基于数据处理的丝兰皂苷提取工艺的参数优化方法
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XI AN RAINBOW BIO-TECH CO LTD
- Filing Date
- 2026-05-14
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional particle swarm optimization (PSO) algorithms lack the flexibility to adjust inertial weight settings in the yucca saponin extraction process, making it difficult to adapt to the dynamic requirements of complex multi-parameter processes at different iteration stages, resulting in low optimization efficiency and insufficient stability.
An adaptive inertia weight mechanism is adopted, which combines a machine learning model and a Gaussian process regression model. By dynamically adjusting the inertia weight and perturbation potential energy, the iterative process of the particle swarm optimization algorithm is optimized. The distribution of multidimensional parameter data and the degree of advantage of the optimal result are used to achieve adaptive control of the search process.
It significantly improves the optimization efficiency and stability of the yucca saponin extraction process, reduces ineffective iterations, avoids premature entry into local optima and excessive oscillations, improves the accuracy and consistency of the extraction process, and reduces energy consumption and time costs.
Smart Images
Figure CN122224312B_ABST