2694 results about "Scattered light" patented technology

The invention discloses a PCB three-proofing coating quality detection method and system. The method comprises the steps that a visible light reflection image of the surface of a PCB and scattering spectrum data of a near-infrared band are acquired; performing spatial filtering processing on the visible light reflection image to extract interface area pixels, calculating coating layer thickness gradient distribution based on Mie scattering characteristics in scattering spectrum data, and fusing to generate an edge gradient distribution map; and according to the gradient amplitude change rate in the edge gradient distribution map, segmenting the effective coverage area of the coating layer by adopting a self-adaptive dynamic threshold algorithm, and extracting curvature extreme point density and boundary fractal dimension parameters at the segmentation boundary. According to the invention, three technical barriers of difficult microdefect capture, unmeasurable interface performance and delayed failure risk in traditional detection are solved, and two-dimensional accurate diagnosis of the structural compactness and interface reliability of the coating layer is realized.

The invention discloses a water quality heavy metal pollution detection method and system based on Raman spectrum.The water quality heavy metal pollution detection method comprises the steps that water body Raman scattering light is collected in situ through a miniature optical fiber probe, and a continuous time sequence spectrum signal flow is generated; wavelet transform is combined with self-adaptive threshold setting, and high-frequency noise and effective spectral signals are separated; dynamically strengthening the characteristic peak of the target heavy metal through a frequency domain characteristic screening module, and inhibiting a water molecule interference peak at the same time; generating a cross-domain fusion feature vector; processing the fusion feature vector through a pre-trained heavy metal concentration prediction model, and outputting concentration prediction values of various heavy metals; a confidence score is dynamically calculated based on a deviation between a current predicted value and historical data distribution, and model parameter update and system calibration are automatically triggered. The method has the advantages that through acousto-optic signal cross-domain fusion and closed-loop self-calibration, the target peak is dynamically strengthened while water molecule interference is inhibited, and the real-time performance, the anti-interference performance and the prediction precision of heavy metal detection are improved.

The invention relates to the technical field of water pollution detection, in particular to a water quality pollution multi-dimensional analysis system which comprises the following steps: synchronously measuring an ultraviolet-visible absorption spectrum of a water sample to be detected and scattered light intensity in an orthogonal direction, and deducting spectrum baseline interference caused by suspended particulate matters in the water sample through a correction algorithm; the interferents in the water sample are selectively converted and degraded by applying a programmed potential to the inflowing water sample to be detected; the reverse pulse is periodically applied, so that the online regeneration function of the electrode is realized; analyzing the spectral data by adopting a multivariate correction algorithm, and calculating the concentration of the target pollutant; the calculation residual error of the multivariate correction algorithm is analyzed, new interference and system performance drift of current measurement are diagnosed, and a corresponding system state code is generated; and responding to the system state code, automatically starting and carrying out state checking and multi-point calibration on the instrument, and using a calibration result to update the analysis model. Through multi-dimensional perception and closed-loop operation, accuracy and reliability of water quality monitoring are guaranteed.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention relates to the technical field of optical lens defect detection, and particularly discloses an optical lens laser-induced damage on-line detection method and system, a linear polarizer and a narrow-band filter are connected in series in a detection laser light path, non-target polarized light is filtered through polarization direction matching, meanwhile, interference of the environment and scattered light is inhibited through the narrow-band filter, and the laser-induced damage on-line detection system is obtained. According to the method, external source noise such as ambient light and non-target polarized light is reduced through polarization matching and narrow-band filtering, randomness of speckle noise is offset through multi-angle collection and signal fusion in a visual detection mode, exposure is dynamically adjusted, an image is fused to improve the signal-to-noise ratio, noise and real signals are decoupled through self-supervised learning, and the real-time performance of the system is improved. Dust interference is removed in combination with morphological operation; a deep learning reasoning model enhances the damage identification capability in a noise environment, defocusing blur caused by mechanical vibration is eliminated through phase conjugate correction, and the influence of photoelectric conversion noise, dust interference and the like on the detection efficiency and accuracy is remarkably reduced through multi-link linkage.

