35 results about "Differential spectrum" patented technology

The invention relates to the technical field of intelligent detection, in particular to a feed mycotoxin detection method and system based on spectral analysis. The method comprises the following steps: acquiring a near-infrared diffuse reflection spectrum of a feed sample to be detected, constructing an original spectrum vector based on absorbance data of the near-infrared diffuse reflection spectrum, and determining a projection component of the original spectrum vector on a pre-constructed qualified matrix space; subtracting the projection component from the original spectrum vector to obtain a residual vector, and calculating a first-order difference spectrum of the residual vector based on a preset sensitivity weight; and performing local feature weighting on the first-order difference spectrum to determine a weighted feature value, determining a residual entropy value of the residual vector and a range value of the residual vector, and determining a detection result of the to-be-detected feed sample by using the weighted feature value, the residual entropy value and the range value. According to the technical scheme, a more accurate detection result of the mycotoxin of the feed can be obtained.

The invention discloses a satellite hyperspectral coastal saline-alkali soil salinity inversion method based on a machine learning model, and the method comprises the steps: S1, collecting an original multispectral image and ground actual measurement data, and obtaining standardized initial feature space data through the radiometric calibration correction preprocessing operation; s2, performing fractional order differential spectrum enhancement on the initial feature space data to obtain an enhanced high-dimensional feature set, and performing importance adaptive optimization on the enhanced high-dimensional feature set by adopting a Boruta feature selection algorithm to obtain an optimal high-dimensional feature subset; s3, based on the optimal high-dimensional feature subset, an initial inversion model is obtained by integrating Optuna hyper-parameter automatic optimization, and based on the initial inversion model, a salinity inversion model is obtained through feature contribution degree analysis optimization and embedding of an SHAP interpreter; and S4, performing salinity prediction on the global remote sensing image based on the salinity inversion model to realize sea saline-alkali land salinity inversion.

The application relates to a visual identification method and system of a liquid crystal angle color-changing material. Specifically, optical images of a material at a preset angle sequence are collected, Lab chroma and rotation-invariant uniform LBP texture features of each image are extracted, and a multi-dimensional initial feature sequence is formed. A double-branch identification model is constructed. One branch performs differential spectrum analysis on the feature sequence, and then extracts features through a multilayer perception machine. The other branch encodes contexts through a bidirectional GRU, and enhances key information by using angle-channel correlation attention, and then pools the key information into a context vector. The outputs of the two branches are concatenated, and are mapped into an encoding vector through a classifier. During training, a polarization loss function is used to drive the encoding vectors of the genuine product and the fake product to converge to two preset opposite polarization directions. During identification, the distances between the encoding vector of a material to be tested and the two polarization vectors are calculated, and the one with a shorter distance is determined as the genuine or fake product.

The present application relates to the field of on-line analysis of material chemical composition, and discloses an on-line monitoring process for the composition of a lead frame electroplating solution, comprising: calculating the concentration of the electroplating solution by conventional monitoring of the spectrum; calculating a spectrum entropy index based on the degree of deviation of the real-time spectrum from a pre-established statistical baseline model, and comparing the spectrum entropy index with a pre-set failure trigger threshold; starting a dynamic calibration step only when the spectrum entropy index exceeds the pre-set failure trigger threshold, the dynamic calibration step generating a model error vector by comparing the physically real differential spectrum with the model virtual differential spectrum through injection of a standard pulse, and correcting the chemometrics model using the model error vector, so that the present application realizes on-demand calibration, ensures real-time accuracy of the model, and reduces the consumption of the standard.

The present application relates to the technical field of nondestructive testing and intelligent sorting of agricultural products, in particular to a kind of soybean based on hyperspectral image nondestructive testing method, comprising: collecting the original hyperspectral image data of soybean sample, generate pre-processing hyperspectral data cube;Calculate spatial gradient field, and apply gradient partition threshold to construct morphological distribution model;Spectrum-morphology coupling operator is constructed, and the output self-referenced differential spectrum feature set;For long-wave band spectral response, execute anomaly detection, generate internal state determination result output detection classification data;According to error distribution characteristics, update the gradient partition threshold of morphological distribution model and the weight parameter of spectrum-morphology coupling operator, generate final detection result for downstream sorting equipment to call;The present application solves the problem that fixed model produces performance drift with sample change, ensures the stability of sorting precision.

