116 results about "Spectral curve" patented technology

The invention discloses a method and a system for reflecting a growth state by a spectral curve of a plant leaf under salt mist stress and a storage medium, and relates to the field of plant growth monitoring. Processing the collected leaf image, extracting a leaf main body area, removing an interference part, and matching the filtered original spectral data to obtain target spectral data of the leaf main body area; the method comprises the following steps: combining target spectral data of a leaf main body area with leaf moisture content data, constructing a spectral analysis model by adopting a partial least square method, taking the leaf moisture content as a covariable into the spectral analysis model, peeling off the interference of leaf moisture change on the spectral data, and calculating the leaf moisture content. And finally obtaining a target spectrum curve of the plant leaf under the salt mist stress. A characteristic wave band corresponding to the target spectrum curve is extracted, a plant growth index is constructed according to the characteristic wave band, and the plant growth index is used for reflecting the growth health state of the plant. The problems that traditional monitoring is high in subjectivity, insufficient in precision and low in efficiency are solved.

The invention provides a spectral image demodulation method, spectral image demodulation equipment and a computer readable storage medium. The spectral image demodulation method comprises the following steps: S1, acquiring a first spectral image; s2, processing the first spectral image to obtain a second spectral image; s3, performing spectrum correlation region division on the second spectrum image to obtain a plurality of spectrum correlation regions; s4, obtaining a plurality of spectrum curves according to the plurality of spectrum correlation areas; and S5, obtaining a spectral image of a target wave band according to the target wave band, the plurality of spectral curves and the first spectral image. The spectral image demodulation method provided by the invention can break through the limitation of the prior art, can realize spectral image demodulation and spectral image inversion, has the advantages of high precision, high accuracy, high reliability and the like, and can be widely applied to various fields and various scenes.

The invention relates to the field of remote sensing image processing, in particular to a farmland soil type classification-oriented remote sensing image data acquisition method, which comprises the following steps of: regularly acquiring remote sensing image data of a target area according to a preset period; obtaining a distance measure between any two pixel points in each remote sensing image, and segmenting each remote sensing image to obtain a ground feature region; analyzing the trend and the amplitude of each pixel point on the spectral curve, and determining the red edge expression degree of each pixel point; determining the vegetation growth performance degree of each pixel point; determining the farmland soil segmentation quality of each remote sensing image; and screening a to-be-classified image from all remote sensing images of the target area, screening a farmland soil area to construct a bare soil mask image, and performing farmland soil classification on a ground feature area in the to-be-classified image in combination with a neural network. According to the method, the farmland soil area in the remote sensing image is accurately divided, and the accuracy of farmland soil type classification is improved.

The invention discloses a material classification method and device based on an ultraviolet electron spectrum, a medium and a product, and relates to the technical field of material surface science and data analysis. The method comprises the following steps: acquiring ultraviolet light electron energy spectrum data of a target material to be analyzed, and extracting a first spectrum curve from the energy spectrum data; obtaining a conductor characteristic identification result of the target material according to the first spectrum curve; under the condition of determining that the target material does not have the conductor characteristics based on the conductor characteristic identification result, extracting a second spectrum curve in a preset valence band top energy interval extending towards the negative energy direction by taking the Fermi level as a starting point from the ultraviolet light electron spectrum data, and performing multi-dimensional characteristic fusion on the second spectrum curve, generating a semiconductor characteristic identification result of the target material based on the fusion result; and determining a classification result of the target material according to the semiconductor characteristic identification result. According to the scheme, the conductive attributes of the target material can be quickly and accurately classified.

The invention provides a peanut seed defect detection method and system based on hyperspectral imaging, and relates to the technical field of agricultural product quality detection.The method comprises the steps that hyperspectral images of peanut seeds are obtained, and an average spectral curve is extracted from the hyperspectral images after reflectivity correction; performing feature selection on the average spectrum curve by using an MS-SPA algorithm, and extracting a feature wavelength of the average spectrum curve; the characteristic wavelength is input into the defect detection model, the spectrum sequence conversion part is used for reshaping one-dimensional characteristic wavelength spectrum data into a sequence format adaptive to one-dimensional convolution operation, continuous correlation characteristics between wavebands are reserved, the convolutional layer extraction part adopts a multi-layer one-dimensional convolutional neural network, and the convolutional layer extraction part adopts a multi-layer one-dimensional convolutional neural network; local features and global change modes related to defects in the spectrum sequence are extracted, the defect prediction part adopts global average pooling operation and a Softmax classifier to output prediction probabilities of various defects, and a final defect detection result is obtained; according to the invention, efficient and accurate nondestructive detection of peanut seed defects can be realized.

