982 results about "High spatial resolution" patented technology

Disclosed are an improved GNSS tropospheric delay (ZTD) real-time grid model construction method and system. Firstly, a global tropospheric delay elevation normalization grid model is constructed using global ERA5 reanalysis data from a plurality of years. Then, elevation normalization and grid horizontal interpolation are performed on GNSS real-time ZTDs to obtain a global tropospheric delay real-time grid model. A user only needs to provide position and time information and use the global tropospheric delay elevation normalization grid model and the global tropospheric delay real-time grid model to be able to obtain a ZTD at a target point. According to the present invention, the high spatial resolution of a numerical weather model (NWM) and the high precision of the GNSS tropospheric product are comprehensively used, and an elevation normalization function form is optimized, so that the construction of a tropospheric delay real-time grid model covering a larger height range is achieved, and high-precision tropospheric delay correction information can be provided for GNSS real-time high-precision data processing, thereby improving the data processing performance.

The invention relates to the technical field of corrosion monitoring, in particular to an intelligent pipeline corrosion monitoring and protection system which comprises a dynamic potential disturbance measurement module, a local microcell scanning module, a corrosion rate calculation module, a corrosion risk distribution analysis module and an environmental parameter compensation module. According to the invention, accurate measurement is realized, the spatial resolution is improved, and the current response change rate is calculated by measuring the reference potential of the surface of the pipeline, applying instantaneous disturbance, combining current change differential operation, dynamically tracking the corrosion process, improving the real-time response capability and arranging a microelectrode array to cover corrosion hot spots; the corrosion current density is solved by combining the electrochemical characteristics of the material, the corrosion rate is dynamically predicted, regional distribution is analyzed based on the change of the corrosion rate, the corrosion risk assessment precision is optimized, environmental factors are monitored, disturbance parameters are adjusted, interference is compensated, the monitoring accuracy under different working conditions is ensured, and data stability and protection measure optimization are guaranteed.

The invention discloses an atmospheric environmental pollution event emergency monitoring and early warning method and system, and belongs to the technical field of environmental management. The method comprises the following steps: acquiring multi-scale meteorological field data and chlorine concentration real-time data of at least five monitoring stations, extracting a wind field direction and a concentration change rate, and determining a position and a wind direction of a potential pollution hot spot area. The information is sent to the unmanned aerial vehicle, and the unmanned aerial vehicle automatically plans a path and collects supplementary concentration data from three angles. And fusing the monitoring data and the supplementary sampling data, and combining with a meteorological field input pollutant three-dimensional concentration distribution model to generate three-dimensional distribution data. And inputting the CALPUFF reverse model, and carrying out inversion to obtain the specific position and the emission intensity of the pollution source. According to the scheme, the accuracy and timeliness of source tracing of the pollution source are improved, high spatial resolution and flexible response capability are achieved, and efficient and intelligent technical support is provided for intelligent early warning, rapid source tracing and scientific response of sudden toxic gas leakage events.

The invention discloses an intelligent operation abnormity monitoring and positioning method for a polypropylene cable, and relates to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: collecting multi-source physical signals in the operation process of the cable, and constructing a multi-dimensional feature matrix fusing multiple physical quantities through multi-scale time window division and space mapping processing; extracting space-time coupling characteristics among nodes by using a graph attention embedding network, and training a running state recognition model in combination with a label perception contrast learning mechanism; constructing a cable topological graph based on an identification result, introducing an improved Bayesian space reasoning network, and calculating abnormal probability distribution of each node; a weighted abnormal heat map is further generated, an abnormal propagation path is extracted through an abnormal state flow model and a directional propagation scoring algorithm, and abnormal node positioning and trend evolution prediction are achieved; the method has the advantages of high spatial resolution, high identification precision and good online adaptability, and is suitable for intelligent state perception and abnormity early warning of the polypropylene cable in a complex operation environment.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention relates to a multispectral image and hyperspectral image fusion method and system, and belongs to the field of deep learning and signal processing, and the method comprises the steps: carrying out the coding and feature extraction of a multispectral image and a hyperspectral image through employing a coder based on a spectrum self-attention mechanism; the hyperspectral image features and the multispectral image features obtained by the encoder are input into a space-spectrum fusion module, deep fusion of the spatial dimension and the spectral dimension is carried out, and unified feature representation is obtained; inputting unified feature representation obtained by fusion into a decoder based on a spectrum self-attention mechanism, inputting layered features passing through a graph-based spectrum sensing module into a corresponding decoder symmetric layer, and then decoding and outputting to obtain a spectrum image with high spatial resolution and a spectrum image with high spectral resolution; through the above strategy, full fusion and complementation of spectral image space information and spectral information are ensured, and the method has important value for research and application of MHIF.

