2149 results about "Space resolution" patented technology

The invention provides an intelligent image signal processing method and system based on multi-modal fusion. According to the invention, a multi-mode input signal is received, is divided into a spatial distribution feature extraction region and a dynamic change trajectory capture region, and is decomposed into a penetrability feature layer and a substance reflection feature layer; carrying out association mapping on missing pixel information in the dynamic change trajectory capture region and energy distribution of the penetrability feature layer to generate enhanced dynamic trajectory data, identifying a determined reflection mode in the substance reflection feature layer, and carrying out frequency domain superposition on corresponding frequency band response and the spatial distribution feature extraction region; generating a composite spatial feature, then constructing a multi-modal joint optimization model, then adjusting the contribution ratio of the two data, and generating a fused image signal; the technical scheme provided by the invention not only solves the problems of detail loss, artifact generation and poor dynamic adaptability caused by single-mode limitation, but also improves the spatial resolution and tracking precision of the image signal.

The invention discloses a panchromatic sharpening method based on multi-resolution panchromatic feature guidance, which comprises the following steps: firstly, designing a multi-resolution feature extraction network based on a spatial frequency Transform module, and respectively extracting multi-scale features from panchromatic and up-sampled multispectral images; secondly, in order to restrain and optimize the extracted detail features, a multi-head self-attention-based texture injection module is adopted to extract a dependency relationship between panchromatic and multispectral features, and more similar detail features are guided to be injected; thirdly, in order to solve information loss caused by multispectral image up-sampling, multi-level detail features are injected into a multispectral reconstruction network, the detail features are learned step by step and reconstructed to a panchromatic image size, and a spectrum fusion module based on adaptive channel attention is utilized to reconstruct a fused image in a high-quality mode; and finally, optimizing the model performance by combining reconstruction loss based on L1 norm, structural similarity constraint and transmission perception loss in training. According to the method, the spectral fidelity and the spatial resolution of the fused image are remarkably improved.

The invention discloses a multi-dimensional carbon flux monitoring method, and particularly relates to the technical field of carbon flux monitoring, and the method comprises the following steps: collecting multi-source heterogeneous sensor data, and carrying out spatial resolution unification, time synchronization alignment and data format standardization preprocessing to ensure data consistency; executing fusion validity detection, and correcting time synchronization deviation and data redundancy conflicts; according to an abnormal detection result, dynamically adjusting a fusion weight or determining a weight based on historical clustering, and combining spatial adjacency interpolation and time interpolation to realize data fusion; and finally, inputting a carbon flux inversion model, and outputting a high-precision carbon flux monitoring value to realize dynamic monitoring and prediction. According to the method, a dynamic fusion weight adjustment mechanism is adopted, the fusion weight of each data source is dynamically optimized, adaptive fusion of multi-source observation data is realized, the problems of data discontinuity and insufficient data coverage in the prior art are solved, and a multi-dimensional, full-coverage and high-resolution carbon flux fusion data set is ensured to be formed.

The present disclosure provides electrophoretic systems, methods and compositions for spatial analysis, which can serve to magnify or demagnify spatial resolution of analytes of interest that are captured using electrophoresis. Some implementations can use a diverging or converging electric field in an electrophoretic capture system. Such a divergent or convergent electric field, as opposed to a parallel electric field, can be generated by, for example, utilizing different sizes of electrodes associated with or imbedded in substrates. Also provided herein are electrophoretic systems, methods and compositions for spatial analysis, which can serve to selectively migrate one or more analytes from a region of interest in the biological sample for capture using electrophoresis.

