841 results about "Spectral imaging" patented technology

The invention relates to the technical field of semiconductor manufacturing, and discloses a semiconductor wafer etching method with a detection function. The method comprises the following steps: acquiring initial image data of a wafer surface through an optical detection device, and extracting geometric features and defect distribution information; processing through a multi-scale feature fusion algorithm, identifying an abnormal region and classifying defect types; dynamically adjusting process parameters of etching equipment according to defect conditions, and planning a self-adaptive etching path; during etching, a real-time monitoring module is used for collecting data, and etching parameters are corrected through a feedback control mechanism; and after etching, the etching precision is secondarily detected and verified by means of a multispectral imaging technology. According to the method, defects can be accurately positioned, the etching process is optimized, the etching precision and quality are effectively improved, the rejection rate is reduced, and the efficiency and reliability of semiconductor wafer manufacturing are improved.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention relates to the technical field of corn kernel quality detection, in particular to a corn kernel quality detection model construction method and system based on multi-source data fusion, and the method comprises the following steps: S1, multi-source data collection: collecting apparent morphology data of corn kernels through a multi-spectral imaging device, synchronously utilizing a near-infrared spectrometer to obtain spectral data of internal components of the corn kernels, and measuring structural density characteristic data of the corn kernels in combination with a sonic sensor; s2, multi-modal feature construction: forming a multi-modal feature matrix; s3, dynamic weighted fusion: generating a mixed feature vector; s4, constructing a dual-channel neural network: constructing a dual-channel deep neural network based on the generated mixed feature vector; and S5, model compression and deployment optimization: generating a lightweight detection model suitable for the embedded device. According to the invention, multiple requirements of an agricultural field on real-time performance, precision and deployment flexibility are met.

The invention discloses a multi-element historic building maintenance and restoration method based on three-dimensional laser scanning, and relates to the technical field of computer processing, and the method comprises the following steps: S01, obtaining the three-dimensional point cloud data of a historic building foundation through a ground laser scanner, the basic three-dimensional point cloud data comprises geometric information of a building facade, a ground structure and a near-ground decoration component; s02, an unmanned aerial vehicle carries a multi-line laser scanning device to scan the top and the high-altitude structure of the building, supplementary point cloud data is generated, and the millimeter-level precision three-dimensional coordinates of the pitched roof, the tower eave and the cornice bracket are covered with the supplementary point cloud data; and S03, synchronously acquiring surface texture data of the historic building by adopting multispectral imaging equipment. According to the method, the millimeter-level precision of three-dimensional reconstruction of the historic building is realized through multi-source data fusion and a hierarchical optimization algorithm, and the maintenance decision efficiency is improved by combining automatic disease recognition and a multi-modal analysis technology.

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention discloses a pumped storage power station safety construction monitoring method based on image recognition, and relates to the technical field of pumped storage power station safety construction monitoring, and the method comprises the steps: carrying out the intelligent monitoring of a construction site through a high-definition camera, an unmanned plane, and a multispectral imaging device, and optimizing the target capturing capability through an environment adaptive adjustment strategy. And in combination with a deep learning and behavior analysis model, identifying construction personnel, equipment and environment states, detecting behaviors such as no safety helmet wearing, illegal retention, equipment abnormity and the like, and performing real-time tracking and updating. Based on a risk level calculation model, a multi-level alarm mechanism is constructed, acousto-optic early warning, remote alarm and task scheduling are triggered, and construction safety is optimized in combination with cloud collaborative management. And self-supervised learning and cross-scene transfer learning are adopted, so that the recognition precision and the system adaptability in a complex environment are improved. According to the method, better effects are achieved in the aspects of real-time performance and accuracy.

The invention discloses a micro-channel aluminum flat tube appearance detection method based on image processing, and relates to the technical field of appearance detection.According to the method, more comprehensive information acquisition is carried out through the multispectral imaging technology in combination with RGB, polarized light, infrared spectrum images and ultraviolet spectrum images, and surface temperature difference changes are detected through infrared spectrums; image preprocessing is carried out through contrast-limited adaptive histogram equalization and bilateral filtering, uneven illumination is inhibited while edge details are kept, the contrast of corrosion defects is improved, details of a low-resolution area are enhanced through a super-resolution reconstruction technology, edge features of a corrosion area are further optimized in combination with a histogram in the gradient direction, and the edge features of the corrosion area are further optimized. The identification capability of small-scale defects is improved; an improved YOLOv3 defect detection algorithm is introduced, multi-scale feature extraction is adopted, and a self-attention mechanism is introduced, so that the model pays more attention to a tiny corrosion area, and meanwhile, the detection precision of a small target is enhanced in combination with focus loss and regression loss.

