170 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 rare earth element content quantitative estimation method and device, equipment and a storage medium. The method relates to the technical field of hyperspectrum, machine learning and quantitative estimation. The method comprises the following steps: acquiring hyperspectral data of each sample, eliminating gross error points, performing data dimension reduction and K-means clustering analysis to obtain effective spectral data of each sample, performing denoising processing, averaging to obtain an average spectral curve, and extracting spectral characteristics; analyzing the correlation between the characteristic data of each wave band and the element content by utilizing Pearson correlation, selecting a high-correlation wave band range, carrying out importance test on the high-correlation wave band range by utilizing a random forest, and screening out a characteristic wave band corresponding to each element; and constructing a data set by using the characteristic wave bands corresponding to the elements and the chemical test contents of the elements, and training and evaluating the machine learning model based on the data set to obtain a quantitative estimation model. The rare earth element content can be quickly scanned and evaluated in time.

The invention discloses a hyperspectral satellite target rock and mineral identification method and system based on spectral characteristic parameters. The method comprises the following steps: S1, collecting and preprocessing a standard rock and mineral reflection spectrum curve; s2, collecting a regional hyperspectral satellite image and carrying out image preprocessing; s3, identifying diagnostic spectral reflection and absorption characteristics of the target rock mine based on the standard curve; s4, screening and extracting a diagnostic spectral range of the hyperspectral satellite image; s5, calculating diagnostic spectral characteristic parameters of the hyperspectral image target rock mine; and S6, on the basis of the S5, processing the spectral characteristic parameters obtained by calculation, and realizing target rock and mine identification based on multi-dimensional diagnosis spectral characteristics. According to the method, the application potential of the hyperspectral satellite image in the field of remote sensing geology is effectively improved and the scientificity and the accuracy of the hyperspectral satellite image in large-range rock and ore recognition drawing application are improved through the target rock and ore standard spectral curve characteristic analysis, hyperspectral satellite image processing and spectral characteristic parameter estimation methods.

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 relates to the technical field of optical systems, and provides a full-automatic collimation method and system for an optical system, a medium and equipment, and the method comprises the steps: extracting pulse features of a pulse sequence, calculating pulse interval jitter, an energy fluctuation rate and a deviation between a pulse repetition rate and a theoretical value, and judging a mode locking state; for the output laser, reconstructing a spectral curve of the output laser, and extracting spectral features; obtaining a mode locking stability score through a deep learning model; the basic parameters of the laser cavity are optimized by maximizing the power and the collimation degree of the output laser; basic parameters of the laser cavity are used as priori, and parameters of all optical elements of the resonant cavity are adjusted by maximizing a mode locking stability score and a target pulse width score; and on the premise of the adjusted parameters of the optical elements of the resonant cavity, the pose of the lens is adjusted by maximizing the power and the collimation of the output laser. High-efficiency and progressive optimization of optical system parameters is realized, and the collimation degree is improved.

The invention relates to the technical field of microbiological detection, in particular to a public area microbiological pollution risk assessment system based on spectral analysis, which comprises a spectrum acquisition module used for acquiring a Raman spectrum curve of microorganisms contained in the air of a public area; the spectrum correction module is used for carrying out baseline correction on the Raman spectrum curve, specifically, baseline values of the Raman spectrum curve at all wavelengths are determined, and the corrected Raman spectrum curve is obtained; the feature analysis module is used for identifying the types of the microorganisms, specifically, the feature evaluation value of each wave crest is calculated, all the wave crests are distinguished, and a feature main peak and a feature shoulder peak are obtained; extracting characteristic peaks contained in the acromion; identifying the types of microorganisms in the air; and the risk assessment module assesses the microbial pollution risk of the public area. According to the method, misjudgment and missing detection caused by characteristic peak overlapping are reduced, and the accuracy of microorganism variety identification is improved, so that the pollution risk of microorganisms in a public area is accurately evaluated.

