15 results about "Peak fitting" patented technology

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.

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.

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 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 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 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 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.