Journal of Industrial Information Integration最新文献_第6页

Multi-dimensional framework for assessing digital twin maturity in construction machinery 工程机械数字孪生成熟度评估的多维框架

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

Journal of Industrial Information Integration

Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.jii.2026.101067

Ruibo Hu, Wenting Gong, Ke Chen, Hanbin Luo

Smart construction machinery enabled by digital twin (DT) technology has significant potential to enhance construction safety and efficiency. However, the lack of a dedicated maturity assessment model for construction machinery DT (CMDT) reveals a critical gap in existing research. This study proposes a structured framework for assessing CMDT maturity, designed to evaluate the readiness and developmental stage of DT implementations in construction machinery. The framework was developed based on a comprehensive literature review and expert interviews, yielding a maturity assessment model comprising five dimensions and 24 indicators across five maturity levels. The model adopts a two-stage assessment approach that integrates expert competency-based grouping and weighting with an improved bi-objective optimization model. To enhance the robustness of expert opinion aggregation, a penalty-weight mechanism is embedded in the objective function, effectively balancing consensus and confidence. The proposed framework was validated through a real-world case study involving an automated construction system (ACS). The results demonstrate the capability of the framework to identify CMDT maturity levels and inform improvement pathways. Overall, this study provides an evidence-based tool to accelerate DT adoption in the construction sector.

数字孪生（DT）技术支持的智能施工机械在提高施工安全和效率方面具有巨大潜力。然而，缺乏一个专门的工程机械成熟度评估模型（CMDT）显示了现有研究的一个重要空白。本研究提出了一个评估CMDT成熟度的结构化框架，旨在评估工程机械中DT实施的准备情况和发展阶段。该框架是在综合文献综述和专家访谈的基础上开发的，产生了一个成熟度评估模型，该模型包括五个成熟度级别的五个维度和24个指标。该模型采用两阶段评价方法，将基于专家能力的分组和加权与改进的双目标优化模型相结合。为了增强专家意见聚合的鲁棒性，在目标函数中嵌入了惩罚权重机制，有效地平衡了共识和置信度。提出的框架通过涉及自动化施工系统（ACS）的实际案例研究进行了验证。结果证明了框架识别CMDT成熟度级别和告知改进途径的能力。总体而言，本研究提供了一个以证据为基础的工具，以加速建筑行业对DT的采用。

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引用次数: 0

Mobility-entropy–aware adaptive UWB sampling for energy-efficient real-time industrial digital twins 节能实时工业数字孪生的移动熵感知自适应超宽带采样

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

Journal of Industrial Information Integration

Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.jii.2026.101062

Min-Ho Han , Keun-Woo Lim , Young-Bae Ko

Industrial digital twins (DTs) must fuse data from operational technology (OT) and information technology (IT) platforms in real time. However, the high-frequency ultra-wideband (UWB) sampling needed for real-time fidelity can rapidly drain battery-powered tags, increasing battery-replacement and maintenance burden in large-scale deployments and jeopardizing service-level accuracy. To address this energy-accuracy trade-off, this paper defines mobility-entropy, a three-dimensional metric that quantifies the dynamic characteristics of a mobile entity. A lightweight on-device machine learning scheduler uses this metric to adjust the UWB sampling rate in real time across the end-to-end pipeline from sensor to DT renderer. Evaluated on a seven-anchor indoor testbed mirrored in real time on the MuJoCo DT platform, the proposed approach extends the average tag sleep time by 65.6% compared to a fixed-rate baseline while achieving a Digital Twin Projection Error (DTPE) as low as 3.15 cm across various mobility environments. The result is longer battery life and reduced telemetry data volume without sacrificing geometric accuracy, improving deployment practicality by lowering maintenance overhead and wireless traffic in industrial settings. We also explain how edge decisions are propagated through the integration layer to DT applications, positioning adaptive sensing within the operational technology (OT) to information technology (IT) to digital twin (DT) data flow. These results highlight the framework’s potential for real-world industrial digital twin applications, including worker and asset tracking as well as safety monitoring, by enabling energy-efficient operation with reduced maintenance and communication overhead.

