25 results about "Peak fitting" patented technology

The invention discloses an underground gas storage gas monitoring method and system based on optical fiber spectrum analysis. The method comprises the following steps: S1, optical fiber network deployment: arranging a corrosion-resistant optical fiber sensor network in a gas storage; s2, spectral data acquisition: transmitting light with a specific wavelength to a gas chamber at the tail end of an optical fiber through a Fourier transform infrared spectrometer, and acquiring a transmitted spectrum after interaction of gas and light; s3, spectrum decoupling and concentration calculation: based on a peak pattern fitting algorithm of the Lambert-Beer law, decoupling the infrared spectrum characteristics of the mixed gas, matching a standard spectrum database, and calculating the concentration of each gas component; s4, constructing a three-dimensional gas model: according to the position of the optical fiber sensor and the gas concentration data, establishing a gas migration model to predict a leakage position and a movement direction; according to the method, the trend, the component distribution, the concentration change and the potential leakage risk of the gas in the storage are effectively evaluated, and technical support is provided for the safety, the effectiveness and the economical efficiency of operation of the gas storage.

The application discloses a low-permeability reservoir pore structure quantitative characterization method based on a nuclear magnetic resonance T2 spectrum multi-peak fitting, relates to the technical field of reservoir pore structure characterization, and comprises the following steps: preprocessing a nuclear magnetic resonance T2 spectrum, and decomposing the data after preprocessing by means of Gaussian multi-peak fitting; combining the peak area, the peak center and the peak width of each sub-peak to quantitatively characterize the porosity contribution and the pore size range of micro-pores, mesopores and macropores; introducing the sub-peak parameters obtained by multi-peak fitting into a permeability prediction model, correcting the model for predicting the permeability based on NMR T2 relaxation data based on the multi-peak fitting results, constructing a permeability model, and more accurately predicting the permeability, which has important significance for promoting reservoir prediction.

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.

This invention belongs to the technical field of laser probe elemental analysis and provides a method for rapid adaptive matching of spectral background using peak fitting. The method includes: LIBS spectral acquisition to obtain plasma spectral data; selecting the peak intensities and widths of multiple peaks within the band containing the peak to be matched, obtaining a fitting equation; using this equation to obtain the width of the peak to be matched, while simultaneously setting the number of single-sided scans, background length, and step size; scanning the background noise to the left and right sides with the set background length and step size; selecting the background spectral segment with the smallest noise value from all obtained background noise values ​​as the background of that peak, using this value as the noise value of that peak, and using the average intensity of the spectral segment as the background value; and calculating the signal-to-noise ratio and signal-to-background ratio of each peak based on the peak intensity. This invention automatically estimates the peak width through peak fitting to achieve rapid adaptive identification and matching of the peak background, improving the analytical efficiency of laser probe technology.

The application discloses a spark spectrum analysis method for the inclusion content distribution on the surface of large-size metal materials, which continuously excites and scans the surface of the large-size metal materials through spark discharge to obtain the mixed distribution data of the solid solution and inclusions of the inclusion elements on the surface of the large-size metal materials, and further obtains the relative frequency distribution graph of the spectral intensity; the relative frequency distribution graph of the spectral intensity is subjected to normal distribution and Gumbel extreme value distribution peak fitting processing to obtain the Gumbel extreme value distribution data of the spectral intensity of the inclusions; the number and size information of the inclusions of the small sample are acquired to obtain the maximum inclusion size information; the inclusion size information of the small sample and the maximum inclusion size information are associated with the spectral intensity distribution data of the inclusions to determine the corresponding relationship between the size and the spectral intensity of the inclusions, and the content distribution result of the inclusions on the surface of the large-size metal materials is obtained. The application can quickly obtain the accurate distribution information of each element inclusion on the surface of the metal materials.

The application provides a kind of anaerobic digestion residue dewatering performance rapid detection method, belong to the technical field of biomass waste dewatering performance detection.Specifically: collect anaerobic digestion in 400-4000cm ‑1 Wavelength range of full-band infrared spectrum data;Using infrared spectrum analysis software to obtain the infrared spectrum data is smoothed;According to the data obtained, select 1550-1750cm ‑1 Wavelength range of infrared water spectrum data related to O-H bending vibration, Gaussian deconvolution and peak fitting are carried out using peak fitting software, and are decomposed into six sub-peaks, p1-p6;And calculate the area ratio of each sub-peak, and the ratio of the area ratio of sub-peak 1-2 and sub-peak 3-6 is represented by the sum of the area ratio of sub-peak 1-2 and sub-peak 3-6, and the value is higher, which means it is more difficult to dewater.The infrared detection method of the application has the advantages of clean, efficient, low dose, low cost, batch detection, etc.

