32 results about "Stepwise regression" patented technology

This invention discloses a water treatment dosing control method and system based on quadratic programming, belonging to the field of water treatment process control and optimization technology. It collects historical water treatment operation data and constructs a mechanistic feature set, using the mechanistic feature set as the independent variable and turbidity reduction as the target variable. The turbidity reduction is used to represent the change in flocculation or sedimentation of suspended impurities in the water, and a full-variable regression model is constructed. Variables in the mechanistic feature set are screened, and the full-variable regression model is optimized using a stepwise regression method to obtain a simplified prediction model. Real-time influent water quality parameters are acquired and input into the simplified prediction model. The process of maximizing turbidity reduction in the simplified prediction model is transformed into minimizing a convex loss function, and the convex loss function is iteratively optimized using a hierarchical constrained projection gradient descent method to obtain the optimal dosing scheme. Through mechanism-driven modeling, convex function optimization, and hierarchical constrained projection, intelligent, efficient, and reliable control of the dosing process is achieved.

The present application discloses a wetland vegetation carbon sequestration potential prediction method and system under the influence of future climate change, belongs to the technical field of wetland vegetation carbon sequestration potential prediction, and comprises the following steps: obtaining the net primary productivity of vegetation, the vegetation coverage, the monthly temperature, the precipitation and the atmospheric carbon dioxide concentration in the historical period and the future period; calculating the annual average net primary productivity of vegetation, the coverage and the multi-year average vegetation coverage; calculating the annual average temperature, the precipitation and the carbon dioxide concentration; extracting the wetland vegetation pixel distribution as the research area range; calculating the ratio of the annual average net primary productivity to the vegetation coverage to obtain the wetland vegetation carbon sequestration potential value in the historical period; adopting the multiple stepwise regression analysis method to build a per-pixel wetland vegetation carbon sequestration potential estimation model; and utilizing the annual temperature, the precipitation and the carbon dioxide concentration in the future period to realize the accurate prediction of the wetland vegetation carbon sequestration potential in the research area range in the future period.

The invention belongs to the technical field of medical information processing, and particularly discloses a regression model establishment method and system for detecting related indexes of myocardial cells of Wuzhishan pigs by using blood biochemical indexes, and the method comprises the following steps: collecting blood and heart tissue samples of the Wuzhishan pigs at different month ages, detecting the blood biochemical indexes including liver function, blood fat and blood sugar indexes, and establishing a regression model for detecting the related indexes of myocardial cells of the Wuzhishan pigs. Related indexes of myocardial cells; analyzing the index expression quantity difference, and screening significant difference indexes; then calculating the correlation between the remarkably different blood biochemical indexes and myocardial cell related indexes, and determining high-correlation blood biochemical indexes; and finally, constructing a first version of multiple regression model by respectively taking the myocardial enzyme index and the myocardial cell related index as dependent variables and the high-correlation blood biochemical index as an independent variable, removing outliers and updating the sample set on the basis of the first version of multiple regression model, and constructing a second version of multiple regression model by utilizing the new sample set, and on the basis of the second version of multiple regression model, constructing a third version of multiple regression model by adopting a stepwise regression method. According to the method, related indexes of myocardial cells can be accurately deduced through blood biochemical indexes, and support is provided for Wuzhishan pig heart disease research and model animal research and development.

This application relates to the field of metal bending component manufacturing technology, and discloses a method for controlling the bending radius in progressive forming of tubular materials by spinning and bending. The method includes the following steps: S1, obtaining an optimal combination of loading parameters affecting the forming quality indicators of the tubular material through orthogonal experiments; S2, establishing a regression function between the bending radius and the spinning and bending deformation parameters based on the optimal combination and the Maximin Latin square test results through stepwise regression analysis; S3, testing the regression function; if the test passes, obtaining the regression model of the bending radius; if the test fails, returning to step S2; S4, controlling the bending radius under the optimal loading parameter conditions using the regression model. This application incorporates a diameter reduction parameter into the control method, significantly improving the accuracy of bending radius control compared to existing geometric control models. It reduces the average error between tubular bending radius prediction and control from 10.28% to 5.2%, offering advantages such as high forming accuracy, wide applicability, and higher reliability.

