241 results about "Spectral curve" patented technology

The invention relates to the technical field of spectral signal processing, in particular to a high-precision spectral signal peak detection method and system. According to the technical scheme, the method comprises the following steps: denoising and smoothing spectral data; dividing a spectrum curve by adopting a dynamic partitioning strategy; generating candidate partitioning points based on various local features, and generating feature blocks through optimization processing; according to the method, the influence of feature differences of different regions on a detection algorithm is reduced through a dynamic partitioning strategy, a multi-scale morphological feature system is constructed to comprehensively describe spectral curve morphological characteristics, potential peaks are gradually mined by applying a layered iteration peak detection algorithm, and local characteristics of spectral signals are adapted by means of an adaptive parameter adjustment mechanism, so that the spectral curve morphological characteristics are accurately detected. The peak value correction and fusion mechanism is utilized to improve the quality and reliability of a detection result, spectral data with a complex structure and a high noise level can be effectively dealt with, an efficient and reliable solution is provided for the field of spectral analysis, and the accuracy and robustness of peak value detection and the conciseness and reliability of the result are improved.

The invention relates to the technical field of quality detection, in particular to a medical refrigerator body manufacturing detection system and method, and the system comprises a coating reflection detection module, a sound wave response measurement module, an optical signal fluctuation analysis module, a surface defect recognition module and a structural feature state evaluation module. According to the method, the reflection amplitude gradient change of the adjacent scanning areas is compared with the spectral curve continuity, so that the sensing precision of the surface reflection distribution continuity is improved, the recognition capability of weak anomalies is enhanced, and the internal potential defects are accurately captured in combination with the sound wave echo time and structural stability analysis of a specific part; a light intensity gradient model is constructed according to a sound wave analysis result, a splicing seam and a light intensity jump area of a sunken area are identified, abnormal forms such as scratches and bubbles are further extracted, coordinates are marked, the whole process depends on dynamic feedback and multi-signal linkage calibration, the identification precision and the coverage rate are improved, and medical box body quality global efficient detection is achieved.

The invention discloses a method for constructing a true and adulterated wine discrimination model, and particularly relates to the technical field of wine detection. The method comprises the following steps: extracting component data, a spectrum curve, smell fingerprints and texture information of wine liquid through nondestructive testing, carrying out standardization processing and feature fusion on multi-modal data, extracting distribution similarity features and domain offset robustness features, constructing a comprehensive feature vector, and evaluating a generalization ability value of a true and adulterated wine discrimination model by utilizing a machine learning model; aiming at a low generalization ability model, adopting a transfer learning method to carry out multi-stage training and optimization, and evaluating the detection accuracy of the model on unseen brands and batches in combination with cross validation; and finally, inputting the multi-modal characteristics of the to-be-detected wine on the optimized model, and outputting a true and false wine judgment result and a confidence score. According to the invention, the stability and adaptability of the true and adulterated wine discrimination model can be significantly improved, and it is ensured that the method has high accuracy, high generalization ability and wide market applicability in detection of wine of different brands and batches.

The invention belongs to the technical field of evaluation, development and research of oil and gas reservoirs, and discloses a T2 cutoff value calculation method based on a saturated nuclear magnetic resonance experiment. The method comprises the following steps: acquiring a saturated / bound water nuclear magnetic resonance T2 spectrum curve of a compact sandstone sample; determining a T2 cut-off value based on a saturated / bound water nuclear magnetic resonance accumulative porosity curve, and recording the T2 cut-off value as T2-Cuff; acquiring a logT2-logphi intersection curve of the saturated nuclear magnetic resonance T2 spectrum, and recording a T2 value corresponding to an inflection point of the curve as T2-Log; and determining a mathematical relationship between the T2-Log and the T2-Cuff, and establishing a T2 cut-off value calculation model based on the saturated nuclear magnetic resonance experiment. According to the method, the T2 cutoff value can be calculated only by performing the nuclear magnetic resonance experiment on the saturated water sample and performing mathematical treatment on the T2 spectrum, and the method has the advantages of being economical, simple, efficient and high in practicability.

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 relates to the technical field of soil detection, and particularly discloses a rapid soil heavy metal pollution detection method based on a spectrum technology, which comprises the following steps: obtaining dual-band spectral reflectivity data and temperature and humidity parameters of to-be-detected soil, carrying out spectral characteristic compensation, generating a spectrum curve, and obtaining environment compensation parameters; extracting spectral characteristic parameters from the spectral curve, combining the spectral characteristic parameters to construct a multi-dimensional spectral characteristic matrix, and inputting the multi-dimensional spectral characteristic matrix into a CNN-RF hybrid model for soil heavy metal pollution detection to obtain a heavy metal pollution detection result. According to the method, in-situ lossless rapid detection of the soil heavy metals is achieved through the dual-band spectrum and the environment dynamic compensation technology, interference of environmental factors on spectral measurement is effectively reduced, the detection efficiency is improved, the pollution condition of the heavy metals in the soil is accurately recognized through the CNN-RF mixed model, the reliability and precision of soil heavy metal pollution are improved, and the method is suitable for popularization and application. Corresponding treatment measures can be taken in time, and the safety of the soil environment is guaranteed.