The invention discloses a multi-mode end point detection system and method for a semiconductor dry process, and relates to the field of gas concentration detection. A gas absorption cell; the reference light path adjusting unit comprises an adjustable attenuator used for adjusting signals; the first photoelectric detector is used for detecting a transmission signal; the second photoelectric detector is used for detecting a reference signal; the lateral scattered light detector is used for detecting a scattered signal of the particulate matters in the gas absorption tank; and the signal processing unit is in signal connection with the first photoelectric detector, the second photoelectric detector and the lateral scattering light detector and is used for performing compensation calculation on the transmission signal based on the reference signal and the scattering signal so as to obtain a corrected gas absorption signal. By adopting the scheme, measurement signal distortion caused by optical mirror surface pollution and particulate matter scattering interference in a semiconductor tail gas optical detection technology is avoided.

The invention provides a recycled carbon fiber quality detection method based on machine vision, and the method comprises the steps: obtaining a surface image of a carbon fiber bundle through a machine vision method, and carrying out the preprocessing of the image, including image denoising and enhancement; for the preprocessed surface image of the carbon fiber bundle, extracting the morphological characteristics of the fiber bundle, including fiber bundle surface texture and fiber orientation distribution, for representing the morphological change of the fiber bundle under the reciprocating bending stress; acquiring a scattered light intensity distribution image on the surface of the carbon fiber bundle, and extracting spatial distribution characteristics of scattered light intensity through an image segmentation method, including uniformity and continuity of scattered light intensity distribution; if the fracture risk exists, positioning the identified high-risk area, and collecting image data of the high-risk area; according to the high-risk area image data, scattered light intensity distribution characteristics are extracted, a quantitative relation between scattered light intensity space distribution and the fiber bundle fracture risk is established, and a quantitative index for evaluating the fiber bundle fracture risk is obtained.

The invention discloses a roadway surrounding rock deformation monitoring method based on an optical fiber sensor, and the method comprises the steps: laying on the surface of roadway surrounding rock through employing a distributed optical fiber sensing technology, and obtaining original optical signal data; processing backscattered light data in the original optical signal data through a Brillouin optical time domain analysis algorithm, extracting frequency deviation of an optical signal, and obtaining strain distribution data; according to the strain distribution data, in combination with a pre-established surrounding rock mechanical model, performing initial inversion calculation by adopting a finite element method to obtain initial three-dimensional deformation distribution; on the basis of the initial three-dimensional deformation distribution, updating dynamic boundary conditions of the surrounding rock mechanical model by acquiring real-time geological parameter data to obtain an optimized mechanical model; carrying out inversion calculation on the strain distribution data again by adopting an optimized mechanical model to generate final three-dimensional deformation distribution; and based on the final three-dimensional deformation distribution, performing comparison judgment through a preset deformation threshold, determining the stability state of the surrounding rock, and generating an early warning signal.

The invention discloses a particle size measuring device based on combination of a dynamic light scattering method and a sedimentation method and a measuring method thereof. The particle size measuring device comprises a laser, a light intensity adjusting unit, a sample pool, a thermostatic bath, a PD detector, an optical fiber head, an APD detector, a data acquisition unit, a PC and a control unit. The laser emits incident laser, the incident laser passes through the light intensity adjusting unit, penetrates through the sample cell and a sample and then enters the PD detector, and a transmission light signal is collected through the data acquisition unit and transmitted to software for calculating the particle size and concentration of settled large particles; scattered light is received by an optical fiber head with a fixed angle of 90 degrees or 160-175 degrees and is converted into a digital signal by an APD detector, and the digital signal enters a data acquisition unit and is transmitted to software for calculating the particle size of particles in a non-settling nanometer range. According to the technical scheme provided by the invention, the particle size distribution information of the wide-distribution particle dispersion system in a nano-micron range can be measured in a testing process.

