Agricultural Products Cold Chain Logistics Management System Based On Big Data

With the gradual prosperity of the social economy,people's demand for agricultural products has gradually increased.Through efficient use of big data technology,it is possible to optimize and innovate the agricultural product cold chain logistics enterprise management system,realize the modernization of enterprise logistics management,and effectively improve the management efficiency of agricultural product cold chain logistics enterprises.The use of big data technology can improve the efficiency of information connection,increase the stickiness of corporate customers,and accelerate the value-added of cold chain logistics data.In order to enable HDFS to meet the needs of storing massive and diverse small files generated by various links in the cold chain logistics of agricultural products,this paper then proposes an improved balanced merging and index caching strategy based on file type and size grouping.It mainly contains three modules,namely file preprocessing module,file balance merging module,and index cache module.Tests were performed from three aspects: write speed,Name Node memory usage,and file read speed.The experimental results show that this method can significantly improve the overall performance of HDFS when storing and reading large amounts of small files.This paper introduces the commonly used clustering algorithms in the field of cold chain logistics,focusing on the spectral clustering algorithm,and proposes an improved adaptive spectral clustering algorithm based on local standard deviation and optimized initial center(DCSC-NJW)Let's analyze the data in the field of agricultural cold chain logistics.Using UCI test data set for simulation experiments,the results show that the improved spectral clustering algorithm not only reduces the error rate,but also significantly reduces the time spent in the clustering process,indicating the effectiveness and effectiveness of the improved spectral clustering algorithm.feasibility.Using the improved spectral clustering algorithm in this paper to cluster analysis of nearly a thousand cold chain logistics related data,the optimal city was successfully selected as the construction point of the large-scale cold storage transfer station.This research can effectively improve the utilization efficiency of cold chain logistics resources and time,and maximize the profitability of cold chain logistics enterprises.Finally,the big data-based agricultural product cold chain logistics management system proposed in this paper is constructed and the overall function of the system is tested,so that the efficiency and practicability of the method designed in this paper can be effectively verified in the system.