561 results about "Calibration curve" patented technology

The invention provides a wavelength division multiplexing-fused multi-core optical fiber temperature detection method and system. Wherein the broadband light source is divided into multiple wavelength channels through wavelength division multiplexing, and a wavelength-fiber core mapping relation is established, so that each fiber core of the multi-core optical fiber transmits a single-wavelength optical signal. An optical fiber is integrated to the surface of an object according to a three-dimensional path, each fiber core covers a plurality of sensing sub-areas, the wavelength drift amount caused by temperature is extracted by detecting the attenuation characteristic of an optical signal at a bending position, the wavelength drift amount is converted into a temperature value in combination with a calibration curve, and a temperature gradient model bound with the fiber core position is constructed. And further performing spatial solution by using fiber core spacing, wavelength mapping and transmission time delay parameters, establishing a corresponding relationship between the coordinates of the sensing sub-regions and the temperature, and finally reconstructing three-dimensional temperature field distribution. According to the technical scheme provided by the invention, high-resolution dynamic reconstruction and gradient analysis of a three-dimensional temperature field on a complex surface are realized through wavelength-space two-dimensional decoupling.

The invention discloses a multi-modal data fused karst forest soil carbon cycle feature extraction method. The method comprises the following steps: acquiring soil physical and chemical protection component carbon data, bioactive component carbon data, isotopic tracing data and the like of different succession stages of a karst forest to establish multi-source heterogeneous data; integrating the obtained multi-source heterogeneous data into a'physical-chemical-biological-isotope 'four-dimensional feature space through feature tensor decomposition; constructing a carbon saturation capacity prediction hybrid optimization model, and outputting lt; the maximum carbon sequestration capacity and component carbon proportion of 53 [mu] m microaggregates; the method comprises the following steps: performing mineralization culture on soil in different succession stages of the karst forest, analyzing a dynamic proportion of exogenous carbon to original soil carbon and an input / output path of soil carbon in combination with a 13C / 15N double-standard-sample time-varying calibration curve, and calculating a carbon stability disturbance threshold. According to the method, collaborative analysis of multi-source data is realized, and the temporal-spatial resolution and the model precision of carbon cycle feature extraction are remarkably improved.

The invention discloses a remote automatic quality control and calibration method and system for a pH meter, and the method comprises the following steps: S1, carrying out the initial temperature compensation correction of a pH value, and generating a corrected pH value; s2, environment parameters collected by an environment sensor and the corrected pH value are combined and formatted; s3, cleaning the standardized data set at an edge computing node, and generating an optimized measurement data set; s4, performing confidence evaluation based on a committee query algorithm, and triggering a calibration process for a detected low-confidence sample; s5, generating an enhanced calibration curve by using a double-neural network regression model, and optimizing measurement data in real time by combining particle filtering; s6, displaying the final data in real time through a visual tool, and uploading the final data to a cloud end; and S7, dynamically adjusting the calibration period, the acquisition frequency and the communication strategy of the sensor through the edge computing node. According to the method, double neural network regression, particle filtering, cloud edge cooperation technologies and the like are utilized, and accurate measurement and remote intelligent calibration of the pH meter are achieved.

The invention discloses a method for establishing a clinical interpretable PD diagnosis model based on multi-sequence magnetic resonance brain characteristics, and aims to promote more accurate diagnosis of PD by extracting multi-level and multi-dimensional brain image information from multi-modal brain magnetic resonance image data and constructing a diagnosis model by means of machine learning. The method comprises the steps of brain image data preprocessing, feature standardization and screening, model construction, independent verification set and cross-disease set verification, a group of brain function and metabolism features taking a PD abnormal perfusion mode and nigra iron distribution as the principal thing are obtained, and the constructed model has high diagnosis efficiency in a training set and the independent verification set. Secondly, clinical interpretability of the model is promoted through curve calibration, DCA and CIC analysis and correlation analysis of predicted individual scores obtained based on the model and clinical symptoms. The diagnostic efficiency of the constructed PD diagnostic model in different disease sets shows that the model has good diagnostic efficiency on PSP and MSA diseases.