The invention discloses a dynamic geological survey system and a construction simulation method. The dynamic geological survey system comprises an intelligent sensing and data acquisition module and a multi-source data fusion processing module, the intelligent sensing and data acquisition module comprises a Raman scattering optical fiber strain sensor array, a three-component seismometer monitoring network and a distributed optical fiber pressure sensing subsystem; the Raman scattering optical fiber strain sensor array, the three-component seismometer monitoring network and the distributed optical fiber pressure sensing subsystem form a multi-dimensional sensing network for shield construction geological monitoring. By means of the integrated intelligent sensing and data acquisition module, geological changes in the shield construction process can be monitored. The Raman scattering optical fiber strain sensor array, the three-component seismometer monitoring network and the distributed optical fiber pressure sensing subsystem jointly form a multi-dimensional sensing network, geological strain, microseismic events, pore water pressure and other key information can be captured, and comprehensive geological data support is provided for construction.

The invention provides a water turbidity measurement method and system for water ecological investigation, and the method comprises the steps: determining the scattering difference characteristics of suspended particles in a to-be-detected water body to light through collecting a scattered light signal of the to-be-detected water body and a light signal of a corresponding environment background of the to-be-detected water body; determining a disturbance error of the flow velocity in the to-be-measured water body during measurement of the suspended particles; determining the turbidity gradient of the suspended particles in the to-be-detected water body based on the turbidity inversion model in combination with the scattering difference characteristics and the disturbance error; determining the viscosity state of the water body power in the to-be-detected water body, and determining the attenuation relationship between the suspended particles in the to-be-detected water body and the temperature according to the viscosity state and the fluctuation characteristics of the ambient temperature around the to-be-detected water body; and dynamically compensating the turbidity gradient of the suspended particles in the to-be-detected water body through the attenuation relationship to obtain a dynamic turbidity value of the to-be-detected water body. By adopting the scheme of the invention, the influence of multi-parameter dynamic interference on the measurement of the water turbidity in the to-be-measured water can be reduced.

The invention discloses a vision-based molded lens edge thickness detection method and a related device. The method comprises the following steps: introducing visible gas into an edge area of a molded lens to form a gas film disturbance layer; the gas film disturbance layer is excited by illumination to generate refractive index change; collecting an interferometric phase image corresponding to the scattered light intensity; carrying out Fourier transform analysis on the interference fringes and carrying out temperature correction; and finally, calculating edge thickness distribution through feature space mapping. A gas film disturbance layer is introduced to the edge of the lens to serve as a physical signal conversion bridge, and a multi-light-path interference imaging and spectrum analysis technology is matched, so that the interference problem of a transparent material in direct optical measurement can be effectively avoided, high-precision and non-contact detection on the edge thickness of the molded lens is realized, and the detection precision is improved. And meanwhile, the device has the characteristics of high environmental interference resistance and good measurement stability.

The invention provides a door and window glass defect feedback control method and device, electronic equipment and a storage medium, and relates to the technical field of door and window glass defect detection. The method comprises the steps of collecting a first image after a polarized light imaging detection device is adjusted to suppress reflected light on the surface of glass and enhance scattered light of bubbles in the glass; after the polarized light imaging detection device is adjusted to enhance reflected light on the glass surface, a second image is collected; calculating an adaptive weight group by analyzing local characteristics of the second image; weighting the first image according to the self-adaptive weight group, and subtracting the weighted first image from the second image to obtain a difference image; and extracting bubble defect information in the glass by analyzing the differential image. According to the method, interference caused by non-uniformity of glass surface reflection is overcome, the adaptability of image differential processing to surface reflection background suppression is improved, and therefore image features of internal bubble defects are extracted more effectively and more accurately.

The invention provides a method for realizing carbon fiber extraction control by using deep learning, and the method comprises the steps: obtaining a scattered light intensity matrix and an interference image of a carbon fiber component, carrying out the feature extraction of image data through combining the scattered light intensity matrix and the interference image, and obtaining a feature vector reflecting the damage state of the carbon fiber component; the extracted feature vectors are input into a pre-trained damage state evaluation model, the damage degree of the carbon fiber component is judged, and if the damage degree exceeds a preset threshold value, the carbon fiber component is marked as a to-be-recycled component; according to the damage degree of the carbon fiber component, a multi-target optimization model of pyrolysis temperature, carbon fiber decomposition rate and carbon fiber purity is constructed, and target pyrolysis furnace temperature field distribution is determined; and according to the current performance evaluation or future performance change interval of the carbon fibers, a grading recycling scheme of the carbon fibers is generated by combining the damage degree of the carbon fibers, and full-life-cycle closed-loop management of carbon fiber recycling is formed.

