基于大数据和随机梯度下降算法的轨迹数据分析模型构建与应用

IF 0.6 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Nanoelectronics and Optoelectronics Pub Date : 2023-10-01 DOI:10.1166/jno.2023.3492
Jianhua Xie, Zhongming Yang, Wenquan Zeng, Yongjun He, Fagen Gong, Xi Zhao, Xibin Sun, Saad Aldosary
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引用次数: 0

摘要

本文研究了基于Hadoop的计算与存储资源管理系统框架的模型构建,以及大数据下轨迹数据分析功能的实现。依托云平台基础架构,为支撑数据快速增长和海量数据处理需求,提供结构化和非结构化数据混合存储和分析平台,利用大数据技术构建高扩展性的分布式数据处理框架。分布式计算、存储系统的整体框架模型、功能模块的构建都是以构建系统为目的考虑的。其次,利用 Hadoop 对原始数据进行预处理,集中研究了大数据系统的数据分层设计模型和关键技术分析,提出了设计模型、功能模块、技术方案和 SGD 算法,以及详细的实现过程。最后,该系统通过合并运行车辆的数据,完成了车辆轨迹、空车与装车、人员装卸等数据分析。
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Construction and Application of Trajectory Data Analysis Model Based on Big Data and Stochastic Gradient Descent Algorithm
This paper studies the model construction of computing and storage resource management system framework based on Hadoop and the implementation of trajectory data analysis function under big data. Relying on the cloud platform infrastructure, in order to support the rapid data growth and massive data processing needs, it provides a mixed storage and analysis platform for structured and unstructured data, and uses big data technology to build a highly scalable and distributed data processing framework. The distributed computation, overall frame model of the memory system, and function module have been built with the aim of constructing the system in consideration. Second, by using Hadoop to preprocess the original data and concentrating on the data hierarchical design model and key technology analysis of big data systems, the design model, functional modules, technological solutions, and SGD algorithm are suggested, along with the detailed implementation procedure. Lastly, by merging the data of running vehicles, the system accomplishes the data analysis of vehicle trajectory, empty and load cars, and load and unload people.
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来源期刊
Journal of Nanoelectronics and Optoelectronics
Journal of Nanoelectronics and Optoelectronics 工程技术-工程:电子与电气
自引率
16.70%
发文量
48
审稿时长
12.5 months
期刊最新文献
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