The invention relates to an underwater image enhancement method based on wavelet Mama, which aims at the problems of color shift, low contrast ratio and fuzzy details of an underwater image, extracts shallow layer features of an underwater low-quality image, inputs the shallow layer features into a multi-scale coding network, and performs joint enhancement on low-frequency color information and high-frequency detail features by using a wavelet Mama unit. Global semantic features are obtained by combining down-sampling layer-by-layer compression, and global modeling of color offset correction and contrast enhancement is completed; the spatial resolution is recovered through up-sampling, and reconstruction and enhancement of texture details and color information are realized in combination with jump connection and a wavelet Mama unit; and finally, the features are mapped to an image domain and fused with input image residual errors, and an enhanced underwater image with natural color, clear texture and balanced contrast is generated. According to the method, frequency domain-space domain joint feature modeling is realized through the wavelet Mama unit, and the color authenticity, the structural definition and the detail perceptibility of the underwater image are remarkably improved.

The invention discloses a blasting design-oriented multi-source data fusion preprocessing method, and particularly relates to the technical field of blasting data scientific processing. Comprising the following steps: acquiring multi-source heterogeneous data of a blasting area through a multi-source sensor array and a geological modeling system, establishing a multi-modal data unified representation framework based on ontology, constructing a depth feature fusion network with physical constraints, developing a blasting parameter optimization-oriented mixed integer programming model, and establishing a blasting parameter optimization-oriented mixed integer programming model; integrating a multi-target genetic algorithm and an expert experience knowledge base to perform parameter optimization; according to the method, a multi-mode unified representation framework based on the ontology is constructed, millimeter-level space alignment and millisecond-level time synchronization of multi-source data are achieved through a space-time reference coordinate system and an improved DTW-B spline composite algorithm, the spatial resolution of blasting design is improved to the centimeter level, the time synchronization precision reaches the millisecond level, and the time synchronization efficiency is improved. Compared with a traditional method, the data utilization rate is increased by more than 40%, and a solid foundation is laid for fine blasting design.

The invention discloses a rescue method and system of a rescue robot for exploration, and relates to the technical field of underground space rescue, and the method comprises the following steps: obtaining multi-path acoustic echo data of a karst cave and motion track data of the robot, and constructing a three-dimensional point cloud model according to the multi-path acoustic echo data and the motion track data of the robot; and identifying unmatched data in the multi-path acoustic echo data and the robot motion trail data, and taking an area where the unmatched data is located as an abnormal area. According to the method, the spatial resolution of signal acquisition is enhanced through the multi-microphone array, robust acoustic fingerprints are extracted in combination with a noise reduction algorithm and short-time Fourier transform, high-confidence human body sound source signals are screened out by using a feature template matching mechanism, accurate extraction and recognition of human body acoustic features in a karst cave complex noise environment are realized, and the accuracy of human body acoustic feature recognition is improved. The problem of misjudgment caused by confusion of sound source features and environmental noise in a traditional method is effectively solved.

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention discloses a night semantic segmentation method and device based on wavelet transform detail enhancement and text prompt, and the method comprises the steps: obtaining a night image, carrying out the preprocessing of the night image, and carrying out the reconstruction of a wavelet image; and inputting the preprocessed night image and the image after wavelet transform reconstruction into a deep learning model for semantic segmentation to obtain a segmentation result of the night scene object. A new three-stage network structure is designed and formed, in the first stage, a three-mode feature extractor composed of an image encoder, a night semantic category encoder and a wavelet image encoder is used for extracting features, in the second stage, a double-branch cross-mode feature interaction module is designed, and the feature extraction is carried out through the image encoder. In the first stage, features of different spatial resolutions and semantic hierarchies and natural language priori of a target object are integrated, all-directional semantic information from coarse granularity to fine granularity is captured, in the third stage, a multi-scale feature segmentation decoder is introduced, details of a low-light area are enhanced, fine texture edges and target contours are captured, and the target object is obtained. Through positioning and understanding of the target area by the natural language prior enhancement model, the precision of night scene semantic segmentation can be effectively improved.