The invention discloses a goat oestrus monitoring system. The goat oestrus monitoring system comprises a multi-mode infrared thermal imaging data acquisition sensing unit and a multi-dimensional environment parameter collaborative real-time sensing unit. A goat body surface temperature field and a multispectral image are acquired through an uncooled vanadium oxide focal plane array infrared detector and a multiband spectral imaging module, environmental parameters are acquired in combination with a distributed sensing network, and a motion track is recorded through depth vision and inertia measurement. The heterogeneous multi-core data processing central unit is used for preprocessing, storing and preliminarily analyzing multi-source data, the feature extraction unit is used for mining multi-dimensional features, and the intelligent decision judgment unit is used for judging an estrus state by combining a deep neural network and dynamic threshold adjustment. The system integrates multiple technologies, can accurately monitor the oestrus state of the goats in real time, provides data support for scientific breeding, and improves breeding benefits.

The invention provides a power violation operation identification method based on multi-modal fusion, and the method combines pixel-level fusion of deep learning and a multi-spectral imaging collection technology to construct multi-modal data. A feature adaptation module is used for calibrating multi-scale feature distribution, visual semantics such as objects, scenes and actions in an image are aligned with vocabulary and sentence semantics in a language, multi-level injection is carried out on all levels of calibrated features, key features are strengthened, and violation related visual elements are highlighted. And capturing an operation dynamic process by adopting a neural network with time sequence coding capability, judging through a full connection layer and a classifier, and outputting a violation result. According to the invention, a multi-modal fusion method is adopted, accurate feature processing is carried out under complex illumination and shielding conditions, the recognition accuracy is greatly improved, the misjudgment rate is reduced, and the operation safety is guaranteed.

The invention provides a multi-spectral imaging photovoltaic defect identification method based on deep learning. The method comprises the following steps: acquiring all photovoltaic module images in an area range to obtain a photovoltaic module standard image, constructing an image segmentation model, and performing image segmentation on the photovoltaic module standard image to obtain each photovoltaic panel image; constructing a global map, numbering the photovoltaic panel images, and mapping the photovoltaic panel images to the global map according to the geographic coordinate information to obtain a photovoltaic module global image; constructing a defect identification model, and inputting each photovoltaic panel image into the defect identification model so as to output the defect category of the photovoltaic panel; according to the method, the defect classification model is constructed, the defect category of the photovoltaic panel is output, the number of the photovoltaic panel corresponding to the defect category is obtained, the defect position of the photovoltaic panel is obtained in combination with global image layout information, defect distribution is visually displayed, workers are assisted in rapid positioning and troubleshooting, and the inspection efficiency is improved.

The invention discloses a marine plankton hyperspectral imaging detection system, relates to the technical field of spectrum detection, and is used for solving the problem of poor spectrum classification and recognition in a water body environment. According to the method, the hyperspectral image and the environmental disturbance parameters are synchronously acquired, and the disturbance mapping sequence is constructed, so that accurate alignment between the image and disturbance is realized. A disturbance contribution weight matrix is constructed based on a dominant wave band, pixel-level spectrum stripping is carried out, a plankton purification spectrum is extracted, the spectrum purity and the recognition accuracy are improved, and a spectrum abnormal drift region is precisely recognized in combination with derivative spectrum change and image texture features; a coupling relation between the drift region and background disturbance is further established, a dynamic interference factor map is generated, and identification model parameters are dynamically adjusted, so that the identification stability and classification accuracy of the system in a complex interference environment are improved, and the method is suitable for multi-scene marine ecological monitoring.

The invention provides an efficient and accurate frequency modulation screen printing color management color matching auxiliary system which comprises a collector, the collector comprises an image collection module, the image collection module can capture a multi-channel filter array of a printing manuscript through a multispectral imaging device, and an analyzer comprises a spectral analysis module and a color matching prediction module. The spectral analysis module is in communication connection with the image acquisition module, after low-dimensional feature vectors are generated, candidate schemes are listed, and the color matching prediction module predicts an ink matching scheme based on the low-dimensional feature vectors and a deep convolutional neural network; relates to the technical field of frequency modulation screen printing toning. According to the method, the limitation of traditional single-factor color matching is broken through by introducing a multi-modal data fusion (spectrum + base material + ambient light) and Bayesian optimization feedback mechanism, the nonlinear mapping problem of high-dimensional spectral data is solved through cooperation of manifold learning dimension reduction and deep neural network prediction, and efficient and accurate frequency modulation network color matching adjustment is realized.