The invention provides an extreme weather power load prediction method and system, and the method comprises the steps: obtaining historical data, and determining a basic load in extreme weather according to the historical data; constructing a total load decomposition model, stripping initial estimation of a basic load and a random load in the total load according to the total load decomposition model, and obtaining a meteorological load mid-value through multiple regression fitting; according to the meteorological load median, screening core meteorological factors through improved grey correlation analysis, and combining stepwise regression screening and extreme value adaptation correction to obtain an accurate meteorological load; constructing a comprehensive predictive factor set according to the precise meteorological load; and inputting each prediction factor of the comprehensive prediction factor set into the trained improved BP neural network model, and outputting a power load prediction value in extreme weather, thereby effectively improving meteorological load separation precision and extreme weather load prediction precision.

The application discloses a kind of track fusion methods of vehicle, ship: first, the measurement data and real data of target track are preprocessed, the size of data graph is determined by stepwise regression algorithm, then one-dimensional track point data is assembled into two-dimensional track data graph, to achieve the purpose of batch processing measurement data, also can capture the motion correlation between track point data.Second, the interaction between features and the spatial features between data are mined through CIN network structure, then the input of CIN is taken as the input of Informer module, the temporal features between data are mined through Informer module, and finally the fusion result is obtained through LSTM module.Finally, the model is trained and the model file is obtained, and the track fusion task is completed on the validation set.The application can quickly process large track measurement data and accurately calculate the track fusion result, improve the accuracy of track fusion.

The invention discloses a machine learning recommendation method for precision marketing and potential customer mining. The method comprises the following steps: S1, data acquisition: establishing a user tag table based on historical data and acquiring user behavior characteristics; s2, data preprocessing: preprocessing the data, including removing abnormal values, filling missing values, and converting category features into numeric features; s3, automatic box separation: performing variable analysis and automatic box separation on the preprocessed data; s4, feature processing: converting a data table obtained by binning into a numerical value form; s5, dividing a data set: dividing the processed data set into a training set and a test set; s6, stepwise regression is carried out to select features: stepwise logistic regression is used to select model key variables; s7, manual binning and modeling data set preparation are carried out; the method has the characteristics of high automation level and high decision-making efficiency.

The invention specifically relates to a significant wave height estimation method based on machine learning. The method comprises the following steps: obtaining a radar echo speed space-time sequence by using a coherent X-band radar; extracting statistical characteristics from the speed space-time sequence; performing Spearman correlation analysis on the statistical characteristic value and the actual significant wave height; screening the characteristic values by using a Spearman correlation coefficient, and constructing a Gaussian process regression and stepwise regression integrated model in combination with significant wave height data; and estimating the significant wave height of the echo to be measured through the integrated model. According to the significant wave height estimation method based on machine learning, key information can be effectively obtained from radar echoes by analyzing the speed space-time sequence of the radar echoes, extracting the statistical characteristics and energy information of the radar echoes and combining with the machine learning technology, and the estimation accuracy of the significant wave height is improved. And a physical correlation model of speed characteristics and wave parameters is constructed by using a machine learning method, so that the accuracy and reliability of inversion are remarkably improved.

The invention discloses a method and equipment for determining a pipeline cleaning strategy, a medium and a product, relates to the technical field of pipeline oil stain cleaning, and aims to solve the problem of how to optimize the pipeline cleaning strategy. The method for determining the pipeline cleaning strategy comprises the following steps: acquiring sample data of pipeline cleaning; based on the sample data, obtaining a cleaning effect multiple regression model, a cleaning effect ridge regression model, a cleaning effect lasso regression model and a cleaning effect stepwise regression model; determining an error evaluation index of the pipeline cleaning strategy determined by each regression model; determining a target cleaning rate model from the cleaning effect multiple regression model, the cleaning effect ridge regression model, the cleaning effect lasso regression model and the cleaning effect stepwise regression model, wherein the target cleaning rate model determines that an error evaluation index of the pipeline cleaning strategy is smaller than a preset index threshold value; and determining a target strategy for pipeline cleaning through the target cleaning rate model.

The invention discloses a blade appearance phenotype multi-dimensional feature-based SPAD value estimation method, system and device, and a medium, mainly relates to the technical field of SPAD value estimation, and is used for solving the problem that the existing scheme introduces too many multi-dimensional features and is easy to cause dimension disaster and overfitting. Comprising the steps of performing foreground and background separation on a digital image to obtain a leaf foreground image; extracting blade appearance phenotype multi-dimensional features from the blade foreground image; through Pearson correlation analysis, screening out significant correlation characteristic parameters of SPAD values from the blade appearance phenotype multi-dimensional characteristics, and screening out an optimal blade appearance phenotype multi-dimensional characteristic combination from the significant correlation characteristic parameters through a stepwise regression method; and obtaining a trained random forest network by using the optimal blade appearance phenotype multi-dimensional feature combination and the SPAD mean value.

