55 results about "Multicollinearity" patented technology

A computer-implemented method for predicting a Seawater intrusion index in coastal aquifers in arid regions with multiple parameters in groundwater for a sustainable groundwater management includes selecting multiple parameters based on the level of informative contribution and the multicollinearity to obtain an input dataset, partitioning the input dataset into a modeling dataset and a testing dataset, dividing the modeling dataset into a training set and a validation set and tuning hyperparameters based on a grid search strategy for each model, training each model based on the training set and hyperparameters, evaluating each model based on the validation set and multiple statistical performance metrics, selecting a prediction model based on the testing dataset and the multiple statistical performance, predicting the Seawater intrusion index from the prediction model, and creating an adaptive groundwater management strategy based on the SWI index.

The invention discloses a spare part demand prediction method based on a ridge regression improved algorithm, and relates to the technical field of spare part demand prediction, and the method comprises the following steps: 1, data collection and preprocessing; 2, performing feature extraction on the time series data; step 3, constructing an improved ridge regression algorithm: combining Huber loss and a kernel function; 4, dividing a training set and a test set according to a time sequence, selecting an optimal parameter by adopting a K-fold cross validation method, performing robust kernel ridge regression model training on the training set, predicting a model generated by training on the test set, comparing with a true value of the training set, and calculating MAE, MSE and a value; and step 5, comparing with a traditional ridge regression prediction result. According to the method, the model overfitting problem caused by multiple collinearity among influence factors in spare part demand prediction can be solved, compared with traditional ridge regression, the nonlinear relation among variables can be captured, robustness is higher, and therefore the spare part demand quantity can be predicted more accurately.

The invention discloses an intelligent decision-making method and system for intraoperative stress injury based on machine learning, and relates to the technical field of electric digital data processing. The intelligent decision-making method for the intraoperative stress injury based on machine learning comprises the following steps: S1, selecting and analyzing a sample; s2, performing multi-collinearity evaluation; s3, evaluating information loss; and S4, intelligent decision making. According to the method, sample selection adjustment is carried out through sample selection analysis data, then principal component dimension reduction is carried out based on multi-collinearity evaluation data, finally potential factors are introduced step by step based on information loss evaluation data, and whether risk weights of high-risk factors are obtained or not is judged based on multi-collinearity evaluation values after the potential factors are introduced. And intelligent decision-making of the intraoperative stress injury is performed in combination with the high-risk factors, so that the screening accuracy of the high-risk factors in the intelligent decision-making of the intraoperative stress injury is improved, and the problem of low screening accuracy of the high-risk factors in the intelligent decision-making of the intraoperative stress injury in the prior art is solved.

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 multi-classification risk prediction method and system for liver diseases based on machine learning, and relates to the technical field of biomedicine, and the method comprises the following steps: collecting fatty liver disease diagnosis results and biochemical indexes of a subject to form a training sample set, the method comprises the following steps: screening out biochemical indexes significantly related to fatty liver diseases through single-factor regression analysis, determining potential risk factors, carrying out multicollinearity test on the factors, screening out risk factors, constructing a plurality of machine learning classification models for training, and selecting a model with the best performance as a reference model. The contribution degree of each important risk factor is evaluated and sorted, classification significant factors are determined, the significant factors serve as classification metadata, a plurality of judgment models are trained, input factors are dynamically selected according to contribution values, and finally a fatty liver disease degree classification result is output, so that complex conditions under different sample features and clinical backgrounds are better handled; and the accuracy of prediction results is improved.

The invention discloses a shared bicycle passenger flow prediction method based on a geographically weighted regression model and a related device. The method comprises the following steps: collecting spatial data and social economic data of a shared bicycle putting point of a target city; inputting the effective independent variable data set, the spatial autocorrelation of the effective independent variables, the multi-collinearity test result and the housing price factor data into a least square regression model and a geographical weighted regression model, and outputting a regression coefficient, a significance level and a goodness of fit; and according to the regression coefficient, the significance level, the goodness of fit, the delivery point category and the actual shared bicycle passenger flow data, calculating the prediction precision and the lifting amplitude at different delivery point categories. According to the method, the spatial heterogeneity relationship between the shared bicycle passenger flow volume and the influence factors is considered through the geographically weighted regression model, the regression coefficient is adjusted according to different geographic positions, the influence characteristics of a local area are reflected more accurately, and the prediction precision is remarkably improved.

