一种基于混合数据回归模型的pm2.5影响因素分析方法和系统
By constructing a hybrid data regression model, the problem of fusing functional and component data was solved, enabling the analysis of dynamic influencing factors of PM2.5 concentration and revealing the dynamic impact of industrial structure, economic level, and temperature on air quality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CAPITAL UNIV OF ECONOMICS & BUSINESS
- Filing Date
- 2026-01-13
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to effectively integrate functional and component data, making it impossible to comprehensively analyze the dynamic impacts of economic and natural factors on PM2.5 concentrations.
A mixed data regression model was established. Through equidistant logarithmic ratio transformation, function basis expansion, and iterative reweighted least squares method, a mixed data regression model was constructed with component data and numerical data as covariates and functional data as dependent variables. The dynamic impact of the proportion of the three industries, per capita GDP, and temperature on PM2.5 in various cities was analyzed.
It enables precise analysis of the dynamic influencing factors of PM2.5 concentration, revealing the dynamic impact of industrial structure, economic level, and temperature on air quality, and providing richer data analysis results.
Smart Images
Figure CN122047706B_ABST