Civil aviation master data identification method based on cloud model and rough set
Guo Li, Zhang Ya, Wang Huai-chao · Jisuanji gongcheng yu sheji · 2020
Aiming at the ambiguity and uncertainty of civil aviation master data and the lack of objectivity in weight determination,a civil aviation master data identification method based on cloud model and rough set was proposed.According to the characteristics of civil aviation master data,the most representative 7 identification indicators were selected and divided into 5 levels.The forward cloud generator was used to generate a comprehensive cloud model with each identification index belonging to each main data level.The membership degree of each entity belonging to each main data level was calculated.The rough set theory was introduced to calculate the weight of each identification index and the degree of certainty belonging to the determination degree of each main data,and the maximum certain degree was used as the master data level entity.Experimental results show that the results obtained using this method are consistent with the expected results,which verifies the effectiveness of the method and provides an idea for the main data identification.