2581 results about "Space resolution" patented technology

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 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 a method for improving satellite data inversion soil salinity by using hyperspectral data of an unmanned aerial vehicle. The method comprises the following steps: collecting a soil sample in a test area, and calculating soil salt content; acquiring and preprocessing satellite remote sensing image data of a test area; storing data, outputting a single-band satellite image, and calculating a spectral index; obtaining and preprocessing hyperspectral image data of the unmanned aerial vehicle in the test area; reconstructing and upscaling unmanned aerial vehicle image data pixels to a spatial resolution of 10m; reconstructing the narrow-band spectral data of the unmanned aerial vehicle into broadband spectral data consistent with a satellite image, storing and outputting a single band, and calculating a spectral index; screening a soil salt content characteristic variable, and constructing a soil salt content inversion model by taking the screened characteristic variable as an independent variable and the actually measured soil salt content as a dependent variable; and fitting a function relationship between the upscaling unmanned aerial vehicle soil salt content inversion model and the satellite inversion model by adopting a numerical regression function method to realize correction of the satellite inversion model.

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 provides a farmland root zone soil humidity prediction method, system and device and a storage medium, and belongs to the technical field of soil humidity prediction, and the method comprises the steps: obtaining multi-source remote sensing data of a farmland; downscaling the spatial resolution of the multi-source remote sensing data to obtain a kernel normalized difference vegetation index kNDVI of a first spatial resolution, a surface soil humidity SSM of a second spatial resolution and meteorological data ERA5 of a third spatial resolution; a root zone soil humidity RZSMt at the t moment is given, the kNDVI, the SSM and the ERA5 at the same moment and the SSM at the t + 1 moment are jointly input into a soil humidity prediction model, and the root zone soil humidity RZSMt + 1 at the t + 1 moment is simulated; and taking the current model output as the model input of the next step, and repeating the step to complete the prediction of the soil humidity of the root zone every day. According to the soil humidity prediction model provided by the invention, the problems of sensitivity and complexity in remote sensing inversion can be effectively solved, and relatively high-precision irrigation area scale root zone soil humidity prediction is realized.

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 an image segmentation network construction method fusing visual Mama and multi-scale features, and the method comprises the steps: designing an MFVM-Unet based on a U-Net network structure, and enabling the MFVM-Unet to serve as an image segmentation network; wherein the MFVM-Unet comprises an encoder, a decoder and a multi-scale feature information guiding module; the encoder is used for gradually extracting multi-level features of an input image; the decoder is used for recovering the spatial resolution of the feature map; and the multi-scale feature information guiding module is used for integrating multi-level features. According to the method, the extraction capability of local and global features can be enhanced, and the segmentation precision of a complex-form region is remarkably 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 discloses a spatial-spectral combined hyperspectral image classification method based on a multi-scale adaptive attention mechanism, and the method comprises the steps: firstly carrying out the extraction and fusion of features of different scales through a multi-scale feature fusion module, and comprehensively capturing the detailed spectral information of a hyperspectral image; secondly, dynamically adjusting a feature extraction process by using an efficient self-adaptive spectrum-space feature extraction module, and optimizing fusion and representation of spectrum-space features; and finally, enhancing the ability of the model to understand the context relationship in the image by using the context-aware fusion network. The three modules provided by the method are based on a deep learning framework, a convolutional neural network in deep learning can utilize multiband rich spectral information, and the method is suitable for hyperspectral remote sensing image data sets with different spatial resolutions, including a foreign hyperspectral data set and a domestic hyperspectral data set. The method has high adaptability and transportability, and can meet the requirement of image classification more easily.

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 cross-modal foreground distillation method for multi-view three-dimensional target detection, and belongs to the technical field of automatic driving perception. According to the method, a laser radar or a multi-modal three-dimensional target detection model is used as a teacher model, a multi-view aerial view BEV perception three-dimensional target detection model is used as a student model, and the model performance is improved through multi-scale feature distillation; a space attention mechanism and a foreground mask module are introduced, and the student model can simulate feature responses of the teacher model in different space positions under multiple scales, focus on a foreground target and suppress background noise; the adaptive module ensures the consistency of the multi-scale features in feature dimensions and spatial resolution, and finally, the difference between the features and regression output is measured through loss functions such as mean square error and KL divergence, so that efficient cross-modal and multi-scale foreground feature distillation is realized. Finally, the multi-view three-dimensional target detection precision and efficiency are improved, and meanwhile, the distillation requirements of multi-scale features are fully considered.

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.

A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and / or image contrast.

