90 results about "Stepwise regression" patented technology

The invention relates to a construction method of a metabolic syndrome phlegm syndrome lipid metabolism characteristic diagnosis model. The construction method comprises the following steps: S1, acquiring a serum sample; s2, carrying out lipid metabolite analysis through a full-quantitative lipidomics formula; s3, carrying out primary screening on differential metabolites; s4, optimizing the characteristic indexes by using a random forest algorithm and stepwise regression; and S5, constructing a differential metabolite diagnosis model through Logistic regression analysis. By analyzing differential lipid metabolites of patients with MetS phlegm syndromes and non-phlegm syndromes, the invention reveals that abnormal accumulation of lipid and lipid metabolites may be the core pathological parenchyma of MetS phlegm syndromes. By integrating lipid metabonomics data, using a random forest algorithm, stepwise regression and other methods to screen feature difference lipid metabolites and construct a MetS phlegm syndrome specific diagnosis model, a novel combined biomarker and evidence-based basis are provided for early diagnosis of MetS phlegm syndromes, and a scientific basis can also be provided for objective diagnosis of traditional Chinese medicine phlegm syndromes.

The invention discloses a method for detecting the nitrogen content of organ tissues of different varieties of oilseed rapes based on visible near-infrared spectroscopy, which comprises the following steps: firstly, collecting oilseed rape leaf, shell, stalk and root system samples under the conditions of multiple varieties and multiple nitrogen fertilizer levels, and acquiring spectral data within the range of 430-2500nm by using a visible near-infrared spectroscopy; and a Kjeldahl method is synchronously adopted to measure the real nitrogen content as a reference value. Preprocessing the spectral data, including de-noising, standard normal variable transformation, multivariate scatter correction and derivative processing, so as to weaken the influence of scattering and baseline drift; characteristic wavelengths related to the nitrogen content are screened through stepwise regression, variable projection importance, competitive self-adaptive reweighted sampling, a continuous projection algorithm and other methods, and a random forest model, a support vector machine model, a partial least squares discriminant analysis model, a partial least squares regression model, a support vector regression model, an XGBoost model and other models are combined. And respectively constructing a classification identification model and a regression prediction model.

The present application relates to a method, apparatus, and device for analyzing a lithography process window based on stepwise regression. The method comprises: obtaining photoresist pattern image data and determining the constraints required for the lithography process window; determining a lithography process model that satisfies the relationship between the photoresist pattern features, exposure energy, and focal length, and a stepwise regression direction for the lithography process model; performing stepwise regression on the lithography process model based on the photoresist pattern image data in the direction indicated by the stepwise regression direction to obtain multiple candidate lithography process models and model selection index values ​​for each candidate lithography process model; determining a target lithography process model from the multiple candidate lithography process models based on the model selection index values; and determining the lithography process window based on the constraints and the target lithography process model. This method can improve the accuracy of the analysis results of focal length energy matrix data.

The invention discloses a forest steppe fire risk assessment method based on variable screening and machine learning, and the method comprises the steps: extracting environment factor variables from preprocessed historical fire remote sensing data, and carrying out the correlation analysis, thereby obtaining a correlation result; performing variable screening on the environmental factor variables based on the correlation result, a cable regression algorithm, a principal component analysis method and a stepwise regression method to obtain screened environmental factor variables; performing data division on the screened environment factor variables to obtain training data and test data, and performing model training and testing on a plurality of to-be-trained fire risk assessment models by using the training data and the test data until a plurality of fire risk assessment models are obtained; and determining an optimal fire risk assessment model based on the plurality of fire risk assessment models and the obtained historical fire records, and then performing inversion on the screened environmental factor variables to obtain a fire risk map of the forest steppe. According to the invention, the real fire risk condition can be accurately reflected.

