3496 results about "High resolution" patented technology

The invention relates to the technical field of industrial automatic quality inspection, and provides a PCBA surface defect detection method and system based on deep learning and a medium, and the method is used for carrying out defect detection on a preset PCBA board. Comprising the following steps: acquiring a surface image of a PCBA board according to a preset multi-angle light source and a high-resolution camera, and performing adaptive illumination compensation and noise removal processing on the surface image to generate a standardized image; performing multi-scale segmentation on the standardized image to obtain image blocks including local details and a global structure; constructing a double-branch deep learning model, wherein the double-branch deep learning model comprises a backbone network, a multi-scale feature fusion module and a defect detection branch; inputting the image blocks into a double-branch deep learning model, and outputting a thermodynamic diagram and probability distribution by the double-branch deep learning model; performing binarization processing on the thermodynamic diagram by using a dynamic threshold segmentation algorithm to generate a defect mask; and outputting a defect detection result of the PCBA board according to the defect mask and the probability distribution, and completing the defect detection of the PCBA board.

The invention relates to the technical field of shock absorber defect detection, and discloses an electric vehicle shock absorber defect detection method and system based on machine vision, and the method comprises the steps: collecting an initial image set under the irradiation of a multi-angle light source through high-resolution imaging collection equipment; performing denoising and filtering processing according to the initial image set to obtain clear image data; performing defect classification and spatial distribution analysis according to the clear image data to obtain surface feature vectors containing defect types and defect spatial distribution; carrying out vibration amplitude acquisition and phase angle measurement operation according to the surface feature vector, and carrying out spectral analysis to construct a performance parameter vector; performing data fusion according to the performance parameter vector and the surface feature vector to obtain a fusion feature; inputting the fusion features into a pre-constructed association prediction model to obtain defect prediction data; and performing defect influence degree analysis according to the defect prediction data to obtain a defect evaluation result. The method provides a basis for quality control and performance optimization of the shock absorber.

The invention relates to the technical field of abnormal event early warning, and provides a large-scene monitoring video abnormal event early warning method based on a multi-mode large model. According to the invention, the problems of delay, low accuracy and limited coverage range of abnormal event early warning of large-scene monitoring videos in the prior art are solved. According to the main scheme, multiple paths of high-resolution monitoring videos are spliced and preprocessed to generate a panoramic video; synchronously acquiring and preprocessing audio and sensor data to construct a multi-modal data set; video key frames are extracted by adopting a traditional small model, and the video key frames and multi-modal data are jointly input into a multi-modal large model based on a Transform architecture for deep feature fusion; abnormal events such as tumble, congestion and fight are identified based on the fusion features; triggering an early warning mechanism to send event type and position information in real time; and storing the full-dimensional data of the abnormal event for tracing analysis. The real-time processing performance is optimized through edge calculation, the complex scene understanding ability is enhanced in combination with a multi-modal large model, and the detection precision and the response speed are remarkably improved.

The invention discloses a packaging box printed matter printing quality detection method, which comprises the following steps of: acquiring a high-resolution printing image through an image acquisition module, preprocessing and segmenting the printing image by an image processing module, optimizing image quality, classifying the preprocessed image by an image classification module according to characters, patterns and color blocks, and detecting the printing quality of a packaging box printed matter. The defect detection module performs multi-dimensional detection on the classified preprocessed images based on image parameter data, the quality analysis module quantitatively evaluates quality scores of characters, patterns and color blocks based on detection results, and the computer terminal generates a comprehensive score through weighting calculation and automatically generates a detection report; according to the packaging box printed matter printing quality detection method, complex defects such as font errors, stroke breakage, pattern deviation, color deviation and stains are effectively recognized through multi-dimensional defects, the manual reinspection cost is reduced through full-process automatic processing, and the quality of printed matter is visually reflected through a quantitative evaluation system.

