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

Navigating digitalization and global value chains: Empirical insights from the Chinese manufacturing industry 数字化导航与全球价值链：来自中国制造业的实证分析

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

Journal of Industrial Information Integration

Pub Date : 2026-01-31 DOI: 10.1016/j.jii.2026.101083

Qingyu Zhang , Fakhar Shahzad , Chiranjibe Jana , Nikola Ivkovic , Gerhard-Wilhelm Weber

In a rapidly digitalized and globalized world, enterprises understand how digitalization shapes the global value chain (GVC) to remain competitive. Previous studies have examined digitalization, trade openness, research and development (R&D) investment, foreign direct investment (FDI), and infrastructure quality, leaving a gap in understanding the integrated determinants of GVC. This study aims to fill this research gap by examining the integrated impact of digitalization on GVC. Unlike previous studies, this study develops a holistic framework that captures a multidimensional analysis of the interaction between digitalization and GVC participation. This study used panel data models to achieve the desired outcomes from China’s manufacturing sector, and the results were obtained using Machine Learning Techniques. This study shows that manufacturing, domestic and foreign digitalization, research and development, productivity, and GVC participation all improve a GVC’s position; however, foreign direct investment hampers this improvement. Trade openness, financial growth, and infrastructure all positively impact the relationship between digitalization and the GVC position. By explicitly integrating digital technologies with broader economic and institutional factors, these findings offer a comprehensive understanding of the drivers of GVC competitiveness and provide actionable insights for the manufacturing sectors of emerging economies undergoing rapid digital transformation.

在快速数字化和全球化的世界中，企业了解数字化如何塑造全球价值链（GVC）以保持竞争力。之前的研究考察了数字化、贸易开放、研发投资、外国直接投资（FDI）和基础设施质量，在理解全球价值链的综合决定因素方面存在空白。本研究旨在通过研究数字化对全球价值链的综合影响来填补这一研究空白。与以往的研究不同，本研究开发了一个整体框架，对数字化与全球价值链参与之间的相互作用进行了多维分析。本研究使用面板数据模型来实现中国制造业的预期结果，并使用机器学习技术获得结果。研究表明，制造业、国内外数字化、研发、生产力和全球价值链参与都提高了全球价值链的地位；然而，外国直接投资阻碍了这种改善。贸易开放、金融增长和基础设施都对数字化与全球价值链地位之间的关系产生积极影响。通过明确地将数字技术与更广泛的经济和制度因素相结合，这些研究结果提供了对全球价值链竞争力驱动因素的全面理解，并为正在快速数字化转型的新兴经济体的制造业提供了可操作的见解。

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引用次数: 0

Enhanced hyper-node faster relational YOLO dwarf mongoose graph attention network for multi-target detection in smart IoT edge-cloud surveillance systems 智能物联网边缘云监控系统中多目标检测的增强超节点更快关系型YOLO矮猫鼬图注意网络

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

Journal of Industrial Information Integration

Pub Date : 2026-01-31 DOI: 10.1016/j.jii.2026.101086

Aishwarya D, R.I. Minu

The demand for multi-target detection within an IoT-based edge-cloud surveillance system is increasing. This is particularly the case in real-world scenarios where there could be several targets in varied lighting and several very mobile objects. Even with the best possible models, object detection models collapse when presented with the randomness of real-world environments, including clutter and the detection of multiple objects within a scene. A new innovation, the Enhanced Hyper-node Faster Relational YOLO Dwarf Mongoose (IHnode-FRYDM) Graph Attention Network (GAN) for multi-target detection in IoT-based innovative edge-cloud surveillance systems is presented herein. The new method uses the PASCAL VOC dataset to create a more efficient detection framework. It starts with the Iterative Dependable Peak-Aware Directed Filtering (IDPADF), a newer technique for pre-processing images, that considerably improves both the input image and feature representation quality. The real detection then executes the Faster-YOLO architecture, which is essential since it strives to balance speed and accuracy for real-time IoT operations. Moreover, it uses a Hyper-node Relational Graph Attention Network (HRGAT) to perform effective relational feature learning and correct identification of multiple targets in intricate and dynamic environments. IDMO's performance maximizes the rate of convergence and stability of the model to meet the computational loads of IoT edge devices. The resultant evaluation provides a mAP of 99.6% and an F1-score of 99.5%, while offering a processing time reduction of 32% in comparison to other traditional approaches. The results suggest that the new framework can be successfully deployed into new IoT edge-cloud surveillance processes with an efficient and accurate process to fulfill technical demands of multi-target surveillance applications.