The invention provides a three gorges reservoir area landslide stage division and main control factor identification method and system. The method comprises the following steps: establishing an initial factor set according to geological features and monitoring data of a landslide; optimizing the initial factor set twice to screen out a core factor set; the influence of each core factor on landslide deformation is researched, a multivariate stepwise regression model considering aging, rainfall and reservoir water level is established, the landslide deformation process is decomposed according to each component, calculation formulas of each component are listed in sequence, and a final multivariate stepwise regression model is obtained; analyzing landslide deformation curves in different time periods, identifying stage turning points in combination with evaluation indexes, and accurately dividing landslide deformation stages; and analyzing primary and secondary influences of internal and external influence factors on the landslide in different stages by comparing the fitting variable amplitude value with the actually measured variable amplitude value, and determining the main control factors of each stage and the current main control factor according to the change of the proportion of each component in each stage. According to the invention, landslide main control factors in different stages can be identified.

The invention discloses an intelligent diagnosis system and method for mammary gland scleroderma and breast cancer, and relates to the technical field of image processing, and the system comprises a data obtaining module which is used for obtaining a diagnosis sample set; the feature engineering module performs feature screening by combining machine learning, expert experience and a stepwise regression method to obtain a key inspection feature set; the data enhancement module adopts a synthesis minority oversampling technology to carry out new sample synthesis to obtain an enhanced diagnosis sample set; the model determination module carries out training and performance evaluation on a plurality of preset machine learning diagnosis models to select an optimal machine learning diagnosis model; the explanatory module explains the contribution degree of each key inspection feature to the prediction result of the optimal machine learning diagnosis model by adopting an SHAP method; and the detection application module receives current examination data of the patient and inputs the current examination data into the optimal machine learning diagnosis model to obtain a disease type. According to the application, precise identification of the scleroderma and the breast cancer accompanied by the scleroderma can be realized, and the method and the device are more convenient.

The invention discloses a method for analyzing influence of climate change and human activity on space-time evolution of water resources. The method comprises the following steps: acquiring meteorological information, hydrological information, land utilization information, soil information, social economic information and water resource information of a to-be-detected area; performing trend analysis on the water resource information to obtain a space-time evolution rule of the water resource information; performing attribution analysis on the time-space evolution rule to obtain influence factors of the time-space evolution of the water resource; obtaining the correlation degree of the influence factors and the space-time evolution rule, and completing the analysis of the climate change and human activity on the space-time evolution of the water resource. According to the method, the influence factors of water resource evolution of the to-be-detected area are researched through the multivariate statistical stepwise regression analysis model, the multicollinearity between the related influence factors of climate change and human activity is reduced through the characteristics of stepwise regression analysis, the calculation accuracy is improved, and the sustainable development of water resources is promoted.

The invention discloses a soil heavy metal content prediction method, device, equipment, medium and product, and relates to the technical field of soil detection, the method comprises the following steps: constructing a multiple regression data set; performing multivariate stepwise regression fitting by adopting the data set to obtain a multivariate stepwise regression model; determining a plurality of soil sampling points from the target soil area, and obtaining a heavy metal content measured value of each soil sampling point; performing heavy metal content inversion on each soil sampling point by adopting a multivariate stepwise regression model to obtain an initial heavy metal content predicted value of each soil sampling point; calculating a difference value between each heavy metal content measured value and the initial heavy metal content predicted value at the corresponding position, and performing spatial interpolation on each difference value by adopting a Kriging interpolation method to obtain spatial distribution of prediction errors; and determining a heavy metal content predicted value of any position on the target soil area according to the spatial distribution of the prediction error and a multivariate stepwise regression model. The soil heavy metal content inversion precision can be improved.

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.

The invention discloses a method for forecasting anthracnose of wine grapes, and particularly belongs to the technical field of plant disease and insect pest forecasting. The method comprises the following steps: carrying out intra-group correlation analysis on climatic factors of threshold number of days before the wine grape anthracnose attack through SPSS to obtain meteorological factors significantly related to the wine grape anthracnose; through stepwise regression analysis, redundant climate factors in the meteorological factors significantly related to the wine grape anthracnose are removed, a multiple linear regression model is constructed, the meteorological factors significantly related to the wine grape anthracnose are screened out, and multicollinearity is avoided; secondly, inputting meteorological data to be measured into the multiple linear regression model, and outputting a prediction result of the disease index of anthracnose; and finally, forecasting the anthracnose of the wine grapes based on the prediction result of the disease index of the anthracnose. The method can prevent and control anthracnose, improve wine grape quality and reduce economic loss.

