Journal of Industrial Information Integration最新文献_第5页

Mixed objective scheduling optimization in mountain orchards under energy-saving for carbon neutrality 碳中和节能条件下山地果园混合目标调度优化

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-12-25 DOI: 10.1016/j.jii.2025.101049

Zhentao Xue , Zhigang Ren , Jian Chen , Xiqing Wang , Shuaisong Zhang

The air-ground cooperative plant protection unmanned formation can effectively deal with the complex terrain challenges of mountain orchards and ensure the uniformity of plant protection operation coverage. The core of this system lies in the principles of Industrial Information Integration Engineering (IIIE). Through dynamic scheduling optimization, it can alleviate the problems of large energy consumption and long non-operation paths. Aiming at the dynamic scheduling planning problem, this study proposes an energy-saving hybrid target scheduling optimization method based on an improved Australian wild dog hunting strategy. A novel mountain orchard path coding technology is designed, and an energy consumption model based on the principle of unmanned formation dynamics is established, which provides a scientific basis for formulating efficient energy-saving strategies. The improved Australian wild dog hunting strategy combines the motion constraints of unmanned formation and the requirements of plant protection tasks, and realizes the efficient optimization of the scheduling scheme. Numerical experiments demonstrated the effectiveness of the proposed method, which reduced the objective function to 65.63% of the initial solution in simulations, outperforming the genetic algorithm. This performance was further validated in a real-world scenario, where the value was reduced to 57.34%. This efficient dynamic scheduling optimization serves as a key enabler for agricultural industry integration and informatization.

地空协同植保无人编队可以有效应对山地果园复杂的地形挑战，保证植保作业覆盖的均匀性。该系统的核心是工业信息集成工程（IIIE）的原理。通过动态调度优化，可以缓解能耗大、非运行路径长等问题。针对动态调度规划问题，提出了一种基于改进的澳大利亚野狗狩猎策略的节能混合目标调度优化方法。设计了一种新颖的山地果园路径编码技术，建立了基于无人编队动力学原理的果园路径能耗模型，为制定高效节能策略提供了科学依据。改进的澳大利亚野狗狩猎策略结合了无人编队的运动约束和植保任务的要求，实现了调度方案的高效优化。数值实验证明了该方法的有效性，在模拟中将目标函数降低到初始解的65.63%，优于遗传算法。在实际场景中进一步验证了这一性能，该值降至57.34%。这种高效的动态调度优化是农业产业一体化和信息化的关键推动因素。

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引用次数: 0

From cognitive to circular: A Digital Twin systematic review 从认知到循环：数字孪生系统回顾

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-12-24 DOI: 10.1016/j.jii.2025.101051

Jairo Francisco de Souza , Fabrício Martins Mendonça , António José Baptista , António Lucas Soares , Jorão Gomes Jr.

This paper aims to clarify the characteristics of Digital Twins (DTs) in their most advanced conceptual development, Cognitive Digital Twins (CDTs), and analyze their support for the implementation of the Circular Economy (CE). A systematic literature review was conducted using a specially developed five-dimensional analytical framework to characterize DT proposals and their potential for CE based on an established framework for circularity strategies. The study indicates that cognitive and hybrid DT approaches tend to cover high levels of interoperability, data flow, system levels, and cognitive processes. However, CDT use in CE demands harmonizing different strategies to cover the complete product lifecycle, which recent research on DTs has not fully addressed. This study is the first to systematically review cognitive digital twins and their relation to circularity, offering an analytical framework that can be expanded for future research in various application areas of Industry 5.0.

本文旨在阐明数字孪生（Digital Twins, dt）在其最先进的概念发展——认知数字孪生（Cognitive Digital Twins, CDTs）中的特征，并分析其对循环经济（Circular Economy， CE）实施的支持。系统的文献综述使用专门开发的五维分析框架来表征DT提案及其基于既定循环战略框架的CE潜力。研究表明，认知和混合DT方法倾向于涵盖高水平的互操作性、数据流、系统级别和认知过程。然而，在CE中使用CDT需要协调不同的策略，以覆盖整个产品生命周期，这是最近关于CDT的研究尚未完全解决的问题。本研究首次系统地回顾了认知数字孪生及其与循环的关系，提供了一个分析框架，可以扩展到工业5.0的各个应用领域的未来研究。