The invention discloses a segmented quantitative analysis method for uplift pressure of a gravity dam. The uplift pressure of the gravity dam is influenced by various factors, and safety monitoring data of the gravity dam often have the problems of multiple collinearity and small samples. The method comprises the following steps: introducing a BFAST time sequence decomposition method, decomposing original uplift pressure monitoring data into a trend component and a seasonal component, and obtaining trend segment points and seasonal segment points; a Bayesian model average modeling method integrating Bayesian model selection is introduced, and a gravity dam uplift pressure statistical model is established in a segmented mode; and finally, upstream and downstream water pressure components of the uplift pressure are separated according to the average multiple models, and quantitative analysis of the uplift pressure of the gravity dam is achieved. According to the method, more accurate fitting and component separation effects can be obtained, and accurate quantitative analysis of the uplift pressure of the gravity dam can be realized.

The present disclosure relates to a method and device for determining influencing factors of flue gas emissions, equipment and medium. A plurality of initial influencing factors and a plurality of flue gas emission information of a target unit are obtained; the initial influencing factors and the flue gas emission information are respectively subjected to time sequence causality analysis, dependency analysis and correlation analysis to obtain time sequence causality information, dependency information and correlation between the initial influencing factors and the flue gas emission information; then, based on the time sequence causality information, the dependency information and the correlation, candidate influencing factors are obtained from the plurality of initial influencing factors, so that the selected influencing factors have high confidence in causality, explainability and information integrity; finally, the system feature dimensionality of the candidate influencing factors is reduced to determine the target influencing factors that affect the generation of a plurality of flue gas emission information of the target unit, thereby eliminating the multicollinearity between different influencing factors. Thus, the explainability of the pollutant emissions of the unit under the complex working condition of deep peak regulation is realized.

The application discloses a kind of based on elastic network regression's wiring error leakage user positioning method, system, terminal and medium, wherein method includes: obtaining the abnormal station area leakage fault day's station area residual current data, user load current data, and constructs station area residual current time series and subordinate user load current time series;With the user load current data obtained as explanatory variable, station area residual current data as explained variable, carry out elastic network regression calculation, obtain the optimal explanatory variable after eliminating multicollinearity and its corresponding regression coefficient and construct regression model;Compare the absolute value of each regression coefficient, and the user whose absolute value of regression coefficient is greater than preset threshold is judged as zero line, ground line wiring error user.Through identifying zero line, ground line wiring error abnormal user, to solve the existing low-voltage station area exists because user zero line, ground line wiring error and lead to user load current data into residual current problem.

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 invention provides a method and related equipment for assessing earthquake landslide susceptibility. Multiple landslide influencing factors are selected from multi-source data of the study area, and multicollinearity analysis and Pearson correlation coefficient analysis are performed on all landslide influencing factors to obtain basic environmental factors. Newmark displacement is calculated based on the physical and mechanical parameters in the multi-source data and the basic environmental factors. The Newmark displacement is stacked with the basic environmental factors to obtain multi-channel image data, which is then input into an earthquake landslide susceptibility assessment model for evaluation, resulting in a coseismic landslide susceptibility zoning map of the study area. Compared with existing technologies, this invention uses Newmark displacement as an independent feature input, compensating for the lack of physical mechanism support in traditional pure data-driven models. It achieves the complementary advantages of geological disaster dynamics mechanisms and deep learning feature extraction capabilities, improving the accuracy and reliability of earthquake landslide susceptibility assessment.