The present disclosure provides a field quantitative analysis method and system of lithium, and relates to the technical field of field quantitative analysis of lithium. The method includes: measuring a laser-induced breakdown spectroscopy of a lithium-containing mineral, to obtain spectral data of the lithium-containing mineral; taking the spectral data as an input, and determining a mineral class of the lithium-containing mineral based on a trained mineral classification model; and taking the spectral data as the input, and determining content of lithium in the lithium-containing mineral based on a calibration curve corresponding to the mineral class.

The invention discloses a huge child risk prediction method based on machine learning, and the method comprises the following steps: S1, multi-modal clinical data acquisition, S2, data preprocessing, S3, multi-algorithm model training and optimization, S4, model evaluation and selection: evaluating the performance of each model based on the accuracy rate, the precision rate, the recall rate, the AUC and a calibration curve, selecting a model with optimal comprehensive performance as a target prediction model; and S5, risk prediction and explanation: inputting a to-be-predicted sample into the target prediction model, outputting a huge child risk prediction result, and quantifying the contribution degree of each feature to the prediction result through a SHapley addition explanation (SHAP) method. Through deep fusion of machine learning and clinical multi-modal data, the technical problems that an existing method is single in dimension, insufficient in interpretability and poor in basic level adaptability are solved, and a solution which is high in precision, explainable and easy to operate is provided for huge child risk prediction.

The invention discloses a water quality total hardness and calcium and magnesium ion content detection method, which comprises: extracting a water quality sample to be detected, and injecting into a cuvette; taking a color developing reagent, injecting the color developing reagent into the cuvette, blowing, stirring and uniformly mixing to obtain a reaction solution; measuring the initial current i0 of the reaction liquid in the cuvette at the wavelength of 475nm and the initial current I0 of the reaction liquid in the cuvette at the wavelength of 640nm; then titrating, and recording a measured current value and a titration volume while titrating; according to the obtained relation curve of the current and the titration volume under the two wavelengths, obtaining the titration volume corresponding to a titration judgment end point, and substituting the titration volume into the calibration curve to obtain the concentration of total hardness or the content of calcium and magnesium ions in the water sample. The measuring method disclosed by the invention not only has extremely high accuracy and precision, but also can be applied to measurement of total hardness or calcium ions under different indicators at the same time, and the turbidity of a water sample or magnesium hydroxide precipitate generated during calcium ion measurement hardly affects a test result.

The invention discloses a diagnosis method for predicting tuberculosis risk by using blood routine indexes, which comprises the following steps: (1) collecting open-source blood routine data, preprocessing, and dividing into a training set, a test set and a verification set; (2) screening blood routine examination and 25 indexes derived from the blood routine examination, and obtaining a preliminary screening result through LASSO regression analysis; (3) inputting the preliminary screening result into seven machine learning models, including a logistic regression model, a random forest model, a naive Bayes model, a K proximity model, a support vector machine model, an XGBoost model and a GBM model, for analysis to obtain a final variable combination and an optimal model; (4) inputting the verification set into the optimal model to obtain a DCA decision curve and a calibration curve; early discovery and precise diagnosis and treatment of tuberculosis are promoted, and meanwhile the pressure of medical resources is effectively relieved.

The invention provides an optical sensing measurement method based on closed-loop adjustment of LED driving, and relates to the technical field of optical sensing. The method comprises the following steps: initializing a system; the microcontroller reads a current value of the analog-to-digital converter and calculates an error; calculating a new set value of the driving current of the light emitting diode according to the error; the microcontroller outputs the new set value to the digital-to-analog converter, the digital-to-analog converter controls the driving circuit to adjust the driving current of the light emitting diode, and after a preset time period, the current value of the analog-to-digital converter is collected; repeating the steps, when the error absolute value is smaller than a preset threshold value, judging that closed-loop convergence occurs, and recording the driving current at the moment and the current value of the analog-to-digital converter as measurement results; and obtaining the sample concentration according to the calibration curve and the driving current in the measurement result. According to the scheme, the dynamic range and the low-concentration precision are remarkably improved, LED aging and temperature excursion are inhibited, the circuit is simplified, and multiple groups of current-limiting resistors and multi-gear change-over switches are not needed.