The purpose of the present disclosure is to reduce erroneous detection of a false image caused by sidelobes and also minimize the deterioration of angular resolution by appropriately using a window function when detecting a target using a radar.　A target angle detection device 2 comprises: an angle spectrum calculation unit 21 that performs digital beamforming on the same reception signal of a radar array antenna by using first and second window functions having different sidelobe suppression levels, and calculates first and second angle spectra, respectively; a differential spectrum calculation unit 22 that calculates a differential-intensity spectrum on the basis of the first and second angle spectra; and a target angle detection unit 23 that detects a target at a spectral angle where the spectral intensity lies within a predetermined range on the basis of the differential-intensity spectrum, and determines that what is detected is a false image rather than the target at spectral angles where the spectral intensity lies outside the predetermined range.

The invention relates to the technical field of communication, discloses an electromagnetic spectrum abnormal signal monitoring method and system based on double acquisition and comparison, and aims to solve the problems of asynchronous double-channel sampling and high abnormal signal omission ratio and false alarm rate caused by clock drift and physical path delay in the prior art. According to the method, a hardware-level self-adaptive clock synchronization compensation circuit is constructed, clock path delay is quantized and offset in real time before analog-to-digital conversion, and a first sampling clock and a second sampling clock which are accurately synchronized are generated; a dual-channel ADC is used for synchronously collecting the same radio frequency signal, and abnormal signals are judged and identified through spectrum coherence analysis and differential spectrum threshold. The system comprises a main clock unit, a self-adaptive synchronous compensation circuit, a dual-channel ADC module and a data processing comparison module. The picosecond-level synchronization precision is achieved, external time service is not needed, the missing detection and false alarm rate is remarkably reduced, and the method is suitable for high-reliability frequency spectrum monitoring in the complex electromagnetic environment.

The invention provides a method for predicting hydrophilicity and hydrophobicity of an organic matter and membrane pollution potential based on a differential spectrum, and belongs to the technical field of water quality monitoring, and the method comprises the following steps: titrating a water sample to be detected by using perchlorate, and predicting based on an ultraviolet visible light differential spectrum before and after titration of the water sample; the inlet water of the membrane system passes through a filter and is subjected to spectrum full scanning to obtain an ultraviolet visible light reference spectrum; the water sample enters a titration system, a perchlorate solution is dropwise added and uniformly mixed, spectrum full scanning is carried out, and volume concentration correction is carried out on the spectrum; performing total organic carbon normalization and difference on the ultraviolet visible spectrum of the sample before and after titration to obtain a difference spectrum, performing Gaussian peak division on the difference spectrum to obtain a Gaussian energy band, and determining the hydrophilicity and hydrophobicity of the organic matters in the water sample and the membrane pollution potential according to the energy band ratio. The problems that an existing method for predicting the hydrophilicity and hydrophobicity of the organic matter and the membrane pollution potential is tedious in pretreatment, complex in analysis procedure, long in time consumption and difficult to meet analysis of large-batch samples are solved.

The invention relates to the technical field of agricultural product nondestructive testing and intelligent sorting, in particular to a green soy bean nondestructive testing method based on a hyperspectral image, which comprises the following steps: collecting original hyperspectral image data of a green soy bean sample, and generating a preprocessed hyperspectral data cube; calculating a space gradient field, and constructing a morphological distribution model by applying a gradient partition threshold value; constructing a spectrum-form coupling operator, and outputting a self-reference differential spectrum feature set; performing anomaly detection on the long-band spectral response, generating an internal state judgment result, and outputting detection grading data; updating a gradient partition threshold value of the morphological distribution model and a weight parameter of a spectrum-morphological coupling operator according to the error distribution characteristics, and generating a final detection result to be called by downstream sorting equipment; the problem that a fixed model generates performance drift along with sample change is solved, and the stability of sorting precision is guaranteed.

A method for diagnosing the noise level of a transformer comprises: a step of acquiring a frequency spectrum of noise from a stacked iron core 21 contained in a transformer, the stacked iron core 21 being formed by stacking electrical steel laminations; a step in which, for the calculation of the excitation vibration of the stacked iron core 21, the transverse moduli of elasticity are determined in two planes, each including a stacking direction of the stacked iron core 21; a step in which the excitation vibration for the stacked iron core 21 is calculated using a constitutive equation that includes an elasticity matrix comprising the determined transverse moduli of elasticity as elements, and a frequency spectrum of an excitation vibration of the stacked iron core 21 is acquired;a calculation stage of the difference between the excitation noise spectrum of the stacked iron core 21 and the excitation vibration frequency spectrum of the stacked iron core 21 as a differential spectrum; and a transformer noise level diagnostic stage based on the differential spectrum.

