Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm

The traditional Hadoop-based logistic data processing platform of universities cannot fully collect and control remote data in the process of data processing and has defects such as poor efficiency, high error, and difficult query. In existing designs, devices often need to store complete block data...

Full description

Saved in:
Bibliographic Details
Main Author: Li Li
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Advances in Multimedia
Online Access:http://dx.doi.org/10.1155/2022/4141552
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832563372502024192
author Li Li
author_facet Li Li
author_sort Li Li
collection DOAJ
description The traditional Hadoop-based logistic data processing platform of universities cannot fully collect and control remote data in the process of data processing and has defects such as poor efficiency, high error, and difficult query. In existing designs, devices often need to store complete block data. When retrieving or verifying specific data on the chain, a large number of blocks need to be traversed to find the corresponding data, which reduces the response speed on the user side. In addition, the traditional consensus algorithm is not suitable for resource-constrained terminal devices. Therefore, this paper proposes an intelligent data processing and optimization method of university logistics combined with block chain storage algorithm. For unstructured data on the cloud storage network, the storage information can be obtained through domain analysis, and the storage frequency can be dynamically adjusted based on the estimated data flapping. Moreover, a data storage audit scheme is designed on the cloud storage network to improve data storage efficiency. In order to ensure the integrity verification efficiency of unstructured big data cloud storage, a multilayer block chain network model suitable for university logistics data is designed by introducing block chain technology. The throughput and average occupancy under the same conditions in different environments are evaluated and analyzed, and it is verified that the proposed method is not affected by the initial priority distribution and has good stability and optimization performance. The results show that the proposed method can significantly improve the cloud storage efficiency of unstructured data, can effectively respond to a large number of requests to access data, and has good big data processing capability.
format Article
id doaj-art-97ecd1bf2d334b7f91d93a0642565b21
institution Kabale University
issn 1687-5699
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Advances in Multimedia
spelling doaj-art-97ecd1bf2d334b7f91d93a0642565b212025-02-03T01:20:19ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/4141552Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage AlgorithmLi Li0Wenzhou University of TechnologyThe traditional Hadoop-based logistic data processing platform of universities cannot fully collect and control remote data in the process of data processing and has defects such as poor efficiency, high error, and difficult query. In existing designs, devices often need to store complete block data. When retrieving or verifying specific data on the chain, a large number of blocks need to be traversed to find the corresponding data, which reduces the response speed on the user side. In addition, the traditional consensus algorithm is not suitable for resource-constrained terminal devices. Therefore, this paper proposes an intelligent data processing and optimization method of university logistics combined with block chain storage algorithm. For unstructured data on the cloud storage network, the storage information can be obtained through domain analysis, and the storage frequency can be dynamically adjusted based on the estimated data flapping. Moreover, a data storage audit scheme is designed on the cloud storage network to improve data storage efficiency. In order to ensure the integrity verification efficiency of unstructured big data cloud storage, a multilayer block chain network model suitable for university logistics data is designed by introducing block chain technology. The throughput and average occupancy under the same conditions in different environments are evaluated and analyzed, and it is verified that the proposed method is not affected by the initial priority distribution and has good stability and optimization performance. The results show that the proposed method can significantly improve the cloud storage efficiency of unstructured data, can effectively respond to a large number of requests to access data, and has good big data processing capability.http://dx.doi.org/10.1155/2022/4141552
spellingShingle Li Li
Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm
Advances in Multimedia
title Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm
title_full Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm
title_fullStr Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm
title_full_unstemmed Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm
title_short Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm
title_sort intelligent data processing and optimization of university logistics combined with block chain storage algorithm
url http://dx.doi.org/10.1155/2022/4141552
work_keys_str_mv AT lili intelligentdataprocessingandoptimizationofuniversitylogisticscombinedwithblockchainstoragealgorithm