The invention discloses a qualitative and quantitative analysis method for autonomously identifying halogenated organic matters in a non-targeted manner, and belongs to the technical field of environmental analytical chemistry and water quality monitoring. The qualitative and quantitative analysis method of the halogenated organic matter comprises the following steps: adding an oxidizing agent or a disinfectant into an original water sample for complete oxidation or disinfection to obtain a water sample to be detected; detecting the water sample to be detected by using ultra-high performance liquid chromatography (UPLC)-Orbitrap mass spectrometry (MS), and detecting the original water sample which is not oxidized or disinfected at the same time to obtain two groups of liquid mass data; performing automatic data processing and contrastive analysis on the two groups of liquid quality data by using Compressed Discovery software, and identifying to obtain a newly generated halogenated organic matter; and establishing a calibration curve by using a total ion chromatogram and an external standard method, and carrying out quantitative analysis on the identified halogenated organic matter. According to the method, the purposes of efficient screening and accurate quantification of the non-targeted halogenated organic matter in the complex water body matrix are achieved, accurate quantification is conducted in combination with an external standard method, and rapid data analysis and result output are achieved.

A sliding bearing dynamic load measuring method belongs to the technical field of mechanical transmission. In order to solve the problems of low accuracy and low precision of the existing dynamic load measurement of the bearing, the method comprises the following steps of: measuring a bearing load value of a calibration point by adopting a loading measurement calibration device, and fitting the bearing load value into a voltage-load calibration curve; the resistance strain gauge is arranged at the stress sensitive position of the bearing seat, and when the resistance strain gauge generates a variable resistance value due to the deformation of the bearing seat, the strain bridge outputs a voltage signal; and according to a voltage signal output by the strain bridge, a corresponding load value, namely a dynamic load measurement value, is obtained in a voltage-load calibration curve. The device is used for carrying out load measurement on the bearing.

The invention discloses a stomach cancer postoperative survival prediction model construction and verification method based on machine learning, and belongs to the technical field of medical and industrial combination. According to the technical scheme, the method comprises the steps of obtaining clinical data of postoperative patients of gastric cancer, preprocessing the clinical data, and dividing the data into a training set and a verification set; screening out independent risk factors through single-factor and multi-factor Cox proportional risk regression analysis; constructing and training a plurality of prediction models based on the independent risk factors; the model is evaluated through time dependence AUC, C-index, a calibration curve and Kaplan-Meier survival analysis; and selecting the model with the optimal performance as a final prediction model. The method has the beneficial effects that the constructed GBM prediction model remarkably improves the gastric cancer postoperative survival prediction accuracy, the model can effectively process a complex nonlinear relationship in clinical data, a clear and explainable prediction basis is provided by using the SHAP value, and the transparency and clinical credibility of the model are enhanced.

The invention provides a short bolt axial stress detection method and device based on electromagnetic ultrasonic resonance, and relates to the technical field of industrial fastener stress detection. The method comprises the following steps: determining a preset period and frequency of an excitation signal according to material characteristics of a to-be-measured short bolt, and calibrating resonant frequency and stress to obtain a calibration curve; classifying the lengths of the short bolts to be measured to obtain bolts with known original lengths and in-service bolts with unknown original lengths; based on the determined preset period and frequency, first-order resonant frequency corresponding to the bolt with the known original length is obtained through an electromagnetic ultrasonic transducer and a single-wave resonance method; based on the determined preset period and frequency, first-order resonant frequency corresponding to the in-service bolt with the unknown original length is obtained through an electromagnetic ultrasonic transducer and a double-wave resonance method; and calculating the axial stress of the corresponding short bolt to be measured according to the calibration curve and the first-order resonant frequency. The problems that in the prior art, the energy conversion efficiency is low, and the short bolt detection effect is poor are solved.