引用次数: 0

A sensorless admittance control system for physical human-robot interaction on a two-wheeled differential drive mobile platform 两轮差动驱动移动平台人机物理交互无传感器导纳控制系统

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-12-23 DOI: 10.1016/j.jii.2025.101046

Jinxiang Deng , Li An , Lingyun Luo , Zhuo Zou , Jie Lu , Li Gong , Li Da Xu , Zhongxue Gan , Yuxiang Guan

Physical human-robot interaction (pHRI) increasingly demands robots that are both compliant and capable of handling substantial payloads. While admittance control has been successfully applied to mobile manipulators, compliant control for the mobile platforms themselves—particularly for widely deployed, non-holonomic differential-drive types—remains an underexplored challenge. This paper addresses this gap by proposing a novel, sensorless admittance control system specifically designed for two-wheeled differential-drive mobile platforms. The core contributions are threefold. First, a detailed kinematic and dynamic analysis is conducted to establish the system's theoretical foundation. Second, we develop a resultant external force/torque estimator that requires no additional sensors, utilizing only motor currents and wheel encoder data, thereby achieving zero hardware cost. Third, we introduce an autonomous payload parameter identification method with k-means for data selection, enabling the system to adapt to unknown and variably positioned loads. Real-world experiments demonstrate that the proposed controller reduces the required human guiding force by approximately 50% compared to the original system. The proposed controller successfully reconciles high compliance with high load capacity, handling payloads ranging from 73 kg to 173 kg. This work provides a systematic, cost-effective solution for deploying compliant, high-payload mobile platforms in future industrial and domestic pHRI applications.

物理人机交互（pHRI）越来越多地要求机器人既兼容又能够处理大量有效载荷。导纳控制已经成功地应用于移动机械臂，但移动平台本身的兼容控制，特别是对于广泛部署的非完整差分驱动类型，仍然是一个未被充分探索的挑战。本文通过提出一种专门为两轮差动驱动移动平台设计的新型无传感器导纳控制系统来解决这一差距。核心贡献有三个方面。首先，进行了详细的运动学和动力学分析，建立了系统的理论基础。其次，我们开发了一个合成的外力/扭矩估计器，不需要额外的传感器，仅利用电机电流和车轮编码器数据，从而实现零硬件成本。第三，我们引入了一种基于k-means的自主载荷参数识别方法，用于数据选择，使系统能够适应未知和可变位置的载荷。实际实验表明，与原始系统相比，所提出的控制器将所需的人类引导力降低了约50%。提出的控制器成功地协调了高依从性和高负载能力，处理有效载荷范围从73公斤到173公斤。这项工作为在未来工业和国内pHRI应用中部署兼容的高负载移动平台提供了一个系统的、经济高效的解决方案。

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引用次数: 0

A multi-layer knowledge and data-driven integrated framework for smart manufacturing process: An experimental application for aerospace sheet metal process planning 面向智能制造过程的多层知识和数据驱动集成框架：在航空航天钣金工艺规划中的实验应用

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-12-21 DOI: 10.1016/j.jii.2025.101045

Murillo Skrzek , Anderson Luis Szejka , Fernando Mas

The aerospace manufacturing industry faces substantial complexity, particularly in the aircraft manufacturing process, which requires integrating advanced components and systems with diverse geometries and materials. This environment necessitates robust information systems to manage information exchange across the product life cycle and reduce disruptions during project development. Traditional manufacturing systems struggle to integrate diverse automation technologies and maintain efficiency in highly customised and technologically complex aerospace production. Interferences caused by project changes can lead to increased costs, longer time commitments, and greater environmental impacts. Based on this context, this research proposes a multi-layer knowledge and data-driven integrated framework to seamlessly integrate digital and physical technologies, facilitating communication and transparency across the complex manufacturing process. It supports manufacturing tasks such as process planning, cost estimation, and quality assurance, ensuring the capture and utilisation of explicit and implicit knowledge. Implementing the multi-layer knowledge and data-driven integrated framework enhances manufacturing efficiency, reduces costs, and improves product quality in the aerospace industry. An experimental case demonstrated the ability to store data and knowledge in a structured way, thereby generating different manufacturing plans, supporting process decision-making, and improving the 72.1% efficiency of plan generation with human validation. Future research will focus on validating the manufacturing plan generated from existing manual process plans, enabling optimisation of manufacturing according to the most suitable plan presented, aiming to refine it further and expand its applicability in the aerospace sector.