工业数字孪生（DTs）必须实时融合来自运营技术（OT）和信息技术（IT）平台的数据。然而，实时保真所需的高频超宽带（UWB）采样可能会迅速耗尽电池供电的标签，增加大规模部署时电池更换和维护的负担，并危及服务水平的准确性。为了解决这种能量-精度的权衡，本文定义了移动熵，这是一种量化移动实体动态特性的三维度量。一个轻量级的设备上机器学习调度程序使用这个度量来实时调整从传感器到DT渲染器的端到端管道中的UWB采样率。在MuJoCo DT平台上实时镜像的七锚室内测试平台上进行了评估，与固定速率基线相比，该方法将平均标签睡眠时间延长了65.6%，同时在各种移动环境中实现了低至3.15cm的数字双投影误差（DTPE）。其结果是在不牺牲几何精度的情况下延长电池寿命，减少遥测数据量，通过降低维护开销和工业环境中的无线流量，提高部署的实用性。我们还解释了边缘决策如何通过集成层传播到DT应用程序，将自适应传感定位在运营技术（OT）到信息技术（IT）到数字孪生（DT）数据流中。这些结果突出了该框架在实际工业数字孪生应用中的潜力，包括工人和资产跟踪以及安全监控，通过减少维护和通信开销实现节能操作。

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引用次数: 0

A human factors approach to design an information interface model for a digital twin 基于人为因素的数字孪生信息接口模型设计

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

Journal of Industrial Information Integration

Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.jii.2026.101063

Claire Palmer , Ella-Mae Hubbard , Rebecca Grant , Yee Mey Goh

A Digital Twin requires a user interface to deliver information relevant to its users, hence a model is required to represent the information required by the interface. The objective of this research is to develop a transdisciplinary human factors approach to information gathering and modelling to design Digital Twin information interfaces. Existing approaches to interface modelling either do not consider human factors or those that do provide a high-level view of information insufficient to capture the complexities required for an information interface for a Digital Twin. The approach presented here consists of capturing the information interface requirements using Cognitive Work Analysis to analyse the human-information interaction and structuring this information via Unified Modelling Language (UML) models. To understand human information requirements when interacting with a Digital Twin interface, personas are used to guide the CWA. To illustrate this approach a Digital Twin of an Industrial Gearbox Product-Service is considered. Validation was conducted through a case study with a research and technology organisation. The approach was found to be clear and able to provide information customised to user needs and the level of detail required. The research described creates a more effective approach to creating a Digital Twin information interface model through reducing the number of iterations required to gather information. By specifically considering human interactions the transdisciplinary approach advanced here will augment the development of software systems.

数字孪生需要一个用户界面来传递与其用户相关的信息，因此需要一个模型来表示接口所需的信息。本研究的目的是发展一种跨学科的人因方法来进行信息收集和建模，以设计数字孪生信息接口。现有的接口建模方法要么不考虑人为因素，要么提供的信息高级视图不足以捕捉数字孪生的信息接口所需的复杂性。这里提出的方法包括使用认知工作分析来捕获信息接口需求，以分析人-信息交互，并通过统一建模语言（UML）模型构建该信息。在与数字孪生界面交互时，为了理解人的信息需求，使用角色来指导CWA。为了说明这种方法，考虑了工业齿轮箱产品服务的数字孪生。验证是通过一个研究和技术组织的案例研究进行的。人们发现，这种方法很明确，能够根据用户的需要和所需的详细程度提供量身定制的信息。所描述的研究通过减少收集信息所需的迭代次数，创建了一种更有效的方法来创建数字孪生信息接口模型。通过特别考虑人类的相互作用，这里提出的跨学科方法将增强软件系统的开发。

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引用次数: 0

Cognitive-based framework for detecting and diagnosing broken bars in induction motors for industry maintenance 基于认知的感应电机断条检测与诊断框架

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

Journal of Industrial Information Integration

Pub Date : 2026-03-01 Epub Date: 2025-11-28 DOI: 10.1016/j.jii.2025.101022

Narco Afonso Ravazzoli Maciejewski , Roberto Zanetti Freire , Anderson Luis Szejka , Thiago de Paula Machado Bazzo , Sofia Moreira de Andrade Lopes , Rogério Andrade Flauzino

Three-phase induction motors are the primary actuators for converting electrical energy into mechanical energy in the productive sector, constituting key assets due to their widespread use and critical function. Reducing maintenance costs and implementing predictive techniques incentivize the development of systems to identify intrinsic defects. The increasing demand for customization in manufacturing affects maintenance due to fast production line adaptations. This leads to unforeseen failures that compromise reliability. There is a lack of research on detecting and diagnosing faults in induction motors under intermittent drives or varying operating conditions. To fill this gap, the present research proposes a methodology for recommending algorithms to diagnose and detect broken bar defects in three-phase induction motors during transient operation based on a cognitive system. The framework explains and detects fault causality. Using experimental data (current, voltage, vibration), three-phase induction motors were tested under normal conditions, applying various severities of broken bar faults with load torque variations. Features were extracted from each signal, and feature selection algorithms of different mathematical natures were applied. Machine learning models were built, validated, and tested with multicriteria measures. To assess robustness, white noise was inserted into the experimental signals. The Consistency-Based Filter algorithm emerged as the most suitable for feature selection combined with Random Forest and Multilayer Perceptron models. The best results were achieved with up to 80 % noise tolerance without compromising predictive capacity for diagnosing defect severity. Features following a Gaussian distribution showed better predictive capacity, resulting in a reliable framework for fault diagnosis in induction motors.

