885 results about "Spectroscopic detection" patented technology

The invention discloses an on-line detection method for gas in transformer oil based on photoacoustic spectrum self-correction, and relates to the technical field of spectrum detection and power equipment state monitoring, and the method comprises the following steps: S1, emitting modulation laser with a specific wavelength to a gas sample released by the transformer oil through a laser, and exciting gas molecules to generate photoacoustic signals; and S2, detecting the photoacoustic signal by using a photoacoustic sensor, converting the signal into an electric signal, and outputting the electric signal. According to the online detection method for the gas in the transformer oil, the correction coefficient is adjusted in real time through a self-correction algorithm, errors caused by light path changes and sensor aging are effectively compensated, the precision and stability of long-term detection are remarkably improved, and the detection accuracy is improved by combining a gas spectrum database and a mode recognition algorithm. According to the method, accurate qualitative and quantitative analysis of various gases is realized, the problem of cross sensitivity of multiple components is solved, and the method also has temperature and pressure compensation functions and can automatically adapt to environmental changes.

The invention relates to the technical field of wood detection, and particularly discloses a wood furniture material rapid detection system, which utilizes a portable spectrum scanner to collect spectrum scanning images of a wood furniture material to be detected from a plurality of positions, and introduces a spectrum image processing technology based on deep learning. Spectral feature extraction, correlation analysis of spectral features of all the positions and complementary information enhanced polymerization processing are performed on the spectral scanning images of the multiple positions, so that global spectral feature description of the to-be-detected wooden furniture material is constructed; based on matching analysis between the spectral characteristics of the wooden furniture material to be detected and the spectral characteristics of the known wood type, rapid and accurate detection of the wooden furniture material is realized. In this way, the limitation of a traditional wood identification method and the defects of an existing spectrum detection method can be effectively overcome, and the accuracy and efficiency of wood furniture material detection are improved.

A multispectral combined method for online detection of slurry components in a mineral processing process, relating to the technical field of spectrum detection. The multispectral combined method comprises: collecting slurry samples having a gradient distribution of moisture content to serve as test samples, and using an NIR system to acquire near-infrared spectrum data of the test samples; performing spectral preprocessing on the near-infrared spectrum data, so as to reduce the interference of spectral noise on subsequent modeling, and establishing an NIR regression model for slurry moisture content; using an LIBS system to acquire plasma spectrum data of the test samples, and establishing an LIBS regression model for slurry concentration; substituting the NIR regression model for slurry moisture content into the LIBS regression model for slurry concentration, and optimizing an algorithm to realize model correction and compensation; and establishing a quantitative analysis model for slurry element content to realize LIBS and NIR multispectral combination. The multispectral combined method can effectively improve the online detection precision of slurry components in a mineral processing process and improve the accuracy of a regression model.

The invention discloses a multispectral pollution analysis method and system for a photovoltaic cleaning unmanned aerial vehicle, and belongs to the technical field of spectrum detection.The multispectral pollution analysis method comprises the steps that a multispectral image is obtained, dark current-radiation-atmosphere-shadow full-link preprocessing is carried out, multiband weighted threshold segmentation and morphological post-processing are constructed, and a pollution area is extracted; establishing a self-learning pollution spectral feature library, and identifying pollution types by using cosine similarity; generating a pollution degree distribution diagram in combination with the pollution type-efficiency attenuation mapping table; and when the overall efficiency loss exceeds a preset value, planning a three-dimensional track based on a cluster priority score and an ant colony-genetic fusion algorithm, and returning an image to perform closed-loop verification and complementary cleaning after the unmanned aerial vehicle performs cleaning. According to the invention, the pollution identification precision, the efficiency evaluation accuracy and the cleaning intelligence level are significantly improved, and the method can be widely applied to unattended operation and maintenance of the photovoltaic power station.

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 discloses a water quality heavy metal real-time detection method and system based on multi-source data fusion, and relates to the technical field of water quality detection.The water quality heavy metal real-time detection method comprises the steps that spectral data and electrochemical data of a water quality sample are obtained, feature analysis is conducted based on the spectral data, a spectral heavy metal detection model is constructed, and a spectral detection result is obtained; performing feature analysis based on the electrochemical data, and constructing an electrochemical heavy metal detection model to obtain an electrochemical detection result; and performing multi-source data fusion according to the spectrum detection result and the electrochemical detection result to obtain a water quality heavy metal detection result. By collecting spectral data and electrochemical data, the limitation of a single detection method is overcome, and the stability and reliability of the model are improved; by establishing a multi-level anomaly detection and processing mechanism and formulating corresponding processing strategies for different levels, the intelligent management of the detection process is realized, and the accuracy and credibility of the detection result are improved.

