Quality Correlation Analysis of Electric Power Material Quality Data Based on Association Rule Algorithm

The quality management of power materials is an important guarantee for the safe and stable operation of power grids. However, due to the diversity of power materials, large procurement demand, and complex construction environment, traditional quality management methods are difficult to meet the needs of modern power grid intelligence and efficiency. This paper introduces a power material quality data analysis method based on the Apriori association rule algorithm, aiming to improve the accuracy and efficiency of quality monitoring. The study constructs a power material quality database, uses the Apriori algorithm to mine frequent item sets and association rules in material quality data, analyzes the potential associations between different quality indicators, and identifies key factors affecting quality. At the same time, in order to improve the applicability of the method, data preprocessing technology is used to optimize the integrity and consistency of the data, and evaluation indicators such as lift and confidence are used to screen association rules. The method based on the Apriori algorithm can effectively identify hidden quality problem patterns. In specific experiments, as the support and confidence thresholds increase, the accuracy, recall rate and F1 value are significantly improved. There is a strong correlation between multiple key factors. For example, Supplier A has a significant correlation with conductivity (confidence level: 0.7).