The invention provides a high-speed defect identification method and system based on optical diffraction imaging, and relates to the technical field of optics, and the method comprises the following steps: constructing a composite optical diffraction unit comprising a programmable optical metasurface and a quantum phase modulator, optimizing and determining modulation characteristics through a multi-target constraint function, and collecting a quantum coding diffraction image. The quantum state phase reconstruction network is used for processing the image and extracting characteristic parameters, and a result is obtained through analysis of a defect identification model. According to the invention, rapid defect identification with high spatial resolution is realized, and the detection efficiency and accuracy are improved.

The invention discloses a modeling method for inverting surface high-precision three-dimensional deformation through InSAR and GNSS data fusion, and belongs to the technical field of surface deformation monitoring. The method includes the steps of resolving InSAR line-of-sight deformation, unifying InSAR and GNSS data reference systems, establishing a stress model based on an elastic theory, calculating a three-dimensional deformation component for the first time based on a least square theory, obtaining an optimal weight matrix by applying a variance component estimation method, calculating final three-dimensional deformation and the like. According to the method, three-dimensional deformation and strain parameters are estimated by fusing InSAR measurement data and GNSS measurement data based on the elastic theory, the stress relation between adjacent points is considered, and unnecessary spatial interpolation of GNSS measurement values is avoided. The variance component estimation method solves the problem of unreasonable weight ratio of different types of observation values, and according to the adjusted optimal weight matrix, a function model is solved based on the least square method, and high-precision and high-spatial-resolution earth surface three-dimensional deformation information within the research range is obtained.

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

The invention discloses a fragmented cultivated land remote sensing extraction method based on space-time deep learning, and the method comprises the following steps: 1, data preparation: selecting a high-spatial-resolution remote sensing image, and capturing the space-time change of cultivated land in different growth cycles and environment conditions in combination with multi-temporal data of different times and seasons; a second step of model construction: constructing a deep learning model fusing ConvNeXt and a U-Net framework, namely a ConvNeXt-U model; 3, model training is carried out; 4, crushing cultivated land extraction; and 5, analyzing and outputting a result. Through multi-dimensional technical innovation, remarkable performance breakthrough and application value improvement are realized in the field of fragmented cultivated land remote sensing monitoring, and the method is specifically embodied in the following three aspects: 1, high precision and robustness improvement are realized; 2, optimizing calculation efficiency and generalization ability; and 3, dynamic evolution analysis and decision support capability.

The invention discloses a high-resolution hyperspectral image reconstruction method based on wavelet domain feature fusion and contrast learning, and belongs to the technical field of image fusion and super-resolution reconstruction. The method comprises the following steps: constructing a fusion network model comprising a wavelet transformation module, a cross-modal feature fusion module, a high-frequency contrast learning module and an image reconstruction module; performing end-to-end supervised training by using a training data set containing the low-resolution hyperspectral image and the high-resolution multispectral image; and after training is completed, inputting a test image pair to realize image reconstruction. According to the method, the detail retention capability is improved by combining wavelet decomposition and a directional fusion mechanism, the cross-modal high-frequency feature alignment capability is enhanced through comparative learning, a fusion image with high spatial resolution and high spectral consistency is finally generated, and the method is suitable for multi-modal image reconstruction tasks such as remote sensing, medical and natural images.