The invention relates to the technical field of drug component detection, in particular to a Raman spectrum-based method for detecting the concentration of multiple drug components in blood, which comprises the following steps: acquiring differential spectrum data corresponding to Raman spectrum data of a serum sample of a patient before and after administration of a target drug; correcting a differential signal corresponding to the target drug in each frame of differential spectrum in the differential spectrum data based on the difference between differential signals corresponding to each endogenous substance in adjacent frames of differential spectrums in the differential spectrum data to obtain a corrected differential signal corresponding to the target drug in each frame of differential spectrum; based on the fusion weight corresponding to each endogenous substance, performing weighted fusion on the correction differential signals corresponding to all the target drugs to obtain fusion differential signals of the target drugs, and finally determining the drug concentration of the target drugs in each frame of Raman spectrum based on the fusion differential signals of the target drugs. According to the invention, the drug concentration detection precision is effectively improved.

A method for measuring anisotropic film based on zero reflection difference differential signal relates to the field of film thickness detection, and comprises the following steps: measuring wide-band reflection difference spectrum of a sample by using a reflection difference spectrometer and a photoelastic modulation reflection difference spectrometer; determining an incident wavelength corresponding to a zero reflection difference signal: traversing all wavelength channels in the wide-band reflection difference spectrum, and determining a wavelength corresponding to a zero-crossing of the reflection difference signal; determining the thickness of the anisotropic film: determining the thickness of the anisotropic film according to a mapping relationship between the wavelength corresponding to the zero reflection difference signal and the film thickness. The application proposes using the position of the zero-crossing point of the reflection difference signal ΔR / R=0 to determine the thickness of the anisotropic film, so that the error introduced by the difference between the polarization angle of incident light and the crystal axis angle of the anisotropic film can be removed, and further, the time for determining the crystal axis can be saved.

The application provides a kind of optical sensing component differential spectral responsivity test system, belongs to the technical field of spectral responsivity test, comprising: modulation light source, for emitting modulation light;Bias light source, for emitting bias light, bias light and modulation light form superimposed light;Standard optical sensing module, obtains the standard value of superimposed light intensity;Measured optical sensing module, obtains the measured value of superimposed light intensity;Signal and control unit calculates the differential spectral responsivity of measured optical sensing module under superimposed light.The application also provides a kind of optical sensing component differential spectral responsivity test method, solves the problem that the test method under darkroom condition in prior art is difficult to test and calibrate the spectral responsivity of optical sensing component under actual application scene condition, realizes the spectral responsivity of measured optical sensing module in the case of background light, and evaluates the anti-interference ability of measured optical sensing module.

The invention discloses a vibration densification process reasoning and mapping system based on decision tree machine learning, and relates to the technical field of machine learning, and the system comprises a data collection unit which is used for carrying out the differential spectrum matching of a target object spectrum at a frequency band of 900nm to 1950nm; the double-domain residual machine learning unit is used for extracting abnormal characteristic parameters window by window by taking the zero pulse of the vibration exciter as a reference signal, judging item by item according to a decision tree model, outputting a continuous strip-shaped result through track constraint and morphological opening and closing operation, and generating a continuous quality map; and the output and feedback unit is used for outputting a continuous compact quality index and speed regulation, pass increasing, vibration switching and water replenishing prompts through a decision tree classifier according to the implicit under-compact probability grid and the water content suitability by taking the vertical acceleration clock of the roller as a unified reference, and writing the continuous compact quality index and the speed regulation, pass increasing, vibration switching and water replenishing prompts into a continuous quality map. According to the invention, the dual-suitable monitoring of the density and the water-containing state is realized, and the problems of unstable abnormal identification and difficult data alignment in the prior art are solved.

The invention relates to the technical field of electrochemical in-situ spectrum monitoring, in particular to a method for monitoring dynamic change of an electrocatalytic reaction intermediate product in real time by using SERS (Surface Enhanced Raman Scattering) spectrum.According to the method, an ultrathin catalytic layer is modified on a high-SERS activity nano substrate in situ, an integrated working electrode is constructed, and a monitoring interface, namely a real reaction interface, is ensured. Through a hardware synchronization system, when dynamic electrochemical excitation is applied, synchronous acquisition of SERS spectrums and electric signals of multiple time scales is implemented. The method comprises the following steps: analyzing fingerprint information, concentration dynamics and evolution sequence of different intermediate products from a spectrum sequence by using algorithms such as dynamic differential spectrum, multivariate curve resolution and two-dimensional correlation spectrum, and finally accurately associating with electrochemical response to construct a dynamic reaction mechanism diagram. According to the invention, real-time and high-sensitivity dynamic analysis on generation, consumption and path evolution of transient intermediate products at a real catalytic interface is realized, and a powerful tool at a molecular level is provided for research on an electro-catalysis mechanism.

