Big Health Data Resource Integration Method Based on Hybrid Cloud and Fog Computing

Aiming at the problems of high latency and poor accuracy of retrieval response in current health data integration related research, this paper presents a health data resource integration method based on Hybrid Cloud and Fog Computing. Based on the general model of health data resource service platform, the framework of large health data resource integration is constructed. Sample reduction and dimension reduction are used to reduce big health data. The field matching method based on participle and weight is used to clean data resources and calculate the field matching degree. When the matching degree is larger than a threshold value, the fields to be matched are similar duplicate records, and the redundant data is removed. The weight of resource data is calculated by Hybrid Cloud and Fog Computing, and the big health data is arranged according to the weight value, so as to realize the classification and integration of health data.The experimental results show that the proposed method has good performance, low data redundancy, low retrieval response delay and high classification integration accuracy.