The invention discloses a multi-source remote sensing image zero sample change detection method, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: obtaining remote sensing images collected by two or more remote sensing sensors at different time points in the same geographic area, the image types including optical images and radar images; preprocessing each source image, unifying the spatial resolution and the registration precision, and denoising and standardizing the image; according to the method, the cross-modal shared semantic embedding space is constructed and unsupervised comparative learning is introduced, so that the semantic consistency of the multi-source remote sensing image is effectively improved, and the change recognition capability of the model under the zero sample condition is enhanced; and meanwhile, a difference fusion calculation and structure consistency constraint module is adopted, so that the boundary judgment precision of a change region and the overall structure consistency are improved, and the accuracy and stability of a detection result are remarkably improved.

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

The invention relates to a sea fog quantitative forecasting method based on a period enhanced Transform, and the method comprises the steps: firstly constructing a Transform model as a forecasting model, enabling the Transform model and a self-attention mechanism to better capture the complex nonlinear relation and long-distance time dependence related to the formation of sea fog, and being superior to a conventional statistical method and an early-stage RNN / LSTM model, a data set for model training is constructed based on a time period enhancement technology and visibility standardization mapping, and the explicit time period enhancement technology enables the model to more accurately learn and forecast seasonal and daily change rules of sea fog; and a sample amplification and training set balancing strategy is executed on the data set, so that the problem of sparse fog samples is effectively solved, and the forecasting capability of the model on key low-visibility events is improved. According to the sea fog quantitative forecasting method based on the period enhanced Transform, the precision, timeliness (forecasting per hour) and spatial resolution of sea fog forecasting of a target area can be improved, and sea fog data characteristics can be effectively processed.

The invention discloses a remote sensing image segmentation method fusing frequency modulation and spatial perception, and the method comprises the steps: obtaining and preprocessing an original remote sensing image, and generating a standardized input image; the image is input into a multi-scale frequency domain enhanced feature extraction network, features are extracted step by step according to a plurality of feature levels, each level realizes frequency adaptive semantic enhancement through frequency domain modulation transformation and spatial feature fusion, and deep feature expression is enhanced through feedforward neural network modeling and residual connection output and cross-level residual fusion introduction; the final multi-scale features are decoded through a decoding module, the spatial resolution is recovered, and a pixel-level segmentation result is generated; and constructing a composite loss function containing classification errors, boundary perception and frequency consistency items, and carrying out optimization training on the network. According to the method, semantic complementarity of a remote sensing image in a frequency domain and a space domain is fully mined, so that segmentation precision and robustness of a ground object target in a complex scene are improved, and the method has good generalization ability and engineering practicability.

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

The invention relates to the technical field of traction backflow monitoring, and provides a railway traction backflow monitoring analysis method and system based on multi-path current fusion. The method comprises the steps of generating multi-path current waveform data based on a mapping relation between a phase variation and traction backflow intensity, performing time-frequency domain combined noise reduction, and aligning with a time axis to obtain aligned multi-path current waveform data; and cross-path collaboration analysis is carried out, synchronous deviation characteristics of multi-path current are extracted, matching verification is carried out on the synchronous deviation characteristics and a preset traction load dynamic change interval, and an abnormal current event caused by harmonic distortion or transient impact and positioning information of the abnormal current event in the longitudinal direction of the steel rail are determined. According to the invention, high-precision detection and positioning of railway traction backflow and identification of harmonic and transient impact can be realized, and the problem of low-precision detection of railway traction backflow caused by weak anti-interference capability, insufficient spatial resolution and multipath current collaboration deficiency is solved.

The invention provides an urban carbon neutralization path analysis system and an analysis method thereof. The urban carbon neutralization path analysis system comprises a data acquisition module, a data arrangement module, a region division module, a carbon balance calculation module, a path setting module, a path analysis module and a result output module, according to the invention, through regular rasterization region division, fine modeling of the city space is realized; the carbon sequestration capacity is dynamically estimated by combining multiple types of carbon sources and multiple types of vegetation carbon sequestration factors with an NDVI time sequence index, so that a carbon-oxygen balance calculation result has time sensitivity and spatial resolution; meanwhile, the path analysis module supports a user to set various energy structures, traffic modes and greenbelt lifting parameter combinations to form a multi-path parallel simulation matrix, the selection space of path decision is effectively expanded, the system can realize comparable carbon neutralization evolution analysis according to regions and time periods, and the effects of improving the analysis precision and enhancing the path deduction capability are achieved.