The invention relates to the technical field of trace detection, in particular to a rapid nondestructive detection method for phellinus igniarius polysaccharide content based on a near infrared spectrum technology. The method comprises the following steps: acquiring an original spectrum set of phellinus igniarius samples from various sources under a near infrared spectrum, and dividing the samples from the same source into the same group of original spectrum curves; evaluating the stability and distinguishing capability of light absorption change in different wavelength intervals, and obtaining the light absorption credibility of each target wavelength of the original spectrum curve; analyzing the morphological difference of a single curve in each group of spectrum curves relative to the whole group of curves, and obtaining an initial judgment coefficient for curve smoothing processing; and combining the light absorption credibility with the initial judgment coefficient, performing corresponding smoothing processing on each original spectrum curve according to the determined window length and processing order, and analyzing the actual polysaccharide content of the phellinus igniarius sample of the corresponding type. According to the technical scheme, the accuracy of polysaccharide content detection of phellinus igniarius through near infrared spectroscopy is improved.

The invention provides a numerical control machining tool wear multispectral imaging detection method and system. The method comprises the steps that a three-dimensional data cube is constructed through a multispectral image sequence of a tool machining area; defining a spectrum detection window of each pixel point in the three-dimensional data cube, dividing the spectrum detection window into a main detection sub-block and auxiliary detection sub-blocks, and generating a spectrum difference feature map based on a difference relationship between a spectrum curve of each pixel in the main detection sub-block and an average spectrum curve of all the auxiliary detection sub-blocks; determining a projection vector according to a target spectral feature in the center detection sub-block and a background spectral feature in the local detection window; and determining a spectral feature map according to the projection vector and the three-dimensional data cube, and recognizing an abnormal wear area in the tool machining area according to a multidirectional gradient feature map constructed by a spatial gradient operator corresponding to each pixel in the spectral feature map and a spectral difference feature map. According to the technical scheme provided by the invention, the subtle difference between the wear region and the background region can be distinguished in a multi-dimensional interference state.

The invention relates to the technical field of spectral analysis, in particular to a disperse dye component spectral analysis and identification system based on trace detection, which comprises a wave band interception module, a perturbation extraction module, a spectrum curve identification turning point recording fluctuation direction and a continuous extraction main peak core area expansion two-side generation analysis area, a phase shifting module is used for analyzing a curve fragment lifting relation and a turning sequence to generate a trajectory diagram; and a dye locking module is used for extracting a dye component with the highest matching degree by matching trajectory features to generate a candidate set. According to the method, a core peak area is locked through the fluctuation direction and continuity of a curve turning point, secondary wave crests are discriminated through an adjacent slowly-changing section, a disturbance sequence is formed by combining a mass center offset ratio and curvature connection, a curve fragment lifting relation and a turning sequence are analyzed to generate track mapping, and dye components are screened through track arrangement and offset symmetric matching. And the frequency distribution interval continuity and peak value overlapping verification ensure reliable identification.

The invention provides a transparent coating surface detection method based on space-spectrum separation imaging, and the method comprises the steps: setting an imaging system which comprises a wide-spectrum illumination unit, a light path separation unit, a space position information obtaining unit and a spectrum information obtaining unit, and the spectrum information obtaining unit comprises a dispersion subunit; the light path separation unit divides light reflected after the wide-spectrum illumination unit irradiates the surface of an object into two paths and then sends the two paths of light to the spatial position information acquisition unit and the spectral information acquisition unit which are jointly calibrated; the spatial position information acquisition unit and the spectral information acquisition unit perform data acquisition on a to-be-measured object; the two types of data acquired by the spatial position information acquisition unit and the spectral information acquisition unit are accurately calibrated; and extracting space image features, feature wavebands and spectral curve features from the two types of data after accurate calibration, fusing the features, and detecting the surface of the transparent coating by adopting a teacher-student neural network.

The invention relates to the technical field of agricultural pest control, and particularly discloses a rice pest infection process inversion device, which comprises a base, crawler belt bodies are arranged on both sides of the bottom end of the base, and through high-frequency acquisition and external data import of an ASD spectrometer, in combination with labeling and data enhancement processing of a spectrum curve, the inversion result of the rice pest infection process is obtained. The comprehensive coverage rice blast database is constructed, deep learning training is carried out based on the comprehensive database, the formed rice blast diagnosis model can accurately capture subtle changes of substances in rice on a spectrum curve, and accurate judgment of the rice blast infection degree is achieved. In addition, by means of the rice growth whole process image reconstruction technology, the WOFOST growth model and the hyperspectral reconstruction technology, the spectral information change curve of the sensitive wave band of the rice in the whole phenological period can be inverted, the rice blast infection process can be visually presented, and the method can be used for identifying the rice blast early-stage recessive characteristics. And a scientific decision basis is provided for pest control.

