10 results about "Stepwise regression analysis" patented technology

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.

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 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 invention discloses a method for evaluating bean curd quality by using soybean protein structure indexes and application thereof, and belongs to the technical field of food evaluation. The invention provides a method for evaluating bean curd quality by using a soybean protein structure index in order to overcome the defects that in the prior art, resource waste and economic loss are caused due to the fact that soybean food is produced by adopting mixed varieties in the soybean processing industry, and a method capable of accurately evaluating bean curd applicable soybean varieties is lacked. Protein structure indexes of different varieties of soybeans and quality indexes of bean curds prepared from different varieties of soybeans are utilized to carry out principal component analysis, and stepwise regression analysis is adopted to establish an evaluation system of a linear regression equation. According to the method for evaluating the quality of the bean curd, objectivity and accuracy of bean curd processing suitability evaluation can be improved, and technical support is provided for rapid and accurate bean curd processing suitability evaluation in the soybean industry.

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 a prediction method for thermal stability of a coordination polymerization organic metal catalyst. The method comprises the following steps: constructing a model of a catalyst active species, an ethylene monomer, a catalyst ligand and a bimolecular ethylene coordinated complex, carrying out geometric structure optimization and frequency analysis to obtain a thermodynamically stable 3D structure, and collecting and sorting corresponding stable molecular structures and thermodynamic correction values of enthalpy values and Gibbs free energy of the stable molecular structures; obtaining corresponding single-point energy, enthalpy value and Gibbs free energy of each molecular structure, bond dissociation enthalpy and bond dissociation energy; quantum chemical descriptors are calculated; combining the quantum chemical descriptors, the key dissociation enthalpy and the key dissociation energy to construct a database; and adopting multiple linear stepwise regression analysis to remove multiple collinearity of the descriptors, and constructing a prediction model. According to the prediction method provided by the invention, the thermal stability of the coordination polymerization organic metal catalyst can be accurately predicted, so that the workload is reduced, and efficient screening of the polymerization catalyst is realized.

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.

The invention belongs to the technical field of plant drought resistance identification, and discloses a method for identifying the drought resistance of corn varieties based on comprehensive weights. According to the method, the yield, the water utilization efficiency and a plurality of physiological and biochemical indexes in the corn growth period are combined, through a membership function method, principal component analysis and D value synthesis, the drought resistance of different varieties of corn under film mulching and non-film mulching treatment is systematically evaluated, and a drought-resistant high-yield and high-efficiency prediction model is further established through stepwise regression analysis; and a basis is provided for later large-scale variety screening. The regression equation of the model for predicting the drought resistance, high yield and high efficiency of the corn is as follows: YWD =-2.878 + 0.120 DIX1 + 0.096 DIX2 + 0.278 DIX3 + 0.897 DIX4 + 0.713 DIX6 + 0.570 DIX7 + 0.127 DIX14 + 1.196 DIX16 + 0.141 DIX18, R2 is 0.97, and the fitting effect is better.