1853 results about "Scattered light" patented technology

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 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 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 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.

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 provides an intelligent optical cable performance prediction method and system for a data center, and relates to the technical field of data center performance analys.The intelligent optical cable performance prediction method comprises the steps that firstly, an optical cable microstructure disturbance quantization model is constructed, and an optical cable is divided into a plurality of virtual quantization units in the longitudinal direction; each unit packages a real-time stress-strain state parameter and a response sensitivity coefficient of the basic structure unit, inputting a Brillouin scattering spectrum frequency shift data flow collected in real time into the model for disturbance source analysis, generating a physical excitation decomposition sequence, driving a virtual quantization unit to perform state evolution iterative operation according to the physical excitation decomposition sequence, and obtaining a Brillouin scattering spectrum frequency shift data flow model; a stress-strain state evolution track set is obtained, a time-space correlation network of optical cable link disturbance propagation is constructed according to the stress-strain state evolution track set, network flow characteristic analysis is conducted on the time-space correlation network, an early warning node set is extracted, the future evolution trend of the early warning node set is calculated, and serialized optical cable performance abnormity early warning information is generated. The optical cable performance abnormity can be predicted in advance, and stable operation of the data center optical cable is guaranteed.

The invention relates to a pile foundation deformation indirect monitoring device and method based on optical fiber sensing, the device comprises a counterweight guide hammer, a steel wire rope, a distributed sensing optical fiber and a BOTDA optical signal demodulation device, the counterweight guide hammer is used for guiding the distributed sensing optical fiber to enter a drill hole, the steel wire rope is bound and connected with the counterweight guide hammer through a hanging ring above the counterweight guide hammer, and the BOTDA optical signal demodulation device is connected with the BOTDA optical signal demodulation device. The BOTDA optical signal demodulation equipment is used for pulling a counterweight guide hammer, the distributed sensing optical fiber comprises a connecting section, a measuring section I, a measuring section II, an optical signal input section and an optical signal output section, and the BOTDA optical signal demodulation equipment is used for collecting and analyzing an output Brillouin scattering light signal; the method comprises the following steps: 1, laying measuring points; 2, strain conversion; 3, geometric transformation; and 4, mechanical conversion. The device solves the limitation that the existing monitoring technology cannot carry out nondestructive testing on the existing pile foundation, and is simple in structural design, efficient in layout and easy to popularize.

The invention provides a defect detection system and a defect detection method, the defect detection system comprises an objective table, an illumination channel and a plurality of detection channels, the illumination channel comprises a light source, and the light source is used for providing detection light spots for a detection area of a to-be-detected object. The detection channel comprises an objective lens and a detector, the objective lens is used for collecting scattered light generated by the detection light spot in the detection area, and the detector is used for determining the signal intensity of the scattered light so as to determine defect information of the detection area based on the signal intensity. The incident plane is perpendicular to a second plane where the optical axes of the detection channels are located, deviation angles exist between the optical axes of the detection channels and the normal direction of the surface of the to-be-detected object, and at least two deviation angles in all the deviation angles meet the angle difference condition. According to the device, scattered light of different types of defects can be efficiently collected, concave defects and convex defects can be accurately detected through a plurality of detection channels, missing detection can be avoided, and the two types of defects existing on the surface of an object to be detected can be accurately detected.

An alternating pulsed lidar system comprising a laser beam generator, a receiver, and an analyzer. The laser beam generator is configured to sequentially emit laser beam pulses from ports for an aircraft into an atmosphere on an alternating basis between the ports. The receiver is configured to receive a backscatter light generated in response to sequentially emitting the laser beam pulses and generate backscatter data from the backscatter light. The analyzer is configured to determine a set of parameters for the aircraft using the backscatter data.

The invention relates to the technical field of wafer detection, in particular to a wafer defect detection method and device, and aims to detect wafer defects by adopting a polarization modulation and decomposition scheme according to corresponding characteristics of wafer surface scattered light and defect scattered light in a wafer defect detection process. The wafer detection method for improving the wafer defect detection accuracy is formed, and the wafer detection system for improving the wafer defect detection accuracy is formed according to the wafer detection method.