The invention discloses a high-detection-efficiency long-wave infrared spectrum imaging device and an imaging method, and belongs to the field of micro-nano optical design, the scheme is as follows: a metasurface spectrum modulation layer is composed of a square matrix formed by arranging a plurality of metasurface periodic units in sequence, and each metasurface periodic unit is composed of a plurality of metasurface structure units; each metasurface structure unit comprises a substrate and a plurality of nano columns, and the plurality of nano columns are distributed on the substrate in a C4 symmetrical structure; reverse design of the high-transmittance metasurface structure units is achieved through a particle swarm optimization algorithm, and structure parameters of the high-transmittance metasurface structure units are obtained; incident light passes through the metasurface spectrum modulation layer to realize spectrum coding, coding information is converted into an electric signal by a long-wave infrared focal plane detection pixel below, and a high-resolution long-wave infrared spectrum image can be reconstructed in cooperation with a deep neural network algorithm. The spectral imaging method provided by the invention is perfectly compatible with semiconductor and MEMS technologies, and long-wave infrared band spectral imaging with high detection efficiency is realized.

The invention provides a multi-source fusion spectrum cross-domain high-precision prediction method and system based on transfer learning. The method comprises the following steps: S1, acquiring spectrograms collected by a spectral imaging device under multiple wavelengths; s2, extracting various types of features from the spectrogram; s3, training a fusion model to obtain a trained fusion model; and S4, inputting a to-be-predicted spectrum into the trained fusion model, and outputting a result. According to the invention, 10 <-4 > nm-level wavelength prediction can be achieved in a common industrial camera system, and the level of a scientific research-level grating spectrometer (0.001-0.002 nm) can be reached / exceeded; on a narrow-band, middle-band and wide-band multi-scene cross-domain data set, the mean absolute error (MAE) can be reduced from the nanoscale to the magnitude of 10 <-4 > nm, and excellent robustness is kept under the conditions of noise, feature deficiency and few samples.

The invention discloses a melamine paper surface defect detection system based on optical detection, and relates to the technical field of industrial optical detection, and the melamine paper surface defect detection system comprises a polarized light signal generation module which employs a high color rendering index white light source to irradiate the surface of melamine paper, obtains a surface reflection light beam array carrying different polarization states, and modulates the surface reflection light beam array into eight groups of independent polarized light signals; the multispectral imaging and registering module inputs eight groups of independent polarized light signals into a free-form surface prism group and performs light splitting to generate a diffuse reflection texture image, a mirror reflection image and a near-infrared perspective image, and constructs a three-channel space registering image matrix; and the light path calibration module is used for extracting defect coordinate data and confidence score based on the pixel-level defect thermodynamic image, and performing light path compensation by adopting a real-time feedback regulation mechanism to obtain a dynamically calibrated stable optical imaging signal.

The invention relates to the technical field of multispectral imaging, in particular to a multispectral imaging fused pattern recognition image acquisition method and device, and the method comprises the following steps: obtaining a multiband substrate response value through a multispectral sensor, generating response difference distribution through moving average filtering, inputting cross-correlation analysis, extracting a space offset vector, and compensating displacement; after the signal intensity of the mark points is captured, Gaussian fitting is carried out to generate registration coordinates, triangular mesh division is carried out, offset is calculated, interpolation mapping is carried out, a registration error distribution diagram is generated, bilinear interpolation remapping is carried out to collect time sequence parameters, and a multispectral imaging data set is output. According to the method, noise is eliminated through moving average filtering, offset vectors are extracted in a cross-correlation mode, array displacement is compensated, mark points are positioned through two-dimensional Gaussian fitting, a registration error graph is constructed through triangular meshes, parameters are collected through bilinear interpolation remapping, geometric distortion is corrected, feature consistency and collection synchronism are enhanced, and the imaging resolution ratio and the signal-to-noise ratio are improved.