This invention provides a method and system for predicting power load under extreme weather conditions. It acquires historical data and determines the base load under extreme weather conditions based on this data. A total load decomposition model is constructed, and based on this model, the base load and random load are initially estimated and separated from the total load. The median meteorological load is obtained through multivariate regression fitting. Based on the median meteorological load, core meteorological factors are screened using improved grey relational analysis, combined with stepwise regression screening and extreme value adaptation correction to obtain the accurate meteorological load. A comprehensive prediction factor set is constructed based on the accurate meteorological load. Each prediction factor in the comprehensive prediction factor set is input into a trained improved BP neural network model, which outputs the predicted power load value under extreme weather conditions, effectively improving the accuracy of meteorological load separation and extreme weather load prediction.

The invention relates to a method for constructing a model for estimating the aboveground biomass of non-uniformly planted rice by using multi-temporal unmanned aerial vehicle image data and an estimation method. The method comprises the following steps: dividing a full life cycle of rice into a vegetative period and a reproductive period by combining a high-resolution unmanned aerial vehicle multispectral image, obtaining a multispectral image of a target area by using an unmanned aerial vehicle in a tillering period, processing the image, extracting a rice pixel proportion, and correcting overground biomass and vegetation indexes by using a rice pixel proportion coefficient to obtain a corrected overground biomass and vegetation index; and then a ternary linear regression model of the vegetation index and the aboveground biomass is established through a multivariate stepwise regression method (SPSS), so that the aboveground biomass estimation precision of the non-uniformly planted rice is remarkably improved.

The invention relates to the technical field of data models, in particular to a yellow rice wine quality prediction method, which comprises the following steps: after establishing a yellow rice wine sensory index evaluation table, respectively carrying out sensory analysis on Fujian yellow rice wine in different years to obtain a comprehensive taste actual score; carrying out correlation and significance analysis on physical and chemical components and sensory indexes of the Fujian yellow wine; performing correlation and significance analysis on the color and sensory indexes of the Fujian yellow wine; performing correlation and significance analysis on volatile matter indexes and sensory indexes of the Fujian yellow wine; selecting parameters with significant levels of less than 0.05 in physical and chemical components, color and volatile matters as independent variables, and establishing a sensory comprehensive evaluation and prediction model by taking the comprehensive taste actual score as a dependent variable and adopting stepwise regression analysis; according to the method, the Fujian yellow rice wine in different years is researched, the quality of the Fujian yellow rice wine is evaluated through sensory analysis, electronic nose analysis and physical and chemical analysis, sensory indexes are quantified, datamation indexes for directly evaluating the quality of the Fujian yellow rice wine are obtained, and management and grading are facilitated.

The invention discloses a natural gas monthly sales volume main influence factor screening method, system and device, and belongs to the technical field of natural gas data, and the main influence factor screening method comprises the steps: preprocessing natural gas monthly sales volume influence factors and natural gas monthly sales volume data; performing correlation analysis on the relationship between the influence factors and the monthly natural gas sales volume to obtain an influence factor set A; determining contribution values of the influence factors to the natural gas monthly sales prediction result to obtain an influence factor set B; determining importance indexes of the influence factors on the monthly natural gas sales volume to obtain an influence factor set C; determining a minimum public set of the influence factor set A, the influence factor set B and the influence factor set C; and through stepwise regression analysis, screening out main influence factors from the minimum public set. According to the method, the main influence factors of the monthly natural gas sales can be comprehensively and accurately identified and screened, and the precision and interpretability of the prediction model are improved.

The application discloses a prediction model for diagnosing infectious mononucleosis in children and a construction method thereof. The application adopts single factor analysis and logistic stepwise regression to screen independent influencing factors by using confirmed IM inpatient cases and suspected IM but EBV-DNA negative child cases, and adopts multiple factor regression to screen six independent influencing factors of age, ALB, GLB, PLT, LYMPH# and LYMPH% to construct a diagnosis model. Researches show that in the confirmed diagnosis scene, the multi-index combined model with blood routine and liver function has excellent diagnosis efficiency (AUC=0.986), realizes the ideal balance of high sensitivity and high specificity, provides a hierarchical tool for the precise diagnosis of IM in different clinical scenes, and is suitable for the precise diagnosis of inpatient children.