在基于物联网的边缘云监控系统中，对多目标检测的需求正在增加。这在现实世界的场景中尤其如此，在不同的照明和几个非常移动的物体中可能有几个目标。即使使用最好的模型，对象检测模型在面对现实世界环境的随机性时也会崩溃，包括场景中的杂乱和多个对象的检测。本文提出了一种新的创新，用于基于物联网的创新型边缘云监控系统中多目标检测的增强超节点更快关系YOLO矮猫鼬（IHnode-FRYDM）图注意网络（GAN）。新方法使用PASCAL VOC数据集来创建一个更有效的检测框架。首先是迭代可靠的峰值感知定向滤波（IDPADF），这是一种用于图像预处理的新技术，它大大提高了输入图像和特征表示的质量。然后，实际检测执行Faster-YOLO架构，这是必不可少的，因为它努力平衡实时物联网操作的速度和准确性。利用超节点关系图注意网络（hypernode Relational Graph Attention Network， HRGAT）在复杂动态环境中进行有效的关系特征学习和多目标的正确识别。IDMO的性能最大限度地提高了模型的收敛速度和稳定性，以满足物联网边缘设备的计算负载。由此产生的评估提供了99.6%的mAP和99.5%的f1分数，同时与其他传统方法相比，处理时间减少了32%。结果表明，新框架可以成功部署到新的物联网边缘云监控流程中，流程高效准确，满足多目标监控应用的技术需求。

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引用次数: 0

Multi-source domain adaptation via evidence-based target pseudo-labels for human–computer collaboration fault diagnosis 基于循证目标伪标签的多源领域自适应人机协同故障诊断

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

Journal of Industrial Information Integration

Pub Date : 2026-01-29 DOI: 10.1016/j.jii.2026.101077

Jilun Tian , Hao Luo , Pengfei Yan , Xinyu Qiao , Shimeng Wu , Jiusi Zhang

Existing data-driven fault diagnosis methods imply the decision-making automatically, but lack adaptation and trustworthiness for varying working conditions. Unsupervised domain adaptation (UDA) relies on the cross-domain distribution disparity to achieve high-performance diagnostics. However, it struggles in complex multi-domain and diverse-source scenarios, which currently lack in-depth analysis. The proposed approach implements a novel multi-source domain adversarial network (MSDA) architecture via evidence-based target pseudo-label learning (ETPL) with dynamic multi-loss weightings. Specifically, MSDA constrains the disparity of diverse source–target pairs to obtain generalized domain-invariant features via an adversarial mechanism, and ETPL performs target pseudo-label learning while applying Dempster–Shafer (DS) evidence theory to assign sample-wise weights through MSDA and an unsupervised algorithm. Meanwhile, this study provides a theoretical analysis including a detailed generalization error bound for multi-source scenarios and target pseudo-labels, illustrating its dependence on distribution discrepancy and pseudo-label quality metrics. Human–computer collaboration approach is adopted to strengthen both advantages from human and machines by sample-wise analysis. Sufficient experimental results on two real-world case studies validate the effectiveness, successfully accomplishing complex cross-domain fault diagnosis and illustrating its potential applications in industrial settings.

现有的数据驱动故障诊断方法意味着自动决策，但缺乏对不同工况的适应性和可靠性。无监督域自适应（UDA）依赖于跨域分布差异来实现高性能诊断。然而，它在复杂的多领域、多源场景中存在困难，目前缺乏深入的分析。该方法通过动态多损失加权的基于证据的目标伪标签学习（ETPL）实现了一种新的多源域对抗网络（MSDA）架构。具体而言，MSDA通过对抗机制约束不同源-目标对的差异以获得广义域不变特征，ETPL在进行目标伪标签学习的同时，通过MSDA和无监督算法应用Dempster-Shafer （DS）证据理论分配样本权值。同时，本文对多源场景和目标伪标签的泛化误差界进行了理论分析，说明了其对分布差异和伪标签质量指标的依赖关系。采用人机协作的方法，通过样本分析强化人与机器的优势。两个实际案例的充分实验结果验证了该方法的有效性，成功地完成了复杂的跨域故障诊断，并说明了其在工业环境中的潜在应用。

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引用次数: 0

Deep acoustic–visual fusion for robust material recognition in intelligent robotic perception 基于深度声视融合的智能机器人感知材料鲁棒识别

