Information Intelligent Acquisition Generated by Matrix Reasoning of Inverse P-Set

More and more attention has been paid to the intelligent acquisition of information in the field of data mining in big data, Internet of things and so on. Unlike popular methods such as machine learning, this paper attempts to propose a matrix reasoning method for intelligent information acquisition from the perspective of set theory. The inverse packet set (P-set) is a new set model with dynamic features. In the inverse P-set, the attribute(\alpha {i})of the element(x{i})satisfies the expansion or contraction paradigm for attribute extraction. Based on the concept of inverse P-set, this paper presents some new concepts as(\alpha ^{F})-information equivalence class,(\alpha ^{\overline{F}})-information equivalence class, and((\alpha ^{F}, \alpha ^{ \overline{F}}))-information equivalence class. Then, this paper gives some theorems as internal inverse P-augmented matrix inference, outer inverse P-augmented matrix inference, and inverse P-augmented matrix inference, which are generated by the above information equivalence class. At last, this paper gives the application of intelligent acquisition of information.