The invention provides a food heavy metal pollution detection system based on plasma emission spectrum analysis, and aims to realize rapid and accurate detection of heavy metal elements in a food sample. The system comprises a sample processing module, an excitation module, a spectrum detection module, a signal processing module, a data analysis module and an output module. The sample processing module converts a food sample into an aerosol or steam state. The spectrum detection module is used for extracting spectrum characteristic information of heavy metal elements. And the signal processing module adopts an AI-driven spectrum denoising algorithm to remove background noise and interference signals. And the data analysis module calculates the concentration of each element based on the characteristic peak intensity and generates heavy metal pollution data. And the output module is used for visually displaying the detection result. According to the method, complex sample pretreatment steps are reduced, the detection efficiency and sensitivity are improved, and the method is suitable for food safety detection scenes and particularly has remarkable advantages in field rapid detection application of agricultural products, drinking water and processed food.

The invention discloses a coal quality online detection multispectral analysis system and analysis method, and belongs to the technical field of coal quality detection. Comprising a detection table, a crushing barrel, a supporting frame, a spectrum detector and a conveying belt are installed at the top end of the detection table, the conveying belt is driven by a second motor, and a flattening assembly, a collecting guide plate, a collecting box and a smoothing assembly are arranged at the top of the conveying belt. Through cooperation of the crushing cylinder and the flattening assembly, coal samples can be conveyed to the conveying belt after being crushed, the coal samples are automatically flattened and concentrated so that the coal samples can be conveyed into a spectrum detector to be subjected to spectrum analysis through a laser head, automatic operation is achieved in the whole process, rapidness and convenience are achieved, the detection efficiency is high, manual participation is not needed, and the labor intensity of workers is reduced. Meanwhile, through cooperation of the collecting box, the collecting guide plate and the smoothing assembly, the detected coal samples can be collected and stored in a targeted mode, and waste of coal resources is avoided.

The invention relates to the technical field of spectrum detection and optical imaging, and provides a time domain stimulated Raman scattering method and system.The method comprises the steps that pumping pulses or Stokes pulses are modulated, two stimulated Raman excitation pulse pairs with adjustable relative delay are introduced, a to-be-detected sample is excited to generate transient stimulated Raman scattering, and the time domain stimulated Raman scattering is obtained; transient stimulated Raman scattering signals can be obtained, acquisition of hyperspectral data covering hundreds of wave numbers can be achieved only through single excitation, and the method has the advantage of being high in time resolution. Meanwhile, the spectral resolution of the vibration level life broadening limit can be obtained through the method, and the main technical bottleneck faced by the traditional stimulated Raman scattering spectrum technology can be solved at a time. The method can be applied to the fields of Raman scattering imaging, spectrum detection, related Raman probes and the like.

The invention belongs to the technical field of spectral analysis, and discloses a spectral analysis and image recognition combined intelligent stoma evaluation system and method.A three-dimensional scanning module captures the three-dimensional form of a stoma and the three-dimensional form of surrounding tissue of the stoma through the high-precision laser or structured light scanning technology; the spectral analysis module is used for accurately analyzing the color change of the stoma; the tiny color difference of the stoma area is detected to help to identify potential tissue abnormality; the image recognition and processing module is used for analyzing the collected stoma image by using a deep learning algorithm and automatically recognizing morphological characteristics of the stoma; the data analysis and feedback system is used for automatically generating a stoma health assessment report according to the collected multi-dimensional data; through a set standard reference value, an actual measurement value is compared, and a corresponding suggestion is given, for example, whether a nursing scheme needs to be adjusted or further medical intervention needs to be carried out. According to the method, the real-time state of the stoma is accurately evaluated through non-contact scanning and spectrum detection.

The invention provides a target detection method and system based on a remote sensing image, and belongs to the technical field of radio detection, and the method specifically comprises the steps: carrying out the determination of a detection deviation target in detection targets through a detection deviation condition, and obtaining the distribution data of the detection deviation target in a target region, and combining detection deviation conditions of different detection deviation targets to determine that the detection accuracy of the target area does not meet requirements, combining different types of spectrums to construct a plurality of spectrum detection combinations, and obtaining the detection deviation conditions of the detection deviation targets in different spectrum detection combinations. And the target detection strategy of the target area is determined by using the detection deviation conditions of different spectrum detection combinations and the spectrum composition data, so that the accuracy of target detection processing is improved.