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

Journal of Industrial Information Integration

Pub Date : 2026-01-28 DOI: 10.1016/j.jii.2026.101074

Bo Zhu , Tao Geng , Jia Zhang , Jianlei Cui , Boxin Ren

Accurate material recognition is crucial for intelligent robotic perception, enabling autonomous interaction, grasping, and navigation in complex environments. While traditional single-modality approaches often lack comprehensive information, which limits their performance, multimodal methods that combine acoustic and visual data provide a more robust solution by leveraging complementary cues. However, existing techniques face challenges in effectively integrating these modalities, resulting in suboptimal recognition accuracy under certain conditions. To address these limitations, we propose

M^{3}

CNet, a novel multimodal material classification network that incorporates adaptive frequency filtering, dual-branch feature fusion, cross-attention, and modality fusion attention. The adaptive frequency filtering block dynamically optimizes acoustic frequency bands to enhance the extraction of discriminative features. Meanwhile, the dual-branch feature fusion block captures local and global visual features at multiple scales, improving texture representation. To strengthen inter-modal relationships, the cross-attention block enables mutual reinforcement between acoustic and visual features, while the modality fusion attention block adaptively balances the contributions of each modality at both the channel and spatial levels. This ensures robustness even in the presence of incomplete or noisy data. Extensive experiments on multiple multimodal texture datasets demonstrate that

M^{3}

CNet consistently outperforms other methods in accuracy, precision, and recall.

准确的材料识别对于智能机器人感知至关重要，可以在复杂环境中实现自主交互、抓取和导航。传统的单模态方法通常缺乏全面的信息，这限制了它们的性能，而结合声学和视觉数据的多模态方法通过利用互补线索提供了更强大的解决方案。然而，现有技术在有效整合这些模式方面面临挑战，导致在某些条件下识别精度不理想。为了解决这些限制，我们提出了M3CNet，一个新的多模态材料分类网络，它结合了自适应频率滤波、双分支特征融合、交叉注意和模态融合注意。自适应频率滤波块动态优化声频带，增强识别特征的提取。同时，双分支特征融合块在多尺度上捕获局部和全局视觉特征，提高了纹理表征。为了加强多模态关系，交叉注意块使声学和视觉特征之间的相互强化，而模态融合注意块在通道和空间层面上自适应地平衡每种模态的贡献。这确保了即使在存在不完整或有噪声的数据时也具有鲁棒性。在多个多模态纹理数据集上的大量实验表明，M3CNet在准确率、精密度和召回率方面始终优于其他方法。

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引用次数: 0

GreenEdge AI: Sustainable federated learning for smart city air quality prediction GreenEdge AI：智能城市空气质量预测的可持续联合学习

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

Journal of Industrial Information Integration

Pub Date : 2026-01-27 DOI: 10.1016/j.jii.2026.101081

Sweta Dey , Rishi Raina , Sudeepta Mishra , Abhinandan S. Prasad , Ramesh Dharavath

Rapid urbanization and industrial growth have intensified air pollution in metropolitan regions, making accurate and energy-efficient Air Quality Index (AQI) prediction critical for sustainable smart city management. Existing centralized and conventional federated learning approaches suffer from high communication overhead, excessive energy consumption, and privacy risks, limiting their applicability in distributed urban sensing environments. This paper proposes GreenEdge AI, a green federated learning framework integrating a green-aware custom LSTM (GA-CLSTM) model with energy-aware training, adaptive aggregation, and a hybrid loss function for decentralized AQI forecasting. The framework enables edge-level learning across heterogeneous IoT-based air quality and meteorological sensors while preserving data privacy and minimizing cloud dependency. Sustainability is explicitly incorporated through green metrics, including energy consumption, Energy–Delay Product (EDP), Energy Efficiency Ratio (EER), and Power-to-Performance Ratio (PPR), which guide both model optimization and federated aggregation. Experimental results on real-world hourly AQI data from five major metropolitan cities demonstrate that GreenEdge AI achieves up to 60% improvement in prediction accuracy and approximately 37% reduction in energy consumption compared to conventional baseline models, while significantly reducing peak power usage and communication overhead compared to centralized and conventional federated baselines. These findings underscore the practical value of GreenEdge AI for municipalities and environmental agencies, motivating future research on scalable, energy-aware federated intelligence for smart city applications.