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 discloses an insulator partial discharge source identification method and system, and the method comprises the steps: carrying out the partial discharge detection of an insulator through a radio frequency antenna, obtaining a detection result, carrying out the feature extraction of a partial discharge signal if a partial discharge signal exists in the detection result, and obtaining a feature vector; and performing feature selection on the feature vectors by using a stepwise regression method to obtain selected feature vectors, inputting the selected feature vectors into a trained artificial neural network for classification to obtain a partial discharge type, and determining a partial discharge source according to the partial discharge type. According to the method, the feature dimension can be effectively reduced by using the stepwise regression method, then the features are classified by using the ANN, the classification efficiency of the partial discharge signals is improved, and the partial discharge source can be accurately identified, so that the partial discharge on the surface of the insulator and the source thereof can be effectively and accurately detected and identified.

The present application relates to the technical field of power system regulation, and more particularly to a power transmission network transient equivalent modeling method and system based on GRNN step-by-step regression, an electronic device and a medium; the method comprises: constructing a power transmission network transient equivalent mathematical model; establishing a detailed simulation model of the power transmission network containing a high proportion of new energy units, and dividing the sample set into a training set and a test set; using the training set to perform step-by-step training on the generalized regression neural network; after the training is completed, calling the regression model, calculating the short-circuit current component of the second branch based on the number of low-penetration units and the voltage correction parameter, and calculating the short-circuit current component of the first branch based on the equivalent impedance, and synthesizing the total short-circuit current. In this way, the technical problem of insufficient short-circuit current calculation accuracy of the existing power transmission network transient equivalent modeling technology in the high proportion of new energy access scene is solved, and the accuracy, adaptability and reliability of the calculation results of the equivalent model are improved.

The invention provides a method for selecting an index combination used for constructing an industrial park carbon emission evaluation model, and the method comprises the steps: according to a correlation analysis result, through a stepwise regression analysis method, calculating the index combination from a night light index, a park scale index and a social economic index, and selecting an index combination in which sub-indexes determined to have no correlation with the total amount of carbon emission based on the correlation analysis result are deleted as an industrial park carbon emission evaluation index combination. Therefore, the invention provides the method for selecting the index combination for constructing the industrial park carbon emission evaluation model, and personalized carbon emission evaluation model index selection can be carried out for a specific industrial park.

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 present invention relates to a method, device, computer equipment and storage medium for automatic slag splashing control of a converter, which belongs to the technical field of converter steelmaking. The method comprises: collecting relevant information of the current heat and historical heats, establishing and classifying a database of historical heats, and obtaining corresponding slag splashing and furnace protection gun position control curves; fitting various slag splashing gun position control curves and performing stepwise regression calculations on various slag compositions and weights to form a curve fitting database and an optimal stepwise regression database; combining the condition information of the current heat, obtaining a recommended slag splashing gun position control curve and recommended slag composition and weight values; calculating the calculated values ​​of the slag composition and weight of the current heat according to material balance; obtaining predicted values ​​of the slag composition and weight of the current heat through recurrent neural network calculations; and finally obtaining the slag splashing mode of the current heat and performing automatic slag splashing operations. The present invention can accurately control the slag splashing gun position, improve the slag splashing and furnace protection effect and converter operation rate, and reduce process production costs.

The invention discloses a coarse aggregate void ratio presumption method, which comprises the following steps of: obtaining a plurality of continuously graded coarse aggregates with known void ratios, respectively extracting the grading screen residue of each coarse aggregate, and fitting by using a stepwise regression method by taking the grading screen residue of each coarse aggregate as an independent variable and the corresponding void ratio as a dependent variable to obtain a presumption formula of the void ratio of the coarse aggregates; and presuming the void ratio of the coarse aggregate by using a presumption formula of the void ratio of the coarse aggregate according to the counting screen residue of the continuous grading coarse aggregate to be detected. According to the invention, the test quantity can be reduced, and the test time of the coarse aggregate void ratio is shortened; the void ratio of the coarse aggregate can be presumed in real time according to the counting screen residue data of each grade in the grain composition test of the coarse aggregate on an engineering site, so that the mix proportion of the concrete is conveniently adjusted, and the construction quality of the concrete is guaranteed.