This application relates to the biomedical field and discloses a predictive model and validation method for ART virological failure in HIV / AIDS patients. The model construction method includes the following steps: S1, collecting clinical data from HIV or AIDS patients; S2, performing multiple imputation processing on the clinical data to generate multiple datasets; S3, using a stepwise regression method to screen predictive factors in each dataset; S4, synthesizing the datasets to determine the final predictive factors; S5, establishing a virological failure prediction model based on the final predictive factors. The predictive model constructed by this invention can serve as an effective tool for predicting the risk of ART virological failure in HIV or AIDS patients in clinical practice. The application of this model helps improve treatment effectiveness, optimize resource allocation, and ultimately improve patient treatment outcomes. Furthermore, by continuously collecting new patient data and re-evaluating model performance, the accuracy and practicality of the model can be further improved.

The application discloses a resonator gyroscope temperature compensation method based on stepwise regression and a Gaussian process, and comprises the following steps: screening significant independent variables from preset independent variables through a stepwise regression model; constructing a sample set based on the significant independent variables, training a temperature compensation model; collecting significant variables at a current moment, estimating a zero bias value of the gyroscope through the temperature compensation model; and correcting an output angular velocity of the gyroscope according to the zero bias value. The technical problem of how to effectively suppress the temperature influence on the output zero bias of the hemispherical resonator gyroscope is solved, the zero bias stability of the output result is improved, and the robustness of the temperature compensation model is enhanced.

The invention discloses a stepwise regression analysis method-based college student physique test score prediction model, and belongs to the technical field of sports and metering economics. The method comprises the following steps: collecting and preprocessing college student physique test multi-dimensional index data; constructing a multiple linear regression model between the total physical test score and a plurality of explanatory variables; screening significant variables by adopting a stepwise regression analysis method, and optimizing a model structure; performing multi-collinearity, heteroscedasticity and sequence correlation test and correction on the model; and finally verifying the validity of the variable set by using a random forest model. According to the method, key influence factors can be automatically identified from numerous physical indexes, a prediction model with high goodness of fit and high interpretation is constructed, accurate prediction of student physical scores is realized, and a scientific basis is provided for physical health management of colleges and universities.

The invention provides an earthquake frequency prediction method based on delay correlation, and belongs to the field of earthquake frequency prediction, and the method comprises the steps: obtaining earthquake catalog data in a fracture system region, generating a complete earthquake catalog, carrying out the statistics of an earthquake frequency time sequence based on a preset earthquake magnitude range and a time unit, and calculating the delay cross correlation coefficient of the earthquake frequency time sequence, selecting a maximum delay cross correlation coefficient and a corresponding lag order from the seismic frequency time sequence according to the delay cross correlation coefficients; and according to the seismic frequency time sequence, the maximum delay cross correlation coefficient and the corresponding lagging order, constructing a seismic frequency time sequence delay correlation influence path network, through stepwise regression, obtaining a regression model, and screening out a regression model with the lagging order less than 0 to carry out seismic frequency prediction. According to the method, the problem that high-precision and quantifiable prediction cannot be carried out on the seismic activity frequency in a fracture system due to the lack of quantitative statistical prediction based on fracture seismic delay correlation in the prior art is solved.

The invention discloses a carbonate reservoir connectivity characterization method based on path analysis and dynamic data optimization. The carbonate reservoir connectivity characterization method comprises the following steps: counting optimized sensitive basic attributes and drilling fluid leakage values at drilling leakage sample points in a research area; constructing a complex nonlinear correlation between the sensitive basic attribute and the drilling fluid leakage through a neural network, and obtaining a predicted communication fusion attribute; picking up an inter-well optimal communication path through an ant colony optimization algorithm, obtaining path parameters, and calculating a rank correlation coefficient between the path parameters and actual well group communication dynamic data; taking the ratio of the predicted mean square error to the rank correlation coefficient as an objective function, improving the combination mode of the sensitive basic attributes and the deep learning parameters of the neural network through a stepwise regression method, enabling the objective function value to reach a set threshold value, and obtaining the high-precision communication fusion attributes matched with the production materials. The problem that effective means for representing the communication structure and predicting the communication path are lacked in the carbonate fractured-vuggy reservoir is solved.

