Journal of Industrial Information Integration最新文献_第2页

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 : 2026-01-08 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 15.7 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub 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

Physical AI in cyber-physical systems: from digital to embodied industrial agents 网络物理系统中的物理人工智能：从数字到实体工业代理

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

Journal of Industrial Information Integration

Pub Date : 2026-01-01 DOI: 10.1016/j.jii.2025.101038

Didem Gurdur Broo

The rapid advancement of digital artificial intelligence has created unrealistic expectations about its transfer to physical industrial systems. This commentary critically examines the fundamental misalignment between digital AI capabilities and the complex requirements of industrial cyber-physical systems. While digital AI excels in pattern recognition and virtual environments, physical intelligence demands understanding of mechanics, materials, energy, and real-world constraints that current AI paradigms inadequately address. The commentary argues that achieving genuine physical intelligence in industrial settings requires a fundamental reorientation toward information integration as the enabling foundation, rather than pursuing ever-larger foundation models. Industrial information integration frameworks must bridge cyber-physical boundaries, handle temporal characteristics properly, represent uncertainty explicitly, and enable human-AI collaboration. This perspective aims to redirect research efforts toward the critical challenges of industrial information integration that will ultimately enable meaningful progress in physical AI for cyber-physical systems.

数字人工智能的快速发展让人们对其向实体工业系统的转移产生了不切实际的期望。这篇评论批判性地审视了数字人工智能能力与工业网络物理系统的复杂需求之间的根本错位。虽然数字人工智能在模式识别和虚拟环境方面表现出色，但物理智能需要理解当前人工智能范式无法充分解决的力学、材料、能源和现实世界的限制。评论认为，在工业环境中实现真正的物理智能需要从根本上重新定位，将信息集成作为实现基础，而不是追求更大的基础模型。工业信息集成框架必须跨越网络物理边界，适当处理时间特征，明确表示不确定性，并实现人类与人工智能的协作。这一观点旨在将研究工作转向工业信息集成的关键挑战，最终使网络物理系统的物理人工智能取得有意义的进展。

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引用次数: 0

A multi-layered data service model for Cyber-Physical Production Networks 面向信息物理生产网络的多层数据服务模型

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

Journal of Industrial Information Integration

Pub Date : 2025-12-31 DOI: 10.1016/j.jii.2025.101043

Ada Bagozi, Devis Bianchini, Massimiliano Garda, Michele Melchiori, Anisa Rula

In modern smart factories, supply chains are no longer isolated; instead, they are evolving into interconnected and dynamic networks, where intertwined supply chains enable real-time collaboration and data sharing for adaptive decision-making across multiple stakeholders. By harnessing data from sensors and connected devices, data-driven decisions can be made to optimize the entire supply chain, and to provide novel and customer-friendly products and services. Cyber-Physical Systems form the foundation of Cyber-Physical Production Systems (CPPS) by enabling real-time data exchange and intelligent automation at the factory level, while horizontal integration connects CPPS across different production facilities to enhance supply chain coordination, thus forming the so-called Cyber-Physical Production Networks (CPPN). In CPPN, the Internet of Services (IoS) paradigm, in combination with the Internet of Things (IoT), plays a crucial role in facilitating horizontal integration and seamless collaboration between intertwined supply chains. Since the IoS paradigm has to enable data sharing and processing within individual smart factories and across factory borders, there is a need to design service-oriented architectures specifically tailored to data governance in both CPPS and CPPN. However, existing service-oriented approaches for CPPS primarily focus on deployment layers (e.g., fog/edge computing or IT/production levels) while neglecting data-oriented aspects, limiting modularity and effective data service design across CPPS and CPPN levels. To bridge this gap, in this paper, we propose a multi-layered service-oriented model for CPPN focused on data services, which includes atomic services for data collection and processing, and composite services for governing the data flow within smart factories and throughout the supply chains they participate in. One of the significant advantages of the multi-layered approach is a clear separation of concerns in service design, with the ability to address issues of modularity, scalability, data sovereignty and data access, by distinguishing between CPPS and CPPN levels. In the paper, we critically evaluate different strategies for the management of a service ecosystem that is compliant with the proposed model.

在现代智能工厂中，供应链不再是孤立的；相反，它们正在演变成相互关联和动态的网络，其中相互交织的供应链能够实现实时协作和数据共享，以便跨多个利益相关者进行适应性决策。通过利用来自传感器和连接设备的数据，可以做出数据驱动的决策，以优化整个供应链，并提供新颖和客户友好的产品和服务。信息物理系统通过在工厂层面实现实时数据交换和智能自动化，构成了信息物理生产系统（CPPS）的基础，而横向集成将不同生产设施的CPPS连接起来，以加强供应链协调，从而形成所谓的信息物理生产网络（CPPN）。在CPPN中，服务互联网（IoS）范式与物联网（IoT）相结合，在促进相互交织的供应链之间的横向整合和无缝协作方面发挥着至关重要的作用。由于IoS范例必须支持在单个智能工厂内和跨工厂边界进行数据共享和处理，因此需要设计面向服务的体系结构，专门针对CPPS和CPPN中的数据治理进行定制。然而，现有的面向服务的CPPS方法主要关注部署层（例如雾/边缘计算或IT/生产层），而忽略了面向数据的方面，限制了CPPS和CPPN级别的模块化和有效的数据服务设计。为了弥补这一差距，在本文中，我们为CPPN提出了一个以数据服务为重点的多层面向服务模型，其中包括用于数据收集和处理的原子服务，以及用于管理智能工厂及其参与的整个供应链中的数据流的组合服务。多层方法的一个显著优点是服务设计中关注点的清晰分离，通过区分CPPS和CPPN级别，能够解决模块化、可伸缩性、数据主权和数据访问等问题。在本文中，我们批判性地评估了与所提出的模型兼容的服务生态系统管理的不同策略。

