工业物联网智能感知-传输-控制融合：关键技术与未来展望

张明强; 马晓聪; 杨雅娟; 李东阳; 李腆腆; 王雷雨; 张海霞; 袁东风

doi:10.11999/JEIT250305

工业物联网智能感知-传输-控制融合：关键技术与未来展望

doi: 10.11999/JEIT250305 cstr: 32379.14.JEIT250305

张明强^1, ,,
马晓聪¹,
杨雅娟¹,
李东阳²,
李腆腆³,
王雷雨⁴,
张海霞^{5, 6},
袁东风^{5, 6}

1.
曲阜师范大学网络空间安全学院曲阜 273165
2.
中国石油大学(华东)海洋与空间信息学院青岛 266580
3.
山东师范大学信息科学与工程学院济南 250358
4.
鹏城实验室深圳 518108
5.
山东大学智能通信技术研究院济南 250100
6.
山东省智能通信与感算融合重点实验室济南 250100

基金项目: 山东省自然科学基金(ZR2023MF085)，国家自然科学基金(62401630, U23A20277)

详细信息

作者简介:
张明强：男，博士，副教授，硕士生导师，研究方向为智能通信与网络、工业语义通信、工业物联网等

马晓聪：女，硕士生，研究方向为物理信息神经网络、工业语义通信、工业物联网等

杨雅娟：女，硕士生，研究方向为工业语义通信、深度学习、工业物联网等

李东阳：男，博士，硕士生导师，研究方向为智能通信、无线智能缓存

李腆腆：女，博士，副教授，硕士生导师，研究方向为超可靠低时延通信、通信-感知-计算一体化设计等

王雷雨：男，博士，助理研究员，研究方向为智能通信、无人机通信

张海霞：女，教授，博士生导师，研究方向为智能通信与网络

袁东风：男，教授，博士生导师，研究方向为人工智能、大数据、云/边缘计算、无线通信、数字孪生

通讯作者:
张明强　mqzhang@qfnu.edu.cn

中图分类号: TN911
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出版历程
- 收稿日期: 2025-04-25
- 修回日期: 2025-08-20
- 网络出版日期: 2025-08-26

Integrating Intelligent Sensing, Transmission, and Control for Industrial IoT Networks: Key Technologies and Future Directions

1.
School of Cyber Science and Engineering, Qufu Normal University, Qufu 273165, China
2.
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
3.
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
4.
Pengcheng Laboratory, Shenzhen 518108, China
5.
Institute of Intelligent Communication Technologies, Shandong University, Jinan 250100, China
6.
Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration, Jinan 250100, China

Funds: Shandong Provincial Natural Science Foundation (ZR2023MF085), The National Natural Science Foundation of China (62401630, U23A20277)

摘要

摘要: 大规模工业物联网设备的高效互联互通与智能管控是我国制造业数字化、网络化、智能化转型升级和高质量发展的关键。由于通信、计算和网络资源受限，传输环境复杂，感知、传输和控制系统分离设计，传统工业网络面临感知传输效率低、异构系统互操作性差和难以高效协同的严峻挑战。首先，该文调研并总结了工业物联网发展的核心需求与瓶颈问题，其次，重点聚焦智能感传控融合的工业网络架构、工业物联网智能感知方法、认知智能驱动的工业语义通信以及边缘智能感知-高效传输-最优控制联合设计等关键技术问题，讨论了工业物联网智能感知-传输-控制融合的研究进展，最后总结了工业大模型与工业智能体、工业5.0、工业跨模态协同交互和工业数字孪生等具有重要意义和发展潜力的未来研究方向。
- 工业物联网 /
- 感-传-控融合 /
- 工业语义通信 /
- 深度压缩感知
Abstract: Significance The edge intelligence-enhanced sixth-generation (6G) mobile networks aim to build an integrated architecture that combines sensing, communication, and computation, continuing the trend of 5G’s rapid expansion into vertical industries. Looking ahead, Industry 5.0—defined by human-centric design and large-scale personalized customization—requires 6G-enabled industrial networks to simultaneously meet the demands of sensing, transmission, and control. The efficient interconnection, communication, and intelligent management of large-scale Industrial Internet of Things (IIoT) devices remains fundamental to the digital, networked, and intelligent transformation of the manufacturing sector and its high-quality development. However, limited device resources, complex industrial environments, and the fragmented design of sensing, transmission, and control systems present major challenges. These include limited capability for comprehensive and accurate information sensing, inefficient interaction among heterogeneous devices and systems, and difficulties in achieving intelligent closed-loop collaboration across sensing, transmission, and control. Integrating Intelligent Sensing, Transmission, and Control (ISTC) is essential to enabling intelligent communications in industrial scenarios, facilitating the intelligent interconnection of humans, machines, objects, and environments, and enhancing intelligent management and control across production lines. Progress Achieving semantic interoperability across heterogeneous industrial systems is the core barrier to the integrated design of sensing, transmission, and control, and is also critical to enabling agile interaction between diverse systems, reducing subsystem development and deployment costs, and building autonomous, self-managing industrial networks. Modern IIoT systems typically integrate parallel subsystems across Information Technology (IT) and Operational Technology (OT) domains, each with independent data models and semantic specifications, resulting in natural interoperability barriers. These barriers restrict efficient interaction and expected collaborative operation across vendors and platforms, significantly limiting large-scale interconnection and data sharing. Therefore, comprehensive and accurate information sensing, reliable and efficient transmission, and responsive feedback control have become key requirements for future IIoT networks. Specifically: (1) Intelligent Sensing: Overcoming the limitations of the Nyquist sampling theorem through interpretable intelligent sensing is a prerequisite for ISTC. (2) Semantic Transmission: The effective extraction and unified representation of industrial semantics, combined with intelligent semantic-level interaction, are critical to ensuring interoperability in heterogeneous systems while maintaining operational efficiency and sustainable performance. (3) Integrated ISTC: Joint design of edge-intelligent sensing, efficient transmission, and optimal control enables streamlined workflows in industrial scenarios, reducing system response time, improving control accuracy, and optimizing energy efficiency. Conclusions This paper proposes an intelligent collaborative architecture for IIoT networks comprising edge nodes or terminals, intelligent gateways, and industrial cloud platforms. The focus is placed on three key technologies within Integrating ISTC: (1) Intelligent sensing methods for IIoT networks: These methods enhance sensing efficiency and accuracy by applying interpretable, physics-informed deep compressed sensing approaches to IIoT devices and systems. (2) Robust Industrial Semantic Communications (ISC) driven by cognitive intelligence: This technology combines industrial knowledge graphs with semantic communication mechanisms to improve semantic interoperability and transmission efficiency across heterogeneous industrial systems. (3) Joint design of edge-intelligent sensing, efficient semantic transmission, and optimal control: By clarifying the intrinsic coupling among sensing, transmission, and control processes, this approach optimizes the collaborative service capability of heterogeneous industrial networks and systems. Prospects Despite progress, ISTC still faces considerable challenges. Future research may focus on the following directions: (1) Industrial large models and intelligent agents: The development of specialized AI models remains essential, particularly in core industrial domains where implicit knowledge is concentrated. (2) Industry 5.0: Achieving efficient, semantic-level human-machine collaborative interaction will be a key breakthrough for future industrial scenarios. (3) Industrial cross-modal collaborative interaction: Integrating data across modalities and mining knowledge from diverse sources present significant challenges but are essential for enabling advanced collaborative interaction in IIoT networks. (4) Industrial digital twins: For complex industrial environments and physical systems, continued advances in digital twin technology—particularly in high-precision semantic perception, real-time efficient interaction, and adaptive fault-tolerant control, will play a critical role in accelerating ISTC development.
- Industrial IoT Networks /
- Integrating Sensing, Transmission, and Control (ISTC) /
- Industrial semantic communications /
- Deep Compressed Sensing