The invention discloses a nut defect detection method based on X-ray imaging, and particularly relates to the field of X-ray nut material internal defect imaging, which comprises the following steps: generating and adjusting an initial light beam of X-rays through a synchronous radiation source, and constructing uniform irradiation field data for nut material detection; acquiring angle scattering light intensity matrix data based on the uniform irradiation field data; and performing phase inversion and sparse constraint optimization on the angle scattering light intensity matrix data to generate electron cloud density optimization distribution data, and constructing density connectivity relation graph data based on the electron cloud density optimization distribution data. By introducing a detection principle of combining high-coherence X-ray scattering imaging based on a synchronous radiation source and electron cloud density disturbance analysis, lossless identification of an early microcosmic physical deterioration region in a nut is realized; the problem that an existing method based on absorption imaging is insufficient in defect detection capacity under the background of weak density gradient and homogeneous component is solved.

The invention discloses a device and method for measuring dust concentration distribution at an inlet of an aero-engine, and belongs to the field of aero-engine sand swallowing tests, the device comprises a pulse laser light source, a shaping module, an optical image acquisition module and a data post-processing module, the pulse laser light source is arranged at a preset position around the aero-engine and is used for emitting laser beams, and the shaping module is used for shaping the laser beams; the aero-engine is located in a sand dust environment to be detected; the shaping module is used for receiving the laser beam and outputting a sheet-shaped beam; the optical image acquisition module is used for receiving coherent scattered light emitted by the grit in the irradiation area of the flaky light beam to form a laser speckle image, and transmitting the obtained laser speckle image to the data post-processing module; and the data post-processing module processes the laser speckle image by adopting an inlet sand dust concentration measurement algorithm to obtain the concentration distribution of the inlet sand dust of the aero-engine. The device is simple in structure, and is suitable for measuring the sand dust concentration distribution at the inlet in the sand swallowing experiments of aero-engines with different sizes or stages.

Provided in the present invention are an imaging device and method for turbid oil abrasive particles based on polarized image enhancement, which belong to the technical field of image processing. The imaging device of the present invention includes a CMOS fixing device, a Stokes polarized CMOS sensor, an optical lens sleeve and an optical magnifying glass, where the Stokes polarized CMOS sensor includes a CMOS sensor and a Stokes analyzer. By using a polarization characteristic difference between scattered light of lubricating oil media and reflected light of abrasive particles, Stokes vector information is measured in real time under turbid lubricating oil scenes, the scattered light of the media is filtered out, image quality of the abrasive particles is improved, coverage duration for monitoring abrasive particle imaging is effectively prolonged.

The invention relates to the technical field of optical particulate matter sensing, in particular to a particulate matter particle size recognition and concentration detection method. The method comprises the following steps: laser emitted by a laser forms a laser beam which is focused and then diverged through a lens; with a laser focus as a reference, at least two photosensitive devices are distributed in the light beam propagation direction and comprise a perifocus device and a far focus device, and receiving surfaces of the near focus device and the far focus device face laser beams to collect scattered light; scattered light signals of each device are collected, and are input into a processing unit after independent current / voltage conversion, signal amplification and analog-to-digital conversion; based on the characteristics that small-particle-size scattered light is preferentially captured by perifocus high irradiance and large-particle-size scattered light is preferentially captured by far focus large beam diameter, a mapping relation between particle sizes and channels is established; the number concentration and mass concentration coefficient of each channel is calibrated through standard particles, and synchronous detection of PM1.0, PM2.5 and PM10 is realized in combination with linear processing, so that the particle size recognition precision and the concentration measurement reliability are effectively improved.