航空航天制造业面临着巨大的复杂性，特别是在飞机制造过程中，这需要集成具有不同几何形状和材料的先进部件和系统。这种环境需要健壮的信息系统来管理跨产品生命周期的信息交换，并减少项目开发期间的中断。传统的制造系统努力集成各种自动化技术，并在高度定制和技术复杂的航空航天生产中保持效率。由项目变更引起的干扰可能导致成本的增加、更长的时间承诺和更大的环境影响。基于此背景，本研究提出了一个多层知识和数据驱动的集成框架，以无缝集成数字和物理技术，促进跨复杂制造过程的沟通和透明度。它支持诸如过程计划、成本估算和质量保证等制造任务，确保获取和利用显性和隐性知识。实施多层知识和数据驱动的集成框架可以提高航空航天工业的制造效率、降低成本并提高产品质量。实验案例表明，该系统能够以结构化的方式存储数据和知识，从而生成不同的制造计划，支持工艺决策，并通过人工验证将计划生成效率提高72.1%。未来的研究将侧重于验证从现有手工工艺计划生成的制造计划，根据最合适的计划进行制造优化，旨在进一步完善并扩大其在航空航天领域的适用性。

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引用次数: 0

DPDC-ILKM: A multi-agent integrated large knowledge model for intelligent maintenance of industrial swarm robotics DPDC-ILKM：面向工业群机器人智能维护的多智能体集成大知识模型

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-12-21 DOI: 10.1016/j.jii.2025.101044

Jiaxian Chen , Yujie Xu , Jie Tang , Xuemiao Xu , Ruqiang Yan , Zhixin Yang , Weihua Li

The rapid advancement of large language models (LLMs) has introduced transformative capabilities into industrial intelligence. However, their direct application to the prognostics and health management (PHM) of industrial swarm robotics remains limited due to the lack of specialized maintenance knowledge and insufficient functional integration. Embodied Intelligence (EI), with its capacities for perception, cognition, reasoning, decision-making, and iterative evolution, offers a promising solution to these challenges. Therefore, a multi-agent integrated large knowledge model framework, termed Diagnosis-Prediction-Decision-Control-ILKM (DPDC-ILKM), is proposed to empower intelligent maintenance in industrial swarm robotics. In the DPDC-ILKM framework, a high-reliability industrial large knowledge model is first constructed by integrating operational maintenance records and corpus knowledge from different industrial robotics to adapt to the PHM tasks of diverse individual robots. Second, a multi-agent EI maintenance system is designed to provide operation and maintenance services, including diagnostic, prognostic, decision-making, and control functions. To support the continual improvement of DPDC-ILKM, a self-evolution mechanism is introduced, enabling adaptive learning and continuous optimization in dynamic industrial environments. Finally, the key challenges and future directions are discussed to support the advancement of EI-enabled industrial artificial intelligence. This work presents a unique framework that combines LLMs with EI for industrial maintenance, offering a novel perspective and technical foundation for intelligent maintenance of industrial swarm robotics.

大型语言模型（llm）的快速发展为工业智能引入了变革能力。然而，由于缺乏专业的维护知识和功能集成不足，它们在工业群机器人的预测和健康管理（PHM）中的直接应用仍然受到限制。具身智能（EI）具有感知、认知、推理、决策和迭代进化的能力，为这些挑战提供了一个有希望的解决方案。为此，提出了诊断-预测-决策-控制- ilkm （DPDC-ILKM）多智能体集成大知识模型框架，为工业群机器人的智能维护提供支持。在DPDC-ILKM框架中，首先通过整合不同工业机器人的运行维护记录和语料库知识，构建高可靠性工业大知识模型，以适应不同个体机器人的PHM任务。其次，设计多智能体EI维护系统，提供运维服务，包括诊断、预测、决策和控制功能。为了支持DPDC-ILKM的持续改进，引入了自进化机制，实现了动态工业环境下的自适应学习和持续优化。最后，讨论了支持工业人工智能发展的关键挑战和未来方向。本文提出了一种将llm与EI相结合的独特的工业维护框架，为工业群机器人的智能维护提供了新的视角和技术基础。

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引用次数: 0

Hierarchical Modeling and Analysis of Power Grid Cyber-Physical Systems: Application and Validation of HM-GCPS 电网信息物理系统的分层建模与分析-Ⅱ：HM-GCPS的应用与验证