The invention discloses a forest carbon sink accounting method based on a nonlinear regression and stepwise regression combined model, and relates to the technical field of forest carbon sink accounting, and the method comprises the steps: collecting the ground actual measurement data and remote sensing observation data of a forest sample plot of a target region, carrying out the canopy height and diameter at breast height proxy index inversion of laser radar point cloud data, and obtaining a target forest carbon sink accounting result; variable space completion is completed in combination with space distribution characteristics of ground actual measurement data, and a fusion data set is formed; fitting a nonlinear regression basic model in a power function form on the basis of the mean value of the diameter at breast height and the mean value of the tree height in the fusion data set and the actually measured biomass of the corresponding sample plot, determining regional exclusive parameters, and calculating to obtain basic carbon sink density in combination with a carbon content conversion coefficient, an initial degradation correction coefficient and a phenological seasonal factor; and constructing a lightweight supervised classification model by using the NDVI time sequence trend, the DBH annual growth rate and the soil organic carbon change rate in the fusion data set, and outputting the degradation grade label of each sample plot.

The application utilizes variational mode decomposition (VMD) and principal component analysis (PCA), and proposes a VMD-PCA-based sub-synchronous oscillation energy characteristic influence factor identification method. The port energy characteristics of sub-synchronous oscillation under different working conditions are extracted through time domain simulation, and a regression model is established to perform regression analysis on the port energy of sub-synchronous oscillation. The process is as follows: the VMD mode decomposition is performed on the measured voltage and current at the outlet of the fan to obtain the voltage and current components under the sub-synchronous oscillation mode, and the transient energy flow at the outlet of the fan is obtained by calculation; the transient energy flow function of the fan outlet is fitted, and the energy flow power is taken as the stability characteristic quantity of sub-synchronous oscillation for research; the principal component method is used to solve the multicollinearity problem between the energy characteristic influence factors, a variable fitting evaluation model of the energy characteristic is established based on the stepwise regression method, and the key influence factors of the sub-synchronous oscillation energy characteristic are identified. The method can judge the current system energy characteristic of the system in time, identify the key influence factors affecting the system energy characteristic, and has important engineering significance for maintaining the safe and stable operation of the new power system.

The application discloses an insect biological parameter inversion method based on feature selection; the application can be used for high-precision inversion of insect size parameters, and can help improve the insect species identification accuracy; the application firstly calculates multi-dimensional scattering features of insects, and based on a stepwise regression method, filters and respectively gives the best inversion feature combination for insect weight, body length and body width, and then based on the filtered feature combination, uses a random forest regression method to realize high-precision inversion of the insect size parameters; the effectiveness of the proposed inversion method is verified through measured data of a total of 76 species and 366 insects.

The invention provides a sintering low-temperature reduction degradation index prediction stepwise regression method based on a factor analysis whole process. The method comprises the following steps: S1, data acquisition and preprocessing; s2, factor analysis dimension reduction and comprehensive factor extraction; s3, constructing a prediction model based on optimal subset regression; s4, a model verification and dynamic updating mechanism; and S5, carrying out model deployment and online prediction. According to the method, advanced accurate prediction and intelligent regulation and control of the sintering production process quality can be realized, the finally established prediction model is integrated into a sintering process control system, real-time online prediction of the RDI index is realized, advanced and accurate data support is provided for operators to adjust key parameters such as alkalinity and fuel ratio, and the service life of the operators is prolonged. Finally, the purposes of stabilizing the sinter quality, reducing the blast furnace fuel ratio and improving the economic benefits of enterprises are achieved.

The invention discloses a method for predicting wheat standard ileum amino acid digestibility of laying hens in a brooding period. The method comprises the following steps: determining physical characteristics, chemical components and amino acid contents of wheat samples from ten different sources; preparing a test feed, and carrying out a feeding test on Jingjing No.8 laying hens in a brooding period (28-31 days old); determining the standard ileum amino acid digestibility of the 31-day-old laying hens to the wheat of each source; and based on the data correlation, establishing a prediction equation of the wheat standard ileum amino acid digestibility of the laying hens in the brooding period by adopting a stepwise regression method. The constructed prediction equation can accurately predict the standard ileum amino acid digestibility of the laying hens in the brooding period to the wheat, an effective tool is provided for achieving efficient utilization of wheat resources and promoting practice of precise nutrition, and a theoretical basis and data support are provided for feed and breeding enterprises to scientifically design a daily ration formula of laying chicks.

