油田大规模视频流流动性分析系统

IF 3.9 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Sensor Networks Pub Date : 2024-02-29 DOI:10.1145/3649222
Qiang Ma, Hao Yuan, Zhe Hu, Xu Wang, Zheng Yang
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引用次数: 0

摘要

本文介绍了为油田设计和实施的基于多视频流的流动性分析系统 LinkStream。LinkStream 结合多种技术,解决了计算能力和网络延迟方面的若干问题。首先,该系统采用边缘-中心架构和基于时空相关性的裁剪,大大降低了计算能力要求和网络成本,在有限的边缘设备上实现了大规模视频流的实时分析。其次,它设计了一套流动性信息来描述油田的流动性状况。最后,利用对象跟踪技术设计了油田中唯一油管对象的计数算法。我们已在伊拉克的一个油田部署了 LinkStream。LinkStream 能以可接受的资源开销对 200 多个视频流进行实时推理。
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A Liquidity Analysis System for Large-Scale Video Streams in the Oilfield

This article introduces LinkStream, a liquidity analysis system based on multiple video streams designed and implemented for oilfield. LinkStream combines a variety of technologies to solve several problems in computing power and network latency. First, the system adopts an edge-central architecture and tailoring based on spatio-temporal correlation, which greatly reduces computing power requirements and network costs, and enables real-time analysis of large-scale video stream on limited edge devices. Second, it designed a set of liquidity information to describe the liquidity status in the oilfield. Finally, it uses object tracking technology to design a counting algorithm for the unique tubing object in the oilfield. We have deployed LinkStream in an oilfield in Iraq. LinkStream can perform realtime inference on over 200 video streams with acceptable resource overhead.

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来源期刊
ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks 工程技术-电信学
CiteScore
5.90
自引率
7.30%
发文量
131
审稿时长
6 months
期刊介绍: ACM Transactions on Sensor Networks (TOSN) is a central publication by the ACM in the interdisciplinary area of sensor networks spanning a broad discipline from signal processing, networking and protocols, embedded systems, information management, to distributed algorithms. It covers research contributions that introduce new concepts, techniques, analyses, or architectures, as well as applied contributions that report on development of new tools and systems or experiences and experiments with high-impact, innovative applications. The Transactions places special attention on contributions to systemic approaches to sensor networks as well as fundamental contributions.
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