The invention discloses a multispectral image fusion model based on a double-branch self-attention-generative adversarial network and a fusion method thereof, and the model sequentially comprises an input preprocessing module which is used for carrying out the same-amplitude mapping, normalization and overlapping block embedding of visible light and infrared original images, and generating a to-be-fused feature block; the double-branch encoder module captures a cross-modal long-distance dependence and overall brightness structure through multi-head deep convolution transpose attention (MDTA) and a gated deep convolution feedforward network (GDFN), and extracts high-frequency texture and edge information by using a reversible residual gating layer and detail DetailNode iteration; the fusion decoder module is used for carrying out multi-level self-attention-convolution reconstruction on the two paths of features after channel dimension splicing, and outputting a single-frame high-resolution fusion image; and the double-domain discriminator module comprises a visible light domain discriminator and an infrared domain discriminator which are respectively used for carrying out adversarial evaluation on the fused image and the corresponding modal truth value image so as to improve the detail authenticity and the thermal target contrast ratio of the fusion result. The technical problems that an existing infrared-visible light image fusion method is insufficient in detail reservation, unbalanced in brightness and contrast, poor in unsupervised training stability and the like are solved, the method can be deployed on embedded platforms needing real-time and multi-modal information enhancement such as night monitoring, unmanned driving and edge security and protection, and high-contrast and high-information-amount fusion imaging is achieved.

Luminance information and luminance change information are obtained at the same timing with high resolution. In one example, a solid-state imaging device includes pixels and a control circuit. Each of the pixels includes a photoelectric conversion element that generates an electrical signal based upon incident light, a first pixel circuit that converts the electrical signal into first information, and a second pixel circuit that converts the electrical signal into second information. The control circuit controls each of the pixels such that the photoelectric conversion element is connected to either the first pixel circuit or the second pixel circuit.

The invention provides a high-resolution machine vision detection method and system for precise chromatic aberration detection and a medium, and the method comprises the steps: carrying out the calibration of a camera based on a camera calibration tool, and synchronously calibrating a light source parameter and a color parameter; setting shooting parameters based on the calibrated camera, obtaining a workpiece image in real time, preprocessing the workpiece image, mapping the preprocessed image to a standard color space from an original RGB space, and extracting color features of the preprocessed image; comparing the color feature with the color feature of the standard sample, calculating a color difference index, and comparing with the color difference index based on a set color difference threshold to obtain a detection result; by calibrating various parameters of the camera, the visual detection precision is ensured, and by performing color space mapping on the workpiece image and analyzing the difference between the color difference index of the color feature and the color difference threshold value, the color abnormal area is accurately analyzed, and the detection precision of the abnormal color is improved.

Embodiments of the present application provide a three-dimensional model reconstruction and image generation method, a device, a storage medium, and a program product. In the method, multi-stage three-dimensional reconstruction is performed on the basis of a single image of a target object; in a first stage, a plurality of view images are generated on the basis of an image generation model, and an initial three-dimensional model is reconstructed on the basis of the plurality of view images; and in a second stage, on the basis of the plurality of view images and an initial prompt containing set marker information, a text-to-image model is used to learn an association relationship between the target object and the set marker information, and a plurality of scene images are generated on this basis. Compared with the plurality of view images, the scene images generated in the second stage have higher resolution and richer image details, and then the initial three-dimensional model is optimized on the basis of the plurality of scene images, so that a target three-dimensional model having higher resolution and clearer model details can be obtained, thereby paving the way for practical application of a three-dimensional reconstruction solution based on a single image.

The invention discloses an image deblurring method based on a rotation perception multi-dimensional attention and blurring sensitivity self-adaptive distribution mechanism, aiming at the defects of the prior art in the aspects of complex blurring processing and image reality sense improvement, and belongs to the technical field of image processing, and the method comprises the following steps: preprocessing a collected picture to obtain a training set; training an image deblurring model by using the training set; and performing image deblurring by using the trained image deblurring model. According to the invention, by designing a plurality of innovative modules and combining fuzzy degree adaptive selection, kernel estimation and dynamic weight distribution driven by a physical model, a multi-dimensional rotation perception attention mechanism, spectrum reconstruction anti-noise deblurring and detail enhancement of perception loss optimization, the image deblurring effect and processing efficiency are effectively improved; the method is especially suitable for processing blurred images with high complexity and high resolution.