A method for obtaining crystallization temperature parameters of crystalline polyolefin is characterized in that a temperature scanning mode of a rotational rheometer is utilized, and the crystallization temperature parameters of the crystalline polyolefin are obtained through a small-amplitude oscillation shearing method at a constant cooling rate. The method comprises the following steps: measuring the change value of the storage modulus G'of a crystalline polyolefin sample along with the temperature T, taking the logarithm of the storage modulus G ', drawing a logG'-T curve by taking the temperature T as the abscissa and logG 'as the ordinate, carrying out interpolation extrapolation on the logG'-T curve by adopting a cubic spline trajectory interpolation method to obtain an interpolation extrapolation curve, carrying out first-order differential processing on the interpolation extrapolation curve to obtain a peak shape curve, and calculating the peak shape of the crystalline polyolefin sample according to the peak shape curve. And carrying out single-peak fitting on a peak curve by using an Asym < 2 > Sig function to obtain crystallization temperature parameter information of the crystalline polyolefin sample. The method is a quick test method with a brand new concept, and has the advantages of wide test temperature range, simplicity in operation, high speed, test sensitivity and the like.

The invention provides a method for detecting a passivation film structure on the surface of a corrosion-resistant titanium alloy, which comprises the following steps of: performing X-ray photoelectron spectroscopy test on a sample by a method of changing energy, performing at least twice test to obtain a full spectrum and a spectrum of Ti, O and C, performing charge correction on the peak position of the C element, and performing peak-dividing fitting by adopting an element high-resolution spectrum to obtain the passivation film structure on the surface of the corrosion-resistant titanium alloy. And when the processed result meets the condition that only the photoelectron signal of the passive film is received at least once and the photoelectron signal from the titanium alloy matrix is received at least once in the test, outputting the structure of the passive film on the surface of the sample according to the signal intensity in the data processing and analysis result. Ion etching sputtering is not used in the XPS test process, the illusion generated by titanium ion reduction caused by argon ion etching in a conventional XPS etching-layer-by-layer stripping method is avoided, data obtained through analysis are more accurate, the detection method is a nondestructive detection method, no damage is caused to a film layer, and the detection accuracy is improved. And the accuracy of the detection result is further improved.

The present invention discloses a fitting detection method and system for spectral lines of arc plasma, relating to the technical field of arc plasma. The detection method obtains the spectrum emitted by the arc plasma, performs multi-peak fitting on the spectral line data corresponding to the spectrum to generate a fitting curve; selects spectral line peaks according to the peak positions of the fitting curve and the spectral line peaks corresponding to the spectrum, and determines the fitting curve data and the spectral line range; performs fitting error detection based on the fitting curve data and the spectral line range to generate fitting detection data corresponding to the spectrum. By comparing the peak positions of the fitting curve and the spectral line peaks corresponding to the spectrum, the present invention determines the correctness of the fitting and selects the spectral line range within the fitting range corresponding to the fitting curve. Based on this spectral line range and combined with the fitting curve data, fitting error detection is performed, which can improve the accuracy of the fitting detection result.

Provided in the present application is a quality detection method for a graphite composite structure. The method comprises: selecting a graphite composite structure as a test sample, wherein the graphite composite structure comprises a graphite core and a coating layer coating the graphite core; selecting a test region of the test sample, and using a Raman spectrometer to sequentially perform Raman tests on different sites in the test region, so as to acquire Raman spectra of at least 100 sites; performing peak fitting on data of the Raman spectra of the test sample, so as to obtain a D peak and a G peak; on the basis of the intensities or areas of the D peak and the G peak, acquiring a Raman characteristic parameter A for the at least 100 sites; on the basis of the Raman characteristic parameter A, detecting the relative concentration of defects at different sites on the surface of the test sample; and / or on the basis of the Raman characteristic parameter A, detecting coating information of the coating layer on the surface of the test sample, wherein the coating information comprises whether a coating layer is present and whether the coating layer is excessively coated.