The invention relates to the technical field of forge piece spectral data analysis, in particular to a spectral analysis method and system for forge piece quality detection, a storage medium and computer equipment. After forge piece spectral data are obtained, a reference forge piece is selected, wavebands are divided based on spectral curve adjacent wavelength data differences and peak positions, and atomic oxygen and auxiliary peak wavebands are extracted. Selecting a target wave band, calculating wave crest difference and data difference by comparing normal forgings to obtain oxidation degree and weight, and correcting by combining the weight to obtain final oxidation degree. And analyzing a component wave band change state, and correcting spectral data in combination with an oxidation degree to complete quality detection. According to the invention, the influence of an oxide film on forge piece spectrum detection can be eliminated, so that accurate forge piece spectrum data can be collected, and related personnel can be helped to obtain a more accurate forge piece quality detection result.

The invention relates to the technical field of remote sensing image processing, in particular to a dahurian larch pest occurrence degree identification method based on spectrum reconstruction, and the method comprises the steps: obtaining a real multispectral image of a forest region invaded by defoliated pine caterpillars, and the ground hyperspectral data of a local region of the forest region; performing consistency processing on the real multispectral image and the ground hyperspectral data obtained in the step to obtain a simulated multispectral curve of a local area; performing interpolation reconstruction on the real multispectral image based on the simulated multispectral curve to obtain simulated hyperspectral images of all the forest regions; extracting a sensitive wave band sensitive to the invasion of the deciduous pine caterpillars and sensitive spectral characteristics from the simulated hyperspectral image; and an integrated learning strategy is adopted, and sensitive wave bands and sensitive spectrum characteristics are utilized to analyze the pest occurrence degrees of all the areas of the forest region. According to the method, spectrum reconstruction and an integrated learning strategy are combined to identify the occurrence degree of larch hairy pests, so that the spectrum reconstruction quality and the pest identification precision are improved.

The invention is applicable to the technical field of agricultural monitoring, and particularly relates to an intelligent agricultural monitoring method and system, and the method comprises the steps: delimiting a target region needing to be subjected to pest and disease damage monitoring, dividing the target region into a plurality of regions, selecting a monitoring point, irradiating the regions through light source equipment which is deployed in the monitoring point in advance, and carrying out the illumination of the regions. Clustering the light source equipment into a fixed group and an adjustable group; and starting the light source equipment of the fixed group, collecting spectral reflectivity data at each monitoring point position, drawing a plurality of spectral curves, and identifying the variation trend of each spectral curve, wherein each wave band corresponds to one reflectivity. By starting the adjustable group, the illumination angle can be adjusted, the illumination coverage range can be optimized, the monitoring dead angle can be reduced, the monitoring requirements of different leaf orientations and plant structures can be met, the monitoring accuracy is greatly improved, and the crop disease and insect pest prevention and control efficiency and the intelligent level are improved.

A method for generating a light source in a detection module and a detection module. The method includes: obtaining a sensor having a sensing spectral curve in a predetermined wavelength range; and arranging a light source to generate a predetermined light having an emitting spectral curve in the predetermined wavelength range. After the sensing spectral curve and the emitting spectral curve are overlapped with each other, a matching curve is generated and a standard deviation of the matching curve is less than 0.1. The matching curve generated after the sensing spectral curve and the emitting spectral curve are overlapped is expressed by the following equation: Z(λ)=W(λ)·S(π), where λ is a wavelength, S(π) is the sensing spectral curve, W(λ) is the emitting spectral curve, and Z(λ) is the matching curve.

In order to explore the effectiveness of the functional data analysis method in the hyperspectral image processing, the application proposes a classification method combining the Gaussian regression mixture model and the MRF hyperspectral function data; first, the polynomial regression is used to fit the hyperspectral image pixel spectrum curve, so as to express the pixel spectrum information in the form of function; then, the neighborhood relationship is introduced to establish the Markov random field model, and the neighborhood Gaussian regression mixture model is established in combination with the Gaussian regression mixture model; finally, according to the maximum posterior probability criterion, the final hyperspectral image classification result is obtained. Since the spatial-spectral information of the hyperspectral image is fully combined, the algorithm has high-precision classification result, and effectively improves the classification performance of the hyperspectral image.