The invention relates to the technical field of metal ultrasonic detection, and discloses a method for performing nondestructive flaw detection on a casting by using ultrasonic waves and a flaw detection system.The method comprises the following steps that rapid modeling and acoustic reference calibration are performed on equipment and a workpiece, and a unified coordinate system, a sound velocity delay reference and an optimal sound beam path are obtained; acquiring and caching multi-view ultrasonic echoes in real time to obtain a high-integrity original data stream; implementing adaptive noise diffusion based on the data stream, and constructing a diffusion data stack matched with scattering characteristics; performing scattering compensation inverse diffusion and super-resolution fusion on the diffusion stack to obtain a high-resolution purified echo image; performing closed-loop regulation and control on image quality indexes, and optimizing scanning parameters in real time; and combining the optimized image and scanning parameters, and adopting a segmentation network and online increment fine tuning to realize defect identification and automatic grading. The system has high resolution, real-time performance and self-adaptive closed-loop control, and the reliability and the accuracy of casting defect detection are remarkably improved.

The invention discloses a quality detection method in a functionally graded material preparation process, and relates to the technical field of functionally graded materials.The quality detection method comprises the following steps that the temperature of a green body in the functionally graded material preparation process is collected, and green body temperature distribution data is obtained; performing real-time thickness measurement on the green body according to the green body temperature distribution data to obtain a green body thickness change curve; identifying an abnormal fluctuation section in the thickness change curve of the green body, and carrying out phase detection on a physical region with the abnormal fluctuation section of the green body to obtain regional phase composition data; performing internal defect scanning on the green body according to the regional phase composition data to obtain an internal defect map of the green body; performing quality evaluation on the green body based on the green body internal defect map to obtain a quality grade judgment result; according to the method, the ultrasonic flaw detection scanning range is guided based on the phase abnormal region, and high-resolution internal flaw detection can be performed on the high-risk region of the functionally graded material in a targeted manner.

The invention discloses a method for clearly imaging a mask under an optical objective, and relates to the technical field of optical detection and image processing, and the method comprises the following steps: S1, constructing a light intensity distribution model based on the boundary condition of the field of view of the optical objective in combination with the reflection characteristic and incident angle change rule of a metal edge material, and determining the coverage range of metal edge reflection crosstalk, generating a mask boundary interference prediction map; s2, according to the mask boundary interference prediction map, performing region division on the micro-reflectivity image, extracting brightness gradient characteristics of reflectivity lifting in an interference region, and generating a brightness gradient parameter set required by image filtering; the method is based on light intensity modeling, fusion direction filtering, pixel stripping, gradient reconstruction and credibility weighted fusion, realizes closed-loop control from interference prediction to pixel restoration, has high resolution and adaptivity, can accurately strip edge reflection artifacts and restore a real image structure, remarkably reduces misjudgment and rework rate, and is suitable for large-scale popularization and application. And the mask yield and the stability of the detection system are improved.

The invention relates to an urban live-action three-dimensional construction method based on a high-resolution satellite image. The method comprises the following steps: carrying out stereo image pair screening and stereo image pair preprocessing on a high-resolution multi-view satellite image; carrying out adjustment optimization based on a rational function model on the satellite stereo image pair; generating an epipolar ray image based on the optimized rational function model; performing dense matching on the epipolar line image by combining building edge straight line segment constraint and an MGM algorithm to generate a disparity map; generating a three-dimensional point cloud and a three-dimensional model formed by a plurality of stereo image pairs through forward intersection based on the disparity map and the optimization parameters; generating a digital elevation model, a digital surface model and a three-dimensional Mesh model based on the three-dimensional point cloud; and carrying out precision evaluation and integrity evaluation by adopting pixel proportions of plane precision, elevation precision and height difference. According to the method, the urban live-action three-dimensional model and the corresponding digital product can be constructed quickly, efficiently and accurately by using the high-resolution satellite image.