The invention discloses a rapid calculation method and device for an energy maximum tracking model, and relates to the technical field of demand analysis, and the method comprises the following steps: dividing an original training set into a training set and a test set, the training set being used for modeling and parameter adjustment, the test set being used for evaluation, and preprocessing the training set; correlation analysis is carried out on training set features, local correlation measurement is used to avoid multiple collinearity, and an energy value F of each feature is calculated; according to the method, the multi-collinearity problem is avoided by calculating the energy value of each feature and selecting the feature with good prediction, redundant features can be identified and removed through local correlation measurement, the accuracy of feature selection and the robustness of the model are improved, orthogonalization processing helps to eliminate the dependency relationship between the features, and the robustness of the model is improved. The introduction of the regularization item is helpful for controlling the complexity of the model, avoids the overfitting caused by the too complex model, and obtains a stable and accurate prediction result.

A computer-implemented method of generating interpretable, composite marker indexes that are discriminative and noise-robust is provided. The method comprises storing remotely collected multimodal digital markers from a first cohort and a second cohort. The method further comprises grouping multicollinear features in the multimodal digital markers into clusters, and then selecting representative features for the clusters for multiple classification tasks that require discrimination between the first cohort and the second cohort. The method further comprises linearly combining the representative features into an interpretable, composite marker index such that relative contributions of each of the representative features to the interpretable, composite marker index are known.

The invention belongs to the technical field of building monitoring, and discloses a three-cantilever beam structure crack width prediction method and system based on a regression problem, and the method comprises the following steps: obtaining simulation data sets of a three-cantilever beam identifier structure under different crack conditions; through multivariable visualization and correlation analysis, identifying the multicollinearity between the crack width data and the cantilever beam strain data, and deleting redundant variables to obtain an optimized data set; constructing a plurality of regression models, training and evaluating each regression model by using the optimized data set, and selecting an optimal regression model as a crack width prediction model according to an evaluation result; and the obtained cantilever beam strain data are input into the crack width prediction model, and corresponding crack width data are predicted through the crack width prediction model. According to the method, multi-dimensional information identification and event prediction of the micro gap of the structural body are effectively realized, and technical support is provided for building structure health monitoring and engineering implementation.

The application discloses a software performance anomaly detection method and device. The software performance anomaly detection method comprises the following steps: collecting data of attributes representing the running state of a target software, and obtaining a first data set; screening out elements not meeting a preset multicollinearity condition in the first data set according to a multicollinearity test standard, and obtaining a second data set; performing noise reduction processing on the second data set by using an adaptive discrete wavelet decomposition method according to the waveform characteristics of key performance elements, and obtaining a third data set; training an anomaly detection model by using the third data set, and obtaining a software performance anomaly detection model; and detecting the target software to be detected by using the software performance anomaly detection model, and obtaining a detection result. By combining the multicollinearity test standard, the adaptive discrete wavelet decomposition method and the anomaly detection model, the problem of excessive noise and unobvious software performance failure characteristics is effectively solved, and the accuracy of software detection is improved.

The present application is suitable for the field of mineral exploration, and provides a lithium geochemical anomaly identification method and system for a lithology complex area, which comprises the following steps: obtaining geochemical data of the area, and preprocessing the geochemical data of the area; determining PLSR independent variable indexes according to the preprocessed geochemical data; constructing a PLSR regression model according to the PLSR independent variable indexes; determining lithium geochemical background values of each sample point in the area according to the PLSR regression model; and identifying lithium geochemical anomalies in the area according to the predicted upper limit of the lithium geochemical background values. The present application uses PLSR to construct a regression model between lithium and lithology indicating elements, and then can determine the lithium geochemical background values of each sample point, effectively solves the multicollinearity problem in lithium multiple linear regression analysis, improves the calculation accuracy of the lithium geochemical background values of each sample point in the lithology complex area, and lays a solid foundation for lithium geochemical anomaly identification.

The invention belongs to the technical field of big data analysis, and discloses an immersed tunnel cost prediction method and system based on big data analysis. According to the method, firstly, an immersed tunnel cost big database is constructed, and key feature parameters are screened out through correlation analysis and multi-collinearity processing; then, on the basis of engineering attributes of the cost influence factors, layering the feature vectors, and respectively establishing an intra-layer nonlinear mapping relationship and an inter-layer coupling relationship reflecting a synergistic effect between different engineering attribute feature layers, so as to construct a cost prediction model fusing multi-level influence; and finally, correcting the preliminary prediction result in combination with the specific parameters of the project to be predicted and the human-material-machine price dynamic data to obtain the final cost. According to the method, the construction cost of the immersed tube tunnel can be accurately and efficiently predicted.