The invention relates to the technical field of metal materials, and discloses a spectral analysis monitoring method for aluminum alloy component detection, and the method comprises the following steps: carrying out standardized surface pretreatment on an aluminum alloy sample; collecting spectral data of the sample by adopting a laser-induced breakdown spectroscopy technology; performing noise filtering and background correction on the spectral data through a wavelet transform algorithm; establishing a spectral feature database for element qualitative identification; carrying out element quantitative analysis by adopting an internal standard method, and calculating the content of each element through a calibration curve; comparing a detection result with a standard mark component range to generate component conformity evaluation; performing deviation analysis and performance prediction on samples which do not meet the standard; and finally, generating a quality grading report based on the multi-dimensional evaluation indexes. According to the method, the accuracy of component qualitative identification is ensured, the detection error is reduced, the repeatability and efficiency of detection are improved, and the effectiveness of risk management and control and the overall reliability of a detection result are improved.

The invention discloses a method for testing heat treatment residual stress of a bearing ring based on nanoindentation, and belongs to the technical field of stress testing. The method comprises the following steps: establishing a calibration curve, preparing and testing a sample, and calculating residual stress. According to the method, the indentation work-residual stress quantitative calibration curve for the bearing steel is established through finite element simulation, the specific constitutive relation of materials is fully considered in the model, and system errors caused by mismatching of material parameters of a general model are eliminated. The change trend of the residual stress value calculated by the method is consistent with that of a prediction result of finite element analysis, and it is proved that the method can accurately, reliably and quantitatively characterize the residual stress distributed in the microscale after heat treatment.

The invention relates to a calibration curve determination method, a sample analyzer and a storage medium. The method comprises the following steps: determining a calibration curve of a detection reagent of a sample analyzer, receiving a task of updating the calibration curve of the detection reagent to be calibrated, obtaining a plurality of initial calibration curves associated with reagent information of the reagent to be calibrated, and respectively calculating preset constraint conditions of the plurality of initial calibration curves to obtain a calibration curve of the detection reagent to be calibrated; and obtaining an evaluation value corresponding to a preset constraint condition, and determining a target calibration curve from the plurality of initial calibration curves according to the evaluation value and a preset threshold value. According to the scheme provided by the invention, the target calibration curve with a good screening effect can be screened, the detection accuracy of the sample analyzer is improved, and the output numerical error is reduced.

The invention relates to the field of environmental monitoring, and discloses a system for collecting and preprocessing a volatile organic compound sample in water based on a headspace principle, which constructs a full-automatic physical execution chain through multi-thread interrupt control, PID closed-loop regulation and a mechanical arm state machine. Comprising the following steps: constructing a pre-packaged intelligent sample bottle, encrypting and storing a freeze-dried internal standard substance, a sealing parameter and a calibration curve in a UHF RFID tag, and eliminating the risk of manual intervention; on the basis of a multi-thread parallel control technology, constant-speed sampling, three-stage gradient filtering and environmental parameter binding are synchronously executed, and full-process automation of data acquisition is realized; a physical model is adopted to drive a state machine, balance time is dynamically calculated through a Henry constant, and nitrogen purging pressure closed-loop control is combined, so that the recovery rate of low-volatile substances is increased; a quantum random number generator and a topological data association technology are utilized to construct a structured data packet containing a timestamp, an environment parameter and a quantum check code, and it is ensured that data cannot be tampered.

The invention provides a gas flow field pressure measuring method and device based on a laser spectrum, which can be applied to the technical field of laser and the technical field of pressure measurement. The method for measuring the flow field pressure based on the laser spectrum comprises the following steps: emitting first laser to a to-be-measured gas flow field by using a laser, so as to induce the to-be-measured gas flow field to emit a filament signal through interaction between the to-be-measured gas flow field and the first laser; performing extraction processing on the light filament signal by using a signal detector to obtain target light intensity information corresponding to the to-be-detected gas flow field; according to a preset light intensity pressure calibration curve, performing inversion analysis on the target light intensity information corresponding to the to-be-measured gas flow field to obtain the target pressure of the to-be-measured flow field, the light intensity pressure calibration curve is established according to different gas pressures and spectral signals obtained after gas flow fields under different gas pressures are induced by laser.