The invention discloses a marine environment pile foundation damage identification method based on optical fiber distributed strain monitoring, and belongs to the technical field of structural health monitoring and ocean engineering. The marine environment pile foundation damage identification method comprises the steps that distributed optical fiber sensors are evenly distributed in the axial direction of a pile foundation and used for strain monitoring; acquiring strain response data of the pile foundation along the pile length under the action of the wave force load through a distributed optical fiber demodulator; identifying modal characteristic parameters of the pile foundation structure by applying a random subspace identification algorithm, wherein the modal characteristic parameters comprise modal frequency and modal shape; calculating modal strain energy based on the modal shape, and obtaining a modal strain energy distribution diagram; identifying a damage area, judging whether a set threshold value is exceeded or not by comparing the change of the modal strain energy between the monitoring state and the reference state, judging the damage, and obtaining a damage identification result. The method provided by the invention has the advantages of high spatial resolution, high recognition precision, good environmental adaptability, accurate positioning, high automation, wide applicability and the like.

The invention discloses a panchromatic sharpening method and system based on cross-resolution adversarial learning and a Mama network. The method comprises the following steps: acquiring a low-resolution multispectral image LRMS and a high-spatial-resolution panchromatic image PAN; extracting features of the low-resolution multispectral image LRMS to obtain a first feature map; extracting features of the high-spatial-resolution panchromatic image PAN to obtain a second feature map; performing wavelet decomposition and weighted fusion on the first feature map and the second feature map to obtain a multi-channel feature map, and performing dynamic fusion on the multi-channel feature map through a cross attention gating mechanism to obtain a fused feature map; and carrying out residual connection on the fused feature map and the low-resolution multispectral image, and then carrying out feature reconstruction to obtain a high-resolution multispectral remote sensing image. According to the method, the panchromatic sharpening performance of the remote sensing image can be improved, and the high-quality and high-resolution multispectral remote sensing image is obtained.

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 medical elastography and material multi-scale mechanical measurement, in particular to an elastography method and system based on an instantaneous scattering displacement field, and the method comprises the steps: driving a passive / active method scattering displacement field excitation device through a frequency domain controllable sine wave signal, and generating a scattering displacement field on a target material, an instantaneous displacement field is obtained through high spatial resolution measurement, then sliding window local interception, two-dimensional autocorrelation calculation and nonlinear fitting based on the Rayleigh / shear wave spatial autocorrelation theory are performed on the single-frame instantaneous displacement field, shear wave velocity distribution is obtained, and then the viscoelasticity modulus of a target area is inverted to achieve comprehensive characterization of the viscoelasticity of the material. According to the scheme, the single-frame scattering displacement field is excited and collected, and the elasticity inversion is performed based on the instantaneous displacement field, so that the dependence of traditional elasticity imaging on high-frame-rate equipment is broken through, the material viscoelasticity evaluation can be efficiently completed at low cost, and the application prospect in the fields of clinical diagnosis and mechanical measurement is wide.

The invention relates to a visible light / short-wave infrared dual-band common-caliber optical system, which comprises a common-caliber part, a plane beam splitter, a visible light channel subsystem and a short-wave infrared channel subsystem, and is characterized in that the common-caliber part is positioned at the foremost end of the optical system and consists of a primary reflector and a secondary reflector which are coaxially arranged; the plane beam splitter is located on a light path behind the secondary reflector, full-wave-band light from an object side is reflected by the primary reflector and the secondary reflector in sequence, then reaches the plane beam splitter to be split, and then enters the visible light channel subsystem and the short-wave infrared channel subsystem to be imaged in different wave bands, and the optical system achieves dual-wave-band common caliber. According to the invention, the design of long focal length and large aperture of the dual-band system is realized, the target detection / identification capability of the system is improved, the system has the advantages of high spatial resolution, long operating distance and high identification probability, meanwhile, the system structure is effectively simplified, the system volume is reduced, the installation and adjustment difficulty is reduced, and a relatively low obscuration ratio and relatively high imaging quality are realized.