A muscle probe is provided for obtaining electromyography data and optical spectroscopy data from muscle tissue. The muscle probe comprises an elongate needle having an outer wall surrounding a needle interior, the needle interior comprising: a core electromyography electrode; and one or more optical fibres; wherein the needle is arranged to be inserted into a muscle, and further arranged to detect electrical activity from the muscle; and wherein the one or more optical fibres are arranged to direct incident light from a light source toward a target area of the muscle, and further arranged to receive scattered light from the target area. The present disclosure aims to provide a muscle probe to improve the diagnostic pathway for patients with neuromuscular disorders, by developing a minimally invasive bedside test of muscle health.

The invention provides a polarization modulation coupling time control phase locking high-temperature noise suppression in-situ Raman method and device, and relates to the technical field of spectrum detection. The method comprises the following steps: exciting pulse linear polarization laser to irradiate a sample to obtain Raman scattering light; a parallel vibration component (P) and a vertical vibration component (S) are obtained, and original spectral data containing polarization information and residual high-temperature noise are collected by adopting an ISCCD detector synchronously triggered by gating. Background noise is removed and a parallel vibration component (P) is extracted through polarization differential processing in combination with adaptive filtering (dynamically adjusting parameters according to signal distribution) and principal component analysis (PCA); and finally, performing signal-to-noise ratio optimization on the pure component by applying a phase locking algorithm to finally obtain a Raman spectrum result with a high signal-to-noise ratio. According to the invention, the in-situ Raman spectrum of the material within the temperature range of room temperature to 3000 DEG C can be measured, and the detection of weak signals is more sensitive.

An optical frequency domain reflectometer with environmental disturbance resistance and large strain measurement capability belongs to the field of distributed optical fiber sensing and comprises an opposite sweep frequency light frequency modulation continuous wave signal generation module, a light splitting device, a modulation device, a circulator, a polarization controller, an optical coupling device, a photoelectric conversion module and a data acquisition and processing module. An optical signal generated by an opposite sweep frequency light frequency modulation continuous wave signal generation module is divided into probe light and local oscillation light through a light splitting device, the probe light is modulated through a modulation device and sent to an optical fiber through a circulator, and a Rayleigh back scattering light signal generated by the optical fiber and the local oscillation light interfere in an optical coupling device to obtain interference light; the photoelectric conversion module mixes beat frequency signals of interference light, and the data acquisition and processing module analyzes and processes the beat frequency signals. According to the invention, the position information of the Rayleigh backscattering point is marked by using the modulated probe light, the Rayleigh scattering signal at the corresponding position is restored, crosstalk between channels is inhibited, and high-precision measurement in a large dynamic range and resistant to environmental disturbance is realized.

The invention relates to an underwater image quality improvement method based on adaptive color correction and contrast enhancement, which comprises the following steps: firstly, designing an adaptive correction strategy to carry out channel compensation on an underwater image to obtain an underwater image after color correction; a brightness channel is extracted, and a color interference layer is filtered out, so that global backscattered light is estimated; and gradient domain detail enhancement is carried out, defogging processing is carried out on the base layer of the underwater image after color correction, brightness adjustment is carried out on uneven illumination, and an enhanced image is obtained. According to the invention, by compensating the attenuation of the underwater environment to the image information, the color distribution balance of the three channels is realized, so that the color channels of the underwater image are naturally distributed; a plurality of prior knowledge of the back scattering light is fused, a Gaussian filter of an adaptive standard deviation is constructed, a color interference layer is separated, and the back scattering light can be accurately estimated without being interfered by a white object and a highlight area; therefore, multi-target-oriented contrast enhancement is realized to improve the overall visibility of the image.

