Research on the Construction of Big Data Classification System Based on Distributed Data Flow

In the context of the comprehensive popularization of network technical services and database construction system, more and more data are used by enterprises or individuals. It is difficult for the existing technology to meet the technical analysis requirements of the development of the era of big data. Therefore, in the development of practice, we should continue to explore new technologies and methods to reasonably use big data. Therefore, on the basis of understanding the current big data technology and its system operation status, this paper designs relevant algorithms according to the big data classification model, and verifies the effectiveness of the analysis model algorithm based on practice.

An Electric Equipment Analysis Method based on Knowledge Reasoning

Facing the increasingly complex power grid architecture and high equipment failure risk, a comprehensive equipment condition analysis method based on knowledge reasoning is proposed in this paper, mainly using the large amount of characteristic information to realize the condition evaluation, fault detection and early warning. Firstly, it statistically analyzes the historical data, extracts the characteristic information of equipment health status and builds a knowledge mapping library of key factors for equipment-centered status analysis; secondly, it establishes an intelligent early warning library of equipment index data, and gets the probability of equipment defects and fault risks through induction-based knowledge reasoning method; finally, it gets the equipment status rating through logic and rule-based knowledge reasoning method and the closed-loop system of equipment status evaluation is established. The reasonableness of the evaluation method is verified by the examples, which realizes supervision equipment operation status mining early warning, sensing equipment operation status in advance and reducing potential operation risk of power grid.