The invention discloses an atmospheric pollution remote sensing intelligent monitoring system and method based on multi-modal data, and belongs to the technical field of atmospheric pollution remote sensing monitoring. Comprising a multi-source heterogeneous data acquisition and preprocessing module, a dynamic adaptive modal fusion module, a sudden change scene adaptive module, a three-dimensional pollution inversion enhancement module, a space-time diagram convolution traceability module and a weak signal pollution source collaborative sensing module. According to the method, the concentration contribution values of a plurality of pollution sources are superposed, the three-dimensional distribution of pollutants in space and time is accurately simulated, a visual basis is provided for pollution source positioning and diffusion path analysis, the synchronization of an inversion result and a real-time environment state is ensured through dynamic parameter updating, and the spatial resolution and prediction precision of pollution monitoring are remarkably improved.

The invention discloses a multispectral image and panchromatic image fusion method and system based on a full-spectrum space, high-resolution multispectral image reconstruction is performed by using a trained improved double-flow fusion network (TSFNet), and the method comprises the following steps: acquiring a panchromatic image and a multispectral image, and capturing respective spatial features and frequency domain features; splicing the frequency domain features of the panchromatic image and the multispectral image to obtain image frequency domain features; carrying out residual fusion on the image spatial features and the image frequency domain features by adopting a residual fusion module (RFB); and reconstructing a high-resolution multispectral image based on the fused features, and outputting an up-to-standard image after quality evaluation. According to the method, the improved double-flow fusion network (TSFNet) is utilized to effectively balance space-spectrum information, the accuracy of frequency domain information is kept while the spatial resolution is improved, the information loss in the fusion process is small, and a high-quality high-resolution multispectral image can be reconstructed and obtained.

The invention discloses a tooth segmentation method of a CBCT (Cone Beam Computed Tomography) image in the technical field of medical image segmentation. The method comprises the following steps: S1, sequentially carrying out standardization and data enhancement processing on a CBCT image data set; s2, inputting the CBCT image preprocessed in the S1 into an encoder part of a network structure, and completing step-by-step extraction of image features to obtain multi-scale features; s3, further fusing the multi-scale feature maps generated by each downsampling layer of the encoder network structure to obtain richer and more effective feature expressions; and S4, performing step-by-step spatial resolution recovery on the encoder features, and finally realizing fine segmentation of the tooth image, the tooth segmentation method of the CBCT image solves the problem that the teeth and surrounding tissue boundary details are fuzzy and the teeth are not clear due to the influence of metal artifacts in CBCT segmentation.

The invention provides a scene semantic occupancy prediction method based on semantic-distance adaptive Gaussian, and relates to the technical field of machine learning. The method comprises the following steps: carrying out sign extraction and point cloud acquisition on a look-around image sequence around a vehicle, inputting a feature map into a double-branch prediction network to obtain semantic information and position information of original sampling points, screening a cloud set of the original sampling points to obtain initialized Gaussian anchor points, and distributing the initialized Gaussian anchor points to levels with different spatial resolutions; according to the hierarchy and semantic information, adjusting a preset basic semantic scale to obtain a correction scale of Gaussian ellipsoids, and randomly adding a rotation vector to each Gaussian ellipsoid to obtain an initial four-dimensional attribute of the Gaussian ellipsoids; and performing multi-round dynamic adjustment on the initial four-dimensional attribute by using a Gaussian attribute iteration model, and finally projecting the initial four-dimensional attribute to a target voxel grid to generate dense 3D semantic occupancy prediction, so that the prediction result is more accurate. And intelligent dynamic allocation of computing resources and efficient and real-time three-dimensional scene understanding capability are realized.