Provided are an anti-reflection film having excellent scratch resistance and a method for producing the anti-reflection film. [Solution] An antireflection film 1 comprises a transparent substrate film 2, an antireflection layer 3, and an antifouling layer 4, in this order toward one side in the thickness direction. The transparent substrate film 2 comprises a transparent resin film 21. The antireflection layer 3 is an alternating laminate consisting of at least one niobium oxide layer 31 and at least one silicon oxide layer 32, and the layer in the antireflection layer 3 closest to the antifouling layer 4 is the silicon oxide layer 32. In addition, in a difference spectrum calculated by Fourier transform infrared spectrophotometer analysis (FT-IR analysis), -1 The maximum absorption peak intensity due to the Si-O-Si symmetric stretching near 1200 cm is normalized to 1. -1 When the absorption peak intensity resulting from the Si-O-Si asymmetric stretching is defined as X, 1 / X is 5.0 or more.

Aspects of the technology provide a method / system of sensing a change in temperature and / or strain in an optical fibre comprising a substantially continuous or quasi-continuous fibre Bragg grating. A difference spectrum is determined between a reference reflected spectrum of the fibre Bragg grating and a monitored reflected spectrum. The difference spectrum is analysed to determine whether the optical fibre is experiencing a change of type a)—a temperature increase or an extension—or of type b)—a temperature decrease or a contraction. The technology may be applied to sensing a change in temperature and / or strain in a superconductor or detecting a risk of a quench in a superconductor.

To achieve both reducing false detection of false images due to side lobes and minimizing degradation of angular resolution when detecting targets using a radar, by appropriately using a window function.SOLUTION: A target angle detection device 2 includes: an angular spectrum calculation unit 21 that performs digital beamforming on the same received signal of a radar array antenna by using first and second window functions with different side lobe suppression levels to calculate first and second angular spectra, respectively; a difference spectrum calculation unit 22 that calculates an intensity difference spectrum for the first and second angular spectra; and a target angle detection unit 23 that detects a target at a spectral angle where the spectral intensity is within a predetermined interval for the intensity difference spectrum, and determines that it is not a target but a false image at a spectral angle where the spectral intensity is outside the predetermined interval.SELECTED DRAWING: Figure 2

Disclosed in the present invention are a spectral difference analysis method and device for gas-solid interface flashover. The method comprises: acquiring several pieces of gas-gas spectral data acquired by a gas-gas gap discharge experimental device; acquiring several pieces of gas-solid spectral data acquired by a gas-solid surface flashover experiment device; performing averaging operations on the several pieces of gas-gas spectral data and the several pieces of gas-solid spectral data to obtain background spectral data and gas-solid flashover spectral data, the gas-gas spectral data corresponding to the background spectral data, and the gas-solid spectral data corresponding to the gas-solid flashover spectral data; and, on the basis of the background spectral data and the gas-solid flashover spectral data, calculating a differential spectral signal, such that a user performs gas-solid interface flashover analysis on the basis of the differential spectral signal. The present invention analyzes the effect of a spectrum generated by a gas itself on a solid flashover spectrum during the experiment of the gas-solid surface flashover experimental device, thus rendering gas-solid interface flashover analysis more accurate.

The invention belongs to the technical field of biology, and particularly relates to a method for detecting magnetic resonance signals of glutaric acid molecules and application. The method comprises the following steps: firstly, positioning a target area by utilizing a magnetic resonance imaging sequence, and then respectively collecting magnetic resonance spectrograms of EditOn and EditOff states by adopting a water suppression sequence, a volume external suppression sequence and a J editing sequence and setting resonant frequencies of saturated pulses by the J editing sequence; and subtracting the spectrogram acquired in the EditOn state from the spectrogram acquired in the EditOff state to obtain a differential spectrum, so that a glutaric acid signal is selectively excited near 2.2 ppm and a lipid signal is inhibited. A good effect of rapidly and noninvasively measuring the magnetic resonance signal of the glutaric acid molecules in the living body muscle tissue is realized, and the method has important application value in the field of scientific research.