The invention relates to an ultrasonic phased array full-focusing imaging method for acoustic wave multi-path propagation compensation of a layered composite material, which is characterized by comprising the following steps of: arranging an ultrasonic phased array probe with N array elements on a composite material piece to be detected, obtaining complete echo data by taking each array element as a transmitting array element and taking all array elements as receiving array elements in sequence in a full-matrix acquisition mode; a two-dimensional pixel grid is established in an imaging area, a Monte-Carlo perturbation mechanism used for representing the acoustic wave propagation uncertainty in the layered composite material is introduced into a propagation model for each emission array element-receiving array element-pixel point combination, and the acoustic beam emission direction, the propagation path and the equivalent acoustic velocity are subjected to random perturbation, so that the acoustic wave propagation uncertainty in the layered composite material can be represented. Generating a plurality of equivalent sound wave propagation paths, calculating two-way propagation time of each path, and performing weighted statistics on energy contributions of different paths according to array element sound beam directivity to obtain equivalent propagation time delays of pixel points; and performing delay correction on a full-matrix echo signal by using the equivalent propagation time delay, and performing coherent superposition on signals of all transmitting-receiving array element combinations to realize dynamic focusing of a full-pixel grid, and finally reconstructing an ultrasonic full-focusing imaging image with high resolution and high signal-to-noise ratio. The time delay error caused by propagation path deviation and sound velocity non-uniformity in the layered composite material can be effectively compensated, the focusing precision and spatial resolution of full-focusing imaging are improved, meanwhile, an existing TFM imaging frame does not need to be changed, and the method has good engineering implementability and popularization and application value.

Development of resists are useful, for example, to form a patterning mask in the context of high-resolution patterning. Development can be accomplished using a halide-containing chemistry such as a hydrogen halide. A metal-containing resist film may be deposited on a semiconductor substrate using a dry or wet deposition technique. The resist film may be an EUV-sensitive organo-metal oxide or organo-metal-containing thin film resist. After exposure, the photopatterned metal-containing resist is developed using wet or dry development.

The invention provides an image restoration and generation method based on a conditional generative adversarial network, and the method comprises the steps: a camera APP obtains contour key point data of a user figure proportion through carrying out the high-resolution feature extraction of an input image, carries out the three-dimensional reconstruction of a waist line and a shoulder breadth size, and obtains a contour key point data of a user figure proportion; generating an initial feature mapping graph containing wrinkle density distribution and local deformation amplitude; according to the initial feature mapping graph, adopting a region segmentation technology to separate a body contour from a clothing region, extracting a texture stretching degree and contact point stress distribution, determining a distribution condition of clothing and body contact points, and generating a segmentation result for dynamic adjustment; and for the to-be-optimized detail area list, performing enhancement processing on the texture stretching degree of the contact points of the clothes and the body and the stress distribution of the contact points, obtaining wrinkle layer depth data of a local area, and generating an optimized image layer.

The invention discloses a hyperspectral image super-resolution reconstruction method based on multi-scale cavity convolution guidance, and the method comprises the steps: carrying out the space downsampling of HrHSI to generate LrHSI, and carrying out the spectrum degradation to generate first LrMSI; after the HrHSI generates HrMSI through a spectral response function, performing space degradation to generate second LrMSI; constructing an initialized image generation module by adopting a double-branch network comprising multi-stage cascaded CCEs (Control Channel Element), and generating high-resolution initial estimation; a dual-path cavity cooperative enhancement module DDCA is constructed, a main path reinforces local details, an auxiliary path compresses global information, and feature calibration and residual fusion are realized through a gating mechanism; wherein the multi-scale cavity fusion module integrates a channel and a space attention mechanism so as to enhance context and detail information extraction capability; a depth image generation network of a double U-Net architecture is constructed based on DDCA, and reconstruction is realized through joint optimization. According to the invention, the feature distinguishing capability and the detail recovery capability are enhanced.