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 technical field of heavy metal detection, in particular to a method and a system for rapidly detecting heavy metals in traditional Chinese medicinal materials, and the method comprises the following steps: respectively collecting X-ray fluorescence spectrum curves of to-be-detected honeysuckle and a honeysuckle sample, and sequentially recording the X-ray fluorescence spectrum curves as a to-be-detected spectrum curve and a standard spectrum curve; determining a peak position difference and a contour difference of each characteristic peak in the spectrum curve to be measured; obtaining the peak shape variation degree of each characteristic peak in the spectrum curve to be measured; determining the peak bottom energy difference of each characteristic peak in the spectrum curve to be measured; obtaining an abnormal correction coefficient of each characteristic peak in the spectrum curve to be measured; obtaining the difference between the measured value of the heavy metal content in the honeysuckle with the known heavy metal content and the real value, recording the difference as the measurement difference, and determining the actual content of each heavy metal element in the honeysuckle to be measured based on the correlation between the measurement difference and the abnormal correction coefficient. Therefore, the accuracy of measuring the content of the heavy metal elements in the to-be-measured honeysuckle is improved.

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 carbon content measurement, in particular to a soil organic carbon content measurement method and system.The method comprises the steps that soil to be measured and a preset number of soil samples with known soil PH and soil organic carbon content are obtained; collecting a spectral curve of each position in each soil sample; recording each wavelength on the spectrum curve as each sampling point; determining a spectrum mutation interference coefficient of each sampling point; acquiring a spectral anomaly correction coefficient of each sampling point on each spectral curve; performing weighted fusion on all the spectral curves of each soil sample based on the spectral anomaly correction coefficient to obtain a corrected spectral curve of each soil sample; forming a training set by utilizing the correction spectrum curves, the organic carbon contents and the PH values of all the soil samples, and training the prediction model; and measuring the organic carbon content of the to-be-measured soil through the trained prediction model. Therefore, the accuracy of measuring the organic carbon content of the soil 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 solid waste classification method based on a spectrometer, and relates to the technical field of solid waste analysis, and the method comprises the following steps: S1, preliminary classification: obtaining a sample of unknown solid waste, and observing the characteristics of the sample to obtain characteristic information; s2, selecting a spectrograph: selecting the spectrograph according to the feature information; analyzing the sample by using a spectrograph to obtain a spectrogram; the spectrogram comprises a spectrum curve; s3, spectrogram preprocessing: carrying out normalization processing on the data of the spectrum curve; s4, similarity analysis: for the spectrum curves of the multiple samples, randomly selecting two samples for comparison to obtain a similarity value; if the similarity value is within a preset threshold range, the two samples to be compared belong to the same kind of solid waste. The method is low in operation cost and low in professional degree requirement on workers, and can realize rapid and efficient analysis and classification of solids.

The invention discloses a dimension reduction processing method for space-time joint foresight imaging of an airborne array radar, and the method comprises the steps: constructing a foresight imaging geometric model of the airborne array radar, receiving a distance, direction and channel three-dimensional echo matrix, converting data in a single coherent processing time interval into a frequency domain for the echo matrix, and compensating a Doppler phase, and then, truncating the effective information of the frequency spectrum to obtain snapshot data after dimension reduction. And then applying the newly obtained space-time snapshots to space-time adaptive iterative super-resolution imaging, estimating space-time super-resolution spectrum curves corresponding to the space-time snapshots of the distance-azimuth units, and performing spectrum accumulation on the super-resolution spectrum curves of the distance-azimuth units according to the motion speed of an aerial carrier and antenna scanning parameters to finally obtain a two-dimensional forward view image. According to the method, effective echo information is intercepted in the Doppler domain, space-time data are effectively compressed, the operation complexity is remarkably reduced, and meanwhile the imaging quality is kept.

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 discloses a hyperspectral information-based target object classification method, apparatus and device, and a medium. The method comprises the steps of obtaining hyperspectral information of sparse sampling points of a scene where a to-be-classified target object is located; obtaining visible light information of a scene where the to-be-classified target object is located; performing affine transformation alignment on the hyperspectral information and the visible light information; extracting a contour region of a to-be-classified target object from the visible light information by using an instance segmentation model; acquiring a spectral curve of each sparse sampling point in the contour area from the hyperspectral information according to the contour area of the target object and an affine transformation alignment result; and obtaining spectral features of the target object according to the spectral curves of all the sparse sampling points in the contour region, and performing feature classification on the target object by using a classification model based on the spectral features. The target objects can be quickly classified, the processing time is saved, and the execution efficiency is improved.