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-12-19 DOI: 10.1016/j.jii.2025.101042

Yang-Rong Chen , Jun-E Li , Jie Zhang , Yu-Fei Wang , Ting Zhao

This paper is the second part of a two-part series. In Part Ⅰ, the hierarchical model (i.e., HM-GCPS) of power grid cyber-physical system (GCPS) is established. Part II presents the application method of HM-GCPS in Part I used to solve a specific problem, and verifies the effectiveness of HM-GCPS by an example. First, a modeling and analysis architecture, describing the problems that can be analyzed by HM-GCPS, is proposed. To verify the feasibility and effectiveness of HM-GCPS, the risk propagation analysis process of GCPS cyber space is presented based on HM-GCPS. Then, taking a provincial power dispatch data network (PDDN) as an example, the failure event chains under five cyber-attack scenarios are analyzed and deduced. These five scenarios target all layers of the GCPS cyber space. Meanwhile, the impacts of the five cyber-attack scenarios on GCPS communication services are simulated. The results show that HM-GCPS is feasible and effective when it is used for analyzing the risk propagation of GCPS cyber space.

本文是由两部分组成的系列文章的第二部分。Ⅰ部分建立了电网信息物理系统（GCPS）的层次模型（即HM-GCPS）。第二部分介绍了第一部分中针对具体问题的HM-GCPS的应用方法，并通过实例验证了HM-GCPS的有效性。首先，提出了一种建模和分析体系结构，描述了HM-GCPS可以分析的问题。为了验证该方法的可行性和有效性，提出了基于该方法的GCPS网络空间风险传播分析过程。然后，以某省级电力调度数据网络（PDDN）为例，分析并推导了五种网络攻击场景下的故障事件链。这五个场景针对GCPS网络空间的所有层。同时，模拟了五种网络攻击场景对GCPS通信业务的影响。结果表明，将该方法应用于GCPS网络空间的风险传播分析是可行和有效的。

引用次数: 0

Automating appliance verification in facilities management using a denoised Voltage-Current feature extraction and classification pipeline 使用去噪电压-电流特征提取和分类管道在设施管理中自动化设备验证

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-12-16 DOI: 10.1016/j.jii.2025.101040

Socretquuliqaa Lee , Faiyaz Doctor , Mohammad Hossein Anisi , Shashank Goud , Xiao Wang

Facilities Management (FM) companies can use load monitoring of electrical appliances (assets) to track energy consumption and predictive maintenance. Reliable algorithms are needed to automatically identify or verify appliances through their energy signatures to improve efficiencies during installation and inspection tasks. Most approaches rely on Voltage-Current (V-I) trajectory. These features are extracted from steady-state current and voltage signals. However, these methods often assume signals are uniformly sampled. In real-world conditions, this assumption does not always hold, leading to misclassified steady-state events when signals are noisy. This paper introduces a novel feature extraction and classification pipeline to ensure the validity of detected steady-state events. The approach measures the approximate entropy of current signals and their correlation with voltage to extract denoised features for appliance type classification. The proposed pipeline is evaluated on a large-scale real-world operational dataset spanning multiple appliance categories. We demonstrate that the extracted denoised features significantly improve the performance of Machine Learning (ML) models used for appliance type classification. Finally, we present a deployment framework for FM settings, enabling digital cataloguing of appliances informing businesses on sustainable choices for appliance requirements.

设施管理（FM）公司可以使用电器（资产）的负载监控来跟踪能源消耗和预测性维护。需要可靠的算法来通过其能量特征自动识别或验证设备，以提高安装和检查任务期间的效率。大多数方法依赖于电压-电流（V-I）轨迹。这些特征是从稳态电流和电压信号中提取出来的。然而，这些方法通常假设信号是均匀采样的。在现实世界中，这个假设并不总是成立，当信号有噪声时，会导致对稳态事件的错误分类。为了保证检测到的稳态事件的有效性，本文引入了一种新的特征提取和分类管道。该方法测量电流信号的近似熵及其与电压的相关性，提取去噪特征，用于器具类型分类。建议的管道在跨越多个设备类别的大规模实际操作数据集上进行评估。我们证明了提取的去噪特征显著提高了用于家电类型分类的机器学习（ML）模型的性能。最后，我们提出了FM设置的部署框架，实现了设备的数字编目，为企业提供了设备需求的可持续选择。