The invention belongs to the technical field of spectrum detection and analysis, and discloses an infrared spectrum-based rubber auxiliary component analysis and quality control method and a system thereof, the method comprises the following steps: using a Fourier transform infrared spectrometer to collect infrared spectrum data of a rubber auxiliary mixture in real time, and preprocessing the infrared spectrum data; based on the preprocessed infrared spectrum data, a partial least square regression method is adopted to construct a multi-component quantitative model for component identification and quantitative analysis; according to a result of component identification and quantitative analysis, generating a content report and quality control information of the rubber auxiliary component; according to the method, the influence of environmental interference on infrared spectrum data can be effectively reduced, and real-time and accurate quantitative analysis and quality control can be carried out on each component in the rubber additive production process.

The invention relates to the technical field of spectrum detection, in particular to a method for detecting the content of trace elements in quartz sand. The method comprises the following steps: acquiring spectral data of a quartz sand solution with gradient change of concentration; in each concentration spectrum, calculating an overlapping index according to the position and peak height difference of a spectrum peak and a previous spectrum peak, and screening abnormal spectrum peaks; analyzing the cross interference of the abnormal spectrum peak and the previous spectrum peak to obtain a distortion representation degree; obtaining the deviation influence degree according to the deviation of the abnormal spectrum peaks influenced by the silicon peaks among different concentrations, obtaining the concentration change interference degree according to the deviation amplitude variation trend of the distortion representation degree in the continuous concentration spectrum data in the ordered concentration, and correcting the spectrum data of the abnormal spectrum peaks to detect the content of the trace elements. According to the method, the spectrum data deviation of the quartz sand under the gradient concentration change is dynamically analyzed and quantified, so that the interference of the silicon element on the trace elements is eliminated, and the accuracy and reliability of the detection result of the content detection of the trace elements in the quartz sand are improved.

The invention belongs to the technical field of terahertz detection, and discloses a terahertz metamaterial enhanced spectrum detection system. Comprising the following steps: constructing a metamaterial enhancement factor tensor matrix according to the interaction between terahertz waves and a metamaterial; performing local field enhancement and surface plasma resonance optimization on the target sample based on the metamaterial enhancement factor tensor matrix to obtain a feature enhancement spectroscopy mapping function; constructing a multi-dimensional detection feature space based on a feature enhancement spectroscopy mapping function; a spectral signal enhancement topology network is formed based on the multi-dimensional detection feature space; enhancing the topological network based on the spectral signal, and generating an enhanced spectral feature discrimination function; obtaining a high-precision terahertz spectrum detection result based on the enhanced spectrum characteristic discrimination function; according to the invention, internal correlation between spectral characteristics can be mined, and comprehensive description of sample information is enhanced, so that high-precision terahertz spectrum detection is realized.

The invention discloses a tunable excited Raman spectrum microimaging method and device, and belongs to the field of spectrum detection and optical imaging. The device comprises a broadband adjustable wavelength light source, a high-resolution wavelength scanning module, a stimulated Raman signal excitation module and a stimulated Raman signal detection module. The stimulated Raman signal excitation and imaging module is used for focusing the two beams of narrow-band femtosecond pulse light which are overlapped in time and space on a sample and exciting a stimulated Raman signal, and meanwhile, moving the sample to realize spot scanning microscopic imaging; and the stimulated Raman signal detection module is used for converting the stimulated Raman scattering signal into an electric signal, demodulating the electric signal by using a lock-in amplifier, and recording spectral information by using a data acquisition card. According to the invention, the broadband tunable characteristic of the optical parametric amplifier is utilized to tune the pump light to the wavelength region of the sample absorption peak length so as to realize electron pre-resonance stimulated Raman; and a balance detection technology is combined at a detection end, so that high-sensitivity, high-resolution and high-signal-to-noise-ratio detection of stimulated Raman signals is realized.