The invention relates to the field of water quality parameter detection, in particular to a full-spectrum water quality multi-parameter analysis detector which comprises a full-spectrum acquisition module, a preprocessing module, an information processing module and an application module. The full-spectrum acquisition module is used for transmitting a full-spectrum signal to a detection sample, detecting transmission light and scattered light signals and generating original spectrum data; the preprocessing module is used for preprocessing the original spectrum data by adopting an edge calculation mode to obtain a standardized spectrum matrix; the information processing module is used for calculating the actual concentration value of each impurity in the detection sample according to the standardized spectrum matrix; according to the method, the influence relation between different types of impurities is introduced, compared with a common prediction method, the prediction precision of the concentration of each impurity is higher, whether early warning is needed or not is judged according to the prediction precision, and the situation of misjudgment is avoided.

The invention discloses a conveyor carrier roller online monitoring method and device and electronic equipment. The method comprises the following steps of: firstly, acquiring a sound wave vibration signal generated in real time when the carrier roller of the conveyor moves; wherein the sound wave vibration signal is obtained based on reverse Rayleigh scattering light in the optical fiber; performing feature extraction from the sound wave vibration signal to obtain an energy feature value of each frame; performing normalization processing on the energy characteristic value to obtain a target characteristic value; judging whether the target characteristic value exceeds a preset threshold value or not: if the target characteristic value exceeds the preset threshold value, inputting the target characteristic value into a pre-constructed target neural network model, so that the target neural network model outputs a fault type corresponding to the target characteristic value; therefore, online monitoring of the conveyor carrier roller based on optical fiber sensing multi-feature fusion is completed. According to the method, the fault type can be automatically and accurately identified and output, so that the overall performance and safety of the conveyor carrier roller are enhanced.

The invention relates to the technical field of material detection, and discloses a material multi-modal X-ray foreign matter detection method, which comprises the following steps of: acquiring a transmission gray-scale time sequence array and a scattered photon energy spectrum distribution matrix output by a multi-modal X-ray acquisition system, generating a composite characteristic body through cross-modal characteristic interactive coding, constructing a material-contour associated nodal topology network, and obtaining a material-contour associated nodal topology network; simulating a foreign matter feature conduction path and outputting a correlation confidence value set, performing space-time dimension mapping transformation to generate a foreign matter-detection coupling prediction curved surface, calculating a sensitivity adjustment factor and generating a standardized coupling coefficient thermodynamic cloud chart, extracting a detection quantitative index set and dynamically matching the detection quantitative index set with a preset reference range; and when the indexes exceed the boundaries, generating a regulation and control scheme. According to the method, the accuracy, reliability and real-time performance of foreign matter detection are improved, and the detection requirements of complex scenes can be met.

The invention discloses a photovoltaic panel self-adaptive cleaning system and method based on sand and dust optical thickness monitoring. The photovoltaic panel self-adaptive cleaning system comprises a sand and dust optical thickness monitoring module, a data processing and control module, a cleaning execution module, a power supply module and a remote monitoring module. The sand and dust optical thickness monitoring module is used for monitoring the sand and dust optical thickness AOD of the environment where the photovoltaic panel is located in real time, calculating the AOD by measuring the light intensity attenuation of a specific wavelength, and correcting the atmospheric Rayleigh scattering optical thickness and the gas absorption optical thickness at the same time; the data processing and control module is in communication connection with the sand and dust optical thickness monitoring module and used for receiving the AOD data and comparing the AOD data with a preset dust retention threshold value, and when the AOD value is larger than or equal to the threshold value, a cleaning instruction is generated; and the cleaning execution module is used for receiving the cleaning instruction and executing cleaning operation. Intelligent and self-adaptive control of photovoltaic panel accumulated dust cleaning is achieved, the cleaning opportunity can be adjusted in real time according to the actual sand and dust optical thickness, and the cleaning pertinence and effectiveness are improved.