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引用次数: 0

A multi-scale digital twin model reconstruction method based on compatibility and exclusivity mechanisms 基于兼容性和排他性机制的多尺度数字孪生模型重建方法

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-12-15 DOI: 10.1016/j.jii.2025.101041

Xiaojian Wen , Wenchao Bian , Shimin Liu , Jie Wen , Jinsong Bao , Dan Zhang

With the deepening of digital transformation in the manufacturing industry, digital twin technology has become a key enabler for enhancing the flexibility and intelligent reconfiguration of manufacturing systems. However, the current construction of digital-twin-based production lines still relies heavily on manual expertise and lacks a systematic approach capable of automatically selecting and configuring appropriate components under task constraints. To address this issue, this paper proposes a multi-scale digital twin model reconstruction method based on compatibility and exclusivity mechanisms. The proposed approach establishes a component selection framework that integrates functional, spatial, and associative semantics, and further incorporates the temporal dimension to capture the dynamic evolution of component compatibility. This enables task-driven dynamic reconfiguration and adaptive optimization of spatial layouts. Experimental results demonstrate that the proposed methyod significantly improves component selection accuracy and the level of automation in configuration processes, while ensuring functional compatibility and spatial coordination. The study provides both theoretical support and an engineering-oriented solution for multi-scale intelligent planning and decision optimization in complex manufacturing systems.

随着制造业数字化转型的深入，数字孪生技术已成为增强制造系统柔性和智能重构的关键使能器。然而，目前基于数字孪生的生产线建设仍然严重依赖人工专业知识，缺乏能够在任务限制下自动选择和配置适当组件的系统方法。针对这一问题，本文提出了一种基于兼容性和排他性机制的多尺度数字孪生模型重建方法。该方法建立了一个集成了功能语义、空间语义和关联语义的组件选择框架，并进一步结合时间维度来捕捉组件兼容性的动态演变。这使得任务驱动的动态重新配置和空间布局的自适应优化成为可能。实验结果表明，该方法在保证功能兼容性和空间协调性的前提下，显著提高了组件选择精度和配置过程的自动化程度。该研究为复杂制造系统的多尺度智能规划与决策优化提供了理论支持和面向工程的解决方案。

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引用次数: 0

Knowledge graph-driven fault diagnosis for aviation equipment: Integrating improved joint extraction with large language model 航空装备知识图驱动故障诊断：改进联合提取与大语言模型的集成

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-12-15 DOI: 10.1016/j.jii.2025.101039

Lunyong Li , Auwal Haruna , Wanming Ying , Khandaker Noman , Yongbo Li

Aviation equipment fault diagnosis faces significant challenges due to the complexity of systems, the scarcity of high-quality labeled data, and the critical need for interpretability in maintenance decisions. While Knowledge Graph (KG) offers a promising solution for structured knowledge management, their broader application is impeded by limitations in knowledge extraction methods from unstructured texts and inefficient retrieval mechanisms. To address these gaps, this study proposes an innovative KG method that integrates an enhanced joint extraction model with Large Language Model (LLM) for aviation equipment fault diagnosis. To overcome the bottlenecks of high complexity and low efficiency in constructing KGs for aviation fault diagnosis, this study proposes an Aviation Equipment Maintenance Cascade Binary Tagging (AemCASREL) model optimized with Bidirectional Encoder Representation from Transformers (BERT) fine-tuning and attention enhancement. This model extracts fault entities and relations from diverse unstructured sources, such as aircraft maintenance manuals and equipment logs, to build a KG database in Neo4j. Additionally, a method integrating an LLM with the KG database is introduced, enhancing the model’s generation ability, enabling intelligent question-answering, and offering robust domain knowledge support for fault diagnosis. The experimental evaluation, using both self-built and public datasets, demonstrates the improved model's superiority over the baseline. On the self-built dataset, the F1 score rises from 0.907 to 0.968, and on the public dataset, it increases from 0.907 to 0.980. The integration of LLM and KG enhances the accuracy and intelligence of the question-answering system for aircraft fault diagnosis and maintenance, making it more adaptable to complex faults. This study provides a feasible knowledge-driven paradigm for multi-source information fusion and integration in complex industrial scenarios.

