Information flow-based second-order cone programming model for big data using rough concept lattice

The purpose of this paper is to study the rough concept lattice and use the information flow to construct a second-order cone programming model for big data. Through the construction of the model, attribute reduction is performed on the original data of the noise in the formal background. Then, construct the concept lattice according to the reduced formal background, and then analyze the big data in the form of information flow. Then, based on the advantages of the beta-upper and lower distribution reduction algorithms of the variable-precision rough set, combine the rough concept. The characteristics of the background of the lattice form, the second-order cone thought method theory is applied, and then a second-order cone calculation model is constructed. The rough concept lattice is applied to the processing of big data, and then it is analyzed and researched through concrete examples. The time required in traditional mode is between 118.3 min and 123.6 min, while the time required for second-order cone and concept lattice fitting is 92.4 min and 98.5 min. Experimental data show that the rough concept lattice uses information flow to construct a second-order cone programming model for big data, which results in a greatly reduced number of nodes in the rough concept lattice and an enhanced anti-noise capability of the system, which saves data statistics and calculation time. The traditional concept lattice algorithm can be traced back to the purification of the formal background, and the purification of the formal background can simplify the concept connotation and study attribute reduction from the perspective of lattice isomorphism. Experimental data show that the rough concept lattice uses information flow to construct a second-order cone programming model for big data, which greatly guarantees the integrity and security of the data by about 15%, and saves 20% of the data processing time compared with traditional and algorithms. It has guiding significance for the efficient and secure development of big data in the future. In this paper, data feature mining and information flow model construction are carried out, the power spectral density feature extraction of big data is carried out from a large number of noisy and fuzzy data, and the second-order cone programming model of big data information flow is carried out by rough concept lattice method.