The invention discloses an intelligent slow-release drug carrier screening method based on material microscopic image recognition, which integrates nano hyperspectrum, cryoelectron microscope and Raman spectrum data through a multi-modal data synchronous acquisition system to realize nanoscale space alignment of microscopic morphology and component distribution. A non-local mean denoising algorithm is adopted to enhance pore structure recognition, and chemical bond state changes are quantified in combination with adaptive baseline correction and multi-peak fitting technologies. A characteristic spectrum library is constructed, a chemical distribution diagram is generated through spectrum angle mapping, and the co-localization of the carrier and the medicine is analyzed in combination with spatial mutual information entropy. A space-spectrum combined encoder is innovatively developed, multi-modal features are fused by using a cross attention mechanism, and quantitative indexes such as a pore filling rate, crystallinity change and an interaction stability score are output. According to the method, the static and single-mode limitation of a traditional method is broken through, and a complete screening system from microstructure analysis to release dynamics prediction is established.

This invention discloses a spectral analysis method, apparatus, device, and storage medium. It includes: acquiring raw spectral data to be analyzed; preprocessing the raw spectral data to obtain preprocessed spectral data; performing multi-peak fitting on the preprocessed spectral data using an iterative residual peak-finding fitting algorithm to obtain a final fitted spectrum and a final residual spectrum; and generating spectral analysis results based on the final fitted spectrum and the final residual spectrum. Preprocessing the raw spectral data effectively eliminates background interference, cosmic ray pseudo-signals, and random noise, improving the signal-to-noise ratio of the spectral data. Using the iterative residual peak-finding fitting algorithm for multi-peak fitting accurately identifies and fits all significant spectral peaks, avoiding underfitting and overfitting problems, and improving the accuracy of multi-peak fitting. The generated spectral analysis results can accurately extract core feature indicators, and the fitting quality can be verified through residual spectroscopy, ensuring that the analysis results are accurate and reliable, truly reflecting the structural characteristics of the material.

The invention discloses a CVD diamond lattice size prediction method, device and equipment and a storage medium, and the method comprises the steps: taking information data of various CVD diamonds with known lattice sizes, and generating an ultraviolet Raman spectrum database according to the information data; by taking the information data as training data, training through a convolutional neural network to obtain multi-dimensional features of the ultraviolet Raman spectrum on Raman peaks affecting the lattice size of the CVD diamond; after multi-dimensional feature extraction, according to different linetypes, carrying out peak-splitting fitting on Raman peaks in each ultraviolet Raman spectrum through a set fitting function, and judging a main peak influencing the lattice size; calculating the lattice size of the CVD diamond by adopting a phonon confinement model; judging whether a judgment decision coefficient R2 of the model is greater than a preset value, and if not, adjusting a preset parameter of the fitting function; and outputting a predicted value of the lattice size of the to-be-tested CVD diamond through the model. According to the method, the accuracy of predicting the lattice size of the CVD diamond can be improved.

The invention discloses a transformer oil aging state prediction method and system based on a Raman spectrum and a medium, and particularly relates to the technical field of power equipment monitoring, and the method is characterized in that a multi-dimensional data feature group in each piece of Raman spectrum data is extracted by using a multi-peak fitting recognition algorithm, and a spectrum data feature set is constructed; inputting the spectral data feature set into a pre-trained feature parameter prediction model, predicting to obtain a data feature prediction set of the transformer oil, and constructing a static data feature set and a dynamic data feature set based on the data feature prediction set; the feature parameter prediction model is obtained by training a CNN-UILSTM model; inputting the static data feature set into a pre-trained static aging grade classification model, and classifying to obtain a static aging grade; inputting the dynamic data feature set into a pre-trained dynamic stability prediction model, and predicting to obtain dynamic stability; and predicting and evaluating the aging state of the transformer oil by using the static aging grade and the dynamic stability to obtain a transformer oil aging state prediction result.

This invention discloses a multispectral microscopic three-dimensional measurement method and apparatus. The method includes acquiring structured light fringe images of the sample under test, performing differential operations and separation extraction to obtain a multi-channel modulation image stack; performing modulation correction and global optimization selection on the multi-channel modulation image stack to obtain relative height values; and performing convergence processing based on the relative height values ​​to generate three-dimensional topographic point cloud data. This invention acquires structured light fringe images at multiple axial positions under different exposure conditions and obtains multiple sets of modulation image stacks through differential operations. After separation extraction according to multiple spectral channels, the multiple modulation curves corresponding to each pixel are corrected and optimally fused, and the relative height values ​​are obtained through peak fitting positioning. Finally, the relative height values ​​are converged to generate three-dimensional topographic point cloud data of the sample under test. This enables complete three-dimensional topographic reconstruction of complex industrial samples with spatially non-uniform reflectance characteristics.