快速的城市化和工业增长加剧了大都市地区的空气污染，使得准确和节能的空气质量指数（AQI）预测对可持续的智慧城市管理至关重要。现有的集中式和传统的联邦学习方法存在通信开销大、能耗大、隐私风险大等问题，限制了它们在分布式城市传感环境中的适用性。本文提出了GreenEdge AI，这是一个绿色联邦学习框架，将绿色感知自定义LSTM （GA-CLSTM）模型与能量感知训练、自适应聚合和用于分散AQI预测的混合损失函数集成在一起。该框架支持跨异构物联网空气质量和气象传感器的边缘级学习，同时保护数据隐私并最大限度地减少对云的依赖。可持续性通过绿色指标明确地纳入，包括能源消耗、能源延迟产品（EDP）、能源效率比（EER）和功率性能比（PPR），这些指标指导模型优化和联合聚合。来自五个主要城市的真实小时AQI数据的实验结果表明，与传统基线模型相比，GreenEdge AI的预测精度提高了60%，能耗降低了约37%，同时与集中式和传统联邦基线相比，显著降低了峰值功耗和通信开销。这些发现强调了GreenEdge人工智能对市政当局和环境机构的实用价值，推动了未来针对智慧城市应用的可扩展、能源感知的联合智能的研究。

{"title":"GreenEdge AI: Sustainable federated learning for smart city air quality prediction","authors":"Sweta Dey , Rishi Raina , Sudeepta Mishra , Abhinandan S. Prasad , Ramesh Dharavath","doi":"10.1016/j.jii.2026.101081","DOIUrl":"10.1016/j.jii.2026.101081","url":null,"abstract":"<div><div>Rapid urbanization and industrial growth have intensified air pollution in metropolitan regions, making accurate and energy-efficient Air Quality Index (AQI) prediction critical for sustainable smart city management. Existing centralized and conventional federated learning approaches suffer from high communication overhead, excessive energy consumption, and privacy risks, limiting their applicability in distributed urban sensing environments. This paper proposes <em>GreenEdge AI</em>, a green federated learning framework integrating a green-aware custom LSTM (GA-CLSTM) model with energy-aware training, adaptive aggregation, and a hybrid loss function for decentralized AQI forecasting. The framework enables edge-level learning across heterogeneous IoT-based air quality and meteorological sensors while preserving data privacy and minimizing cloud dependency. Sustainability is explicitly incorporated through green metrics, including energy consumption, Energy–Delay Product (EDP), Energy Efficiency Ratio (EER), and Power-to-Performance Ratio (PPR), which guide both model optimization and federated aggregation. Experimental results on real-world hourly AQI data from five major metropolitan cities demonstrate that <em>GreenEdge AI</em> achieves up to 60% improvement in prediction accuracy and approximately 37% reduction in energy consumption compared to conventional baseline models, while significantly reducing peak power usage and communication overhead compared to centralized and conventional federated baselines. These findings underscore the practical value of <em>GreenEdge AI</em> for municipalities and environmental agencies, motivating future research on scalable, energy-aware federated intelligence for smart city applications.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101081"},"PeriodicalIF":10.4,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A nested goal programming model integrated with an improved genetic bee colony algorithm supported by machine learning methods 基于机器学习的改进遗传蜂群算法的嵌套目标规划模型

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

Journal of Industrial Information Integration

Pub Date : 2026-01-26 DOI: 10.1016/j.jii.2026.101082

N. Gosheh Dezfouli, Behnam Vahdani, E. Mehdizadeh, H.R. Gholami

Formulating engine oil additives is challenging because it requires simultaneously optimizing production efficiency, cost, and compliance with strict quality standards. This study presents an advanced optimization framework for 10W-40 API SL engine oil that combines a nested goal programming model with machine learning (ML) techniques to predict production rates and quality metrics that cannot be expressed in closed-form equations. To address the inability of conventional ML approaches to generate novel additive combinations, we propose an enhanced genetic bee colony algorithm incorporating arithmetic crossover, Makinen–Periaux–Toivanen mutation operators, and a Cauchy distribution-based local search. These modifications significantly improve the algorithm’s ability to explore and evaluate new formulations. The resulting framework achieves 98.76% of nominal production capacity—very close to the theoretical optimum—while reducing quality-related costs by an average of 20.44%. These results represent substantial improvements in production efficiency, cost savings, and overall formulation quality, providing a powerful and practical tool for the engine oil industry.