The invention relates to the technical field of chemical or physical testing, in particular to a differential absorption spectrum-based respiratory inflammation marker detection system, which comprises the following steps of: determining an effective spectrum interval of an original spectrum curve by using a marker wave band of a marker to be detected; splitting the effective spectral interval by using a wavelet basis to obtain a plurality of spectral decomposition hierarchies; determining the separation effectiveness of the spectral decomposition hierarchy waveform by using the difference of adjacent data points in the spectral decomposition hierarchy waveform and the difference of frequency peak values of adjacent spectral decomposition hierarchies; determining an effective hierarchy in the spectral decomposition hierarchy by using a differential value of the separation effectiveness sequence; and determining a target filtering frequency threshold value by utilizing respective separation effectiveness of the effective hierarchy and the current spectral decomposition hierarchy corresponding to the effective hierarchy, and further filtering the spectral decomposition hierarchy to obtain the content of the to-be-detected marker. According to the technical scheme, the accuracy of detecting multiple gas markers caused by inflammation of the respiratory tract is improved.

The invention relates to the technical field of blood concentration detection by optical means, in particular to a clinical blood concentration detection method. The method comprises the following steps: acquiring the delay responsivity of spectral data to drug concentration and the change complexity of other wavebands except a preset characteristic waveband; further obtaining the interfered change credibility of the drug concentration; according to the morphological characteristics of the spectrum curve corresponding to each drug concentration in the preset characteristic wave band and the distribution difference characteristics of the data points between the corresponding spectrum curve and the preset spectrum curve in the preset characteristic wave band, obtaining the spectrum shape distortion of the spectrum curve corresponding to each drug concentration in the preset characteristic wave band; obtaining the processing beneficial confidence of the spectral data; filtering the spectral data to obtain filtered spectral data; and detecting the blood concentration. According to the method, interference components in spectrum detection are removed, so that the quality of spectrum data and the detection precision of the blood concentration are improved.

The invention relates to the technical field of spectral analysis, in particular to a crop growth state real-time monitoring method, which comprises the following steps of: acquiring hyperspectral data and temperature data consisting of all wavebands and reflection intensities at each sampling moment; acquiring a spectrum curve at each sampling moment and a progressive line of each wave band; calculating an asymptotic line noise evaluation coefficient, and further obtaining an asymptotic line difference coefficient; calculating a spectrum progressive temperature drift coefficient at each sampling moment, forming a temperature drift verification sequence at each sampling moment according to the hyperspectral data and the temperature data, and further obtaining a temperature drift verification factor at each sampling moment; calculating a noise temperature drift verification coefficient, and obtaining a self-adaptive wavelet threshold value of each decomposition layer number at each sampling moment; and denoising the hyperspectral data at each sampling moment according to a self-adaptive wavelet threshold to obtain the growth condition of the crops. The invention aims to solve the problems of incomplete noise reduction and excessive smoothness of signals caused by a fixed threshold in traditional wavelet threshold filtering.

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 computer vision, and discloses a laser deinsectization method and system for a flying robot, and the method comprises the steps: collecting multispectral data of the flying robot for operation in an operation region; fitting spectral curves of pixels, identifying curve characteristics of the spectral curves to identify target pests of multispectral data, identifying GIS thermodynamic diagrams of the target pests, analyzing high-risk areas and pest outbreak windows of the target pests, and analyzing dynamic killing priorities of the target pests; converting the pixel coordinates of the target pest into a target spatial position, and analyzing the coordinate variation of the target spatial position in continuous frames in the multispectral data so as to analyze the target movement track of the target pest; carrying out cooperative path planning on the flying robot to obtain a flying path so as to reach a deinsectization position of the flying robot; and laser output parameters of the flying robot are adjusted, and deinsectization work of the flying robot is executed. The laser deinsectization accuracy of the flying robot can be improved.