The invention discloses a microwave power detection system based on a diamond NV color center, which belongs to the technical field of precision measurement and comprises a laser, the diamond NV color center, a photoelectric detector, a microwave generating device, a lock-in amplifier and a main control device. The laser generates laser with a set wavelength, so that the diamond NV color center generates fluorescence after being irradiated by the laser; the photoelectric detector performs photoelectric conversion on the fluorescence to generate a voltage signal; the microwave generating device outputs a microwave signal modulated by the modulation signal; the lock-in amplifier receives the voltage signal and the modulation signal, demodulates the voltage signal and the modulation signal, and outputs an ODMR differential spectrum signal; the main control device generates a differential spectrum line covering a formant area to be measured according to the ODMR differential spectrum signal, the differential spectrum line is fitted, a spectrum line broadening parameter is obtained, and the microwave power is obtained through calculation according to the spectrum line broadening parameter. According to the invention, the problem that the experiment construction and debugging difficulty is large due to the complex structure of the light path and the control circuit in the prior art can be solved.

The application provides a Raman spectrum cosmic ray peak identification method based on multi-feature fusion, which can accurately identify typical high and narrow cosmic ray peaks and narrow but low, high but wide and overlapping variant peaks. The identification steps comprise: 1) performing first-order differential processing on the original Raman spectrum to obtain a differential spectrum, and then performing threshold screening on the positive and negative differential vertexes in the differential spectrum to obtain a candidate peak set; 2) taking the positive and negative differential vertexes of the candidate peak as starting points, performing percentile threshold detection on the differential spectrum to preliminarily locate the peak boundary, and then performing percentile threshold detection on the original Raman spectrum to accurately locate the left and right boundary points of the peak; 3) calculating the left edge pixel width, right edge pixel width, boundary point pixel width, half-peak height pixel width and differential vertex pixel width of the candidate peak, then performing weighted fusion calculation on the pixel width to obtain a score value, and finally identifying the cosmic ray peak according to the score threshold.

SOLUTION: A device comprises: a detection part for detecting a fluid spectrum that is a spectrum of light indicating a state of a fluid flowing in a flow path; a differentiation part for deriving a differential spectrum by differentiating the fluid spectrum; an extraction part for extracting a first solvent spectrum by principal component analysis of the solvent of the fluid; a calculation part for calculating a difference spectrum between the differential spectrum and the first solvent spectrum; and a prediction part for predicting a state of the fluid, by using data related to a peak of the calculated difference spectrum.SELECTED DRAWING: Figure 1

A method is provided for multi-energy X-ray imaging of a field of view in order to display a contrast material, located in the field of view, having an atomic number of at least 65, (e.g., at least 73), and / or a k-edge of at least 60 keV. In the method, two image datasets having different X-ray spectra are recorded and are combined in order to ascertain display information of the contrast material, wherein the X-ray spectra are generated by using at least one spectrally acting filter, starting from a source spectrum of an X-ray source in such a way that the value of their differential spectrum has a maximum, which includes a range, beginning at the k-edge of the contrast material, of higher absorption by the contrast material.

The invention discloses a wide dynamic range sulfur dioxide concentration detection method and a wide dynamic range sulfur dioxide concentration detection device based on UV-DOAS combined with wave band self-competition spectrum reconstruction. The method comprises the following steps: acquiring SO2 data to be detected; determining an SO2 spectral line, and processing the spectral data of SO2 into a differential spectrum of SO2 to be detected based on the determined SO2 spectral line; an adjustable sliding window is constructed, and dynamic wave band selection is achieved through the sliding window; a mapping matrix is obtained through the mapping relation of the standard spectrum, and the differential spectrum of SO2 to be detected is converted from a wavelength domain to an intensity domain by using the mapping matrix; in the intensity domain, noise and interference signals in the spectrum are removed in a first-order linear fitting mode for fitting, and a reconstructed linear spectrum is obtained; the reconstructed linear spectrum is converted from the intensity domain to the wavelength domain through the inverse matrix of the mapping matrix, the differential spectrum of the to-be-detected SO2 after denoising is obtained, the concentration of the to-be-detected SO2 is calculated, dynamic waveband selection is achieved by means of the waveband self-competition technology, and therefore the detection dynamic range is effectively widened.