三相感应电动机是生产部门将电能转化为机械能的主要执行器，由于其广泛的应用和关键的功能，构成了关键资产。降低维护成本和实现预测技术激励系统开发以识别内在缺陷。由于生产线的快速适应，制造业对定制化需求的不断增长影响了维护。这会导致无法预料的故障，从而降低可靠性。对于异步电动机在间歇驱动或变工况下的故障检测与诊断，目前还缺乏相关的研究。为了填补这一空白，本研究提出了一种基于认知系统的推荐算法来诊断和检测三相异步电动机在瞬态运行过程中的断条缺陷。该框架解释和检测故障因果关系。利用实验数据（电流、电压、振动），在正常情况下，对三相异步电动机进行了不同程度的断条故障和负载转矩变化的测试。从每个信号中提取特征，并应用不同数学性质的特征选择算法。机器学习模型的建立、验证和测试采用多标准措施。为了评估鲁棒性，在实验信号中插入白噪声。基于一致性的滤波算法与随机森林和多层感知机模型相结合，成为最适合特征选择的算法。在不影响诊断缺陷严重程度的预测能力的情况下，获得了高达80%的噪声容忍度的最佳结果。服从高斯分布的特征具有较好的预测能力，为异步电动机故障诊断提供了可靠的框架。

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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 : 2026-03-01 Epub 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

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 : 2026-03-01 Epub 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

Design software network: A collaborative EaaS business model for CNC manufacturers, customers, and designers 设计软件网络：面向CNC制造商、客户和设计师的协同EaaS商业模式

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

Journal of Industrial Information Integration

Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.jii.2026.101079

İsmail Yoşumaz , Ali Gülbaşı , Safiye Süreyya Bengül

Purpose

Industry 5.0 accelerates the shift from asset ownership to benefit-based business models. This study develops a collaborative EaaS framework for the CNC sector that simultaneously monetizes the measurable benefit (active machining time or produced part volume) rather than the machine itself, and integrates 3D product designers as active, revenue-generating stakeholders in the value chain.

Design/methodology/approach

A qualitative research design combining document analysis and descriptive content analysis was employed. From 101 documents, 41 were selected through purposive sampling.

Findings

The proposed Design Software Network model establishes a triadic ecosystem connecting CNC manufacturers, customers, and designers. By leveraging existing digital twin and IoT infrastructures for real-time measurement of machining outputs, the Design Software Network model implements pay-per-use pricing for physical equipment while generating an entirely new revenue layer: automated, blockchain-enforced royalties paid to designers for every part produced using their licensed 3D models. This dual monetization mechanism, which combines benefit-based pricing of machine usage with recurring monetization of digital designs, addresses the current exclusion of designers from EaaS value capture and fosters collaborative innovation.

Originality

Pay-per-use models have begun to emerge in the CNC sector, remaining strictly limited to the manufacturer–customer dyad. The DSN’s originality lies in extending these established measurement systems to systematically include 3D product designers through scalable, usage-based royalty streams. This integration does not yet exist in the literature or industry implementations. The model thereby completes the transition to a genuinely human-centric, triadic Industry 5.0 ecosystem.

工业5.0加速了从资产所有权到基于利益的商业模式的转变。本研究为CNC行业开发了一个协作的EaaS框架，同时将可衡量的利益（主动加工时间或生产零件量）货币化，而不是机器本身，并将3D产品设计师集成为价值链中活跃的、产生收入的利益相关者。设计/方法/方法采用文献分析和描述性内容分析相结合的定性研究设计。从101篇文献中，通过有目的抽样抽取41篇。所提出的设计软件网络模型建立了一个连接CNC制造商、客户和设计师的三元生态系统。通过利用现有的数字孪生和物联网基础设施实时测量加工输出，设计软件网络模型实现了物理设备的按使用付费定价，同时产生了一个全新的收入层：为使用其许可的3D模型生产的每个部件向设计师支付自动化的、区块链强制的版税。这种双重货币化机制结合了基于收益的机器使用定价和数字设计的循环货币化，解决了目前设计师被排除在EaaS价值获取之外的问题，并促进了协作创新。在CNC领域，按次付费的模式已经开始出现，但仍然严格限于制造商-客户的模式。DSN的独创性在于通过可扩展的、基于使用的版税流，将这些已建立的测量系统扩展到系统地包括3D产品设计师。这种集成在文献或行业实现中还不存在。因此，该模型完成了向真正以人为中心的三元工业5.0生态系统的过渡。