The invention discloses a multi-frame photoacoustic image reconstruction method based on optical flow alignment and depth feature fusion. The method comprises the following steps: S1, obtaining photoacoustic signal data; s2, reconstructing a photoacoustic cross-sectional image; s3, performing optical flow calculation and image alignment; and S4, training the deep feature fusion network. According to the method, through optical flow motion correction and a potential space learning mechanism, space-time information and complementary features in the aligned multiple frames of images are dynamically integrated, complementary information in the multiple frames of images is adaptively fused, noise is suppressed, and finally reconstruction of high signal-to-noise ratio and high spatial resolution images of biological tissues is achieved. Experimental results show that the method can significantly improve image quality, recover image distortion and detail loss caused by motion and noise, and provide a new effective scheme for promoting robust clinical application of a photoacoustic imaging technology.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

The invention discloses a laser ultrasonic defect imaging method based on sampling-imaging double-order compressed sensing, and relates to the technical field of laser ultrasonic detection and material defect detection, and the method comprises the following steps: carrying out sparse scanning on a to-be-detected sample through a random sparse sampling strategy; according to a compressed sensing principle, global ultrasonic intensity distribution of full-matrix scanning under a high sampling rate is reconstructed, and global sparse positioning is realized through extreme value detection; then, on the basis of global sparse positioning, carrying out local fine scanning by adopting high spatial resolution; and generating a sparse imaging result with low spatial resolution by using global sparse positioning and local fine scanning data through a synthetic aperture focusing technology, and performing high-resolution compressed sensing reconstruction imaging by using a compressed sensing principle again. Therefore, by adopting the laser ultrasonic defect imaging method based on sampling-imaging double-order compressed sensing, efficient imaging of tiny defects can be realized, and scanning points are reduced while the imaging precision is improved.

A high-density tactile sensing array based on a bionic microstructure comprises a flexible substrate for providing mechanical flexibility and packaging protection; the bionic microstructure layer is composed of a pyramid array or a hemisphere array and is used for enhancing sensitivity and spatial resolution; the carbon nanotube (CNT) sensitive layer uniformly covers the surface of the microstructure and converts pressure change into an electric signal; according to the thin film transistor signal reading system, active matrix design is adopted, pixel-level signal extraction is achieved through a row-column addressing strategy, and electric signals are rapidly and accurately processed. According to the preparation method, photoetching, wet etching and hot reflux processes are combined, precision machining of a microstructure is achieved, and a uniform CNT sensitive layer is formed through a spraying technology. According to the design method, microstructure parameters and sensitive layer characteristics are optimized through equivalent circuit model construction and simulation verification, and collaborative optimization of high sensitivity and wide dynamic range is achieved. The invention has the advantages of high sensitivity, high spatial resolution, low crosstalk, quick response and the like.

The invention discloses an area intelligent identification alarm system for tower crane hoisting, which relates to the technical field of alarm systems and comprises a front-end data acquisition module, an acquired data processing module, a wireless network communication module, a hoisting safety analysis module and a safety alarm execution module. The visual detector, the detection sensor and the environment sensor form a multi-element acquisition module, hoisting parameters and environment information can be synchronously acquired in real time on the hoisting site of the tower crane, the comprehensiveness of information acquisition is ensured, and the problem that the traditional configuration is single in acquisition element and the cost is low is solved through the redundancy design of multiple acquisition elements. The method solves the problem that the existing tower crane is easy to fail, realizes real-time intelligent identification and graded early warning of potential safety hazards in a tower crane hoisting operation area, has the characteristics of high dynamic adaptability, high spatial resolution, intelligent response, high deployability and the like, and provides scientific and efficient technical support for safety management of tower crane operation.