The invention discloses a surface defect detection device and method based on polarized optical dark field imaging, and belongs to the technical field of industrial defect imaging detection, the device comprises a polarization camera, an imaging lens, a polarization annular LED light source and a sample to be detected which are coaxially arranged; wherein the polarization camera and the imaging lens form a polarization imaging system, the polarization annular LED light source emits linearly polarized light with an inclination angle, and the linearly polarized light is reflected and scattered on a sample to be detected; the linearly polarized light enters the surface of a to-be-detected sample, a small amount of reflection and most of scattered light of the to-be-detected sample are collected by the imaging lens and the polarization camera through a circular hole in the center of the polarization annular LED light source, and finally imaging is achieved. The defect depth can be calculated according to the defect width and the defect inclination angle, so that the defect depth is inversed by the low-frequency linear polarization degree, and the problem that the defect depth information cannot be effectively extracted in the prior art is solved.

According to the mining dust monitoring system and method based on multi-wavelength fusion and dynamic compensation, a light chamber is arranged in an inner cavity of a shell, an inlet of the light chamber is connected with a main gas inlet of the shell through a gas inlet pipeline, and an outlet of the light chamber is connected with a main gas outlet of the shell through a gas exhaust pipeline; the laser detection module is mounted in the light chamber and comprises a lens group, a laser source I, a laser source II, a light scattering signal receiving module, a light transmission signal receiving module and a light trap; the first laser source and the second laser source form a dual-wavelength laser source; the micro fan is mounted in the main air outlet; the environment sensing module is mounted in the exhaust pipeline; the method comprises the following steps: constructing an environment drift prediction model; testing the dust concentration in the environment to be tested, and calculating the dust concentration I Csct and the dust concentration II Ct based on the scattered light electric signal and the transmission photoelectric signal; and carrying out environment drift prediction, and obtaining corrected dust concentration data through a fusion calculation mode. According to the system and the method, an accurate dust concentration monitoring result can be obtained.

The invention relates to the technical field of optical fiber monitoring and maintenance, and discloses an optical fiber monitoring method, device and equipment, a storage medium and a computer program product, and the method comprises the steps: enabling pulse light of a preset period to be coupled into a to-be-detected low-loss optical fiber based on an optical fiber amplifier and a variable optical attenuator, and generating a backward scattering light signal; converting the backward scattering light signal into an electric pulse signal by using a single-photon detector, and counting the electric pulse signal by using a preset period as a period through a synchronous clock to obtain a counting result; adjusting the power of the variable optical attenuator according to a counting result, and drawing a target signal curve of the to-be-measured low-loss optical fiber based on an adjustment result; and analyzing the target signal curve to obtain a monitoring result of the to-be-measured low-loss optical fiber. The power is dynamically adjusted in different measurement sections, and the multi-section measurement results are superposed, so that the dynamic range of the single-photon optical time domain reflectometer is remarkably expanded, and the loss and dead pixels of the low-loss optical fiber can be reflected more comprehensively and accurately.

The invention provides an anorectal surgery postoperative care rehabilitation evaluation method and system. Wherein a vibration sensor is constructed through a machine tool machining vibration monitoring technology, vibration frequency offset of the dressing after absorbing wound seepage is detected in real time, and the vibration frequency offset is associated with dressing weight variation; meanwhile, an optical scattering measurement technology is adopted for carrying out scattering spectrum analysis on wound exudate, and the protein concentration index is analyzed. And carrying out time sequence correlation on the weight change data corresponding to the vibration frequency offset and the protein concentration index to generate a seepage component dynamic change curve. And through a wireless transmission unit in the remote measurement system, performing remote synchronous comparison on the dynamic change curve and a preset healing stage reference curve, and when the vibration frequency offset exceeding the standard seepage amount continuously appears in the curve and the protein concentration index breaks through the threshold range, triggering a remote early warning signal of wound healing abnormity. According to the technical scheme provided by the invention, the nursing rehabilitation efficiency and precision are improved.

