69 results about "Multicollinearity" patented technology

The embodiment of the invention provides a landslide disaster negative sample optimization method based on an improved frequency ratio, and belongs to the technical field of data processing, and the method specifically comprises the steps: collecting disaster-pregnant environment data, and carrying out the preprocessing of the disaster-pregnant environment data, and obtaining raster data; defining two parameters of grading precision and grading bandwidth, introducing information entropy and a maximum frequency ratio to adaptively set parameter values of the grading precision and the grading bandwidth, and calculating a comprehensive frequency ratio; based on the statistical distribution of the comprehensive frequency ratio, taking a peak point as a negative sample screening threshold value, dividing a negative sample screening area based on the threshold value, then realizing calculation of a positive and negative sample ratio, and further determining a negative sample label under the constraint of a buffer area; according to the frequency ratio distribution of each raster data, an attribute interval with a frequency ratio greater than 1 is extracted as a significant feature, then the significant features of each raster data are classified, and an optimal feature element is determined through multi-collinearity analysis. Through the scheme of the invention, the accuracy and reliability of negative sample selection are improved.

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 carbon emission prediction method and device based on the relation between capture variables and a medium, and relates to the technical field of traffic carbon emission prediction. The method comprises the following steps: based on a preset traffic carbon emission influence factor set, calculating the correlation between the traffic carbon emission influence factor set and the traffic carbon emission, and carrying out preliminary screening to obtain a preliminary variable candidate pool; performing multi-collinearity detection on the preliminary variable candidate pool and updating the preliminary variable candidate pool to generate a screened variable candidate pool; performing nonlinear relation analysis on the screened variable candidate pool through an XGBoost algorithm, and screening to obtain a key variable sequence; inputting the key variable sequence and the traffic carbon emission into a Transform model, carrying out feature embedding and position coding, and generating a time sequence vector; and respectively predicting traffic carbon emission levels under different levels for the time sequence vectors. According to the method, the scientificity of the variable screening process and the reliability of model construction are ensured, and then the prediction capability of the model is enhanced.

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 relates to an immunotherapy prognosis evaluation system based on combined penalty likelihood modeling, and belongs to the technical field of precision medicine. Based on a generalized linear hybrid model, various types of clinical endpoints, such as objective remission rate and disease-progression-free lifetime, are effectively integrated. By introducing the random effect, the model can capture potential correlation between end points, and the statistical effectiveness of analysis is enhanced. For the colinearity between clonality mutation features, key variables are screened through a penalty likelihood method, the influence of multiple colinearity is reduced, and the explanatory property and prediction accuracy of the model are improved. In order to overcome the challenge of limited sample size, cross-subgroup data fusion likelihood modeling is introduced, the strategy not only retains the model specificity of each subgroup, but also improves the adaptability of the model to small sample data and the prediction accuracy through information sharing among the subgroups.

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 provides a regional mountain torrent zoning method and device. The method comprises the steps of obtaining initial data according to collected mountain torrent breeding background data of a target region; performing correlation analysis and multi-collinearity analysis on the data of each dimension in the initial data to obtain input data in a grid form; each grid in the input data is regarded as a node, edges between the nodes are determined according to the distance between the nodes, and therefore the input data are constructed into graph structure data G containing a node matrix, a normalized adjacent matrix and an attribute matrix; aiming at the condition that the number of the partitions is not set in the target area, traversing calculation is carried out on all the number of the partitions by taking G as input and using a graph neural network, and the optimal number of the partitions is determined by taking a clustering effectiveness index as an optimization target through an optimization method, so that a partition result of the grid scale is determined; and post-processing a partition result of the grid scale, and drawing a final mountain torrent partition map. The method is high in partitioning precision, high in automation degree and high in universality.

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.

Embodiments of the disclosure provide a method, apparatus and electronic device for business processing. The method includes: obtaining a plurality of performance indicators associated with a business indicator; determining, in the plurality of performance indicators, a target performance indicator with a multicollinearity problem; determining a first performance indicator; determining a first regression coefficient between the target performance indicator and the first performance indicator; determining a plurality of second regression coefficients between the business indicator and a plurality of second performance indicators; and restoring a regression coefficient of the target performance indicator to the business indicator based on the first regression coefficient and the plurality of second regression coefficients, the regression coefficient of the target performance indicator to the business indicator being directly proportional to a degree of impact of the target performance indicator on the business indicator.

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 invention relates to the field of blast furnace ironmaking process monitoring and fault diagnosis, and aims to provide an SFENOSA-based blast furnace ironmaking process monitoring method related to KPI (Key Performance Indicator). The method comprises the following steps: firstly, extracting low-speed characteristics of monitoring variables through an SFA model, and then establishing an ENOSA model to construct monitoring statistics and control limits; due to the fact that the problems of overfitting and multiple collinearity in process variables are solved through ENOSA, process data and KPI data are divided into three orthogonal subspaces, and therefore independent monitoring of KPI related components and KPI unrelated components is achieved. According to the SFENOSA method provided by the invention, low-speed features extracted from process variables are taken as input, the dynamic nature of process data is overcome, and the modeling performance and the robustness are improved by utilizing an EN method; due to the fact that the dynamic nature, overfitting and multicollinearity problems of the blast furnace ironmaking process are considered, the process monitoring and fault detection efficiency can be effectively 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 mining subsidence area line tower deformation disaster-causing evaluation factor determination method. The method comprises the following steps: step 1, preliminarily selecting an influence factor which influences the safety of a power transmission and distribution line tower; step 2, analyzing each primarily selected influence factor, judging whether the influence factor has an influence on the safety of the tower, if so, retaining the influence factor, and if not, deleting the influence factor; step 3, performing multi-collinearity analysis on each factor which affects the safety of the tower, and finally determining an evaluation factor which affects the safety of the tower; the technical problems that in the prior art, determination of the line tower deformation disaster-inducing factors in the mining subsidence area is affected by the mountain microtopography, and a previous goaf deformation research method is not completely applicable are solved.

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.