The invention relates to the technical field of remote sensing image quality control, and discloses an unmanned aerial vehicle hyperspectral remote sensing image inland water flare judgment method and device, and the method comprises the steps: obtaining the wavelength data of each pixel point in a remote sensing image and the radiation brightness data corresponding to each wavelength data; determining a saturated flare characteristic wave band and an unsaturated flare characteristic wave band according to the spectral curve characteristics of each content in the remote sensing image; identifying saturated flare pixels and unsaturated flare pixels in the remote sensing image according to the wavelength data and the radiation brightness data of each pixel point in the saturated flare characteristic wave band and the unsaturated flare characteristic wave band; and judging whether the remote sensing image is available or not according to the positions and proportions of the saturated flare pixels and the unsaturated flare pixels relative to the focus region in the remote sensing image. According to the method, whether the content contained in the pixel is the flare pixel is judged according to the spectral curve characteristics of the pixel points in the remote sensing image, and then whether the remote sensing image is available is judged according to the position and proportion of the flare pixel in the focus region of interest.

This invention relates to the field of near-infrared analysis technology, specifically to a system for analyzing the uniformity of multi-bin cotton blending in a cotton cleaning machine based on near-infrared spectroscopy. The system includes: acquiring near-infrared spectral curves and cotton bin humidity at different sampling times; analyzing the effects of baseline shifting and curve scaling based on fluctuations in the spectral curves to identify abnormal curves; determining the scattering coefficients of abnormal states based on the abnormal state parameters and humidity changes of all abnormal curves; removing the wavelength range absorbed by hydroxyl bonds from the abnormal curves to obtain de-hydroxyl bond curves; determining the degree of influence of hydroxyl bond absorption effect by combining the difference in scattering coefficients between the abnormal curves and the corresponding de-hydroxyl bond curves; and finally, adjusting the ventilation efficiency in the cotton bins according to the degree of influence to achieve uniformity analysis. This invention can effectively analyze the impact of cotton bin humidity on noise interference generated during spectral detection, improving the accuracy of detection results when detecting the uniformity of multi-bin cotton blending.

The present application relates to the technical field of electronic equipment, and more particularly to a self-luminous square hole gray scale target for electronic endoscope and a detection method, which comprises a tunable light source unit, a special frosted glass, a gray scale coating and a constant current source and a power parameter control system connected with the tunable light source unit in turn, the center of the special frosted glass is provided with a square through hole, the gray scale coating is uniformly covered on the special frosted glass, and the center tunable light source of the tunable light source unit is attached to the square through hole; the power parameter control system is adjusted to make the brightness, spectral curve and color temperature of the center tunable light source reach the required effect, the target is collected and analyzed by the electronic endoscope, the brightness response characteristics, signal-to-noise ratio and static image tolerance of the electronic endoscope are measured, the implementation of the target can ensure better background space brightness uniformity in the measurement process, and a more uniform and stable measurement gray scale target is provided for the electronic endoscope detection device, and the measurement value is more accurate.

The invention belongs to the field of deep learning and remote sensing image processing, and particularly discloses a hyperspectral few-sample classification network construction method and a hyperspectral ground feature classification method, and the method comprises the steps: obtaining a hyperspectral remote sensing image and sample label data thereof; performing cross-domain reconstruction and spectral curve extraction, and filtering out common information among categories through eigenvalue decomposition to obtain a discretized spectral curve; extracting physical invariance features, and taking the physical invariance features as constraint rules to generate virtual samples; the reconstructed hyperspectral data and the spectrum self-supervision auxiliary information are input into a double-branch variational automatic encoder network, multi-loss constraint of cross reconstruction is carried out, and the hyperspectral data and the spectrum self-supervision auxiliary information of the same category show greater similarity in a potential space; and outputting a final surface feature prediction result based on the multinomial logistic regression classifier, and completing the construction of the hyperspectral few-sample classification network. According to the invention, high-precision and high-robustness hyperspectral ground feature classification can be realized under the condition of sample scarcity.