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引用次数: 0

Emerging perspectives on embodied intelligence in future smart manufacturing 未来智能制造中具身智能的新兴观点

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

Journal of Industrial Information Integration

Pub Date : 2026-03-01 Epub Date: 2025-11-29 DOI: 10.1016/j.jii.2025.101020

Dan Xia , Pengpeng Xu , Guangjie Han , Jinfang Jiang

Embodied intelligence has emerged as a transformative paradigm in artificial intelligence, representing the convergence of multimodal perception, cognitive reasoning, and physical interaction with the environment. In the context of smart manufacturing, it is increasingly recognized as a key enabler for future intelligent systems capable of adapting to dynamic, unstructured, and human-centric production environments. With the rapid development of large multimodal models, embodied intelligence is poised to achieve unprecedented levels of generalization, autonomy, and task versatility through continuous learning and real-world interaction. Therefore, this article conducts a systematic review of the current research status and development trends of embodied intelligence in smart manufacturing, analyzes its key technologies, summarizes typical application scenarios, and further discusses the challenges and future research directions, aiming to provide new insights and guidance for smart manufacturing driven by embodied intelligence.

具身智能已经成为人工智能的一个变革范式，代表了多模态感知、认知推理和与环境的物理交互的融合。在智能制造的背景下，它越来越被认为是未来智能系统的关键推动者，能够适应动态、非结构化和以人为中心的生产环境。随着大型多模态模型的快速发展，具身智能将通过持续学习和现实世界的互动，实现前所未有的泛化、自主性和任务多功能性。因此，本文系统回顾了智能制造中具体智能的研究现状和发展趋势，分析了其关键技术，总结了典型应用场景，并进一步探讨了面临的挑战和未来的研究方向，旨在为以具体智能为驱动的智能制造提供新的见解和指导。

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 : 2026-03-01 Epub 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

Prioritizing and overcoming barriers to unmanned aerial vehicles adoption in agriculture using an integrated intuitionistic fuzzy decision-making approach 利用综合直觉模糊决策方法确定农业中无人机采用的优先级和克服障碍

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

Journal of Industrial Information Integration

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.jii.2025.101047

Fei Gao

Unmanned aerial vehicles (UAVs) have garnered increasing attention due to their efficiency, cost-effectiveness, and performance, leading to various efforts to implement UAVs in agriculture, especially with the rapid development of low-altitude economy. However, successful UAV application in agriculture is not always achieved, and understanding the barriers and potential solutions is crucial for effective implementation. To this end, this study employs intuitionistic fuzzy sets, the modified Delphi method, the fuzzy weight with zero consistency (FWZIC) method, and the weighted aggregated sum product assessment (WASPAS) method to identify and prioritize barriers and solutions for UAV application in agriculture. Firstly, 32 barriers are identified and categorized into five main categories. The intuitionistic fuzzy FWZIC method is then utilized to calculate weights for prioritizing the barriers. Subsequently, the intuitionistic fuzzy WASPAS method is applied to assess and rank solutions for these barriers. The results indicate that “risk of failures” is the most significant sub-barrier hindering UAV application in agriculture. Additionally, “design and prompt more reliable UAV technologies” is the most effective solution for mitigating these barriers. This study provides a systematic framework to address barriers to UAV application in agriculture, and the findings can assist practitioners by guiding their efforts toward overcoming the most significant barriers and facilitating successful UAV application in agriculture.

无人驾驶飞行器（uav）由于其效率，成本效益和性能而受到越来越多的关注，导致各种努力在农业中实施无人机，特别是随着低空经济的快速发展。然而，无人机在农业中的应用并不总是成功的，了解障碍和潜在的解决方案对于有效实施至关重要。为此，本研究采用直觉模糊集、改进德尔菲法、零一致性模糊权（FWZIC）法和加权累计和积评价（WASPAS）法对无人机在农业应用中的障碍和解决方案进行识别和排序。首先，确定了32个障碍，并将其分为五大类。然后利用直觉模糊FWZIC方法计算障碍物的优先级权重。然后，应用直觉模糊WASPAS方法对这些障碍的解决方案进行评估和排序。结果表明，“故障风险”是阻碍无人机在农业领域应用的最重要的子障碍。此外，“设计和促进更可靠的无人机技术”是缓解这些障碍的最有效解决方案。本研究提供了一个系统的框架来解决无人机在农业应用中的障碍，研究结果可以帮助从业者克服最重要的障碍，促进无人机在农业中的成功应用。

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引用次数: 0