The invention discloses a method for determining and analyzing primary and secondary elements in dolomite and magnesite based on ICP-AES (Inductively Coupled Plasma-Atomic Emission Spectrometry), and relates to the technical field of determination and analysis of the primary and secondary elements, the method comprises the following steps: obtaining determination samples of the dolomite and the magnesite, pretreating the determination samples, obtaining spectral line interference conditions of each element based on the determination samples, and determining the spectral line interference conditions of the elements based on the spectral line interference conditions; the method comprises the following steps: pre-processing measured data, drawing a calibration curve of each element, obtaining the interference condition of the calibration curve of each element, and carrying out ICP-AES measurement on the pre-processed measured data to obtain a characteristic spectral line. According to the method, the source and the influence degree of the interference spectral line are determined by analyzing the spectral line interference condition, then targeted measures are adopted for correction, and meanwhile, concentration measurement deviation caused by chemical interference, physical interference, ionization interference and other factors is found by evaluating element concentration interference factors, so that the accuracy of the concentration measurement is improved. And correction is carried out by means of optimizing a sample pretreatment method, adjusting instrument working conditions and the like, so that the reliability and the stability of the analysis method are improved.

The invention belongs to the technical field of isotope analysis, and particularly relates to a data correction method for measuring hydrogen and oxygen isotope components of plant water. The data correction method for measuring the hydrogen and oxygen isotope components of the plant water comprises the following steps: performing isotope spectrum measurement on the plant water to be measured to obtain delta18O plant water, delta2H plant water and residual R plant water; obtaining the corrected delta18O plant water in the to-be-detected plant water according to the correction curve formula 1; obtaining the corrected delta2H plant water in the plant water to be detected according to the correction curve formula 2; delta < 18 > O plant water correction = delta < 18 > O plant water-Y1 formula 1; delta 2H plant water correction = delta 2H plant water-Y2 formula 2. The correction method provided by the invention is simple to use, is widely applicable to the process of measuring the hydrogen and oxygen isotope component in the plant water by an isotope spectrometer, and can greatly improve the accuracy of a measurement result.

The invention relates to the technical field of material performance detection, and discloses a non-destructive evaluation method and system for the strength of microorganism mineralized river sand, and the method comprises the following steps: applying a sweep frequency sound wave excitation signal to a microorganism mineralized river sand standard sample model; performing penetration simulation operation on the microorganism mineralized river sand standard sample model according to the sweep frequency sound wave excitation signal; extracting mineralization specific characteristic parameters corresponding to the sample model through the penetration response signal; according to the mineralization specificity characteristic parameters, constructing a biological cementation index of microorganism mineralization, and according to the biological cementation index, analyzing the compressive strength of the microorganism mineralization river sand standard sample model; constructing a destructive calibration curve of the microorganism mineralized river sand standard sample model; the target compressive strength of the to-be-tested sample model is analyzed through the destructive calibration curve, and the strength non-destructiveness of the microorganism river sand corresponding to the to-be-tested sample model is analyzed based on the target compressive strength. According to the method, the accuracy of non-destructive evaluation of the strength of the microorganism mineralized river sand can be improved.

The invention provides a cultural relic age determination method and system, and relates to the technical field of cultural relic age determination, the method comprises the following steps: collecting a sample from a to-be-tested cultural relic, and cleaning, drying and crushing the sample to obtain a treated sample; performing elemental composition analysis on the sample through an energy dispersion X-ray fluorescence spectrophotometer to obtain the types and content ratios of main elements and trace elements in the sample; collecting similar cultural relic samples of known years, performing elemental composition analysis, and establishing a standard sample ratio of the known years; comparing the ratio of the types and contents of the main elements and the trace elements in the sample with the ratio of a standard sample of a known age, and fitting a calibration curve through a least square method according to a comparison data set; and optimizing the calibration curve, and calculating an absolute age estimation value of the cultural relic according to the optimized calibration curve. According to the method, interference of single element variation is reduced through multi-element correlation analysis, and historical relic age determination is carried out.