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图 1 云边端协同的工业物联网智能感知-传输-控制融合架构

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图 2 基于可解释深度压缩感知的多模态工业语义信息智能感知与传输

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图 3 认知智能驱动的工业语义信息鲁棒传输与工业智能应用

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图 4 基于工业语义通信的边缘智能感知、高效传输与最优控制一体化联合设计框架

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图 5 基于工业大模型与工业语义信息交互的工业智能体系统构建路径

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表 1 深度压缩感知算法特点与主要任务

方法	文献	年份	提出的算法	算法特点	主要任务
深度压缩感知	[24]	2010	LISTA	稀疏编码的最佳近似	稀疏编码与重建
	[25]	2015	SDA	堆叠降噪自编码器，可学习测量矩阵	数据重建与恢复
	[26]	2017	CSGM	基于生成对抗网络，不考虑稀疏性	数据重建
	[27]	2019	DCS	基于元学习和生成对抗网络，隐变量优化	数据重建
	[28]	2021	LISTA-based	网络深度的动态调整	稀疏信号恢复
	[30]	2022	CASNet	自适应采样率分配、细粒度可扩展	数据重建与恢复
	[31]	2023	TransCL	基于块CS，不同退化的鲁棒适应性	分类、分割、重建
	[32]	2023	DPC-DUN	性能-复杂度权衡来实现动态调整	数据重建
	[33]	2025	IDM	两级可逆重构机制	数据重建与恢复

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表 2 物理信息神经网络算法特点与主要任务

方法	文献	年份	提出的算法	算法特点	主要任务
物理信息神经网络	[42]	2019	PINN	逆问题求解，时空函数逼近	偏微分方程发现
	[43]	2021	PIML	引入物理定律约束的机器学习方法	逆问题求解，综述
	[44]	2021	PINN-SR	稀疏回归，多维非线性时空PDE、交替方向优化	数据稀缺偏微分方程
	[45]	2023	PICS	Navier-Stokes问题求解更新先验，CS稀疏采样恢复	数据重建与恢复
	[46]	2021	CNN-based	数据、物理驱动损失函数设计	故障检测
	[47]	2024	PINN-based	以经验退化和状态空间方程建模	电池状态监测
	[48]	2025	MLP-based	物理知识引入损失函数设计	剩余寿命预测

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表 3 信源信道联合编码算法特点与主要任务

方法	文献	年份	提出的算法	算法特点	主要任务和使用的数据集
信源信道联合编码	[61]	2019	DeepJSCC	自编码器压缩、降噪，抗“悬崖效应”	图像通信，Cifar10, ImageNet, Kodak
	[62]	2020	DeepJSCC-f	利用信道反馈提升重建质量	图像通信，Cifar10, ImageNet, Kodak
	[63]	2023	Generative JSCC	基于生成对抗网络实现率-失真-感知折中	图像通信，CelebA-HQ, ImageNet
	[64]	2023	DeepJSCC-V	动态压缩比，预测自适应码率控制	图像通信，Cifar10, ImageNet, Cifar100, Kodak
	[65]	2025	SwinJSCC	Transformer-单模型适应多信道和速率	图像通信，DIV2K, Kodak, CLIC2021, Cifar10
	[66]	2025	D²-JSCC	两步速率控制、自适应先验建模	图像通信，Kodak, CLIC2021

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