Methods of studying characteristics of a sample over time are disclosed. The method includes irradiating a sample containing particles with a light beam to generate scattered light by interaction of the light beam with the sample, obtaining measurements of the scattered light over a measurement period divided into a plurality of shorter sub-processes, classifying each sub-process, and determining sample particle characteristics for the sub-processes. The method may also include transforming each sub-process prior to classifying each sub-process. Determining sample particle characteristics for the sub-processes may be performed prior to classifying each sub-process. The method may also include determining a change in the sample particle characteristic over time in the sub-process.

The invention relates to the technical field of dust concentration detection, in particular to an online detection method and system based on limited space dust concentration, and the method comprises the following steps: based on each detection area, analyzing scattered light signals, classifying the scattered light signals into different particle size categories according to angles and coordinates, and performing signal compensation in combination with real-time humidity; adjusting the irradiation direction of the LED array to optimize illumination distribution, comparing light intensity differences, dynamically scheduling uniform illumination areas, subdividing detection points, analyzing layering signals, and obtaining particle size layering concentration data. According to the invention, distribution mapping with distinct particle size category difference is realized by using a spatial multi-dimensional layered structure, signal adaptability is improved by fusing a humidity dynamic response mechanism, particle size, humidity and illumination multi-source data are dynamically fused, local environment interference is eliminated through layering and compensation linkage, and the accuracy of the system is improved. The continuity of data expression and the response speed of concentration monitoring are improved, and continuous dynamic tracking of the dust state in a limited space complex environment is supported.

The invention discloses an underwater image enhancement method and system based on binocular vision and polarization imaging. The method comprises the following steps: firstly, synchronously acquiring an orthogonal polarization image pair through a binocular polarization imaging unit, recovering scene depth information from the polarization image pair by using a binocular stereoscopic vision technology, and calculating a transmissivity graph; analyzing the polarization characteristic difference between target reflected light and backscattered light, establishing a polarization difference model, and separating background scattered light; solving an underwater imaging equation in combination with the depth information and a polarization difference model to obtain a preliminary restoration result of the target reflected light; and finally, further improving the image quality through the steps of multi-scale spectrum analysis, adaptive filter construction and post-processing by adopting a spectrum adaptive image enhancement technology. The system comprises a binocular polarization imaging unit, a calculation processing unit and an active illumination unit. According to the method, the problems of color distortion, low contrast and fuzzy details of the underwater image are solved, and the visual quality and usability of the underwater image are improved.

The invention relates to the technical field of underwater facility measurement, and provides an underwater facility defect geometric feature measurement method and system, a medium and equipment, and the method comprises the steps: calculating the actual sound velocity based on the water temperature, water pressure and salinity, correcting a sonar point cloud, calculating the sound velocity change rate, and adjusting the size of a filtering window, performing smooth filtering on the corrected sonar point cloud; for the surface image, calculating an image dark channel, after smoothing the dark channel image through scattering filtering, calculating transmissivity, fusing a scattering coefficient, removing surface image scattered light, and correcting the scattering coefficient through turbidity; based on the salinity and the water temperature, the underwater refraction angle of the laser is calculated, and the three-dimensional scanning point cloud is corrected; registering and fusing the point clouds to generate a facility surface point cloud model; based on the surface image, texture information of the facility surface is extracted, after the texture information is mapped to the facility surface point cloud model, a defect area is segmented, and geometric feature parameter extraction is carried out. And the detection reliability in a complex water area environment is improved.

The invention relates to the technical field of particle size analysis, solves the technical problem of signal distortion in rodlike abrasive dust particle size monitoring in a high-intensity magnetic field environment, and particularly relates to an inverse Fourier transform particle cross detection method and system for a laser particle analyzer, and the method comprises the following steps: collecting an original scattered light intensity signal and magnetic field intensity data; calculating a magnetic field change gradient based on the magnetic field intensity data; calculating a predicted phase offset used for eliminating signal distortion caused by rotating speed abrupt change by using the magnetic field change gradient; and identifying an oscillation risk period based on the magnetic field change gradient and a preset risk determination threshold. The method breaks through the hypothesis of a traditional Mie theory on spherical particles, establishes a scattering kernel function of the rod-shaped particles based on a T matrix theory, introduces a magnetic field coupling term, and quantifies the influence of a magnetic field on particle orientation and scattering signals. The situation that single particles are misjudged as multi-size mixed particles due to scattering double-peak artifacts caused by directional arrangement of the rod-shaped particles in a high-intensity magnetic field is avoided, and the particle size inversion precision is remarkably improved.