The invention relates to the technical field of remote sensing monitoring, solves the technical problems that an existing soil salinity inversion method is insufficient in precision and poor in generalization ability, and particularly relates to a bare soil salinity inversion method fusing partial least squares and a random forest. Comprising the following steps: acquiring spectral reflectivity data and salinity spectral index of a multiband range and spatial resolution, and preprocessing to establish a digital orthoimage; removing a water body and a vegetation coverage area in the digital orthoimage by using a normalized vegetation index to obtain bare soil data containing 24-dimensional feature variables; establishing a fusion model and carrying out training evaluation; and performing prediction by taking Sentinel-2 satellite remote sensing data as input of the fusion model to obtain the salt content of the bare soil. According to the method, the precision, reliability and generalization ability of soil salinity inversion can be effectively improved, compared with a traditional single model, the nonlinear relation and noise in remote sensing data can be better processed, and the generalization ability of salinity inversion is remarkably improved.

A method and device for generating a satellite remote-sensing image with high spatial, temporal and spectral resolutions is provided. The method includes: fusing a multispectral image with high temporal and spatial resolutions but a low spectral resolution and a hyperspectral image with a high spectral resolution but low temporal and spatial resolutions to generate a fused image with high spatial, temporal and spectral resolutions. Compared with a current spatial-temporal-spectral integrated fusion method, the proposed method is more in line with resolution characteristics of current mainstream satellite-borne remote-sensing data and can achieve high-fidelity fusion performance.

The invention relates to the technical field of satellite remote sensing application, and discloses a crop classification method based on a multi-source satellite image, and the method comprises the steps: firstly obtaining multi-temporal satellite image data, extracting spectral reflectivity characteristics, generating a matrix, and constructing a classification model set; setting a feature parameter category set, and establishing a feature fusion correlation model based on the obtained texture, vegetation index parameters and spatial resolution data of the historical image; extracting spectrum and texture feature parameters of a crop area in the current image, and generating an optimized feature set in combination with model optimization; updating classification logic based on the optimized feature set and the classification model, generating a target classification scheme and calibrating a spatial relationship; and finally, acquiring crop growth cycle data, and establishing a time sequence feature association rule to optimize a crop type spatial distribution map. According to the method, through multi-source feature fusion and dynamic optimization, the crop classification precision and reliability are improved, and the method is suitable for a precision agricultural management scene.

The invention relates to the technical field of satellite-borne hyperspectral image processing and analysis, and solves the technical problem that the huge spectral and spatial resolution difference between low-resolution hyperspectral data and high-resolution multispectral data cannot be fully considered in the existing method. The forest hyperspectral satellite image-oriented deep and shallow double-branch super-resolution method comprises the steps of constructing a double-branch network architecture, performing feature fusion reconstruction on output features of the double-branch network architecture, and obtaining high-resolution hyperspectral data with low spectral variation characteristics of forest vegetation in spaceborne hyperspectral image data. According to the method, the problem of modal difference between low-resolution hyperspectral data and high-resolution multispectral data is effectively solved by learning on different feature levels and scales, and the model is enabled to pay more attention to low-spectral variation characteristics of different forest vegetation in a satellite image through a plurality of feature attention mechanisms. And the requirements of subsequent fine monitoring tasks of various forest resources can be met.

The invention relates to the technical field of vibration monitoring, in particular to a self-powered transmission line fitting aeolian vibration damage diagnosis system and method, and the system comprises a sensing collection module, a signal decoupling module, a damage identification module, a damage association module and a risk assessment module. According to the method, stress wave velocity and acceleration data are synchronously collected, time alignment is implemented, feature coupling precision is enhanced, wave crest offset and energy density are respectively extracted by using moving average filtering and wavelet transform, effective data segments are dynamically screened, and environmental noise interference is suppressed. A stress wave propagation change rate is quantified based on a path attenuation model, a continuous energy abnormal node is matched to realize damage positioning, a breeze response abnormal region is identified by combining vibration direction change and a signal envelope offset degree, multi-dimensional features are coded and subjected to risk judgment through a neural network, a damage positioning and risk assessment closed-loop framework is formed, and the risk assessment accuracy is improved. And the spatial resolution and evaluation precision of aeolian vibration damage identification under complex working conditions are significantly improved.

