model data with high spatial resolution; screening clear sky pixels participating in modeling of the XGBoost model; an XGBoost model is adopted, and a plurality of model relations between a low-spatial-resolution land surface temperature annual change model coefficient and a land surface attribute and between a day-by-day land surface temperature residual error and the land surface attribute are constructed; obtaining a high-spatial-resolution surface temperature annual change model coefficient and a day-by-day surface temperature residual error based on the constructed multiple model relationships, and obtaining a high-spatial-resolution daily surface temperature; the accuracy and the stability of a surface temperature reconstruction result are improved.