This invention discloses a method for testing the crosslinking degree of hard carbon precursors and a method for predicting the capacity of hard carbon, belonging to the technical field of performance evaluation of battery anode materials. The method includes the following steps: preparing a control sample and a test sample of the precursor; collecting infrared spectral data of the control sample and the test sample under the same conditions; extracting the peak area of ​​characteristic functional groups in the infrared spectral curves using peak fitting; calculating the crosslinking degree of the hard carbon precursor according to a formula; verifying the accuracy of the detection method using conventional dynamic mechanical thermodynamic analysis; and establishing a mathematical relationship between the crosslinking degree of the precursor sample and the specific capacity of the derived hard carbon. This allows for the measurement of the crosslinking degree of the precursor sample and the prediction of the sodium storage capacity of the hard carbon anode material. Compared with existing technologies, this invention has stronger universality, simpler operation, quantitative analysis, and better data stability, making it an advanced detection technology for hard carbon anode materials.

As the key target object in the scene of river and lake management, the segmentation of the skin, clothes and water of pedestrians is very important, therefore, the application provides a human and water segmentation method and device based on spectral reflectance characteristics. Firstly, according to the change trend of the spectral reflectance curve of the skin, clothes and common water, and the separability from other material spectral curves, the feature wave band is selected and the separability calculation and verification are carried out, then the spatial direction and spectral direction of the collected multi-spectral image data are normalized and pretreated, and input into the double-flow lightweight model of the established spectral feature and spatial image feature fusion to segment the skin, clothes and water of the pedestrians in the scene, finally, the segmentation result is subjected to morphological operation, overexposure correction, generation of the circumscribed rectangle frame and other post-processing; and based on the multi-spectral image data acquisition system, the design and construction of the human and water segmentation device based on spectral reflectance characteristics are realized.

A kind of under-forest ginseng year nondestructive identification method based on convolutional neural network model and near-infrared hyperspectrum belongs to under-forest ginseng year nondestructive identification technical field.The method solves the problems of time-consuming, high cost, poor repeatability and often destructive to under-forest ginseng in the prior art. The method of the present application first acquires the near-infrared hyperspectrum images of under-forest ginseng of different years which have been marked with years, then performs irradiation correction, interested region extraction and format conversion, and then performs mixed noise reduction processing on the obtained original near-infrared hyperspectrum curve of under-forest ginseng, extracts characteristic spectral band using the uninformative variable elimination method, establishes a convolutional neural network model, and iteratively trains the convolutional neural network to obtain a trained neural network model. Finally, the trained neural network model is used to nondestructively identify the under-forest ginseng to be detected to obtain the year of the under-forest ginseng to be detected. The method can realize rapid, real-time, nondestructive and accurate identification of under-forest ginseng of different years.

The embodiment of the invention provides an extrapolation method and device for the reflectivity of a colorimeter, the ratio of the reference light intensity to the measurement light intensity can be calculated to obtain the reflectivity, window data collection is carried out on the reflectivity obtained through calculation, and therefore a fitting error function can be created according to model parameters, and the reflectivity of the colorimeter can be obtained. And screening the plurality of candidate models according to the fitting error function to obtain a plurality of target models, adaptively adjusting the size of a preset window according to the plurality of target models, and finally calculating to obtain the tristimulus value of the color, thereby improving the consistency of measurement through a local adaptive mechanism while maintaining the smoothness of the spectrum curve, and improving the accuracy of the measurement. And the physical rationality of data is ensured.

The invention discloses a granite identification method and device based on SDGSAT-1 satellite thermal infrared remote sensing, and the method comprises the steps: carrying out the preprocessing of obtained SDG SAT-1 satellite thermal infrared remote sensing data, and obtaining a remote sensing image of a research region; establishing a corresponding relation between mineral spectral curve characteristics of the research area and a satellite sensor spectral domain; according to the remote sensing image and the mineral spectrum curve in the corresponding relation, a GTI granite index graph is extracted through a GTI granite index formula, and according to the GTI granite index graph, a granite distribution result graph meeting the preset precision is extracted by combining an image segmentation and edge detection method.

This invention provides a method and system for pollution identification based on the hyperspectral fingerprint effect, belonging to the field of power transmission and transformation equipment condition monitoring technology. The method includes: acquiring hyperspectral image data of the surface of the insulator under test and ambient light intensity; preprocessing the hyperspectral image data; segmenting the image into multiple pollution level regions and calibrating the spectra of the polluted regions using the spectra of clean insulators of the same model; extracting a multidimensional spectral fingerprint feature vector containing amplitude, waveform morphology, and derivative from the calibrated spectral curve; identifying components based on the fingerprint features and extracting pollution component influence coefficients; calculating equivalent salt density and ash density, correcting the pollution component influence coefficients and equivalent salt density using ash density and ambient light intensity, and weighted fusing the corrected equivalent salt density, ash density, and corrected pollution component influence coefficients to output the insulator pollution level. This invention improves the accuracy, outdoor robustness, and interpretability of pollution component identification.