The invention discloses a thunder and lightning positioning method and system based on multi-source data fusion, and belongs to the technical field of thunder and lightning monitoring and positioning. The method comprises the following steps: in a preset time window, collecting first and second arrival time of a lightning event received by each ground lightning detection device, and calculating the arrival time difference; and extracting a lightning detection distance, a capture time difference and an observation included angle in combination with the lightning area image, obtaining an electric field change rate, a radar reflectivity and an equipment distance, substituting into a combined lightning positioning formula, and calculating a space distance between a lightning striking point and the first receiving equipment. Through the position information of the equipment, the geographical coordinate of the lightning striking point is calculated. According to the scheme, the spatial resolution and accuracy of lightning positioning can be effectively improved, positioning errors caused by a traditional radar or single-point monitoring method are reduced, simultaneous positioning of a plurality of lightning striking points is supported, and accurate data support and spatial decision basis are provided for subsequent lightning risk early warning, power equipment protection and disaster traceability.

The invention provides a medical image segmentation method and system based on spatial detail enhanced vision, and relates to the technical field of image processing. The method comprises the following steps: performing initial feature mapping on an input and preprocessed medical image to obtain an embedded feature map; the embedded feature map is input into an encoder for feature extraction, and multi-scale features are obtained; the encoder comprises a plurality of encoding stages, and the number of channels is doubled and the spatial resolution is halved through down-sampling operation between the encoding stages; the multi-scale features are subjected to up-sampling and spatial enhancement reconstruction step by step through a decoder, and a high-precision segmentation result is generated; wherein the decoder comprises a plurality of decoding stages, the decoding stages correspond to the encoding stages, and feature fusion is carried out between the corresponding stages of the encoder and the decoder through jump connection. According to the method, the boundary description precision and the segmentation robustness of the low-contrast image can be improved without increasing the linear complexity, and the method is suitable for medical image segmentation scenes of skin lesions, gastrointestinal polyps and the like.

The invention discloses a seismic signal-based soil-structure co-evolution seismic liquefaction analysis device and method, and the method comprises the steps: obtaining geological and seismic data of a target region, and constructing a geometric ratio, time ratio and acceleration ratio co-scaling system based on a Bockingham Pi similarity theory; a seismic liquefaction multi-field coupling evolution device is constructed to evolve the soil liquefaction process of a target area, a distributed optical fiber sensor array and a micro pore pressure meter group are adopted to form a millimeter-level spatial resolution monitoring network, and a high-speed camera system is matched to synchronously capture pore water pressure, structural strain and a soil shear band expansion process. Total element time sequence correlation analysis of vibration triggering, seepage developing and structural catastrophe is achieved, the influence degree of the total element time sequence correlation analysis on the earthquake is further analyzed, and a direct basis is provided for engineering restoration priority and a reinforcement scheme.

The invention discloses a method and a device for multi-scale fusion of a hyperspectral image and a multispectral image in the technical field of hyperspectral image processing, and aims to solve the problems of insufficient retention of spectrum and spatial information of a fusion result and poor spatial resolution when the hyperspectral image and the multispectral image are processed in the prior art. The image acquisition unit acquires a hyperspectral image and a multispectral image; splicing and fusing the hyperspectral image after up-sampling and the multispectral image to obtain a fused hyperspectral image; performing feature extraction on the fused hyperspectral image to obtain shallow spectral spatial features; performing wavelet transform on the shallow spectrum spatial features to obtain a multi-scale sub-band feature map; multi-scale feature extraction is achieved through wavelet transformation, the local feature extraction module is added to the Mamba module, spectral information and spatial information are fully obtained, multi-scale fusion of a hyperspectral image and a multi-spectral image is achieved, feature extraction precision is improved, and an image with higher quality is obtained.