The invention belongs to the technical field of debris flow assessment, and provides a method for determining a debris flow susceptible area driven by multiple initiation mechanisms in a cold and cold mountainous area. Comprising the steps of disaster-inducing factor selection, data projection and resampling, bilinear interpolation and nearest neighbor interpolation, index statistical data calculation, multi-collinearity data rejection, model coupling, susceptibility spatial distribution diagram generation and debris flow susceptibility area determination. According to the invention, through multi-model coupling, mining of relationships among different types of data is realized, and the classification precision of the model is improved; the disaster-inducing factors are selected by combining the debris flow formation mechanism and the geographic features of the cold and cold mountainous area, debris flow susceptibility mapping under multiple triggering mechanisms is achieved, and the reliability of the model is improved.

The present invention provides a method and system for online compensation of vector magnetic interference of a rotary-wing UAV, belonging to the field of geomagnetic vector measurement technology. In order to solve the problem that the magnetic vector measurement value is affected by the magnetometer noise and attitude measurement noise at the same time, resulting in the accuracy of geomagnetic vector calculation being limited and the magnetic compensation effect being limited; and the problem that the maneuvering attitude of the rotary-wing UAV is limited and the multicollinearity of the measurement data is serious, resulting in difficulty in solving the model parameters. In the recursive process, the present invention uses an adaptive exponentially weighted moving average noise covariance estimator to quickly estimate the noise, adjust the noise covariance matrix of the recursive total least squares method in real time, reduce the influence of noise, and improve the compensation accuracy under the influence of geomagnetic vector error. At the same time, in the recursive process of the recursive total least squares method, the covariance matrix is ​​adaptively regularized to improve the compensation parameter estimation accuracy under the influence of multicollinearity.

This invention discloses a method for predicting the risk of landslides based on connectivity and a geographic detector. It employs a discretization approach to transform the comprehensive soil erosion index and influencing factors into characteristic parameters of categorical variables. Multicollinearity analysis is performed on all characteristic parameters to filter out those exhibiting collinearity. The geographic detector is used to calculate the explanatory power of each characteristic parameter for landslide risk in the target area and the frequency of landslide occurrence at sample points within different grids. The product of the explanatory power and frequency of landslide occurrence for different characteristic parameters within each grid is accumulated to obtain a spatial distribution map of landslide risk prediction for the target area. The beneficial effects of this invention are: by combining soil erosion and sediment transport potential, and using a geographic detector to analyze the explanatory power of each characteristic parameter for landslide occurrence, highly accurate landslide risk assessment results are obtained.

The invention discloses an air conditioner load prediction method based on PCA and GRU. The method comprises the steps that firstly, historical air conditioner load data and corresponding temperature, weather and date type multivariate influence factors are obtained; carrying out dimension reduction on the multivariate influence factors by utilizing a principal component analysis method, eliminating multiple collinearity, and extracting a plurality of comprehensive principal components of which the cumulative contribution rates exceed a preset threshold value; carrying out abnormal value identification, correction and normalization preprocessing on the historical load data; aligning the preprocessed load data with the comprehensive principal component according to a time sequence, and constructing an input feature sequence for training a gating cycle unit model; and finally, predicting the air conditioner load in a future time period by using the trained model. According to the method, model input is effectively simplified through PCA, the data quality is improved, the strong time sequence modeling capability of the GRU model is combined, the precision and efficiency of short-term air conditioner load prediction are jointly improved, and reliable technical support is provided for power system dispatching and demand side management.