The invention relates to the field of optical detection, in particular to spectrum detection equipment for measuring the thickness of a coating film, and the spectrum detection equipment comprises a feeding unit which comprises an erecting assembly; the erecting assembly is used for placing a to-be-detected coating optical element, a turntable in the feeding unit can transfer the coating optical element to the position of the measuring unit, and two spectrum detectors in the measuring unit can measure the coating thickness of two surfaces of the coating optical element at the same time; compared with an existing measuring device, the designed spectrograph coating thickness measuring device has the advantages that firstly, the coating thicknesses on the two surfaces of a coating optical element can be measured at the same time at a time in the aspect of efficiency, the efficiency is doubled, and the spectrograph coating thickness measuring device is suitable for large-batch coating thickness measuring tasks; in the aspect of accuracy, when the coating thicknesses on the two surfaces of the coating optical element are measured at the same time, the coating optical element does not need to be taken out, turned over and then fed, the problem of turn-over errors is avoided, and the coating thickness of each surface is accurately measured.

The invention provides an optical image and LIBS detection area registration method and device, a medium and a program product, and the method comprises the steps: collecting an optical image, three-dimensional shape data and spectrum detection data of a to-be-detected sample, and building a sample space coordinate system based on the optical image and the three-dimensional shape data, the spectrum detection data is collected from a preset spectrum detection area on the surface of the to-be-detected sample; establishing a mapping relation between optical image pixel coordinates and LIBS laser focus coordinates in the sample space coordinate system; extracting image features of the optical image and spectral features of the spectral detection data, and fusing and updating the mapping relation based on the image features and the spectral features to obtain a registration result of the optical image and the spectral detection area; and performing superposition display on the optical image and the spectrum detection result aligned based on the registration result in a visual interface. According to the method, the influence caused by mechanical positioning errors, manual calibration deviations and sample morphology differences is effectively reduced, and the registration precision and robustness are improved.

The invention belongs to the technical field of spectrum detection, and particularly relates to a spectrum smell recognition module which comprises a light source device, a first concave reflector assembly, a second concave reflector assembly, two angle-adjustable reflectors and a spectrum sensor assembly. A gas spectrum absorption cell and a gas spectrum reference cell are formed between the first concave reflector and the second concave reflector, a transparent optical window is arranged between the gas spectrum absorption cell and the gas spectrum reference cell for isolation, and an angle-adjustable reflector is arranged at each of the two ends of the gas spectrum reference cell; by synchronously switching the angles of the two angle-adjustable reflectors, the real-time switching of the spectrum smell recognition module between the gas spectrum absorption cell and the gas spectrum reference cell is realized, and the intensity of the gas spectrum absorption signal is superposed and accumulated through multiple times of switching to carry out spectrum signal analysis. According to the invention, the intensity of a gas spectrum absorption signal and the accuracy and stability of a spectrum signal are improved, and meanwhile, the self-calibration function of the spectrum smell recognition module is also realized.

The invention relates to the technical field of soil pollution detection, in particular to a soil pollution real-time detection and analysis method and system based on spectral analysis, and the system comprises a soil pollution detection platform, a soil database, a regional pollution division unit, a sample check analysis unit, a regional identification analysis unit, a pollution diffusion analysis unit and a display response unit. Analysis is carried out from two points of an unmanned aerial vehicle hyperspectral technology and a sample collection and detection technology, namely, regional pollution division feedback analysis is carried out on a spectral feature image to judge whether a heavy metal pollution area exists in a target soil area or not and the pollution degree condition, and regional pollution matching check analysis is carried out on sample parameters to judge whether the target soil area has a heavy metal pollution area or not. According to the method, the unmanned aerial vehicle hyperspectral detection can be checked and fed back through sample experiment detection, and whether the two types of detection are consistent or not can be visually known through an information feedback display mode, so that the availability of a detection result is ensured.

The invention provides a fiber optic spectrometer and a spectrum detection method, and belongs to the technical field of spectrum detection. The optical fiber spectrometer comprises an optical splitter and a detector, wherein the optical splitter comprises a multimode optical fiber and a diffraction grating which is formed by writing on the end face of the emergent end of the multimode optical fiber and is arranged opposite to the light sensing surface of the detector. The spectrum detection method comprises the following steps: calibrating a fiber optic spectrometer by using a tunable laser to obtain an initial transmission matrix for representing response characteristics of the fiber optic spectrometer under different wavelengths; correcting the initial transmission matrix by adopting a singular value decomposition algorithm to obtain a final transmission matrix; measuring a to-be-measured light source by using a fiber optic spectrometer to obtain a to-be-measured diffraction image; and reconstructing the spectrum of the to-be-measured light source according to the to-be-measured diffraction image and the final transmission matrix. According to the invention, the diffraction image of the light source to be detected is measured by means of the optical fiber spectrometer with a simple structure, and the spectrum of the light source to be detected can be rapidly and reliably detected in combination with the transmission matrix obtained by calibrating the optical fiber spectrometer.