The invention relates to a Raman spectrum intelligent analysis method and device, equipment and a storage medium, and the method comprises the steps: recognizing preprocessed Raman spectrum data of a to-be-detected carbon material based on a multi-task learning model, and outputting a material type recognition result and a peak characteristic parameter prediction value; according to a material type identification result, calling a corresponding target peak-dividing fitting template and a target physical constraint rule in a material Raman physical mechanism knowledge base; initializing fitting parameters in the target peak-dividing fitting template based on the predicted value of the peak characteristic parameter, and executing nonlinear fitting on the preprocessed Raman spectrum data based on the initialized fitting parameters and a target physical constraint rule to obtain mathematical peak parameters; inputting the mathematical peak parameters into a quantitative correlation model stored in a material Raman physical mechanism knowledge base, calculating to obtain material physical parameters, and outputting a structured spectrum analysis result; compared with the prior art, the technical scheme provided by the invention can realize automation, high throughput and high reliability of Raman spectrum analysis of the carbon material.

The present invention relates to the technical field of mass spectrometry analysis, and more specifically, to an analysis method and system for improving the quality of mass spectrometry. The method comprises the following steps: scanning a sample using a mass spectrometer, collecting raw mass spectrometry data, and preprocessing the raw mass spectrometry data; identifying potential peak positions based on the preprocessed raw mass spectrometry data using a multi-scale Gaussian fitting technique, and screening candidate peaks by calculating the signal-to-noise ratio of each peak; fitting each screened candidate peak using an asymmetric Gaussian function model to describe the candidate peak shape; constructing a mass offset correction model to correct the mass spectrometry data, and performing super-resolution processing on the corrected mass spectrometry data using a convolutional neural network model. The present invention employs a multi-scale Gaussian fitting technique, a signal-to-noise ratio screening mechanism, and an asymmetric Gaussian function model for peak fitting.

The invention provides a fabric waterless dyeing distinguishing method, which comprises the following steps: sample preparation: selecting a sample, preparing the sample into a powder sample, tabletting, and transferring into an X-ray photoelectron spectrometer; instrument parameter setting: selecting an X-ray source, and setting analyzer parameters; data acquisition: scanning powder sample tablets in a spectrum scanning range, and confirming the position of a Na1s peak and coexisting elements; setting a high-resolution narrow spectrum according to the position of the Na1s peak, scanning the powder sample tablet within the range of the high-resolution narrow spectrum, and collecting a correction reference; data processing and analysis: correcting binding energy by adopting a correction reference, and carrying out peak-dividing fitting through a software tool; and by referring to the binding energy, calculating the atomic concentration of the Na element by adopting a sensitivity factor method, and distinguishing waterless staining and water staining according to the atomic concentration of the Na element and a preset distinguishing standard. The method has the advantage that the supercritical waterless dyed fabric and the water dyed fabric can be effectively distinguished.

The embodiment of the invention provides a non-contact temperature detection method and related equipment, and belongs to the technical field of temperature measurement. The method comprises the following steps: acquiring absorption spectrum data to be analyzed through an acquisition card; carrying out peak searching fitting processing on the to-be-analyzed absorption spectrum data to obtain a preset fitting coefficient matrix; performing spectral data processing on the to-be-analyzed absorption spectrum data through a preset function approximate solution algorithm according to preset parameter range data and a preset fitting coefficient matrix to obtain preset absorption peak data; wherein the preset function approximation solution algorithm is obtained by optimizing a Voi gt function approximation solution through a rapid analysis approximation strategy; and according to the preset absorption peak data, carrying out absorption peak area calculation, and according to the preset absorption peak area data obtained through calculation, carrying out calculation through a double-line temperature measurement algorithm to obtain target temperature data. According to the embodiment of the invention, the efficiency and engineering suitability of non-contact temperature detection can be effectively improved, and the dependency on manpower is reduced.

The application discloses an intelligent sustained-release drug carrier screening method based on material microscopic image recognition, integrates nanometer hyperspectral, cryo-EM and Raman spectral data through a multi-modal data synchronous acquisition system, realizes nanoscale spatial alignment of microscopic morphology and component distribution, adopts a non-local mean denoising algorithm to enhance pore structure identification, combines adaptive baseline correction and multi-peak fitting technology, and quantifies chemical bond state changes, constructs a characteristic spectral library, generates a chemical distribution map through spectral angle mapping, combines spatial mutual information entropy to analyze the co-localization of carriers and drugs, innovatively develops a space-spectrum joint encoder, fuses multi-modal features by using a cross attention mechanism, and outputs quantitative indexes such as pore filling rate, crystallinity change and interaction stability score. The method breaks through the static and single-modal limitation of traditional methods, and establishes a complete screening system from microscopic structure analysis to release kinetics prediction.