配制机油添加剂是一项具有挑战性的工作，因为它需要同时优化生产效率、成本，并符合严格的质量标准。本研究提出了一种先进的10W-40 API SL机油优化框架，该框架将嵌套目标规划模型与机器学习（ML）技术相结合，可以预测无法用封闭形式方程表示的生产率和质量指标。为了解决传统机器学习方法无法生成新的加性组合的问题，我们提出了一种增强的遗传蜂群算法，该算法结合了算术交叉、Makinen-Periaux-Toivanen突变算子和基于Cauchy分布的局部搜索。这些修改显著提高了算法探索和评估新公式的能力。最终的框架实现了98.76%的名义产能——非常接近理论最优——同时平均降低了20.44%的质量相关成本。这些结果代表了生产效率、成本节约和整体配方质量的大幅提高，为发动机润滑油行业提供了一个强大而实用的工具。

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引用次数: 0

A multi-level multi-source digital twin model for performance enhancement and optimization decision-making in precision milling machines 精密铣床性能提升与优化决策的多级多源数字孪生模型

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

Journal of Industrial Information Integration

Pub Date : 2026-01-23 DOI: 10.1016/j.jii.2026.101080

Yang Xie , Shulong Mei , Fei Wang , Chaoyong Zhang

The transition of CNC machining toward digitalization and low-carbon manufacturing is essential for the advancement of intelligent production. However, conventional parameter configuration methods fail to balance efficiency and sustainability. To overcome this limitation, this study proposes an intelligent optimization framework that integrates digital twin (DT) technology with multi-objective optimization. A multi-level virtual machine tool model is established to enable operational condition mapping and structural response modeling of key machining parameters. A Simulation Augmentation Collaboration Mechanism (SACM) is further introduced, in which the DT generates high-fidelity distribution information to guide a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) in producing realistic samples under critical operating conditions. These augmented data iteratively refine the model, significantly enhancing predictive generalization. An Improved Meta-Learning-Optimized XGBoost (IMeta-XGBoost) model is then established to predict three performance indicators: spindle energy consumption, specific cutting energy, and machining noise. A Predicted-Fitness-Guided Multi-Objective Deep Q-Network (PF-MO-DQN) is then employed for global optimization, followed by entropy-weighted TOPSIS to determine the optimal machining parameters experimental validation demonstrates reductions of 8.95% in spindle energy consumption, 18.03% in specific cutting energy, and 10.15% in machining noise, confirming significant improvements in energy efficiency, productivity, and noise mitigation. This work provides a robust and scalable approach for multi-objective optimization in complex machining environments.

数控加工向数字化和低碳制造的过渡是推进智能生产的必要条件。然而，传统的参数配置方法无法平衡效率和可持续性。为了克服这一限制，本研究提出了一种将数字孪生（DT）技术与多目标优化相结合的智能优化框架。建立了多级虚拟机床模型，实现了运行工况映射和关键加工参数的结构响应建模。进一步介绍了一种仿真增强协作机制（SACM），其中DT生成高保真的分布信息，指导WGAN-GP在关键操作条件下生成真实样本。这些增强的数据迭代地改进了模型，显著增强了预测泛化。然后建立改进的元学习优化XGBoost （i - meta -XGBoost）模型，预测主轴能耗、切削比能量和加工噪声三个性能指标。然后采用预测适应度引导的多目标深度q -网络（PF-MO-DQN）进行全局优化，然后采用熵加权TOPSIS来确定最优加工参数。实验验证表明，主轴能耗降低了8.95%，比切削能量降低了18.03%，加工噪声降低了10.15%，证实了能源效率、生产率和噪声缓解方面的显着提高。这项工作为复杂加工环境下的多目标优化提供了一种鲁棒性和可扩展性的方法。

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引用次数: 0

A review on machine learning and deep learning techniques for plant leaf disease detection and classification with IoT in agriculture industry 机器学习和深度学习技术在农业物联网植物叶片病害检测与分类中的应用综述

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

Journal of Industrial Information Integration

Pub Date : 2026-01-22 DOI: 10.1016/j.jii.2026.101078

Priyadharshini Arputharaj, Kalaivanan Karunanithy

Agriculture serves as a major source of food and plays a key function as the backbone of most countries’ economies. However, farmers are encountering many challenges in this sector, such as drought, flooding, diseases, nutrient deficiency, and so on. The technological advancements in the field of agriculture, also called smart agriculture, are necessary to address the requirements of the expanding population and manage the associated challenges. Among those, plant leaf diseases are the primary concern that severely impacts crop yield and economic stability. This technical review examines various Machine Learning (ML) and Deep Learning (DL) approaches used to identify and classify different plant leaf diseases. This review gives an overview of the current state-of-the-art ML, DL, and IoT-enabled disease prediction systems and their recent advances in developing an intelligent system in smart agriculture. It provides insights into the various technological developments and discusses the benefits and opportunities of AI-based models in plant disease management.