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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 : 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

A thorough assessment of the non-IID data impact in federated learning 对联邦学习中非iid数据影响的全面评估

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

Journal of Industrial Information Integration

Pub Date : 2025-12-27 DOI: 10.1016/j.jii.2025.101052

Daniel M. Jimenez-Gutierrez, Mehrdad Hassanzadeh, Aris Anagnostopoulos, Ioannis Chatzigiannakis, Andrea Vitaletti

Federated learning (FL) allows collaborative machine learning (ML) model training among decentralized clients’ information, ensuring data privacy. The decentralized nature of FL deals with non-independent and identically distributed (non-IID) data. This open problem has notable consequences, such as decreased model performance and longer convergence times. Despite its importance, experimental studies systematically addressing all types of data heterogeneity (a.k.a. non-IIDness) remain scarce. This paper aims to fill this gap by assessing and quantifying the non-IID effect through an empirical analysis. We use the Hellinger Distance (HD) to measure differences in distribution among clients. Our study benchmarks five state-of-the-art strategies for handling non-IID data, including label, feature, quantity, and spatiotemporal skews, under realistic and controlled conditions. This is the first comprehensive analysis of the spatiotemporal skew effect in FL. Our findings highlight the significant impact of label and spatiotemporal skew non-IID types on FL model performance, with notable performance drops occurring at specific HD thresholds. The FL performance is also heavily affected, mainly when the non-IIDness is extreme. Thus, we provide recommendations for FL research to tackle data heterogeneity effectively. Our work represents the most extensive examination of non-IIDness in FL, offering a robust foundation for future research.

联邦学习（FL）允许在分散的客户信息中进行协作机器学习（ML）模型训练，从而确保数据隐私。FL的分散特性处理非独立和同分布（non-IID）数据。这个开放的问题有显著的后果，比如降低模型性能和延长收敛时间。尽管它很重要，但系统地解决所有类型的数据异质性（又名非数据异质性）的实验研究仍然很少。本文旨在通过实证分析，对非iid效应进行评估和量化，填补这一空白。我们使用海灵格距离（HD）来衡量客户之间分布的差异。我们的研究在现实和受控的条件下，对处理非iid数据的五种最先进的策略进行了基准测试，包括标签、特征、数量和时空倾斜。这是对FL时空倾斜效应的首次综合分析。我们的研究结果强调了标签和时空倾斜非iid类型对FL模型性能的显著影响，在特定的HD阈值下，性能会出现显著下降。FL的性能也受到很大的影响，主要是在非idness非常大的情况下。因此，我们为FL研究提供了有效解决数据异质性的建议。我们的工作代表了对FL非iidness最广泛的检查，为未来的研究提供了坚实的基础。

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引用次数: 0

Two-stage dynamic reconstruction biased learning for anomaly detection in attributed networks of smart manufacturing 面向智能制造属性网络的两阶段动态重构偏学习异常检测

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

Journal of Industrial Information Integration

Pub Date : 2025-12-27 DOI: 10.1016/j.jii.2025.101050

Jing Long , Jiahao Zeng , Zhifei Yan , Min Shi , Kun Xie , Meng Shen , Naixue Xiong

In smart manufacturing systems, interconnected systems composed of equipment nodes such as intelligent machine tools and sensors can be abstracted as attributed networks. However, such networks are vulnerable to security risks like cyber-attacks and equipment failures, which directly threaten the stable operation of smart manufacturing systems. In the unsupervised setting, existing anomaly detection models intrinsically lean towards fitting the overwhelming majority of normal patterns during training. However, they cannot escape being influenced by anomalous characteristics, which degrades the detection of anomaly patterns in smart manufacturing environments. To address this challenge, this paper proposes a novel anomaly detection method called ARENA for attributed networks in smart manufacturing, which adopts two-stage dynamic reconstruction bias learning. Firstly, a graph autoencoder uncovers latent data patterns in smart manufacturing scenarios by minimizing reconstruction error. Then, the dynamic reconstruction biased learning module adjusts the training process in two stages to filter out pseudo-normal nodes and pseudo-anomalous nodes, enabling the model to adaptively fine-tune, mitigating the impact of anomalous data during training. Finally, the classification module further amplifies the anomaly score, making abnormal patterns more pronounced and easier to detect. The overall anomaly score is calculated by combining the results of the graph reconstruction and classification modules. Experimental results show that the ARENA method significantly improves performance, with an increase of 3.73% in AUC and 21.1% in AUPRC, including the success of the case study, providing strong support for the intelligent operation and maintenance of equipment in industrial manufacturing systems.

