Development And Application Of Big Data Mining System Based On Logistics Website

With the rapid development of big data,massive amounts of data have been regarded as one of the most valuable assets of this era.The explosion of data in all walks of life has led to data mining being pushed to the forefront of research,How to efficiently and reasonably apply data mining techniques to their respective fields has become a problem that many scholars have yet to solve.At the same time,the logistics industry has become one of the most important components of the modern service industry driven by e-commerce.Because the logistics industry is closely linked with manufacturers,suppliers,retailers and consumers,the data is not only large in quantity but also rich in type.It was the research goal of this paper to obtain more hidden value from abundant logistics data.This paper aimed to establish a comprehensive data mining system based on logistics website data.The system integrated real-time data collection and cleaning,efficient data storage and access,data mining models,and GIS functional modules,and provided a platform for ordinary users to more easily and efficiently conduct logistic data mining work.Author adopted the development mode of high-level programming language and scripting language to develop the data mining system based on logistics website.The system structure was C/S,took Visual Studio as the development platform,C# language as the basis,and GIS-related functions using ArcGIS Engine components for secondary development.Due to needing to collect large amounts of data,the system also used Python for network data capture and data cleaning,and KNN algorithm,K-means algorithm,association rule algorithm and community discovery algorithm were encapsulated into different functional modules,and they were organically combined.Author made use of the data mining system based on logistics website to carry out Case Analysis,which mainly includes the research of logistics spatial structure and the research of logistics network structure.The main work and conclusions are as follows:(1)Using the number of regional logistics lines and the number of regional logistics companies' courier companies to explore the level of regional logistics development,using clustering algorithms to divide the national logistics development level into 8 levels,and the results show that the development of logistics in our country still has the unbalanced development between the east and the west.(2)Using the association rule algorithm to explore the relationship between the start,destination and cargo type of the logistics transportation.Tianjin to be the place where most of the steel cargo was shipped and other association rule were discovered.(3)Using correlation analysis to explore the relationship between the level of logistics development and economic development,the number of employees in the logistics industry and the convenience of transportation,and provide suggestions on how to speed up regional logistics development.(4)Analyzing the logistics network structure through big data,this paper finds that the regional logistics network which takes cities as its nodes belongs to the hub-network structure.Shanghai,Beijing and Guangzhou the hub cities within the Chinese mainland control the entire network,and different research objects and research scope may produce different hub cities.At the same time,the number of connections in the north-south network is much larger than that in the east-west direction,indicating that the logistics links between the north and the south are more frequent.(5)Using the community discovery algorithm to divide the logistics network into 8 communities,exploring the main influencing factors of the logistics development in each community,and comparing them with the provincial administrative divisions and existing urban agglomerations.The results show that the community structure is consistent them,but there are still some differences.It is of great significance to increase the internal links of each community and optimize the network structure for accelerating the development of logistics.