农业是粮食的主要来源，是大多数国家经济的支柱，发挥着关键作用。然而，农民在这一领域面临着许多挑战，如干旱、洪水、疾病、营养缺乏等。农业领域的技术进步，也被称为智能农业，是解决不断增长的人口需求和管理相关挑战所必需的。其中，植物叶片病害是严重影响作物产量和经济稳定的首要问题。本技术综述探讨了用于识别和分类不同植物叶片疾病的各种机器学习（ML）和深度学习（DL）方法。本文综述了当前最先进的机器学习、深度学习和物联网疾病预测系统，以及它们在智能农业中开发智能系统的最新进展。它提供了对各种技术发展的见解，并讨论了基于人工智能的模型在植物病害管理中的好处和机会。

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引用次数: 0

A survey of integrated multi-layer security for continuous-process industrial control systems: Insights from a steel manufacturing sector 连续过程工业控制系统的集成多层安全调查：来自钢铁制造部门的见解

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

Journal of Industrial Information Integration

Pub Date : 2026-01-22 DOI: 10.1016/j.jii.2026.101072

Fang Wang , Aiai Ren , Jun Cheng , Yijie Zheng , Xu Zhou , Li Qiao , Jun Yan , Fang Dong , Qian Zhao , Jun Shen

Continuous-process industries such as steel operate under tight safety and availability constraints while facing a rapidly expanding attack surface across device, network and behavioural layers. This survey consolidates evidence on legacy endpoints, protocol exposures, and process-level risks in steel manufacturing, and organises it in a structured, multi-layer taxonomy that clarifies how local faults can escalate to plant-wide disruption. Using a transparent literature search and screening protocol, the survey synthesises prior work on device hardening, network segmentation, and anomaly detection, and foregrounds what is distinctive about steel, including near-zero downtime operations and multi-vendor operational technology ecosystems. Building on this synthesis, the survey grounds actionable guidance in established industry standards by linking security controls to recognised programme requirements and mapping adversary techniques to an industrial control systems-focused attack framework, thereby providing plant-ready implementation cues. The survey also distils a phased integration workflow that locates analytics at the industrial edge and couples them with existing safety interlocks and operational change control. Case evidence from steel incidents is used to illustrate typical intrusion chains and to motivate layered mitigations. The review concludes by identifying priority research needs in data governance and benchmarking, as well as the edge-centric and safety-cased deployment of AI models, and supply-chain-aware machine learning operations. Taken together, these contributions provide a domain-grounded roadmap for strengthening resilience in steel-manufacturing industrial control systems while preserving operational continuity, and a transferable template for other continuous-process sectors.

钢铁等连续加工行业在严格的安全和可用性限制下运行，同时面临着跨设备、网络和行为层快速扩展的攻击面。该调查整合了钢铁制造中遗留端点、协议暴露和过程级风险的证据，并将其组织在结构化的多层分类法中，以澄清局部故障如何升级为整个工厂的中断。通过透明的文献检索和筛选协议，该调查综合了之前在设备硬化、网络分割和异常检测方面的工作，并展望了钢铁的独特之处，包括近零停机操作和多供应商操作技术生态系统。在此综合基础上，该调查通过将安全控制与公认的程序需求联系起来，并将对手技术映射到以工业控制系统为中心的攻击框架，从而为已建立的行业标准提供可操作的指导，从而提供工厂就绪的实施线索。该调查还提炼出了一个分阶段的集成工作流程，将分析定位于工业边缘，并将其与现有的安全联锁和操作变更控制相结合。来自钢铁事故的案例证据用于说明典型的入侵链，并激励分层缓解。最后，该审查确定了数据治理和基准测试方面的优先研究需求，以及人工智能模型的边缘中心和安全部署，以及供应链感知机器学习操作。综上所述，这些贡献为在保持业务连续性的同时加强钢铁制造业工业控制系统的弹性提供了一个基于领域的路线图，并为其他连续加工部门提供了一个可转让的模板。

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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 : 2026-01-22 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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