The invention discloses a remote sensing image panchromatic sharpening method and system fusing Mama and CNN under detail enhancement guidance, and the method comprises the following steps: S1, enhancing the texture feature information of a multispectral image LRMS with low spatial resolution through the high-pass information of a panchromatic image PAN, and obtaining an enhanced LRMS image; s2, extracting local and global feature information of the PAN image and the enhanced LRMS image through a convolutional neural network CNN and Mama, and integrating the extracted feature information to a fusion branch based on the CNN step by step so as to carry out full interaction and fusion of the local and global information of the two modals; s3, finally, reconstructing and generating a multispectral image HRMS with high quality and high spatial resolution; according to the method provided by the invention, the CNN focused on local feature extraction and the Mama focused on global feature modeling are effectively combined, so that a good panchromatic sharpening effect of the remote sensing image is achieved.

The invention discloses a plasma ion three-dimensional flow velocity distribution detection system and method, and belongs to the technical field of plasma diagnosis. The system comprises a Mach probe, a controllable voltage source, a sampling resistor and a voltage acquisition system, the Mach probe, the sampling resistor and the controllable voltage source are connected in series through wires to form a loop, and the voltage acquisition system is connected in parallel with the controllable voltage source and the sampling resistor and is used for acquiring the voltage of the controllable voltage source and the sampling resistor. According to the detection method, on the basis of the multi-axis vector synthesis principle, ion flow velocity components in different planes are obtained by inserting a Mach probe in the three-axis direction, and a three-dimensional flow velocity component is calculated through a vector synthesis algorithm. According to the invention, the local flow field structure in the ion source can be diagnosed, the spatial distribution characteristic of the ion flow velocity can be revealed, the spatial resolution is high, the velocity information of particles can be directly provided, the three-dimensional data of the ion flow velocity measurement can be synthesized without complex optical path rotation or multiple scanning, and the experimental device is simple and low in cost.

The invention discloses a DEM intelligent super-division method based on high-spatial-resolution remote sensing data, and the method comprises the steps: inputting a high-resolution remote sensing image to a guide selection branch module, inputting a low-resolution DEM to a terrain reconstruction branch module, carrying out the interaction through a terrain perception cross attention module, obtaining guide information and DEM features, and carrying out the super-division of the high-spatial-resolution remote sensing image. And performing multi-source feature fusion on the guide information and the DEM features to obtain fused features, training the guide information, the DEM features and the fused features by adopting a total loss function until the total loss function is converged, and outputting a target high-resolution DEM. According to the method, the high-resolution remote sensing image which is easy to obtain is used as guide information to guide the super-resolution reconstruction process of the low-resolution DEM, so that a high-resolution DEM product which is high in precision and rich in terrain details can be generated with relatively low cost and relatively high efficiency.

The invention belongs to the technical field of ocean observation, and discloses an underwater sound velocity distribution super-resolution construction method based on multi-modal data fusion. According to the method, a sound velocity distribution forecasting model is constructed, the model carries out temperature preliminary reconstruction on original space area temperature data through a Unet neural network, and sea surface temperature features are extracted through a Mama neural network; correcting the preliminarily reconstructed temperature data through sea surface temperature features to obtain high-spatial-resolution temperature data; and finally, obtaining high-spatial-resolution sound velocity distribution data through a sound velocity distribution calculation module. According to the method, the underwater temperature and the sea surface temperature are subjected to data fusion, so that the sound velocity distribution of the whole space area is quickly and accurately forecasted under the condition of original low-spatial-resolution data, and the problem of insufficient spatial resolution of ocean sound velocity distribution estimation is solved.