The invention provides an imperfect soybean hyperspectral imaging classification method based on deep learning. Efficient and accurate classification is realized by fusing spectrum and image features. Acquiring spectrum and image data by using a hyperspectral imaging system; the data quality is improved through a preprocessing method; and constructing a dual-channel feature fusion model, and reinforcing key feature extraction in combination with an attention mechanism. According to the method, the classification precision of 95.13% and 94.00% in the visible light-near infrared band and the short wave infrared band is achieved, and compared with a traditional support vector machine and a convolutional neural network method, the classification precision is remarkably improved. The model deeply digs spectral image features through a deep learning network, analyzes the association between the spectral image features and soybean quality, and enhances the robustness and generalization ability of the model. The method has the advantages of being high in precision, rapid in detection, high in adaptability and the like, is suitable for different varieties and large-scale detection scenes, and provides an efficient solution for agricultural product quality monitoring, automatic sorting and agricultural scientific research.

The invention discloses an unmanned aerial vehicle target segmentation and detection method based on large model visual perception, and the method comprises the steps: obtaining a to-be-detected data stream in real time through a multi-region spectral imaging module carried by an unmanned aerial vehicle, effectively supplementing the deficiency of single sensor information through the fusion of multi-spectral data, and improving the multi-dimensional information expression of an image; the image of the image quality evaluation network is used for noise evaluation and image optimization processing, the quality of the image is further improved, and the accuracy of a subsequent detection result is ensured; through an adaptive enhancement algorithm and weighted fusion, multi-system feature maps can be fused more accurately, so that multi-level and multi-scale feature information can be fully integrated, and a comprehensive feature map with cross-topology information is obtained. Based on a target segmentation detection model of a deformable structure, an accurate pixel-level segmentation mask and a bounding box can be generated for the problems of target deformation and posture change.

The invention belongs to the technical field of glass production and manufacturing, and particularly discloses a glass substrate edge detection system and a repairing method thereof, and the glass substrate edge detection system comprises a transmission unit, a detection module, a processing control module, a repairing execution module and an auxiliary unit. The detection module adopts a multispectral imaging and laser displacement sensing technology: a visible light imaging unit captures surface texture defects, a near-infrared imaging unit detects internal microcracks in a penetrating manner, and an ultraviolet imaging unit excites a fluorescent substance to enhance the defect contrast; the laser displacement sensor is combined with an adaptive focusing technology to realize three-dimensional contour submicron-level measurement. And the processing control module identifies the defect type through a deep learning algorithm and generates a repair instruction based on the three-dimensional data. And the repairing execution module is used for quickly changing a laser repairing head, a glue repairing head or a grinding head through a mechanical arm and executing repairing processes such as laser cladding, nano spraying or UV glue repairing according to parameters such as the edge breakage depth and the crack length, so that the detection precision and the repairing efficiency of the edge defects of the glass substrate are remarkably improved.

The invention discloses a water environment monitoring method based on image vision, and relates to the technical field of water environments, and the method comprises the steps: continuously shooting water body images of different depths at preset intervals in the vertical direction through an underwater camera; calculating the color difference of two adjacent pictures, and determining a water quality mutation layer; when it is detected that the turbidity in the water quality abrupt change layer interval exceeds a preset threshold value, infrared spectral imaging and ultrasonic ranging cooperative work is switched, the penetrability characteristic of an infrared image and ultrasonic depth calibration data are combined, the layering positioning error is corrected, and the water quality abrupt change layer interval is obtained. Through vertical interval shooting, color difference analysis, infrared and ultrasonic collaborative correction and a dynamic sampling strategy, the precision and efficiency of water quality layered monitoring are improved.

The invention discloses an intermediate infrared polarization spectrum imaging method and device. The method comprises the following steps: carrying out joint coding on an intermediate infrared light beam carrying to-be-measured target space, spectrum and polarization information by adopting an intermediate infrared polarization spectrum modulator consisting of metasurface units which are arranged in an array and have non-correlation response characteristics; projecting the coding light field to a two-dimensional detector array to generate a two-dimensional coding image under single exposure; and decoding the two-dimensional coding image through a reconstruction unit so as to reconstruct and obtain a space-spectrum-polarization four-dimensional data cube of the target to be measured. The problem that polarization information cannot be rapidly obtained in the prior art is solved, target full information can be obtained under single exposure, and the system has the advantages of being high in imaging speed, simple in structure, easy to miniaturize and the like.