The invention relates to a phase-sensitive optical time domain reflectometer based on multi-frequency reference switching. Continuous laser generated by a continuous laser is divided into a local path and a detection path through an optical coupler; the local path is input to the polarization coherent receiving module, the detection path is incident to a light intensity modulator driven by the signal generation circuit module, and the light intensity modulator is converted into a multi-frequency pulse laser sequence with intensity modulation; the multi-frequency pulse laser sequence enters the sensing optical fiber to be measured through the optical circulator; generated Rayleigh backscattered light and local continuous laser are subjected to beat frequency coherent receiving in a polarization coherent receiving module through a circulator; and the signal acquisition circuit adopts a demodulation algorithm to compensate phase drift and sensitivity difference of detection pulse demodulation signals with different frequencies so as to realize dynamic strain sensing. Compared with the prior art, the phase drift and the sensitivity difference between the frequencies are extracted to realize the compensation of the interleaved signals, and the method does not depend on the fixed reference frequency, so that the robustness and the adaptability of the system in a complex environment are remarkably enhanced.

The invention discloses a light field surface defect identification system and method based on multi-angle scattering, and relates to the field of optical image analysis. The system comprises a multi-angle scattered light field acquisition module and a defect intelligent identification module. The acquisition module obtains a hemispherical space scattered light field image of an optical element to be detected through one-time exposure of a collimation laser light source, a hemispherical dome and the like. The recognition module integrates a data preprocessing unit and an SFD-YOLO network, and on the basis of YOLOv11, the network replaces CBS in a shallow layer with a PSiam attention module to strengthen local significant feature perception, and replaces C3K2 in a deep layer with an OfficitMambaCSP module to strengthen global semantic modeling. The method comprises the steps of image acquisition, data set generation preprocessing, network training and defect identification. The problems that in the prior art, single-view-angle information is limited, and scattering data is insufficient in utilization are solved, Top1ACC reaches 95.6%, the parameter quantity is only 1.25 M, and the method is suitable for high-precision real-time detection of optical element defects.

The invention discloses a nanosecond particle velocity measurement method based on orthogonal line polarization beam splitting, and belongs to the technical field of particle image velocity measurement. The method comprises the following steps: providing two linear polarization state orthogonal double-pulse lasers, and respectively outputting S polarization pulse light beams and P polarization pulse light beams; the four pulse light beams are combined through the double-path laser beam combining mirror set and output to the sheet light mirror set, and a plane illumination jet flow field with the thickness smaller than or equal to 1 mm is formed; scattered light is separated according to a polarization state by using a polarization beam-splitting cubic prism, and particle images under S polarization and P polarization are respectively captured by two double-exposure cameras which are orthogonally arranged; five kinds of time sequence control are triggered through a multi-channel synchronous controller; processing the view field deviation of the two cameras by adopting a block polynomial alignment method; and calculating a particle velocity field based on a particle tracking velocity measurement algorithm. According to the invention, the polarization debugging error is reduced, the homogeneity error after beam combination is obviously reduced, the coincidence degree of the four beams of planar light is improved, and a high-precision universal alignment scheme is provided for the multi-camera PTV technology.

The invention discloses a passive imaging-based multi-spectral polarized light transmission characteristic test method, which relates to the technical field of optical engineering, and comprises the following steps of: performing space-time alignment and weighted fusion on a multi-spectral polarized image data set by using a multi-dimensional image fusion method to generate a polarization coupling matrix; inputting the polarization coupling matrix into a non-uniform polarized light transmission model, performing depolarization feature extraction by a multi-scale convolutional layer, performing inter-spectrum polarization correlation analysis by an attention mechanism layer, outputting a multi-spectrum polarized light transmission characteristic test scheme, inputting the multi-spectrum polarized light transmission characteristic test scheme into an optical simulation engine, and performing optical simulation on the multi-spectrum polarized light transmission characteristic test scheme. And driving the LabVIEW unit to execute polarized scattered light imaging simulation of the smoke environment. The advanced multi-dimensional image fusion technology and the non-uniform polarized light transmission model are adopted, the image data fusion and imaging simulation precision is improved, the problem of transmission characteristic inversion errors caused by registration deviation is effectively solved, and meanwhile the reliability and precision of transmission characteristic testing in the complex environment are greatly improved.