Research on the processing method of multi-source heterogeneous data in the intelligent agriculture cloud platform

Abstract

Abstract With the development of big data and blockchain technology, a large amount of multi-source heterogeneous data has been accumulated in the agricultural field by before, during and after production. Agricultural information service systems are often targeted at specific regions, specific applications and specific data resources. Due to the lack of effective analysis and refining, the conversion efficiency of data resources into useful information is too low, resulting in contradiction between the continuous enrichment of agricultural data resources and the relative lack of agricultural information services. Therefore, in view of the multi-source heterogeneous characteristics of agricultural data and the specific business needs of different agricultural scenarios, the intelligent processing method of agricultural data is analysed, and a heuristic algorithm based on K-Means limited clustering number is proposed to judge the accuracy of abnormal data processing. By inputting test sample data for testing, the algorithm has improved accuracy by nearly 30% compared to traditional K-Means.