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.

The invention belongs to the field of image spectrum detection, and particularly relates to a high-precision detection device and method for a liquid meniscus contour in a capillary tube. Comprising a telecentric lens, an industrial camera, a displacement platform, a light source, a capillary tube and a culture dish, the industrial camera, the capillary tube and the culture dish are installed on different displacement platforms respectively, the light source is provided with a rotary telescopic rod and can move in all directions, when the liquid meniscus contour in the capillary tube is shot, the light source, the meniscus of the capillary tube and the industrial camera are adjusted to be aligned to the same horizontal plane, and on the basis of the telecentric imaging principle, according to the light refraction principle, the liquid meniscus contour in the capillary tube is obtained. And deriving a distortion correction function of the meniscus contour in the capillary tube. According to the invention, the industrial camera and the telecentric lens are matched for use, the problem of shape distortion caused by object distance change is avoided, and the difficulty of image distortion correction is reduced. The meniscus contour distortion correction function derived based on the telecentric imaging principle can accurately calculate the actual value of the meniscus contour and restore the actual meniscus contour.

The invention relates to an airglow layer gravity wave spectrum temperature imaging system and a two-dimensional temperature field point-surface fusion inversion method. The airglow layer gravity wave spectrum temperature imaging system comprises a fine spectrum detection subsystem, a rotation temperature spectrum measurement subsystem and a large-view-field gravity wave imaging subsystem. The three subsystems are respectively provided with three progressively-increased field angles and carry out synchronous detection, a detection area and a detection target are organically cooperated, image and data processing is carried out in the later period, and the fine spectrum detection subsystem and the rotating temperature spectrum measurement subsystem carry out cooperative calibration, so that high-precision inversion of the atmospheric temperature can be realized. And the linear relation between the airglow radiation intensity and the atmospheric temperature is obtained. A small, medium and large field-of-view collaborative observation mode is adopted, and through a point-surface fusion detection method and a data inversion algorithm, a linear relationship between high-accuracy airglow radiation intensity constructed by single-point detection and atmospheric temperature is expanded and fused to an airglow radiation field with a horizontal two-dimensional resolution capability; therefore, the temperature field with the same detection precision and the horizontal two-dimensional resolution capability is obtained.

The invention relates to the technical field of grading gangue selection of coal gangue, in particular to a grading gangue selection method of coal gangue, which comprises the following steps: acquiring mineral texture data through a multispectral imaging device, analyzing the spatial distribution of aluminum and silicon elements in combination with an X-ray diffractometer, and generating a three-dimensional component map through spatial registration and fusion with infrared heat value data; impurities are dynamically removed through sulfur element spectrum detection, and the air pressure and the material flow of winnowing equipment are adjusted by matching roasting process parameters; components of the sorted waste are analyzed through an X-ray fluorescence spectrometer, particle size data are obtained through a laser particle analyzer, a genetic algorithm is input to iteratively optimize detection channel combination and sampling density, a resource distribution instruction is generated, and equipment parameter correction is fed back. According to the method, aluminum-silicon ratio critical value accurate identification, sorting parameter dynamic adaptation and waste recycling path optimization are realized, and the problem of overburning caused by insufficient quantitative analysis of element distribution in the prior art is solved.

The invention relates to the technical field of spectrum detection, in particular to a Raman spectrum-based rice oil detection method, which comprises the following steps: extracting main and auxiliary peaks to construct a peak group structure, performing sliding comparison to output a conformity region, identifying jump to remove an abnormal signal, calibrating a peak position to generate a correction structure, and performing homing numbering to complete a detection scheme. According to the method, the feature matching rule is constructed by combining the peak position spacing, the intensity ratio and the wavenumber index, the recognition precision of the multi-peak structure in the spectrum is enhanced, non-continuous response signals are recognized and eliminated through derivative change trend and proportion deviation constraint, and the accuracy of stable response extraction is improved. Dynamic calibration and position adjustment of peak positions are realized by utilizing an offset trend sequence, the consistency of a peak group structure in an overall direction and the continuity of a local structure are ensured, a numbering system and structure attribution division is completed after homing sorting, and the integrity of feature expression in a complex sample and the orderliness and reliability of spectral data processing are improved.