在智能制造系统中，由智能机床、传感器等设备节点组成的互联系统可以抽象为属性网络。然而，这种网络容易受到网络攻击和设备故障等安全风险的影响，直接威胁到智能制造系统的稳定运行。在无监督环境中，现有的异常检测模型本质上倾向于在训练过程中拟合绝大多数正常模式。然而，它们无法逃脱异常特征的影响，这降低了智能制造环境中异常模式的检测。为了解决这一挑战，本文提出了一种新的智能制造属性网络异常检测方法ARENA，该方法采用两阶段动态重构偏差学习。首先，图形自编码器通过最小化重构误差来揭示智能制造场景中潜在的数据模式。然后，动态重构偏置学习模块分两个阶段调整训练过程，过滤掉伪正常节点和伪异常节点，使模型能够自适应微调，减轻训练过程中异常数据的影响。最后，分类模块进一步放大异常评分，使异常模式更加明显，更容易被发现。结合图重构和分类模块的结果计算总体异常评分。实验结果表明，ARENA方法显著提高了性能，AUC提高了3.73%，AUPRC提高了21.1%，包括案例研究的成功，为工业制造系统中设备的智能运维提供了强有力的支持。

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引用次数: 0

Federated split learning-driven multimodal physical-virtual integration framework: high-fidelity full-cross-section deformation field reconstruction in precise metal tube bending manufacturing 联邦分裂学习驱动的多模态物理-虚拟集成框架：高精度金属管材弯曲制造中的高保真全截面变形场重建

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.2025.101048

Zili Wang , Xinlei Hu , Shuyou Zhang , Lemiao Qiu , Yaochen Lin , Liangyou Li , Yongzhe Xiang , Jie Li

During the metal tube bending (MTB) process, high-fidelity reconstruction of full cross-section (FCS) deformation is critical to the robustness of closed-loop control in tube-bending manufacturing systems. However, the distributed nature of industrial data, the spatiotemporal discontinuity of physical sensing, and the heterogeneity of multimodal physical–virtual data hinder effective integration of distributed sources and precise reconstruction of the transient deformation of tube surfaces. To address these challenges, we propose a Federated Split-Learning–Driven Multimodal Physical–Virtual Integration (FSLD-MPVI) framework. Leveraging a hybrid distributed–centralized architecture with cross-level collaborative fusion, FSLD-MPVI enables efficient integration and knowledge sharing of local high-fidelity visual data, global low-fidelity finite-element (FE) simulation data, and static process parameters that are dispersed across manufacturing nodes. Within the split learning (SL) distributed architecture, three cascaded, heterogeneous subnetworks are deployed, each dedicated to fusing a specific class of hybrid modality inputs, thereby providing the infrastructure needed to integrate modalities originating from different workshops. In the federated learning (FL) layer, a centralized server aggregates the parameters of each subnetwork respectively, mitigating cross-node data isolation while preserving data locality. Experiments demonstrate that FSLD-MPVI achieves high-accuracy global reconstruction (R² = 0.9973); in the 90° bending case, the shape deviation remains within 0.2 mm. These results verify that multimodal physical–virtual integration strongly supports precise global reconstruction of FCS deformation fields and establishes a new paradigm for intelligent process monitoring in advanced manufacturing systems.

在金属管材弯曲加工过程中，全截面变形的高保真重建对管材弯曲制造系统闭环控制的鲁棒性至关重要。然而，工业数据的分布式特性、物理感知的时空不连续以及多模态物理-虚拟数据的异质性阻碍了分布式数据源的有效集成和管道表面瞬态变形的精确重建。为了应对这些挑战，我们提出了一个联邦分裂学习驱动的多模态物理虚拟集成（FSLD-MPVI）框架。FSLD-MPVI利用混合分布式集中式架构和跨层协作融合，实现了本地高保真视觉数据、全球低保真有限元（FE）仿真数据和分散在制造节点上的静态过程参数的高效集成和知识共享。在分离学习（SL）分布式体系结构中，部署了三个级联的异构子网，每个子网专门用于融合特定类别的混合模态输入，从而提供集成来自不同车间的模态所需的基础设施。在联邦学习（FL）层，集中式服务器分别聚合每个子网的参数，在保持数据局部性的同时减轻了跨节点数据隔离。实验表明，FSLD-MPVI实现了高精度的全局重建（R²= 0.9973）；在90°弯曲情况下，形状偏差保持在0.2 mm以内。这些结果验证了多模态物理-虚拟集成强有力地支持了FCS变形场的精确全局重建，并为先进制造系统中的智能过程监控建立了新的范例。

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引用次数: 0

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