The invention relates to the technical field of regional water and soil conservation and environmental governance, and relates to a method and device for determining a vegetation configuration mode. The method comprises the steps that a to-be-restored area is divided into a plurality of micro site types, and vegetation survey information of each micro site type is acquired; based on the vegetation survey information, calculating the importance values of different types of plant species, the importance values representing the importance degree of the plant species in the community; primarily selected plant species are determined based on the important values, and the ecological niche width and the ecological niche overlapping value of each primarily selected plant species are calculated according to the important values of the primarily selected plant species; taking the micro site factor and the vegetation factor as independent variables, taking the water and soil conservation function index as a dependent variable, and adopting a stepwise regression method to construct a multiple linear regression model; based on a multiple linear regression model, the water and soil conservation function and community stability of different plant combinations are evaluated according to the important value, the ecological niche width and the ecological niche overlapping value of the primarily selected plant species, a target plant combination is determined according to the evaluation result, and a vegetation configuration scheme is formed.

The application discloses a runoff change attribution analysis method based on underlying surface spatial characteristics, and belongs to the technical field of runoff attribution analysis. First, in the constructed underlying surface spatial characteristic factor set, the key underlying surface spatial characteristic factors capable of effectively explaining the change of Budyko parameters are screened through collinearity test and stepwise regression. Second, an optimal parameter relationship model between the Budyko parameters and the key underlying surface spatial characteristic factors is established. Finally, combined with a water-heat coupling balance equation, the runoff change in different analysis periods is attributed and decomposed, so that the contribution amount of climate change, the contribution amount of underlying surface spatial characteristic change and the absolute contribution proportion are obtained. Through the relationship between the parameters and the underlying surface spatial characteristics, the underlying surface spatial heterogeneity is allowed to participate in the runoff change attribution analysis in a parameterized manner, so that the expression capability of the attribution result to the comprehensive influence of the underlying surface is improved, and guidance is provided for the basin water resource regulation and ecological protection management.

The invention provides a method for constructing a Hu sheep lamb weaning weight prediction model, which comprises the following steps of: weighing and recording lamb birth weight when a set number of to-be-delivered Hu sheep ewes lamb, recording the number of lambs with the same birth and lamb sex, weighing and recording lamb weaning weight at the weaning day age of the lambs, and recording the lamb weaning weight at the weaning day age of the lambs; acquiring a data set including lamb birth weight, the number of lambs in the same fetus, lamb gender and lamb weaning weight, and dividing the data set into a training set and a verification set; performing multivariate stepwise regression analysis on the training set, establishing a prediction model of the weaning weight of the male lambs and the weaning weight of the female lambs, and checking the prediction model by using a root-mean-square error, a variable coefficient and a decision coefficient; and substituting the verification set into the prediction model, performing correlation analysis on a prediction result and an actual measurement result, and verifying the accuracy of the prediction model. According to the method, the construction process is relatively simple, the constructed prediction model is relatively high in reliability, and the weaning weight of the Hu sheep lambs can be accurately predicted according to the birth weight and the number of lambs of the same birth.

This invention provides a method for calculating the content of micro-components in coal seams, comprising the following steps: Step S1, measuring the content of micro-components, ash content (Aad), and total sulfur content (St) of the coal core; Step S2, obtaining the well logging curve values ​​for the depth segment of the coal seam where the coal core is located; Step S3, selecting sensitive well logging curves reflecting the micro-components of the coal core through mathematical statistical analysis of the correlation between the micro-component content of the coal core and the sensitive well logging curves; Step S4, performing multivariate stepwise regression analysis on the micro-component content of the coal core and the sensitive well logging curves respectively to obtain the relationship between the micro-component content of the coal core and the sensitive well logging curves; Step S5, obtaining the well logging curve values ​​corresponding to the coal seam segment for which the micro-component content of the coal core to be measured is obtained, and substituting the well logging curve values ​​into the relationship to obtain the micro-component content of that coal seam segment. The technical solution of this invention can quickly and accurately predict the content of micro-components, providing a basis for development deployment and decision-making, and reducing development risks.