基于“云-管-边-端”物联网架构的碳排放监测

doi:10.11959/j.issn.2096-3750.2022.00281

物联网学报 ›› 2022, Vol. 6 ›› Issue (4): 53-64.doi: 10.11959/j.issn.2096-3750.2022.00281

基于“云-管-边-端”物联网架构的碳排放监测

邢方圆¹^,², 贺诗波², 孙铭阳², 陈积明²

¹ 东南大学网络空间安全学院，江苏南京 211189
² 浙江大学控制科学与工程学院，浙江杭州 310027

修回日期:2022-09-13 出版日期:2022-12-30 发布日期:2022-12-01
作者简介:邢方圆（1991- ），女，博士，东南大学副研究员，主要研究方向为物联网及无线通信网络
贺诗波（1983- ），男，博士，浙江大学教授，主要研究方向为工业物联网、大数据网络科学、人工智能
孙铭阳（1988- ），男，博士，浙江大学研究员，主要研究方向为低碳电力系统智能优化与决策、人工智能系统安全与隐私保护
陈积明（1978- ），男，博士，浙江大学教授，主要研究方向为网络优化与控制、网络系统安全、工业大数据与物联网
基金资助:
国家自然科学基金资助项目(U1909207);国家自然科学基金资助项目(U20A20159);国家自然科学基金资助项目(62103371)

Carbon emission monitoring based on internet of things with cloud-tube-edge-end structure

Fangyuan XING¹^,², Shibo HE², Mingyang SUN², Jiming CHEN²

¹ School of Cyber Science and Engineering, Southeast University Nanjing 211189, China
² College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Revised:2022-09-13 Online:2022-12-30 Published:2022-12-01
Supported by:
The National Natural Science Foundation of China(U1909207);The National Natural Science Foundation of China(U20A20159);The National Natural Science Foundation of China(62103371)

摘要/Abstract

摘要：

近年来，我国积极参与全球气候治理进程。碳排放的精准、快速与可靠监测是实现碳排放态势感知、整体布局优化的核心基础，目前该方向的研究仍缺乏系统性梳理。首先，调研了碳排放监测的发展进程，归纳了国内外的现行标准。然后，分别探究了基于终端设备与基于物联网的碳排放监测方法，总结出基于物联网的监测方法具有结构清晰、设计灵活、传输可靠等优势。最后，基于“云-管-边-端”物联网架构提出一种工业碳排放监测体系，并总结了实现“云-管-边-端”4 个方面的潜在关键技术，旨在对未来碳排放监测的相关研究提供一定参考。

关键词: 物联网, 碳排放, 云-管-边-端, 实时监测

Abstract:

In recent years, China has participated in the global climate governance with a positive stance.The accurate, rapid, and reliable monitoring is fundamental for the situation awareness and layout optimization of the carbon emission.However, the state-of-the-art review with systematic studies of this topic has hardly been identified.The development process and the existing standards of carbon emission monitoring were investigated.The terminal equipment-based methods and the internet of things (IoT)-based applications for carbon emission monitoring were explored, and the IoT-based methods were concluded which hold the advantages including clear structure, flexible design, and reliable transmission.Based on the IoT, an industrial carbon emission monitoring method using the “cloud-tube-edge-end” IoT structure was established.The enabling technologies that can implement the functions of the “cloud-tube-edge-end” were summarized, which are expected to provide guidance for future carbon emission monitoring.

Key words: IoT, carbon emission, cloud-tube-edge-end, real-time monitoring

中图分类号:

TP393

邢方圆, 贺诗波, 孙铭阳, 陈积明. 基于“云-管-边-端”物联网架构的碳排放监测[J]. 物联网学报, 2022, 6(4): 53-64.

Fangyuan XING, Shibo HE, Mingyang SUN, Jiming CHEN. Carbon emission monitoring based on internet of things with cloud-tube-edge-end structure[J]. Chinese Journal on Internet of Things, 2022, 6(4): 53-64.

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自动监测技术	适用场景	优点	缺点
气相色谱法	常用于检测CH₄	具有较高的灵敏度和重复性	对载气、助燃气和工作标气需求较大
			易受环境温度影响
非分散红外法	常用于检测CO₂、CH₄、N₂O	原理简单、成本低	测量CH₄、N₂O精度较低
		测量CO₂精度较高
傅里叶变换红外光谱法	常用于检测浓度较小范围内的 CO₂、	可实现多组分同时检测	检测下限易受到气体吸收光程的影响
	CH₄、N₂O
光腔衰荡光谱法	常用于检测CO₂、N₂O	精密度最高、稳定性好	仪器价格和运维成本昂贵
离轴积分腔输出光谱法	常用于检测废气中的 CO₂、N₂O，以及	灵敏度高、响应时间快	系统噪声较大
	勘探深海可燃冰		频率调制技术装置比较复杂
可调谐半导体激光吸收光	常用于检测含氟温室气体及有毒有害	高灵敏度	易受测量环境温度影响
谱法	气体等	检测速度快
		可实现多组分同时检测

基于“云-管-边-端”物联网架构的碳排放监测

Carbon emission monitoring based on internet of things with cloud-tube-edge-end structure

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参考文献 48

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对比项	大气	固定污染源废气	所需传感器/检测器
CO₂	非分散红外法	非分散红外法	红外二氧化碳传感器
	傅里叶变换红外光谱法	傅里叶变换红外光谱法	催化二氧化碳传感器
	光腔衰荡光谱法	离轴积分腔输出光谱法	热传导二氧化碳传感器
		可调谐半导体激光吸收光谱法
CH₄	气相色谱法	气相色谱法	红外甲烷传感器
	非分散红外法	非分散红外法
	傅里叶变换红外光谱法	傅里叶变换红外光谱法
		离轴积分腔输出光谱法
		可调谐半导体激光吸收光谱法
N₂O	非分散红外法	非分散红外法	红外一氧化二氮传感器
	傅里叶变换红外光谱法	傅里叶变换红外光谱法	量子级联激光一氧化二氮分析仪
	光腔衰荡光谱法
含氟等其他温室气体	气相色谱法	气相色谱法	氟气传感器

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