The invention provides a carbon fiber composite material microcrack image segmentation method based on a two-stage super-resolution dynamic attention network, and the method comprises the steps: collecting a microscopic image of a to-be-detected carbon fiber composite material, and carrying out the preprocessing of the image, so as to improve the discrimination between a crack and a background; scanning the preprocessed image by adopting a multi-scale sliding window, carrying out microcrack feature extraction in combination with morphological Top-hat transformation, and positioning a candidate region with microcracks; cutting an image corresponding to the candidate region into sub-images, inputting the sub-images into a pre-trained super-resolution reconstruction network, and amplifying the sub-images to 2-8 times of the original size to obtain a high-resolution candidate region image; inputting the super-resolution enhanced candidate region image into a pre-trained convolutional neural network model for pixel-level crack segmentation to obtain a segmentation result of cracks in the candidate region; and mapping the segmented micro-crack region back to an original image coordinate system, marking the position and shape of the crack on the original image, and outputting a final micro-crack segmentation result.

The invention provides an optical and acoustic image registration method for underwater structure crack detection, and the method comprises the steps: collecting a multi-mode image through the vision field and time synchronization, building a one-to-one correspondence relation according to the pose and time metadata, and improving the image quality through the denoising, distortion correction and other means; optical and acoustic multi-scale features are respectively extracted through a dual-channel network, and cross-modal semantic alignment is realized in combination with a shared weight and a channel attention mechanism; generating a high-resolution scale offset field by using local cross-correlation and micro-upsampling, and introducing manifold regularization constraint to ensure that a vector field is smooth and continuous; according to the method, the local scale field and the affine parameters are combined, non-rigid space mapping is achieved through thin-plate spline interpolation, feedback iterative optimization based on edge structure consistency is assisted, the registration precision of a key structure is enhanced, the automatic registration effect of the underwater multi-modal image is improved, and the method has high robustness and practical application value.

The invention provides a small target detection method and device based on a high-resolution fusion feature map. The method comprises the following steps: dividing an image to be detected into a plurality of overlapped sub-regions and recording position information of the overlapped sub-regions; performing feature extraction and multi-scale fusion on each sub-region to generate a high-resolution fusion feature map; generating geometric parameters and initial confidence of candidate frames based on each spatial position on the image, and performing preliminary screening to obtain a candidate frame set of the sub-regions; mapping candidate frames in all the candidate frame sets to a global coordinate system according to the position information, and combining repeated candidate frames pointing to the same small target to form a global candidate frame set; for each candidate frame in the global candidate frame set, cutting a local image area in the to-be-detected image to carry out fine judgment to obtain a fine trimming confidence coefficient; and fusing the initial confidence coefficient and the refined confidence coefficient to obtain a comprehensive confidence coefficient, and outputting a final small target detection result according to a comparison result of the comprehensive confidence coefficient and an adaptive threshold value. According to the invention, the tiny target in the image can be accurately captured.

The invention relates to the technical field of collapse hill identification methods, in particular to an unmanned aerial vehicle image collapse hill intelligent identification method based on a coordinate attention mechanism. The system comprises an unmanned aerial vehicle image acquisition and preprocessing module, a CA-Unet model construction module, a coordinate attention calculation module and an intelligent collapse hill identification output module. According to the method, a high-resolution image and laser radar point cloud data are collected through an unmanned aerial vehicle, a digital orthoimage and a digital elevation model are generated through orthorectification, and a deep learning data set is constructed; a coordinate attention mechanism is embedded in a U-Net model, the recognition capability of long-distance gullies and steep edges of collapse hills is enhanced through feature coding and dynamic weighting in the horizontal and vertical directions, direction-sensitive feature enhancement is achieved through a coordinate attention calculation module, and finally collapse hill boundary vectors and morphological parameters are output. According to the method, the limitation of a traditional method in the aspects of spatial dependence modeling and boundary extraction is broken through, and accurate recognition and surveying and mapping of the collapse slope erosion landform are achieved.