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 provides a flue-cured tobacco part oil content nondestructive quantitative characterization method and device based on visible light spectrum.The method comprises the steps that visible light spectrum data of flue-cured tobacco leaf samples at different parts are collected through a hyperspectral camera, and a series of pretreatment is carried out, the series of preprocessing at least comprises the steps of carrying out black and white board radiation correction on the visible light spectrum information, and extracting pixel points of which the characteristic wave bands are within 500.14-649.75 nm and the spectral reflectivity values are less than 0.3 or the characteristic wave bands are within 700.52-799.26 nm and the spectral reflectivity values are greater than or equal to 0.5 as regions of interest based on a threshold segmentation method; calculating a spectral reflectivity mean value of all pixel points in the region of interest as a single spectral curve representing the tobacco leaf sample of the tobacco bundle; and finally, on the basis of the preprocessed spectrum curve, constructing a flue-cured tobacco upper, middle and lower part classification model and constructing a part flue-cured tobacco oil score prediction model.

The invention discloses a hyperspectral anomaly detection method based on local spectral contrast and multi-branch convolution, and the method comprises the steps: segmenting a multi-scale window region A in an original hyperspectral image into 3 * 3 region blocks, setting a middle block B0 as a target region, and setting the other region blocks as a background region Bi; determining an average spectral curve Bavg of the Bi, and solving a spectral angular distance matrix D according to the spectral curve Ap of each pixel in the multi-scale window region and the average spectral curve Bavg of the background region; then determining the maximum value Tmax of the spectral angular distance in the target area; determining a spectral angular distance average value Mi in the background area, determining a local spectral contrast Ci, taking a minimum value in the Ci as a change coefficient c, and determining a contrast score u according to the c and the test pixel dtest; window areas with different scales are selected to traverse the whole image, and a feature map U is obtained; and putting the feature map U into multi-branch convolution, extracting feature maps of different scales through convolution operation of three different branches, and fusing the feature maps to obtain a hyperspectral anomaly detection result map.

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.

The present invention proposes a fitting method for the ultra-low reflectivity of optical coatings to solve the technical problem of large measurement errors in the ultra-low reflectivity of optical coatings. The steps of the present invention are as follows: simulating and calculating the effective refractive index of the waveguide of an uncoated FP laser chip at both ends to obtain the reflectivity of the uncoated FP laser chip and air; performing a P-I test on the uncoated FP laser chip at both ends to obtain a threshold current; performing an ultra-low optical coating on one end of the uncoated FP laser chip to obtain a laser chip with one end coated; applying different current values ​​to the laser chip with one end coated according to the threshold current and testing the corresponding spectral curves; using the spectral curves under different current values, the reflectivity of the ultra-low optical coating is obtained according to the reflectivity fitting. The present invention can accurately estimate the ultra-low reflectivity of the optical coating, playing a decisive role in the optimization of SLD and SOA products.

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.

The invention relates to the technical field of communication, in particular to a channel quality monitoring and evaluating method and system capable of being detected in real time, and the method comprises the steps: taking a sampling point from a digital signal at an interval of preset frequency from a starting frequency, and storing the spectrum data of each sampling point in preset time; all spectrum data of each sampling point in preset time are summed and averaged, average spectrum data of each sampling point is calculated, an average spectrum curve and a background noise threshold curve are calculated according to the average spectrum data of each sampling point, and real-time acquisition can be realized through time domain-frequency domain data combination. The real-time performance of spectrum detection and evaluation is improved; the background noise threshold obtained through time domain-frequency domain data combination processing is more real-time, and the accuracy of a channel quality evaluation result can be improved; and the original single frequency point monitoring is changed into the current frequency band quality evaluation, so that the use scene of the function is enriched.

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 invention provides a method for detecting antibiotic residues in milk, comprising the following steps: preparing milk antibiotic residue samples of different concentrations; extracting casein from the milk antibiotic residue samples and collecting fluorescence hyperspectral images of the casein antibiotic residue samples and pure milk casein samples; performing image correction and calculating average spectral curve data; performing PCA data dimensionality reduction processing on a spectral curve data set of the casein antibiotic residue samples, inputting the data into a BP neural network algorithm and training the data to obtain an antibiotic residue detection model; extracting casein from milk to be tested as a casein antibiotic residue sample, collecting spectral curve data to be tested of the casein antibiotic residue sample, inputting the spectral curve data to be tested into the antibiotic residue detection model and obtaining a prediction result, wherein the presence and type of antibiotic residues can be determined by only obtaining the casein in the milk and the corresponding spectral curve data.