Intelligent Analysis of Logistics Information Based on Dynamic Network Data

In order to realize the intellectualization of logistics information analysis, this paper proposes an intelligent analysis method of logistics information based on dynamic network data cloud mining. This paper selects the data of a shipping logistics platform to realize the intelligent analysis experiment of logistics information based on cloud clustering mining. The purpose of the experiment is to find out the advantages of logistics information intelligent analysis based on cloud mining by comparing the performance differences between cloud clustering mining and traditional clustering mining in logistics information intelligent analysis. This paper builds an experimental environment based on Hadoop and MapReduce parallelization based on K-means algorithm. Taking the obtained logistics data as the analysis object, preprocess it and get the results based on cloud clustering mining. The experimental results show that the parallel mining analysis method is 179.2% slower than the traditional mining analysis method in dataset data1, 60.4% slower in dataset data2, and 2.8% faster in dataset data1. The intelligent analysis method of logistics information based on cloud clustering mining has good scalability and speedup ratio. Conclusion. Applying cloud mining to logistics information analysis and realizing the intelligent analysis of logistics information has great advantages, and can well meet the content and efficiency needs of logistics information analysis stakeholders.

Abstract licence: CC BY

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