The invention discloses a typical tree species growth prediction method, system and device based on multi-stage multi-factor regression and a medium, and relates to the technical field of tree species growth prediction.The method comprises the steps that multi-source heterogeneous data are collected and preprocessed; dividing the independent variables into different types of influence factors, and performing statistical test on each factor to obtain a preliminary candidate variable set; calculating a variance expansion factor of the candidate variables, and when the expansion factor exceeds a threshold value, reducing the correlation among the preliminary candidate variables by adopting a collaborative path method to obtain a final variable; performing regression modeling through a three-stage modeling method based on the final variable to generate a regression model, and establishing a regression sub-model for each partition; and based on the obtaining mode of the final variable, extracting a judgment rule of the tree species and outputting the judgment rule in a structured format. According to the method, high-precision prediction of the growth under multi-factor driving can be realized, and the problems of multi-collinearity, unstable variable selection, insufficient nonlinear structure expression and the like in a traditional regression model are solved.

The invention belongs to the technical field of building monitoring, and discloses a three-cantilever beam structure crack angle prediction method and system based on a multi-classification problem, and the method comprises the following steps: obtaining simulation data sets of a three-cantilever beam identification body structure under different crack conditions; according to the simulation data set, through multivariable visualization and correlation analysis, identifying multicollinearity between the crack angle data and the cantilever beam strain data, and deleting redundant variables to obtain an optimized data set; on the basis of the optimized data set, a crack angle prediction model is constructed by adopting multiple algorithms oriented to a multi-classification problem; and the obtained cantilever beam strain data are input into the crack angle prediction model, and corresponding crack angle data are predicted through the crack angle prediction model. According to the invention, the dynamic change of the existing crack under the load effect can be monitored, and the trend information of the new crack can be identified.

This application relates to the fields of remote sensing image processing and ecological environment monitoring technology, and discloses a method for extracting peat bog information based on the dense temporal characteristics of multi-source remote sensing. The method includes acquiring time series of microwave radar and optical vegetation index, performing alignment and cleaning, extracting low-frequency and continuous first-order derivative sequences from the microwave radar to generate temporal basis features; extracting historical delay features for effective optical observation times, splicing the basic state term and derivative modulation product term to generate a bilinear distributed lag observation matrix; performing scale unification processing on the lag observation matrix, constructing a diagonal regularized matrix based on time-lapse attenuation characteristics, and solving for the solution vector; calculating the normalized asymmetric hysteresis intensity and reconstruction quality factor, and combining dual thresholds to determine the output distribution data. This invention incorporates the physical hysteresis effect of hydrological evolution into the computational framework, overcoming multicollinearity and observation sparsity problems, and achieving high-precision extraction of peat bogs.

The invention belongs to the technical field of tunnel construction, and particularly relates to a method and system for rapidly predicting the wind speed of a tunnel face in drilling and blasting method tunnel construction. The method comprises the following steps: acquiring the concentration and wind speed data of harmful gas at a tunnel face measuring point within 30 minutes of ventilation after blasting of the tunnel face; constructing a multiple linear regression model of the harmful gas concentration and the wind speed; based on correlation analysis, testing the reliability effectiveness of a control variable in the multiple linear regression model; checking whether multiple collinearity exists among independent variables in the multiple linear regression model based on multiple collinearity analysis; determining a prediction model based on the reference regression analysis result; and the wind speed value is quickly predicted based on the concentration values of CO, CO2 and SO2. The relation between the harmful gas and the wind speed is established through the concentration of the harmful gas and the wind speed data, the change of the wind speed of the tunnel face can be rapidly predicted, the frequency of the draught fan can be dynamically regulated and controlled, and high efficiency and safety of ventilation of drilling and blasting method tunnel construction are facilitated.

The application provides a balanced intrusion detection method and system based on width focus learning, and belongs to the field of information security intrusion detection. The method comprises the following steps: obtaining a standardized data set, screening and filtering information features of the standardized data set through information variance and multicollinearity, eliminating invalid features and redundant features, and optimizing data quality; adopting a generative adversarial network to construct a balanced data set, introducing a deep belief network to optimize generated data, increasing data diversity and authenticity, and using a sigmoid model as a discriminator of the adversarial network. Finally, a width learning network is used to learn and train the balanced data set, a focal loss mechanism is introduced to strengthen the attention degree of the width learning model to different types of attack samples, so that the detection and recognition of different attack samples are achieved. The balanced intrusion detection model based on width focus learning improves the precision and reliability of intrusion detection attack detection, shortens the training time, and improves the detection rate of different attack samples.