The invention relates to the technical field of spectral imaging detection, in particular to a spontaneous-stimulated Raman co-localization single-cell substructure atlas analysis system and a spontaneous-stimulated Raman co-localization single-cell substructure atlas analysis method. The system comprises a femtosecond pulse excitation module used for outputting two beams of broadband femtosecond pulse laser with different frequencies and continuously adjustable optical power; the Stokes light modulation module is used for modulating the Stokes light and carrying out space beam combination on the Stokes light and the pump light; the pump light modulation module is used for modulating pump light; the spontaneous Raman laser continuous adjusting module is used for outputting spontaneous Raman laser; the stimulated Raman exciting light co-positioning module is used for combining the combined laser and the spontaneous Raman laser again, focusing the combined laser and the spontaneous Raman laser on a sample, exciting a stimulated Raman scattering signal, converting the stimulated Raman scattering signal into a digital signal and carrying out data acquisition; and the spectrum detection module is used for collecting the spontaneous Raman scattering light which is reflected and returned along the original light path, and collecting a spectrum signal to form a Raman spectrum. The method has the advantage of improving the accuracy of single-cell heterogeneity structure and cancer cell type discrimination.

The resolution-customizable micro spectrometer based on the transverse dispersion metasurface comprises a dispersion metasurface array structure and a focal plane pixel array, and the dispersion metasurface array structure comprises a plurality of dispersion metasurface sub-arrays which are distributed in an array mode and used for preliminary spectrum decoupling and directional convergence. The dispersion metasurface subarray comprises a plurality of dispersion metasurface units and all-dielectric substrates thereof, the dispersion metasurface units are nano-columns with four-fold symmetry, the nano-columns are periodically arranged, and the phase of transmission light is dynamically regulated and controlled according to the frequency omega of incident light. The focal position (x ', y') of the transmission light on the focal plane is in a linear mapping relation along with the change of the omega; the focal plane pixel array comprises a multi-element plane array composed of a plurality of pixels, and the multi-element plane array is in pixel-level registration with the dispersion metasurface sub-array. According to the invention, pixel-level precise condensation is realized, crosstalk is effectively inhibited, the detection precision is improved, and real snapshot spectral imaging is realized.

The invention provides a method and system for detecting metal abrasive particle defects in lubricating oil, and the method comprises the following steps: scanning a lubricating oil sample collected by a machine by using a multispectral imaging system to obtain a two-channel image of a visible light wave band and a near-infrared wave band; the dual-channel image comprises a visible light image and a near-infrared image; respectively enhancing the visible light image and the near-infrared image, and carrying out fusion analysis on the enhanced images to obtain a dual-channel fusion image; performing window sliding on the dual-channel fusion image, and constructing a direction constraint factor; performing threshold segmentation on the dual-channel fusion image through a direction constraint factor to obtain an abrasive particle region; and an actual metal abrasive particle defect image is collected to analyze the abrasive particle area, and an identification result is obtained. By performing enhanced fusion identification on the visible light image and the near-infrared image and performing dual identification on the contour and the texture, the accuracy is improved, and the misjudgment rate is reduced.

The invention discloses an ultra-high-speed imaging device based on acousto-optic filtering modulation. The ultra-high-speed imaging device comprises a spectrum modulation system, a spectrum imaging system and a data processing system. The spectrum modulation system utilizes an acousto-optic tunable filter (AOTF) to dynamically modulate broadband continuous spectrum light, narrow-band illumination light which rapidly changes along with time is generated, and time information of a dynamic scene is effectively mapped to a spectrum dimension. And the spectral imaging system images the spectral coded dynamic scene to the hyperspectral camera to realize acquisition of spectral space-time information. The data processing system performs spectral crosstalk correction, channel separation and demosaicing reconstruction on the acquired original mosaic image, and recovers a time sequence image according to a spectrum-time mapping relation, thereby realizing single-shot superspeed imaging in a dynamic process. The device has the advantages of being simple and compact in structure, flexible in time window, high in imaging fidelity and the like, and has wide application prospects in the fields of rapid dynamics such as microfluidics, laser processing and ultrafast physics.

The invention provides a video-level polarization spectrum imaging system and method. The system comprises an imaging unit, a polarization spectrum coupling unit, a data processing unit and an output unit, the imaging unit is used for imaging a target object on an imaging plane; the polarization spectrum coupling unit comprises a multi-channel spectrum modulation diaphragm and a micro-polarization focal plane array; the multi-channel spectrum modulation diaphragm is used for dividing incident light into a plurality of different spectrum channels; the micro-polarization focal plane array is used for converting the spectrum modulation light into polarized light of a plurality of different angles; the data processing unit is used for fusing the spectral information, the polarization information and the pixel information into multi-dimensional polarization spectral data; and the output unit is used for transmitting the polarization spectrum data to a computer. The system obtains spectrum information through a spectrum modulation diaphragm so as to distinguish the material composition and the chemical property of a target object; according to the system, polarization imaging is carried out through the micro-polarization focal plane array, and the detectability and the recognition rate of a target object can be improved.