The invention provides a power station boiler pipeline creep damage state characterization method and device based on magnetoacoustic detection, and the method comprises the steps: obtaining samples in different creep damage states, and carrying out the magnetoacoustic detection of the samples to obtain corresponding original magnetoacoustic signals; de-noising processing and characteristic parameter extraction are carried out on the original magnetoacoustic signal, and a first characteristic parameter x and a second characteristic parameter y are extracted; constructing a nonlinear mapping relation between the comprehensive creep state parameter z and the first characteristic parameter x and the second characteristic parameter y, and solving a polynomial coefficient through an optimization algorithm, so that the comprehensive creep state parameter z is monotonically increased along with the creep damage state t; and performing normalization processing on the comprehensive creep state parameter z to form a creep state normalized calibration curve t = f (x, y) for realizing quantitative characterization of the creep damage state of the power station boiler pipeline. According to the method, the normalized calibration curve of the creep state can be constructed based on the magnetoacoustic detection characteristic parameters, and the creep damage state characterization of the power station boiler pipeline is realized.

The invention discloses an LIBS (Laser-induced Breakdown Spectroscopy) method for detecting heavy metals in wheat based on round hole constrained ultrasonic extraction, which comprises the following steps: mixing wheat powder and a hydrochloric acid solution, performing ultrasonic oscillation and centrifugal separation, and extracting supernate; dropwise adding the supernatant into a circular hole restraint sheet attached to a glass slide, and drying to obtain a sample; and measuring the LIBS spectrum of the sample in an inert gas atmosphere by adopting an LIBS method, taking the characteristic spectral line intensity of the Cd element in the LIBS spectrum as input, and obtaining the Cd element concentration of the sample based on the established calibration curve, thereby completing the detection of the Cd element in the wheat. According to the method, the coffee ring effect of the UAE method is improved, and the spectrum stability is improved. By adopting the CHC-UAE method, the LIBS technology can realize high-sensitivity detection of harmful heavy metal Cd in wheat, the stability of the LIBS spectrum is improved, and the requirements of actual application of heavy metal detection in wheat are met.

The invention provides an open-type comprehensive current sensor data analysis method, which comprises the steps of simulating asymmetric distribution of a magnetic field according to an actual offset coordinate of a guide mass center in a clamp opening coordinate system, and generating a magnetic field compensation coefficient about a current eccentric state; compensating the real-time magnetic field intensity data according to the magnetic field compensation coefficient related to the current eccentric state, and eliminating random noise to obtain calibrated symmetrical magnetic field signal distribution; generating a reference curve of dynamic measurement current and magnetic field conversion according to the optimized conductor eccentric position state description, and determining a current measurement calibration curve; performing conversion processing on the input magnetic field signal through the current measurement calibration curve so as to correct signal distortion caused by eccentricity, and obtaining a comprehensive current reading without eccentric interference; and according to the relevance between the comprehensive current reading without eccentric interference and historical reference current data, analyzing the stability of a current measurement state, and obtaining a real-time clamp operation state feedback record.

The invention discloses a rotating blade defect online monitoring and identifying method and system, and relates to the technical field of turbine blade state monitoring and diagnos.The method comprises the steps that vibration parameters of a to-be-calibrated moving blade are obtained; the vibration parameters comprise a first resonance parameter and first blade end offsets at different rotating speeds; the first resonance parameter comprises a first amplitude and a first frequency; establishing a calibration curve based on the vibration parameters; acquiring an actual rotating speed and a blade tip timing signal of the to-be-measured moving blade; on the basis of the actual rotating speed and the blade tip timing signal, actually-measured moving blade vibration parameters are determined; the actually measured moving blade vibration parameters comprise a second resonance parameter and second blade end offsets at different rotating speeds; the second resonance parameter comprises a second amplitude and a second frequency; and substituting actually measured moving blade vibration parameters into the calibration curve to obtain a defect identification result of the moving blade to be detected. According to the invention, quantitative monitoring and fault early warning of the health state of the blade can be realized.

