8 results about "Stepwise regression" patented technology

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

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

The application discloses a greening maintenance quality monitoring system based on soil characteristics, which comprises a sensing layer, a network layer, a platform layer and an execution layer which are sequentially communicated and connected, forming a closed-loop control architecture. The sensing layer collects soil characteristic parameters and plant growth characteristic parameters through a vibrating string soil pore water pressure sensor and the like, and uploads the parameters after pretreatment. The network layer adopts adaptive LoRa-Mesh networking technology, dynamically adjusts the transmission period to ensure stable data transmission. The platform layer establishes the quantitative correlation between soil characteristics and plant growth state based on an improved polynomial stepwise regression model, calculates the maintenance quality evaluation value and divides the level, and generates targeted maintenance instructions. The execution layer executes irrigation and fertilization operations and feeds back the execution state. Through accurate monitoring of core parameters, quantitative model evaluation and closed-loop control, the application improves the maintenance accuracy and efficiency, reduces resource waste, and is suitable for various greening areas.

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

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

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

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

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