The invention belongs to the technical field of deep learning and image processing, and discloses an underwater image enhancement method based on tensor learnable prior. By designing learnable prior based on a Tensor Train kernel, explicit modeling of the underwater degradation law is realized. The four Tensor Train kernels correspond to the height, the width, the channel and the image block modality respectively, so that the system can simultaneously describe the vertical structure change, the horizontal scattering, the spectral absorption difference and the regional scale attenuation, thereby obviously enhancing the description capability for the underwater multimode degradation. The Tensor Train kernel is dynamically generated based on the kernel prediction sub-network, the prior structure of the method can be adaptively adjusted along with the input image, and the stability and generalization ability of the enhancement result are improved. According to the method, color shift is effectively corrected and scattering blur is inhibited while the structure edge is kept, so that the enhancement process is changed from traditional black box type mapping to physical consistency structured reasoning, and more natural colors, higher contrast and better detail recovery effects are obtained in a complex turbid environment.

The invention relates to the technical field of medical image information processing, discloses an ICG fluorescence image and MRCP three-dimensional model registration fusion display method and system, effectively solves the problems of deep biliary tract structure blurring and form distortion caused by liver tissue scattering effect in intraoperative fluorescence imaging, uses a preoperative model as anatomical prior, and improves the accuracy of the intraoperative fluorescence imaging. A viewpoint correlation optical path depth field is constructed through reverse ray tracing, a spatial variation point spread function is generated accordingly, physical reverse recovery of scattering halo in a fluorescence image is achieved, non-specific background noise such as vascular leakage is effectively inhibited in combination with topological gravitational constraint of a central skeleton, and the detection accuracy is improved. The real pipe diameter and the center line position of the deep bile duct are accurately recovered, non-rigid registration correction is performed on the three-dimensional model based on the recovered high-fidelity image, and projection errors caused by respiratory movement and soft tissue deformation are eliminated.

The invention discloses an online quality monitoring method and system for fine dried noodle production and a storage medium. The method comprises the following steps: acquiring initial optical characteristic data of surface and internal tissues of the fine dried noodles, acquiring multiband reflection and transmission light signals through spectral analysis equipment, and determining initial characteristic distribution of surface dispersion and internal pore structures of the fine dried noodles; light source parameters are adjusted according to the initial optical characteristic data, and optimized light source parameter configuration is obtained; applying the optimized light source parameter configuration to a detection area through an optical fiber light guide technology to obtain scattering spectrum data; processing the scattered spectrum data to obtain an enhanced spectrum signal; analyzing the enhanced spectral signal through a spectral decomposition algorithm, and determining the tissue uniformity distribution of the fine dried noodles; according to the fine dried noodle tissue uniformity distribution, judging fine dried noodle quality indexes by adopting a classification algorithm to obtain a quality evaluation result; and generating real-time feedback data of the fine dried noodle production line according to a quality evaluation result, integrating quality indexes of multiple batches of fine dried noodles, and determining production optimization parameters.

The invention discloses a vector flow velocity measuring device and method based on a bidirectional scanning spectral domain optical coherence tomography technology. Comprising a super light-emitting diode light source, an optical fiber coupler, a polarization controller, an interferometer comprising a sample arm and a reference arm, and a collimating lens, the collimating lens, the grating, the focusing lens group and the linear array camera are sequentially connected through a light path; the linear array camera, the computer and the two-dimensional galvanometer are sequentially connected through a line; according to the method, the fixed bias vector velocity is introduced by utilizing the bidirectional scanning technology, the dynamic light scattering optical coherence tomography method and the Doppler optical coherence tomography method are combined, decoupling of each parameter of the vector flow velocity is efficiently and accurately realized, and a quantitative index is provided for vector flow velocity analysis. The vector flow velocity measuring device based on the bidirectional scanning spectral domain optical coherence tomography technology is simple in system setting and low in cost, the time resolution is improved by about 2.5 times, and the measuring time is shorter.

