化工行业人工智能物联网技术及应用研究综述

张伟; 纪雅婷; 赵利强; 王坤峰

doi:10.11959/j.issn.2096-6652.202604

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化工行业人工智能物联网技术及应用研究综述

综述与展望 | 更新时间：2026-04-27

- 化工行业人工智能物联网技术及应用研究综述
- A review of artificial intelligence of things technologies and applications in the chemical industry
- 智能科学与技术学报 2026年8卷第1期页码：12-32
- 作者机构：
  
  1.北京化工大学信息科学与技术学院，北京 100029
  2.北京化工大学化学工程学院，北京 100029
- 作者简介：
  
  [ "张伟（1998- ），男，北京化工大学信息科学与技术学院博士生，主要研究方向为计算机视觉、多模态感知。" ]
  [ "纪雅婷（2002- ），女，北京化工大学信息科学与技术学院硕士生，主要研究方向为多模态融合感知。" ]
  [ "赵利强（1982- ），男，北京化工大学化学工程学院副教授，主要研究方向为能源化工的智能检测与智能控制。" ]
  [ "王坤峰（1982- ），男，北京化工大学信息科学与技术学院教授、博士生导师，主要研究方向为计算机视觉、多模态感知、智能无人系统。" ]
- 基金信息：
  
  国家自然科学基金项目(U22A2061);北京市自然科学基金项目(L241017)
- DOI：10.11959/j.issn.2096-6652.202604
  中图分类号： TP39
- 收稿：2025-12-13，
  
  修回：2026-03-14，
  
  录用：2026-03-17，
  
  纸质出版：2026-03-15
- 稿件说明：
移动端阅览
张伟,纪雅婷,赵利强等.化工行业人工智能物联网技术及应用研究综述[J].智能科学与技术学报,2026,08(01):12-32.

Zhang Wei,Ji Yating,Zhao Liqiang,et al.A review of artificial intelligence of things technologies and applications in the chemical industry[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):12-32.
张伟,纪雅婷,赵利强等.化工行业人工智能物联网技术及应用研究综述[J].智能科学与技术学报,2026,08(01):12-32. DOI： 10.11959/j.issn.2096-6652.202604.

Zhang Wei,Ji Yating,Zhao Liqiang,et al.A review of artificial intelligence of things technologies and applications in the chemical industry[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):12-32. DOI： 10.11959/j.issn.2096-6652.202604.

摘要

化工行业人工智能物联网技术在在线监测与智能巡检、设备管理与预测性维护等方面发挥着关键作用，是推动工业5

0进程的重要技术支撑。其中，人工智能作为核心驱动力，通过赋能物联网系统具备数据智能感知、异常检测、预测性维护、智能控制与自主决策能力，显著提升了整体系统的智能化水平、响应能力与运行效率。从人工智能物联网的系统架构出发，系统梳理了感知

层数据采集、网络层数据传输、边缘层与平台层数据处理、执行层控制与执行等关键技术的发展现状；然后结合典型化工应用案例，分析了人工智能物联网在实际生产环境中的部署成效与技术路径；最后，总结该领域当前面临的挑战，并展望未来发展趋势。

Abstract

Artificial intelligence of things (AIoT) has become an important enabling technology for the chemical industry

especially in online monitoring and intelligent inspection

equipment management

and predictive maintenance

providing strong support for the transition toward Industry 5.0. From the perspective of AIoT system architecture

the recent progress of key technologies was reviewed

including data acquisition at the perception layer

data transmission at the network layer

data processing at the edge and platform layers

and control and actuation at the execution layer. Typical application scenarios in the chemical industry were further analyzed to summarize the deployment effectiveness and technical pathways of AIoT in real production environments. The analysis indicates that AIoT can significantly enhance state perception

anomaly identification

predictive analysis

and collaborative control under complex operating conditions. However

challenges remain in heterogeneous data management

model generalization and interpretability

secure communication

and large-scale engineering deployment. Future research should focus on cloud-edge-end collaborative intelligence

lightweight and adaptive deployment

and physics-data hybrid modeling for safety-oriented closed-loop optimization.

关键词

Keywords

references

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