The present application relates to the ICP emission spectrum adaptive baseline calibration method and system of water body metal detection, aims at improving the adaptive calibration ability of baseline drift in the continuous scanning period of inductively coupled plasma emission spectrometer. Through point-by-point differentiation and noise suppression of the original spectrum intensity sequence, the standardized differential spectrum sequence is generated and the Shannon entropy is divided into sub-windows to calculate the differential entropy time sequence reflecting the local chaos degree, the mean, standard deviation and slope change rate are extracted to form a three-dimensional entropy flow state vector, and the spectrum signal anomaly detection and baseline fitting parameter freezing are realized by combining the incremental update isolated forest algorithm. Then, the limited step gradient descent fine tuning is carried out in the local window, the dynamic calibration parameter group is generated and the baseline compensation is completed. The method effectively suppresses the baseline drift and parameter oscillation caused by environmental and sample changes, improves the stability of the spectrum calibration data, and provides a reliable data basis for subsequent metal characteristic peak detection.

The application discloses an electromagnetic ultrasonic adaptive filtering method applied in a 27.5KV / 50Hz strong electromagnetic environment and belongs to the technical field of nondestructive testing and signal processing. Aiming at how to effectively suppress strong periodic power frequency interference and strong random pulse interference and how to extract electromagnetic ultrasonic echo signals from seriously distorted signals with high fidelity, the method comprises the following steps: firstly, signals of a main channel and a reference channel are synchronously collected, and when an instantaneous amplitude exceeds a preset dynamic threshold, it is determined that strong pulse interference exists through real-time amplitude detection; then, the repaired reference channel signal is taken as input, and periodic power frequency interference in the main channel is cancelled through a least mean square adaptive filter; finally, a method based on singular value decomposition and singular value differential spectrum peak is used to suppress residual broadband background noise. The application can extract electromagnetic ultrasonic echoes from seriously distorted signals with high fidelity, and effectively solves the problems of signal distortion under pulse interference and poor power frequency cancellation effect of a traditional method.

The invention relates to a soil organic carbon functional group differential spectrum quantitative analysis method based on mineral peak correction. The method comprises the following steps: dividing a to-be-detected soil sample meeting a preset standard into two parts, firing one of the two parts to form an original soil sample and an ash content sample, collecting a functional group characteristic signal from the sample through a Fourier transform infrared spectrometer, generating an original soil spectrum and an ash content spectrum, and calculating the content of the ash content in the original soil spectrum and the ash content spectrum according to the original soil spectrum and the ash content spectrum. The method comprises the following steps: carrying out spectrum pretreatment on an original soil spectrum and an ash spectrum, carrying out spectrum subtraction treatment after pretreatment to obtain a differential spectrum, carrying out peak area integration and proportion calculation on the differential spectrum, and establishing a reliable correlation between the peak area of the differential spectrum and the absolute content of organic carbon so as to directly output the absolute content of a target carbon functional group. By adopting the method, the defect that the traditional spectral analysis method can only be qualitative or semi-quantitative can be effectively overcome, the anti-interference capability and the standardization degree are improved, and the method has better repeatability.

The invention discloses a photovoltaic arc fault detection method and system based on singular value decomposition and wavelet transform, and relates to the technical field of photovoltaic power generation system arc fault detection, and the method comprises the steps: collecting current data, carrying out the filtering processing, and constructing a Hankel matrix for the filtered current data; performing singular value decomposition on the Hankel matrix to obtain a singular value sequence; constructing a differential spectrum by using the singular value sequence, carrying out de-noising processing, and carrying out SVD reconstruction on the de-noised fault current data; and performing wavelet transform based on the reconstructed current data, extracting a wavelet detail coefficient, and when the wavelet detail coefficient continuously exceeds an alarm threshold, judging an arc fault and triggering an alarm. According to the method, the singular value decomposition and wavelet transform methods are utilized, the fault current detection precision is improved, the sensitivity is high, the omission ratio is low, and the anti-interference performance is high.

The present application relates to the technical field of drug component detection, and in particular to a method for detecting concentrations of multiple drug components in blood based on Raman spectroscopy. The method obtains differential spectrum data corresponding to Raman spectrum data of serum samples of a patient before and after administration of a target drug. Based on the differences between differential signals corresponding to each endogenous substance in adjacent frame differential spectra in the differential spectrum data, the differential signals corresponding to the target drug in each frame differential spectrum in the differential spectrum data are corrected to obtain corrected differential signals corresponding to the target drug in each frame differential spectrum. Based on the fusion weight corresponding to each endogenous substance, the corrected differential signals corresponding to all target drugs are weighted and fused to obtain a fusion differential signal of the target drug. Finally, based on the fusion differential signal of the target drug, the drug concentration of the target drug in each Raman spectrum is determined. The present application effectively improves the detection accuracy of drug concentration.