The invention discloses a hydrogen leakage on-line detection system and method based on a quadrupole mass spectrometry technology, and belongs to the technical field of hydrogen detection.The detection system comprises a gas inlet module, a dynamic dilution module, a six-way valve quantitative sample injection module and a quadrupole mass spectrometry detection system, and rapid and sensitive quantitative detection of hydrogen can be achieved; the usage amount of gas standard substances and samples is saved, and the method is green, efficient and energy-saving. The detection method comprises the following steps: establishing a calibration curve, and calculating the concentration value of an unknown hydrogen sample according to the absolute signal intensity value of the unknown hydrogen sample. The gas standard substance with high concentration is diluted on line to obtain samples with different concentrations, and quantitative detection of trace hydrogen components can be reliably realized for samples with different pressures such as pipelines / gas cylinders, ambient air and the like. The device can be used for on-line measurement of hydrogen components in various industrial gases in high-pressure pipelines or steel cylinders, on-line monitoring of hydrogen leakage in hydrogen related places such as hydrogen refueling stations, and on-line measurement of hydrogen components in scenes such as closed spaces and field exploration.

The invention discloses a method, a system, equipment and a medium for determining impurity components in graphite ore, and relates to the technical field of component determination. The method comprises the following steps: preparing a standardized sample; respectively measuring a to-be-measured sample and the standardized sample by using an X-ray fluorescence spectrophotometer, and determining the elemental analysis line intensity of the to-be-measured sample and the elemental analysis line intensity of the standardized sample; performing regression analysis on the elemental analysis line intensity of the standardized sample by using a mathematical correction mode, and determining related parameters; the related parameters comprise a calibration curve constant and a spectral line overlapping interference correction coefficient; and content calculation is carried out based on the elemental analysis line strength of the sample piece to be detected and the related parameters, and the impurity components in the target graphite ore are obtained. The method provided by the invention can improve the determination precision of impurity components in graphite ore.

The invention discloses an ICP-MS (Inductively Coupled Plasma Mass Spectrometry) detection method for phosphorus in trichlorosilane, and belongs to the field of analysis of high-purity electronic chemicals. According to the invention, a low-temperature matrix digestion technology is adopted to quantitatively form a SiFcomplex and inhibit silicon volatilization loss; according to the matrix matching calibration system, a calibration curve is constructed by adopting a weighted least square method, so that matrix interference is effectively corrected; a detection technology screening strategy is carried out by combining a cascade strategy of high-resolution inductively coupled plasma mass spectrometry, so that the interference of a complex matrix is successfully avoided. Experiments show that when the method is used for detecting phosphorus, the linear correlation coefficient R is greater than 0.999, the detection limit is as low as 0.02 ng / g, and the method accords with ISO 17025 and semiconductor-grade material quality control standards, and is suitable for accurate quantitative analysis of ng / g-grade phosphorus elements in high-purity trichlorosilane.

The invention relates to the technical field of biological medicine, in particular to construction and application of a candida patient death risk prediction model. The model is based on traditional SOFA scoring, core variables are screened through single-factor and multi-factor logistic analysis and LASSO regression analysis, respiratory system, blood coagulation function and circulatory system SOFA scoring and key indexes such as the maximum lactic acid value, the minimum albumin value and the maximum blood urea nitrogen (BUN) are integrated, and a prediction model is constructed through logistic regression. Through multi-center data verification, the AUC of an internal verification queue reaches 0.826, the AUC of an external verification queue is 0.813, and a calibration curve shows that the predicted death rate is highly consistent with the actual death rate. Patients can be divided into a high risk group and a low risk group through a 13-score threshold value, and the 28-day survival rate difference is significant (plt; 0.05) of the method. An efficient and convenient early risk assessment tool is provided for critical candida patients, and clinical precise intervention and prognosis improvement are assisted.