Physicochemical reaction mechanism study and model optimization based on big data analysis

Abstract

The huge amount of data generated by information technology, intelligence and automation has serious redundancy and duplication, which becomes a hidden cost of consuming resources. In the field of physicochemical analysis, for the explanation of mechanism, the analysis of big data can provide reference and integration. The purpose of big data-based analysis is to remove redundancies and inconsistencies in the given raw characterization data to better improve its completeness and accuracy. The paper takes the density matrix of quantum bits, which is different from the classical real number representation, etc. as the representation of feature data, and investigates the cleaning of feature data based on quantum representation from the perspective of the intersection of theoretical physics and computer science. This paper comprehensively describes the basic theories and methods of data mining. On the basis of understanding and analyzing a variety of data mining techniques, it focuses on the BP neural network-based data mining techniques to provide in-depth analysis and elaboration. Further, this paper proposes an improved algorithm with multi-algorithm advantage integration and joint optimization for the deficiencies in the BP neural network algorithm, which is fully demonstrated in the nonlinear function simulation. In addition, based on the HA-BP algorithm, this paper has designed anomaly data detection model and variable factor analysis model, and their reliability and practicality are also fully demonstrated in the nonlinear function simulation in the analytical study of physicochemical reaction mechanism.