The invention discloses a blurred image NeRF modeling method, system and device based on a scattering light path model and a medium. The modeling method comprises the steps of blurred image initial pose calculation, camera pose interpolation, neural radiation field NeRF-based 3D scene modeling, scattering light parameter learning guided by an internal scattering light path model, blurred image synthesis prediction by using a scattering sensing volume rendering method, and parameter joint optimization based on blurred image luminosity loss. The system, the equipment and the medium are used for implementing the method. A light propagation phenomenon is represented as internal scattering of light at a medium or surface intersection point through an internal scattering light path model, a scattering light path direction and a sampling point distance are autonomously learned by means of a feedforward neural network, and image rendering is performed by integrating contributions of sampling points in the directions of a main light path and the scattering light path. The real imaging process of a blurred image in a complex illumination environment can be effectively simulated, the geometric ambiguity problem is effectively avoided, the convergence stability of the neural network is improved, and a finer reduction effect on geometric details of a fine object is achieved.

The invention discloses a high-spatial-resolution distributed optical fiber sound wave sensing system based on FPGA real-time demodulation. The system comprises a narrow linewidth laser source module, a first coupler, a high-bandwidth sweep frequency signal modulation module, a circulator, a coherent receiving and photoelectric conversion module and an FPGA integrated module. A laser source emitted by the narrow linewidth laser source module is divided into about 10% of reference light and about 90% of detection light through the first coupler. The FPGA integrated module is connected with the high-bandwidth sweep frequency signal modulation module, and transmits a sweep frequency radio frequency pulse signal and a frequency shift radio frequency pulse signal to the high-bandwidth sweep frequency signal modulation module; the high-bandwidth sweep frequency signal modulation module is connected with the sensing optical fiber through the circulator so as to output sweep frequency optical pulses; the sensing optical fiber is connected with the coherent receiving and photoelectric conversion module through the circulator to transmit Rayleigh backscattered light; the coherent receiving and photoelectric conversion module is connected with the FPGA integrated module, and the FPGA integrated module carries out signal processing to obtain vibration information on the optical fiber.

The invention relates to a respiratory dust concentration online monitoring device and method for a coal mine, and belongs to the technical field of respiratory dust concentration monitoring. The device comprises a virtual impact separation module, a light scattering detection module, a flow control module and a data processing module. The virtual impact separation module is used for separating respirable dust and non-respirable dust; the respirable dust enters the light scattering detection module, the respirable dust is irradiated by a laser beam which is incident in the direction perpendicular to the flowing direction of the respirable dust, a scattered light signal obtained after dust scattering is transmitted to the data processing module, and the concentration of the respirable dust is calculated in real time; the flow control module is used for controlling the rotating speed of the sucking pump in real time. Through multi-technology fusion and intelligent algorithm compensation, the accuracy and environmental adaptability of respiratory dust concentration measurement are improved, the problems that a traditional separation device is prone to blockage and needs frequent maintenance are solved, and long-term continuous separation monitoring of respiratory dust can be achieved.

The invention belongs to the related technical field of laser processing quality detection, and discloses a laser processing real-time defect monitoring device based on scattered light detection, which comprises a scattered light detection module, a signal processing module and a display early warning module, the detection module is used for realizing a scattered light detection function of focusing, filtering and attenuating; the signal processing module carries out various processing and enhancement on the detection signal based on a coordinate attention mechanism so as to obtain a defect judgment result; and the display early warning module is used for displaying data such as defect judgment results and the like, and giving alarms of different levels according to the severity of the defects. The invention further discloses a corresponding monitoring method. According to the invention, real-time and accurate identification of surface layer and subsurface layer defects in the laser processing process can be realized, so that the method is suitable for providing a more efficient and reliable solution for laser processing quality control in the fields of semiconductors, optical elements, aerospace and the like.