The invention relates to the technical field of steel smelting, in particular to a comprehensive metallurgical performance judgment method, system and equipment for steelmaking lime and a medium, and the method comprises the steps that furnace information of multiple historical furnaces is obtained, the furnace information comprises independent variable parameters and dependent variable parameters, and a historical database is established; dividing a secondary database according to a preset interval; stepwise regression analysis and symmetric neural network operation are performed on each secondary database, first and second optimal combination information is extracted, and third optimal combination information is generated through a recurrent neural network; constructing a metallurgical performance rating model based on all the third optimal combination information; and inputting the heat information of the to-be-evaluated heat into the lime metallurgical performance rating model to obtain the metallurgical performance grade of the lime used by the heat, supplementing the heat information into the historical database, and updating the lime metallurgical performance rating model. According to the method, the judgment comprehensiveness, precision, real-time performance and long-term adaptability can be 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 present invention provides a method and system for training a face detection model. The method includes: performing data augmentation on a training dataset to obtain an augmented image set; performing basic feature extraction on each image in the augmented image set to obtain an original feature map of a first branch; performing feature enhancement based on a validity mechanism on the obtained original feature map to obtain an enhanced feature map of a second branch; initializing training parameters; constructing a stepwise classification group and a stepwise regression group comprising the first branch and the second branch according to a selective strategy; and performing a weighted summation of the stepwise losses of the constructed stepwise classification group and stepwise regression group for supervised model learning until the model converges. Furthermore, the present invention provides a face detection method and system.

The invention provides a multi-intersection signal lamp cooperative control method based on stepwise regression and Webster. The multi-intersection signal lamp cooperative control method comprises the following steps: acquiring initial traffic flow data of each traffic intersection and preprocessing the initial traffic flow data; defining physical vectors of different motion modes of vehicles, using a vector statistical method to count the traffic flow of different physical vectors of each traffic intersection, and constructing a multivariate vector stepwise regression model to predict the total traffic flow of each traffic intersection; based on the total traffic flow of each traffic intersection in unit time, optimizing the traffic light duration of the signal lamps by using a Webster timing method, and performing cooperative control on the signal lamps at multiple intersections according to an optimization result; according to the method, on the basis of unknown traffic preposition information and a classic machine learning mode, the traffic flow of the whole intersection of the main road is predicted through the vector stepwise regression model, then the signal lamps are adaptively controlled and optimized in combination with the Webster timing method, and the method has the advantages of high benefit, high credibility and high robustness.

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.

The invention discloses a method for calculating a target heart rate of exercise, which comprises the following steps of: 1, obtaining the consensus of the relation among age, resting heart rate and target heart rate in the biomedical field and the sports science field at present by looking up academic literatures; 2, designing a research scheme, and determining a method and a tool for collecting data; 3, collecting data on site to obtain 324 samples; 4, through statistical analysis, carrying out multivariate stepwise regression analysis by taking the heart rate after exercise as a dependent variable and taking age and resting heart rate as independent variables, so as to obtain a double-drop target heart rate model; 5, establishing and calculating target heart rate matrix data, determining the age range to be 20-70 years old, determining the resting heart rate range to be 55-95 times per minute, and calculating the matrix data of the target heart rate lower limit value; the problem of correctly calculating the bull's-eye rate according with the medical physiology principle is solved.

The invention discloses a quantitative analysis method for cell damage, and belongs to the technical field of building materials. The method comprises the following steps: S1, acquiring a cell original image data set, performing image background segmentation, and extracting a segmented cell image; s2, extracting gray value information of the segmented cell image, wherein the gray value is 256 dimensions between 0 and 255; s3, calculating the number of pixel points corresponding to each gray value, and generating gray value distribution data; and S4, the gray value distribution data is preprocessed, and the preprocessing comprises abnormal value elimination and standardization processing of the data. S5, stepwise regression is adopted to screen out features significantly related to cell damage; and S6, based on the features screened in the step S5, performing nonlinear modeling on the screened features by adopting a multi-layer perceptron method, and analyzing the cell damage degree. According to the method, the fineness, the fitting capability and the wide applicability of cell damage analysis are improved.