The invention discloses a method for optimizing micro-crack segmentation based on deep learning and super-resolution reconstruction, and the method comprises the steps: cutting an image in real time, obtaining an image block which takes a component as a target main body, and synchronously recording a homography matrix for geometric mapping; selecting an amplification strategy to improve the resolution, and recording a scale mapping relation; inputting the enhanced image block into a double-flow network; adaptive fusion and reconstruction are carried out on the two branch features, and a high-resolution texture image is output; generating a geometrically corrected ortho-image, fusing the geometrically corrected ortho-image with original illumination information, and outputting a corrected image with a known pixel size; identifying cracks, spalling and honeycomb diseases in parallel; generating a unified defect confidence map; calculating real geometric parameters of the BIM in a BIM global coordinate system through coordinate back projection; and generating quantitative defect reports and maintenance suggestions. The method has the advantage that seamless connection between the detection result and the BIM global coordinates is realized.

The invention relates to the technical field of crop disease and insect pest image recognition, and particularly discloses a crop disease and insect pest image recognition method based on a large model, and the method comprises the steps: obtaining a multi-angle leaf image through high-resolution imaging equipment under a controllable illumination condition, and obtaining a target image in a unified format; extracting scab texture complexity features in combination with a local binary pattern and a gray-level co-occurrence matrix algorithm, and performing multi-channel statistical analysis on RGB and HSV color spaces to generate color heterogeneity feature vectors; further fusing the two types of features into a composite disease feature vector, inputting the composite disease feature vector into a probability model constructed based on a support vector machine and a Monte Carlo Dropout mechanism, and outputting probability distribution and confidence score of disease and pest categories; and dynamically adjusting a model training strategy according to a confidence level, triggering a feedback mechanism for a low-confidence sample, generating a synthetic image by using a conditional generative adversarial network, and optimizing model parameters in combination with incremental learning to realize stable identification modeling of rare or complex disease types.

The invention relates to the technical field of data processing, and discloses a video understanding method and device based on dynamic sparseness, equipment and a medium, according to the scheme, spatiotemporal feature extraction and conversion are performed on a video frame sequence through a spatiotemporal feature encoder, spatiotemporal information of a video can be fully reserved, and video features with rich semantics can be output. A dynamic sparse attention mechanism is utilized to perform sparse attention calculation on video semantic features, and attention distribution is dynamically adjusted according to time-space characteristics of video contents, so that important context information in a video is accurately captured, redundant calculation is reduced, calculation complexity during video processing is effectively reduced, and video understanding efficiency is improved. The context feature vectors are analyzed and calculated through the text generation encoder, efficient and accurate video semantic understanding and text description generation are achieved, and therefore the video understanding efficiency in the application scene of processing mass transaction data in the financial field and processing high-resolution medical images in the medical field is improved.

The invention discloses a multispectral image panchromatic sharpening method based on plug-and-play gradient feature guidance fusion, through effective extraction and integration of gradient guidance features (GRAD), excellent recovery results are obtained in the aspects of structural fidelity, detail retention and spectral consistency, and in addition, the method has the advantages of being simple in structure and convenient to use. Gradient feature guidance is fused and designed into a highly flexible plug-and-play method, and the existing architecture can be seamlessly enhanced. In particular, the present invention explicitly models a GRAD by integrating fine spatial details of a high resolution PAN with intrinsic spectral information of an MSI. Meanwhile, an attention mechanism and learnable weighting and residual connection are adopted, and selective aggregation and adaptive fusion of image information are realized, so that the MSI quality is remarkably improved. In addition, the GRAD is systematically analyzed from the aspects of structure, detail and spectrum, and the result is optimized through a multi-objective loss function. In this way, gradient information is fully mined, high-frequency details are captured, cross-modal structure guide alignment is achieved, panchromatic sharpening of the multispectral image is flexibly and efficiently achieved, and the panchromatic sharpening performance is improved.

An additive manufacturing method based on micro-selective laser melting technology. By using a spot size of 30 μm or less, a metal powder layer thickness of 10 μm or less and ultrafine metal powders having a D90 particle size of 30 μm or less, and in combination with the control of a scanning laser power, a scanning rate, a linear energy density and a volumetric energy density within specific ranges, the method can print complex-structured metal parts having extremely high resolutions and surface smoothness, and can obtain a high-density internal structure of a component even under a relatively low energy density or power condition, thereby effectively protecting an optical system. Moreover, a printed component exhibits a defect-free microstructure, and has excellent electrical properties, thermal properties and mechanical properties. Particularly, ultrafine grains and a high dislocation density provide superior mechanical properties compared to conventional manufacturing materials. An infrared laser device can be used, thereby achieving low costs.

