Abstract
To solve the problem of not being able to intuitively lock the cause of defects when processing massive quality inspection datasets with multiple batches, types, and models of measurement terminals, a MapReduce-based Apriori algorithm is proposed. After the MapReduce algorithm model calculation, the processing efficiency of the quality inspection dataset has been significantly improved. Through the multi-node calculation and analysis of the single-phase electricity meter quality inspection dataset and the application of association rule mining, reasonable suggestions were given, which improved the professional value and quality supervision level of the quality inspection business.
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