The present invention discloses a negative social emotion representation analysis system for colleges and universities, which relates to the technical field of social emotion analysis, and includes a data collection and preprocessing module, a feature extraction and anomaly detection module, an intelligent perception and negative emotion representation module, and an anomaly risk assessment and response module: The data collection and preprocessing module obtains emotion feature information in real time based on multi-dimensional data sources, and cleans and preprocesses the obtained emotion feature information. The present invention solves the problem of multicollinearity among multi-dimensional data features by calculating the data consistency offset coefficient and the strong correlation coefficient, avoiding unstable model regression coefficients. The system real-time identifies abnormal correlations and classifies and processes them in combination with risk thresholds to ensure robust model output. By continuously collecting data to establish an analysis set, it supports long-term emotion trend analysis and provides personalized responses according to the risk level, such as reminders, interventions or emergency measures, to achieve precise management and timely adjustment of students' emotional states.

An improved frequency ratio-based landslide disaster negative sample optimization method is provided in an embodiment of the present invention, belonging to the technical field of data processing. Specifically, it includes: collecting disaster-bearing environment data and performing preprocessing to obtain raster data; defining two parameters of classification accuracy and classification bandwidth, introducing information entropy and maximum frequency ratio to adaptively set the parameter values of classification accuracy and classification bandwidth, and calculating the comprehensive frequency ratio; based on the statistical distribution of the comprehensive frequency ratio, using the peak point as the negative sample screening threshold, dividing the negative sample screening area based on this threshold, then calculating the ratio of positive and negative samples, and further determining the negative sample labels under the buffer constraint; according to the frequency ratio distribution of each raster data, extracting the attribute interval with a frequency ratio greater than 1 as the significant feature, then classifying the significant features of each raster data, and determining the best feature elements through multiple collinearity analysis. Through the solution of the present invention, the accuracy and reliability of negative sample selection are improved.

The invention discloses a karst region soil thickness mapping method based on a stacked integrated model, and relates to the technical field of soil mapping, and the method comprises the following steps: taking climate, terrain and stony desertification elements as spatial information; obtaining optical remote sensing images of the research area in recent ten years, processing the remote sensing images to obtain vegetation, soil and stony desertification indexes, and generating various topographic factors through topographic software; performing correlation analysis on environment covariables to eliminate multiple collinearity, converting qualitative variables and unifying resolution; dividing the data into two groups containing and not containing stony desertification information, and eliminating and screening an optimal variable by using six models through recursive features; taking the six models as base models, and taking prediction results as input to construct three meta-models to realize stacking integration; and finally, soil thickness prediction mapping is completed, and the uncertainty is predicted through quantile regression quantification. According to the method, the stacked integrated model and stony desertification information are combined, and the precision and resolution of soil thickness prediction are improved.

The invention discloses a method for predicting low-temperature performance of aged asphalt, and belongs to the technical field of performance evaluation of road materials. According to the method, firstly, an asphalt sample is subjected to simulated aging treatment, then improved column chromatography is adopted for separating and measuring the content of saturates, aromatics, colloids and asphaltene of aged asphalt, a chromatographic column is of an activated aluminum oxide and silica gel double-layer adsorption structure, gradient elution is combined, and the separation efficiency and reproducibility are remarkably improved. The method comprises the following steps: firstly, measuring a creep rate m value of aged asphalt, analyzing the correlation degree between the m value and components of the aged asphalt by using grey correlation, and establishing a multiple linear prediction model based on ridge regression by taking the contents of four components as independent variables and the m value as a dependent variable; the model effectively overcomes the multicollinearity problem among components, component analysis data are input into the model for unknown samples, the low-temperature performance of the samples can be predicted, and the research and development efficiency and the engineering quality control level are greatly improved.