The invention discloses a building engineering multi-dimensional monitoring system based on panoramic imaging, and relates to the technical field of building engineering monitoring and sensing. Comprising a panoramic image acquisition module, an image splicing and panoramic reconstruction module, a multi-source data synchronous acquisition module, an image-data fusion construction module, an image-physical association modeling module and a risk identification and intelligent response module, an image acquisition array formed by high-resolution cameras is installed at key positions of a building structure, images of all the cameras are obtained, and an original image sequence covering a monitoring area is output. According to the invention, through fusion of the panoramic image and the multi-source sensing data, visualization, intelligent identification and risk prediction of the structure state are realized, a closed-loop monitoring system is constructed, the problems of single traditional monitoring means, data splitting and the like are solved, and the safety monitoring intelligent level of constructional engineering is improved.

The invention relates to the field of environmental monitoring, and particularly discloses a non-negative matrix factorization and adaptive peak recognition fluorescence feature extraction and traceability system, which comprises a spectral data preprocessing module, a spectral data non-negative matrix factorization module, a component number automatic selection module, an adaptive peak recognition module, a feature library construction module and a similarity comparison module. An improved non-negative matrix factorization model is adopted to decompose the three-dimensional fluorescence spectrum matrix of a single sample, and an optimal component number K is automatically determined through multiplicative update rule iterative optimization; the self-adaptive peak identification module carries out selective filtering, accurately extracts the position and intensity of a fluorescence peak through multiple mechanisms, and carries out peak position calibration in a neighborhood; the Hungary algorithm is adopted to carry out characteristic peak matching to calculate the comprehensive similarity between the samples, and rapid and accurate identification of the pollution source is realized. The method has the advantages of high resolution, strong anti-interference capability, low requirement on the number of samples, automation and the like, and is suitable for water quality fingerprint feature extraction of a water sample in a complex environment and real-time source tracing of sewage.

The invention discloses a retina thickness prediction method and system based on a multi-modal image, and the method and system achieve the effective estimation of the retina thickness under a low-cost condition through feature alignment and fusion modeling, and improve the basic screening and follow-up visit capability. According to the invention, through fusion of the multi-mode retina image data, the structure and function information of the optic nerve can be more comprehensively obtained, and the prediction accuracy of the thickness of the retina nerve fiber layer (RNFL) is improved. The OCT high-resolution hierarchical structure and the wide-view texture features of the eye fundus image are combined, so that anatomy and pathological states of optic nerves can be truly restored. The method can be used as an auxiliary method for early screening and early warning of optic neurodegenerative diseases such as glaucoma, and provides support for low-cost and high-efficiency primary screening and clinical auxiliary decision making.

The invention discloses a multi-light-spot anti-interference deformation measuring device and method based on stroboscopic energy spectrum.The multi-light-spot anti-interference deformation measuring device comprises a point light source, a first cylindrical mirror is arranged below the point light source, a first linear array sensor is arranged below the first cylindrical mirror, and the first linear array sensor is electrically connected with a data acquisition device; light emitted by the point light source passes through the first cylindrical mirror and then is focused into a line segment to be imaged on the first linear array sensor, and the first linear array sensor converts optical signals of the focused line segment into digital signals and outputs the digital signals to the data acquisition device. According to the multi-light-spot anti-interference deformation measuring device, a multi-dimensional displacement sensor measuring system is established by applying the cylindrical mirror, the linear array sensor and the point light source, a displacement measuring system with high precision, high resolution and high sampling rate can be realized, and the multi-light-spot anti-interference deformation measuring device is few in related components, small in size, simple to maintain, low in cost, high in system stability and easy to popularize. The method is more suitable for outdoor multi-point monitoring of engineering structures.