由于系统的复杂性、高质量标记数据的稀缺性以及维护决策对可解释性的迫切需求，航空设备故障诊断面临着巨大的挑战。知识图谱（Knowledge Graph， KG）为结构化知识管理提供了一个很有前景的解决方案，但其广泛应用受到非结构化文本知识提取方法和低效检索机制的限制。为了解决这些问题，本研究提出了一种创新的KG方法，该方法将增强的联合提取模型与大语言模型（LLM）相结合，用于航空设备故障诊断。为克服航空设备故障诊断中KGs构建复杂、低效的瓶颈，提出了一种基于BERT（双向编码器表示）微调和注意力增强的航空设备维修级联二值标记（AemCASREL）模型。该模型从不同的非结构化来源（如飞机维护手册和设备日志）中提取故障实体和关系，以在Neo4j中构建KG数据库。此外，提出了一种将LLM与KG数据库集成的方法，增强了模型的生成能力，实现了智能问答，并为故障诊断提供了鲁棒的领域知识支持。使用自建数据集和公共数据集进行的实验评估表明，改进的模型优于基线。在自建数据集上，F1得分从0.907上升到0.968，在公共数据集上，F1得分从0.907上升到0.980。LLM和KG的集成提高了飞机故障诊断与维修问答系统的准确性和智能化，使其更能适应复杂故障。本研究为复杂工业场景下的多源信息融合与集成提供了一种可行的知识驱动范式。

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引用次数: 0

Energy-efficient task offloading in the Industrial Internet of Things: A Lyapunov-guided multi-agent deep reinforcement learning approach 工业物联网中的节能任务卸载：lyapunov引导的多智能体深度强化学习方法

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-12-09 DOI: 10.1016/j.jii.2025.101037

Zihang Yu , Zhenjiang Zhang , Sherali Zeadally

Multi-access Edge Computing (MEC) integrated with the Industrial Internet of Things (IIoT) is vital for intelligent manufacturing and industrial automation because it enables low-latency and high-efficiency task offloading from resource-limited devices to an edge server. However, dynamic wireless channels and stochastic task arrivals introduce significant uncertainties, leading to queuing delays, inefficient resource utilization, and high energy consumption. Moreover, the lack of future system information makes real-time offloading decisions particularly challenging. To address these issues, we construct both task queues and delay-aware virtual queues, and we formulate a stochastic optimization problem for joint task offloading and resource allocation. The objective is to minimize long-term energy consumption while ensuring queue stability and satisfying task deadline constraints. To solve this problem, we propose a novel Lyapunov-guided multi-agent deep reinforcement learning framework (LYMADDPG), which integrates Lyapunov optimization with Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Specifically, we use Lyapunov optimization to transform delay constraints into a virtual queue stability control problem, converting the original long-term problem into a series of per-slot optimizations. Next, we use MADDPG to learn optimal offloading and resource allocation policies in a distributed and adaptive manner. Extensive simulation results demonstrate that our method significantly outperforms baseline algorithms in reducing energy consumption, ensuring queue stability, and meeting task deadlines. These results confirm the practical effectiveness of our approach and highlight its strong potential for real-world deployment in MEC-enabled IIoT systems.

与工业物联网（IIoT）集成的多访问边缘计算（MEC）对于智能制造和工业自动化至关重要，因为它可以将低延迟和高效的任务从资源有限的设备卸载到边缘服务器。然而，动态无线信道和随机任务到达引入了显著的不确定性，导致排队延迟、资源利用效率低下和高能耗。此外，缺乏未来系统信息使得实时卸载决策特别具有挑战性。为了解决这些问题，我们构建了任务队列和延迟感知虚拟队列，并提出了一个联合任务卸载和资源分配的随机优化问题。目标是在确保队列稳定性和满足任务截止日期约束的同时最小化长期能量消耗。为了解决这一问题，我们提出了一种新的Lyapunov引导的多智能体深度强化学习框架（lyaddpg），该框架将Lyapunov优化与多智能体深度确定性策略梯度（madpg）相结合。具体来说，我们使用Lyapunov优化将延迟约束转化为一个虚拟队列稳定性控制问题，将原来的长期问题转化为一系列逐槽优化。其次，我们使用madpg以分布式和自适应的方式学习最优卸载和资源分配策略。大量的仿真结果表明，我们的方法在降低能耗、确保队列稳定性和满足任务截止日期方面明显优于基线算法。这些结果证实了我们方法的实际有效性，并突出了其在支持mec的工业物联网系统中实际部署的强大潜力。

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引用次数: 0