Analysis of civil aircraft quality data under the support of big data

In the production assembly manufacturing process, a large amount of quality data has been generated by civil aircraft equipment system. With the passage of time and the accumulation of data, these massive data cannot be dealt with effectively using traditional statistical analysis of discrete manufacturing industry. To solve this problem, the method of quality data analysis for unsupervised learning presented in this paper was developed, after evaluating the generating characteristics of the civil aircraft quality data and the problems associated with the processing of the traditional quality data analysis. On this basis, in this paper, according to the disorder association, complex structure and large amount of data of the civil aircraft quality data, the data mining association analysis Apriori algorithm and the big data Splunk platform are introduced to effectively reduce the complexity of the quality data analysis through the complementary advantages of both, and put the data in an orderly, coherent state. The results show that the developed method is effective with high efficiency value.