利用人工智能和边缘计算优化企业管理

Shanshan Wang
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引用次数: 1

摘要

在互联网时代,每天都会产生大量的数据。借助云计算,企业可以更方便地存储和分析这些数据。随着物联网的出现,越来越多的硬件设备接入网络,产生海量数据。数据在很大程度上依赖于云计算进行集中的数据处理和分析。然而,数据量的快速增长已经超过了云计算的网络吞吐能力。边缘计算将计算节点部署在本地网络的边缘,使设备能够在本地网络中完成数据采集和预处理。从而克服了云计算对海量原生数据传输效率低、传输时延大的问题。本文设计了一个面向企业管理的人的轨迹培训系统。仿真结果表明,该系统能够支持企业管理人员的轨迹跟踪和预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing
In the internet era, huge data is generated every day. With the help of cloud computing, enterprises can store and analyze these data more conveniently. With the emergence of the internet of things, more hardware devices have accessed the network and produced massive data. The data heavily relies on cloud computing for centralized data processing and analysis. However, the rapid growth of data volume has exceeded the network throughput capacity of cloud computing. By deploying computing nodes at the edge of the local network, edge computing allows devices to complete data collection and preprocessing in the local network. Thus, it can overcome the problems of low efficiency and large transmission delay of cloud computing for massive native data. This paper designs a human trajectory training system for enterprise management. The simulation demonstrates that the system can support human trajectory tracing and prediction for enterprise management.
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