The invention provides a high-frequency eddy current-based metal pipeline conductivity measurement method, which comprises the following steps: designing an absolute eddy current sensor, positioning a sensor coil on the inner side of a metal pipeline and coaxially arranging the sensor coil and the pipeline, and introducing high-frequency alternating current into the coil to generate eddy current; establishing a simplified high-frequency eddy current detection analysis theoretical model, and setting geometric dimensions of a sensor and a pipeline; a Bessel function and a spatial resolution expression are used for analyzing the relation between the real part of the eddy current impedance change under high-frequency excitation and the conductivity of the pipeline; setting the conductivity of the metal pipeline according to the analysis result, and fitting the relation between the impedance change and the conductivity through a power function; and according to a coil impedance measurement result, combining the fitted power function curve, and performing inversion to obtain the actual conductivity of the metal pipeline. According to the invention, the conductivity of the metal pipeline can be effectively measured.

The invention discloses a multi-curvature high-resolution bionic miniature visual tactile sensor for fingertips, which relates to the technical field of sensors and comprises a fingertip structure, a transparent support structure, a flexible silica gel sensing layer and an image acquisition module. A cavity is formed in the fingertip structure; the transparent supporting structure is clamped in the cavity; the flexible silica gel sensing layer is adhered to the transparent supporting structure, and black circular array mark points are attached to the surface of the flexible silica gel sensing layer; the image acquisition module is arranged on the fingertip structure, the acquisition end of the image acquisition module is arranged above the transparent supporting structure, and the view field area of the acquisition end covers the whole flexible silica gel sensing layer area; compared with other fingertip tactile sensors, the visual tactile sensor is a human fingertip simulated visual tactile sensor with the highest spatial resolution, the smallest overall size and the lowest manufacturing cost.

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 relates to the technical field of remote sensing image processing, and particularly discloses a remote sensing hyperspectral image super-resolution fusion algorithm and system based on quaternion, and the method comprises the steps: employing a panchromatic image quaternion feature extraction module, a hyperspectral image quaternion feature extraction module and a self-attention fusion module, constructing a double-U-Net structure network based on a multi-scale residual link and a jump link; and inputting a panchromatic image and a low-resolution hyperspectral image, and calculating to obtain a high-spatial-resolution hyperspectral image. According to the method, a plurality of spectral channels of a hyperspectral image and high-resolution spatial information of a panchromatic image are embedded into four components of a quaternion, and spectral and spatial characteristics are modeled uniformly by means of algebra of the quaternion; and then feature extraction, fusion and reconstruction are carried out through a deep learning network, and finally a fusion image with both high spectral fidelity and high spatial resolution is generated, so that the spatial resolution of the hyperspectral image can be significantly improved, and the spectral consistency of the hyperspectral image can be maintained at the same time.

According to the reconfigurable flexible pressure sensor based on the magnetic ionic liquid drops and the preparation method of the reconfigurable flexible pressure sensor, the superparamagnetic nanoparticles and the ionic liquid drops are combined to prepare the magnetic ionic liquid drops which are high in fluidity and can be magnetically controlled, and the magnetic ionic liquid drops serve as a pressure sensitive layer of a pressure sensing device; and the flexible stretchable pressure sensor based on the magnetic ionic liquid drop array is constructed. The invention also provides a distributed / combined non-contact multi-element in-situ reversible reconstruction method of the flexible pressure sensor, and an external magnetic field is utilized to control the magnetic ion liquid drops in the sensor to carry out programmable low-resistance rapid movement and deformation between the super-hydrophobic interconnected microfluid channel and the chamber array. The spatial distribution, component / volume and geometrical shape changes of the magnetic ionic liquid drops in the array are regulated and controlled, and distributed / combined non-contact multi-element in-situ reversible reconstruction of the flexible stretchable pressure sensor in the performances of spatial resolution, sensitivity, detection limit, pressure response mode and the like is realized through one or more change combinations.

The invention discloses a gas extraction pipeline tiny leakage positioning method based on multi-sensor fusion, which belongs to the technical field of positioning, is used for gas extraction pipeline leakage positioning, and comprises the following steps: establishing a state equation and an observation equation of a motion process of an inspection robot, and realizing inspection robot positioning under a navigation system based on Kalman filtering. And realizing coal mine gas extraction pipeline leakage source positioning under a microphone array coordinate system based on a beam forming algorithm, and obtaining coal mine gas extraction pipeline leakage source positioning under a navigation system through coordinate conversion. According to the method, the leakage source position of the gas extraction pipeline under the navigation system is directly given, maintainers can conveniently and quickly reach the leakage source position, and the first-aid repair time is greatly shortened; the positioning error of the inspection robot can be effectively reduced, and the phenomenon that the positioning error is accumulated along with time when the inspection robot only uses an accelerometer and a gyroscope for positioning is avoided; the influence of background noise on a positioning result can be greatly reduced, and the spatial resolution of a leakage positioning result is improved.

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.