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 provides a panchromatic sharpening joint optimization method and system based on three-stage progressive fusion. The method comprises three stages of coarse fusion, deblurring detail enhancement and fine fusion. The method comprises the following steps: firstly, performing feature extraction and preliminary fusion on a low-resolution multispectral image and a high-resolution panchromatic image through a dual-path mutual enhancement network; secondly, a multi-layer stacked deblurring module is introduced to perform deep enhancement on fusion features, and the details and definition of the image are effectively improved in combination with partial large kernel convolution, channel mixing and an element-level attention mechanism; and finally, realizing fine optimization of spectrum consistency and a space structure, and outputting a fused image with high spatial resolution and high spectral fidelity. According to the method, the stability and reconstruction quality of panchromatic sharpening under a complex degradation condition are effectively improved through multi-source remote sensing image collaborative modeling and progressive feature fusion, and the method has good practical value and popularization prospect and is superior to a current panchromatic sharpening method based on deep learning.

The invention discloses a flood monitoring and deduction method based on unmanned aerial vehicle time sequence radar remote sensing, which comprises the following steps: S1, collecting multi-source flood disaster high-spatial-resolution time sequence radar remote sensing data through unmanned aerial vehicle radar remote sensing, and processing the data into a remote sensing data set with consistent space-time reference; s2, preprocessing each time sequence radar remote sensing image in the remote sensing data set; s3, segmenting a submerged area and a non-submerged area of the preprocessed time sequence radar remote sensing image, removing fine water in the submerged area, and extracting a radar remote sensing high-resolution submerged range; and S4, quantitatively extracting flood evolution characteristic elements, and performing flood disaster potential influence area deduction on the current submerging range in combination with the digital elevation model and hydrological connectivity. According to the method, the problem that the large-range flood evolution process is difficult to dynamically monitor is solved, and support is provided for rapid emergency and risk avoiding transfer of watershed flood by forward deduction of the potential influence area of flood disasters.

The invention discloses a hyperspectral and multispectral image fusion method and system based on Reform residual iteration. The method comprises the following steps: acquiring an image data set of a hyperspectral remote sensing image and a multispectral remote sensing image in the same region and preprocessing the image data set; performing spatial down-sampling on the preprocessed data set to generate a low-resolution hyperspectral image and a low-resolution multispectral image; an image priori network model is constructed, the image priori network model comprises a space priori module and a spectrum priori module, the space priori module captures a long-range spatial dependency relationship through a Reformer layer, and the spectrum priori module extracts spectrum features through a depth separable convolutional layer; establishing a multi-level iteration model, setting a residual connection reconstruction function, and training the model by adopting an optimization algorithm and a space-spectrum joint loss function to obtain a trained model; and finally, generating a hyperspectral image with high spatial resolution. According to the method, the parameter quantity required by fusion can be effectively reduced.

The invention discloses a temperature difference-deformation decoupling method for thermal expansion coefficient in-situ monitoring, and belongs to the technical field of material physical property testing, and the method comprises the following steps: obtaining high spatial resolution temperature distribution data and full-field deformation data; performing three-dimensional reconstruction on the high-spatial-resolution temperature distribution data to generate dynamic temperature field data; theoretical thermal stress is calculated based on the dynamic temperature field data and the material mechanical parameters, and interference deformation data is generated; a dynamic correction coefficient is introduced to decouple full-field deformation data, and a real thermal expansion deformation field is output; calculating a thermal expansion coefficient based on the real thermal expansion deformation field and the temperature change trend data; and monitoring a uniformity index of a real thermal expansion deformation field, and adjusting a dynamic correction coefficient and a material mechanical parameter on line. According to the method, the temperature field and the deformation field are synchronously collected, thermal stress interference deformation caused by temperature difference is theoretically calculated and eliminated, and a closed-loop feedback optimization mode is combined, so that high-precision dynamic monitoring of the thermal expansion coefficient of the material under the non-uniform temperature condition can be realized.