The utility model relates to the technical field of detection equipment, and provides multi-spectral detection equipment for defects in industrial places, which comprises a three-axis cradle head bracket, the detector outer shell is rotationally connected with the three-axis holder bracket, and the position adjustment of the detector outer shell is controlled by the three-axis holder bracket; the visible light lens I, the visible light lens II, the infrared lens and the ultraviolet lens are mounted on the detector outer shell, and lenses of the four lenses are positioned on the same side surface of the detector outer shell; and the visible light lens I, the visible light lens II, the infrared lens and the ultraviolet lens are distributed in a crossed manner. According to the technical scheme, visible light pictures and infrared or ultraviolet pictures under the same field angle can be collected, the four lenses are distributed in a crossed mode, coordinate transformation of the pictures can be achieved conveniently, information result fusion of visible light detection and infrared or ultraviolet detection is achieved, and the detection accuracy is improved. And the detection accuracy and reliability of industrial place defects and the like can be improved.

The invention relates to the technical field of spectrum detection, and discloses a quality control method for traditional Chinese medicine capsules, which comprises the following steps: continuously scanning by using a micro light spot probe during capsule movement to obtain a spatial spectrum response sequence; performing adjacent micro-area differential operation on the sequence, and filtering a shell background and retaining a particle scattering signal by utilizing a microstructure difference between a capsule shell continuous film and a powder content discrete accumulation; according to the method, a traditional path of a shell standard model is established, through a spatial frequency domain decoupling mechanism, reference errors caused by batch drifting of shell physical attributes are avoided, and the detection sensitivity of trace component fluctuation and foreign matter mixing is improved.

The invention discloses a visible light hyperspectral measurement method based on multispectral sensing, and belongs to the technical field of spectrum detection. According to the method, visible light eight-channel data is collected through a multispectral sensor, after optical crosstalk correction and filtering preprocessing, key principal components are reserved through PCA dimension reduction, a multispectral and hyperspectral mapping model is constructed through an SVR algorithm, and finally the hyperspectral reflectivity is reconstructed through PCA inverse transformation. The problems that traditional hyperspectral equipment is high in cost and poor in portability are solved, low-cost and high-precision visible light hyperspectral measurement is achieved, and the device and the method can be widely applied to food freshness detection, agricultural product quality evaluation and other scenes.

The invention relates to the field of cavity ring-down, in particular to a cavity ring-down spectrum detection system and method, and the system comprises a detection unit, a gas path system, a constant temperature control unit and a control and processing unit. The gas path system can be used for filling inert protective gas into the ring-down cavity and maintaining a micro-positive pressure state in a non-measurement period of the detection system, so that external pollutants and water vapor are effectively prevented from invading, and lens pollution and internal adsorption are reduced; the constant-temperature control unit is used for carrying out overall temperature control on the ring-down cavity and the gas path system and inhibiting signal drift caused by water molecule adsorption / desorption; and the gas path system adopts a low-adsorption material, a dead-cavity-free structure and a low-leakage sealing joint, so that the memory effect and the leakage risk are further reduced. The invention also provides a corresponding detection method which comprises the steps of micro-positive pressure maintenance, constant temperature control, strong purification, sample measurement and the like. According to the system and the method, the measurement precision and the stability are remarkably improved, the maintenance frequency and the startup purging time are reduced, and the maintenance requirement is reduced.

The invention discloses a material quality detection system, method, medium and equipment, the material quality detection system comprises a feeding module, a conveying module, a spectrum detection module, a visual detection module, a sorting module and an information management module, the spectrum detection module is arranged at the downstream of the conveying module; comprising a camera obscura, a visible near-infrared spectrometer, a plurality of optical fiber probes, a plurality of first light sources, a plurality of second light sources, a laser distance measuring sensor and an adjustable rack, and the adjustable rack is arranged on the inner wall of the camera obscura and located above a conveying module. The information management module controls the adjustable rack to move so as to drive the multiple optical fiber probes, the multiple first light sources and the laser distance measuring sensor on the adjustable rack to move, and the multiple second light sources are arranged in the camera obscura and located below the conveying module. By arranging the spectrum detection module with the adjustable rack, optical signals of materials can be received from multiple angles, and multi-mode detection such as diffuse reflection, slow transmission and transmission is achieved.