The invention discloses a shale logging resistivity correction method, device and equipment and a medium, and relates to the technical field of shale gas exploration, a shale core sample is pretreated, and shale organic matter is obtained; the method comprises the following steps: performing X-ray photoelectron spectroscopy experiment and data correction on shale organic matters to obtain first carbon atom scanning atlas data, performing peak-splitting fitting on the first carbon atom scanning atlas data to obtain a first peak area, and calculating a first graphite bond peak area proportion; performing X-ray photoelectron spectroscopy experiment and standard correction on the graphene sample to obtain second carbon atom scanning spectrum data, performing peak-splitting fitting on the second carbon atom scanning spectrum data to obtain a second peak area, and calculating a second graphite bond peak area proportion; the graphitization degree of the organic matter is calculated according to the first graphite key peak area proportion and the second graphite key peak area proportion, the logging resistivity of shale is corrected, the graphitization degree of the organic matter is accurately and rapidly measured, and the accuracy of shale logging resistivity correction is improved.

The present application relates to the technical field of nondestructive analysis, in particular to a near-infrared spectrum resolution real-time correction method combined with a neural network, which is realized based on a near-infrared spectrum detection correction system, wherein the system comprises a xenon lamp, a fiber probe A, a calibration electric control shutter, a Y-shaped fiber, a near-infrared spectrometer, a measurement electric control shutter, a fiber probe B, a sample to be measured, and a halogen tungsten lamp.The xenon lamp is integrated in the spectrometer device through the Y-shaped fiber, which can realize automatic and rapid switching of the light path without manual intervention, supports online measurement, and combines the xenon lamp data with the neural network, so that a spectrum broadening function can be constructed through Gaussian peak fitting and polynomial fitting only by collecting one frame of xenon lamp spectrum, and then the physical model is used to guide the neural network training to realize the reconstruction from low-resolution measurement spectrum to high-resolution corrected spectrum.This combination scheme not only realizes the high integration of the calibration system and the automation and real-time of the calibration process.

The invention belongs to the related technical field of residual stress detection, and discloses a residual stress measurement method based on a Bayesian model, which comprises the following steps of: (1) constructing the Bayesian model based on the acquired diffraction intensity and scanning angle value of a sample, and inputting the acquired diffraction intensity and scanning angle value into the Bayesian model; the output is a Bragg diffraction angle; and (2) inputting the acquired diffraction intensity and the scanning angle value of the to-be-measured piece into a Bayesian model to obtain a Bragg diffraction angle, and further calculating to obtain a residual stress value based on the obtained Bragg diffraction angle. According to the method, Rietveld fitting is used for carrying out peak pattern fitting on the residual stress, the super-robust Bragg diffraction angle 2theta value is recognized through the Bayesian posterior model, then the residual stress value with good reliability is obtained through formula calculation, existing X-ray stress measurement is optimized, and then precision and reliability are improved.

The invention discloses a method for evaluating the period failure risk and the service life of a high-temperature alloy part by using HEXRD, which comprises the following steps: (1) setting a creep experiment in a synchrotron radiation device, applying constant stress and constant temperature to a high-temperature alloy part sample, starting HEXRD real-time monitoring at the same time, and collecting two-dimensional diffraction ring data at a preset sampling interval; and (2) software is used for processing data, the two-dimensional diffraction ring data is converted into a lattice strain and FWHM evolution curve along with time, peak splitting fitting is conducted on mixed peaks, a fitting result is obtained, and the high-temperature alloy can be Ni-based high-temperature alloy. According to the method, real-time and quantitative monitoring of the initial creep internal stress of the high-temperature alloy is achieved through the in-situ synchrotron radiation technology, the bottleneck that internal stress evolution cannot be dynamically tracked through a traditional method is solved, and direct technical support is provided for service safety evaluation.

The invention discloses a quantitative characterization method for a low-permeability reservoir pore structure based on nuclear magnetic resonance T2 spectrum multi-peak fitting, and relates to the technical field of reservoir pore structure characterization, the nuclear magnetic resonance T2 spectrum is preprocessed, and Gaussian multi-peak fitting decomposition is performed on the preprocessed data; according to the peak area, the peak center and the peak width of each sub-peak, carrying out quantitative characterization on the porosity contribution and the aperture range of the corresponding micropores, mesopores and macropores; the sub-peak parameters obtained through multi-peak fitting are introduced into the permeability prediction model, the model for predicting the permeability based on the NMR T2 relaxation data is corrected based on the multi-peak fitting result, the permeability model is constructed, the permeability is predicted more accurately, and the method has great significance in promoting reservoir prediction.