The invention discloses a farmland soil moisture content real-time surveying system and method based on unmanned aerial vehicle multispectral imaging. The method comprises the steps that multispectral sensor parameters are configured, and an unmanned aerial vehicle flight scanning path is planned; a space coordinate system and time synchronization mechanism is established, and high-precision positioning and time synchronization are achieved; collecting and calibrating visible light, near-infrared and short-wave infrared original image data to form a spectral signal set; calculating an image signal-to-noise ratio, screening a low-quality image, and executing noise suppression; extracting timestamp information from the suppressed image, performing spatial positioning, and establishing a spatial corresponding relation database; calculating time synchronization precision and performing image correction; evaluating the spatial resolution according to the distortion coefficient; calculating a comprehensive quality parameter, comparing the comprehensive quality parameter with a threshold value, and obtaining a soil moisture content distribution parameter when a condition is met; a farmland moisture state map is generated, partition marking is carried out according to a multi-level standard, a monitoring result is output, and high-precision and real-time monitoring of the farmland soil moisture content is achieved.

The invention discloses a reconstruction spectral imaging system based on an electromagnetic drive Fabry-Perot resonant cavity, and belongs to the field of spectral imaging. The system comprises an optical lens group, an electromagnetic driving device, a Fabry-Perot resonant cavity, a photoelectric detection array and a calculation unit, the cavity length of the Fabry-Perot resonant cavity can be adjusted through the electromagnetic driving device. The optical lens group receives light reflected or emitted by an external to-be-measured target scene and carries out collimation processing, and then the light enters the Fabry-Perot resonant cavity. The Fabry-Perot resonant cavity screens the incident light based on the resonance condition of the cavity length of the Fabry-Perot resonant cavity, and the screened light is emitted to the photoelectric detection array; the photoelectric detection array converts the incident light intensity into an electric signal and outputs the electric signal to the calculation unit; and a spectrum reconstruction model is built in the calculation unit, and a hyperspectral image of the to-be-measured target scene is obtained by using the spectrum reconstruction model based on the electric signal. The working range and the spectral imaging precision of the spectral imaging system based on the Fabry-Perot resonator are improved, the technological requirements are reduced, and the engineering applicability of the spectral imaging system based on the Fabry-Perot resonator is improved.

The invention belongs to the technical field of component prefabrication, particularly relates to a digital twinning-based fabricated building prefabricated component production process, solves the problems of insufficient quality and low construction efficiency in the prior art, and constructs virtual mapping of a design-production-construction whole process through a digital twinning technology. The method realizes data-driven intelligent decision making and flexible production, and specifically comprises the following steps: an intelligent design cooperation stage: a complete quality guarantee system is constructed through arrangement of structures such as an AI visual detection system and a block chain traceability system, the AI visual detection system adopts a multispectral imaging and deep learning technology, and the AI visual detection system adopts a multi-spectral imaging and deep learning technology; various defects on the surface of the component can be accurately identified, a visual thermodynamic diagram is generated, and a quality closed loop is realized by matching with an automatic repairing function of a mechanical arm; the block chain traceability system links the whole process quality data in real time through an encryption technology, and when a detection value does not reach the standard, the intelligent contract automatically triggers a rectification process and locks a problem component to form a complete quality prevention and control chain.

The invention discloses a lithium battery laser welding detection method, and belongs to the technical field of nondestructive testing. The method comprises the following steps: acquiring a three-dimensional path point sequence of a to-be-detected welding seam; controlling the transient thermal excitation unit to apply transient thermal excitation along the path; a high-speed multispectral imaging unit is used for synchronously collecting the thermal radiation attenuation process of the weld joint under at least two different spectral bands, and a space-time spectrum data cube is formed; and finally, inputting the data cube into an artificial intelligence model for defect identification. The system correspondingly comprises a three-dimensional visual sensor, a transient thermal excitation unit, a high-speed multispectral imaging unit and a synchronous control and data processing unit. According to the method, different types of welding defects such as air holes and incomplete fusion can be effectively distinguished by analyzing the heat diffusion characteristic difference under different spectral bands, the interference of uneven surface emissivity is inhibited, and the online detection accuracy and reliability of the three-dimensional complex welding seam are improved.