The invention discloses an accurate lossless identification method for the real concentration of a filling body, belongs to the technical field of filling, and solves the problem that the real concentration of the filling body cannot be accurately measured after a goaf is maintained for a certain days. The method comprises the following steps: performing a hyperspectral scanning experiment on a test block by using a hyperspectral imager, extracting a surface spectral curve of the test block, collecting a modeling data set, dividing the modeling data set into a training set and a test set, performing iterative training on a concentration prediction model, loading a surface spectral curve of a filling body, executing the concentration prediction model, and outputting the concentration of the filling body. According to the method, the real concentration of the filling body is identified by utilizing a hyperspectral imaging technology, a sample does not need to be damaged, the method has the advantage of nondestructive detection, after the filling body concentration identification model is established, the corresponding concentration can be obtained after a test block with unknown concentration is scanned, the experiment detection period is greatly shortened, and the method has high efficiency.

The invention discloses a mutton moisture content detection method and device, a medium and a product, and relates to the technical field of image nondestructive testing, and the method comprises the following steps: obtaining a target image; performing feature extraction on each pixel point in the target image, and determining a spectral curve of the mutton sample to be detected; preprocessing the spectrum curve of the mutton sample to be detected by using an optimal preprocessing method to obtain a preprocessed spectrum curve of the mutton sample to be detected; screening the preprocessed spectrum curve of the mutton sample to be detected by using a continuous projection algorithm to obtain the reflectivity of the mutton sample to be detected under a plurality of characteristic wavelengths; and inputting the reflectivity of the mutton sample to be detected under each characteristic wavelength into the mutton moisture content prediction model to obtain a predicted value of the moisture content of the mutton sample to be detected. The detection precision of the moisture content of mutton is improved.

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 field of coastal salt marsh wetland vegetation monitoring, and discloses a suaeda salsa adaptive hyperspectral index and identification method based on a hyperspectral remote sensing image. The method mainly aims at solving the problem of difficulty in identifying the suaeda salsa in the intertidal zone caused by serious phenomena of same object and different spectrum and same spectrum and foreign matter in the coastal wetland; the method comprises the following steps of: firstly, selecting representative samples and acquiring an average spectral reflectivity curve of the representative samples by considering different vegetation types, coverage difference and soil water-salt difference of the coastal wetland; then, analyzing the spectrum curve and selecting a hyperspectral image optimal wave band combination which is most suitable for distinguishing the suaeda salsa from other surface features; then, setting an adaptive rule and constructing a suaeda salsa adaptive hyperspectral index (SSAHI); and finally, quickly and accurately identifying and extracting the range of the suaeda salsa in the intertidal zone based on SSAHI. According to the method, the range of the suaeda salsa in the intertidal zone can be rapidly and accurately monitored based on the hyperspectral remote sensing images of different sensors and different time, and the problems that existing suaeda salsa identification based on the hyperspectral remote sensing images is difficult and low in precision are solved.

The invention relates to the technical field of optical detection, in particular to a nano material detection method based on surface enhanced Raman spectroscopy, which comprises the following steps: acquiring spectral data at different acquisition moments based on a nano material, generating a spectral curve, and preprocessing; analyzing the preprocessed spectrum curve to obtain an initial shape feature as a crest factor, and constructing a particle set; calculating a fluctuation influence factor through the particle set, obtaining an overlapping parameter of the particle according to the preprocessed spectrum curve, and combining the fluctuation influence factor with the overlapping parameter to determine a final shape feature of the particle; carrying out clustering analysis on the ultimate shape features of the particles to obtain a plurality of clusters, calculating the feature change degree of each cluster, carrying out screening and redundant peak elimination according to the feature change degree, and correcting the spectrum curve after elimination; quantitative analysis is carried out based on the corrected spectrum curve, so that the method has higher adaptability to data noise, redundant information and external environment change, and the result has higher robustness.

The invention relates to an image processing method and apparatus, an electronic device, a storage medium and a program product. The method comprises the steps of obtaining a plurality of partition image data and a spectrum type of a spectrum curve corresponding to each partition image data; for the partition image data corresponding to the spectrum curve of which the spectrum type is a narrow-band spectrum, determining a target gain of each color channel corresponding to the partition image data based on the partition image data, the spectrum curve corresponding to the partition image data and the human eye spectrum response curve corresponding to each color channel; adjusting the partitioned image data based on the target gain of each color channel corresponding to the partitioned image data to obtain adjusted image data corresponding to the partitioned image data; and obtaining target image data based on the multiple pieces of adjusted image data. By adopting the method, the image quality can be improved.

The invention relates to the technical field of iron content detection, in particular to an iron ore total iron content detection method and system based on spectral analysis, and the method comprises the steps that after spectral data of iron ore are obtained, an improved envelope line method is adopted for preprocessing the spectral data, determining the local maximum value point of the spectral data by adopting a sliding window method, specifically, determining the size of a window for searching the local maximum value point of the spectral data by adopting the sliding window method according to the number of data points of a maximum characteristic peak of a spectral curve of the spectral data, so that the robustness of determining the local maximum value point of the spectral data is improved; when the envelope line node is determined, not only is the local maximum value considered, but also the intersection point of the fitting curve of the spectrum curve is considered, and the feature points in the two aspects are taken as the envelope line node, so that the envelope line node is determined more accurately, and the characteristics of the spectrum curve can be reflected better.

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.