The present application relates to the field of traditional Chinese medicine processing and spectral detection, in particular to a spectral detection equipment for traditional Chinese medicine processing; it comprises a gantry, a floating support, a flexible light-transmitting roller, a spectral probe, a brush roller and a data processing module; the equipment applies dynamic compaction to irregular traditional Chinese medicine through the floating support and the flexible light-transmitting roller, forms a flat detection belt, and makes the internal probe synchronous lifting to keep constant detection distance; the core is to extract the real-time lifting height of the support and convert it into a scattering compensation coefficient, and to carry out baseline correction of the original spectral curve based on the tiny pores to quantitatively analyze the effective components; the present application effectively eliminates the distance error caused by the overall form fluctuation of the material, and improves the stability of the spectral baseline and the consistency of the component quantitative analysis when the complex material is detected online.

A detection method for detecting chemical element content by using flame photometric spectral curve, comprising data preparation and detection steps, the data preparation comprises: obtaining standard spectral constant matrix containing all to-be-detected chemical elements by experimental measurement; the detection step comprises: S1. finding maximum light intensity value point in input light intensity matrix; S2. checking whether wavelength value belongs to characteristic wavelength value, yes, entering step S4, otherwise, entering step S3; S3. discarding maximum light intensity value point and re-performing step S1; S4. determining chemical element type according to characteristic wavelength value and calculating concentration value; S5. obtaining intermediate matrix; S6. judging whether intermediate matrix meets continuous detection condition, yes, continuously repeating steps S1-S5 until not meeting continuous detection condition, and terminating process. The present application can complete single compound and mixture analysis and detection.

The application discloses a liquid level height automatic calculation method and system based on spectral characteristics, a medium and equipment, and particularly relates to the technical field of container liquid level automatic calculation and dynamic monitoring. The method improves the algorithm through spectral curve change point detection, accurately captures the spectral feature difference between gas phase and liquid phase through spectral sequence modeling, introduces the running length index and dynamic updating mechanism, filters out the real gas-liquid interface change point through the multiple check mechanism to filter the false change point, infers the type according to the mean difference before and after the change point and the running length change, takes the clear mutation point or the midpoint of the gradual transition zone as the interface, combines the pixel resolution and the bottom starting position, and obtains the actual liquid level height through formula calculation, establishes the conversion relationship between the pixel quantity and the actual liquid level height through the proportional relationship between the image axis pixel number and the absolute height of the container, and finally takes the average value of the calculation results of three wave bands as the absolute height of the liquid level based on the pixel number of the spectral curve feature point.

The invention provides a rapid positioning detection method and system for a spectral peak value of a laser, and relates to the technical field of laser performance detection.The method comprises the steps that spectral signals of the laser in a set wave band are collected in real time, the spectral signals are transmitted to a data processing unit, and original spectral data are obtained; processing the original spectral data to generate a smoothed spectral curve; identifying all local maximum value points on the smoothed spectrum curve, and recording a wavelength value and an intensity value corresponding to each local maximum value point; a main peak point is screened out from all the local maximum value points according to the highest intensity principle, so that the peak wavelength and the corresponding peak intensity of the laser are preliminarily determined; and dynamically determining three spectrum configuration points based on the smoothed spectrum curve and the corresponding peak intensity. The method can improve the positioning precision and detection reliability of the spectrum peak wavelength, and meets the requirements of a high-precision application scene for laser performance detection.

The application relates to the technical field of intelligent sensors, in particular to a lubricating oil oxidation degree rapid detection method and system, which comprises the following steps: obtaining a Fourier infrared spectrum curve of a lubricating oil sample to be detected; quantifying spectral overlap based on the geometric morphological variation of a carbonyl characteristic peak; quantifying a spectral deformation disturbance factor based on the fluctuation characteristics and variation trend of the spectral curve; quantifying a spectral interference coefficient based on the spectral overlap and the spectral deformation disturbance factor to construct a spectral comprehensive distortion rate, correcting the peak area of the carbonyl characteristic peak of the lubricating oil sample to be detected, and detecting the oxidation degree of the lubricating oil sample to be detected. The application solves the area distortion problem caused by compound overlap and baseline drift in lubricating oil spectral detection, accurately deducts the false area by quantifying the distortion rate, and improves the precision of lubricating oil oxidation degree detection.