Recently, foundation models (such as ChatGPT) have emerged with powerful learning, understanding, and generalization abilities, showcasing tremendous potential to revolutionarily promote modern industry. Despite significant advancements in various fields, existing general foundation models face challenges in industry when dealing with the data of specialized modalities, the tasks of varying-scenario with multiple processes, and the requirements of trustworthy output, which makes industrial foundation model (IFM) a necessity. This article proposes a system architecture of termed IFMsys, including model training, model adaptation, and model application. Specifically, in model training, a base model is constructed by pretraining on multimodal industrial data and fine-tuning with fundamental industrial mechanisms. In model adaptation, the base model is developed into a series of task-oriented and domain-specific IFMs through fine-tuning with representative tasks and domain knowledge. In model application, an industrial agent-centric collaboration system and a comprehensive application framework of IFM are proposed to enhance the industrial product lifecycle applications. In addition, a prototype system of the IFM, namely, MetaIndux, is delivered, with application examples presented in typical industrial tasks. Finally, future research directions and open issues of IFM are prospected. We hope this article will inspire the advancements in the theories, technologies, and applications in this emerging research field of IFM.

With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation mod-els in the process industry.This paper provides a comprehensive overview of the challenges and oppor-tunities presented by the use of foundation models in the process industry,including the frameworks,core applications,and future prospects.First,this paper proposes a framework for foundation models for the process industry.Second,it summarizes the key capabilities of industrial foundation models and their practical applications.Finally,it highlights future research directions and identifies unresolved open issues related to the use of foundation models in the process industry.

Smart manufacturing has been transforming toward industrial digitalization integrated with various advanced technologies. Metaverse has been evolving as a next-generation paradigm of a digital space extended and augmented by reality. In the metaverse, users are interconnected for various virtual activities. In consideration of advanced possibilities that may be brought by the metaverse, it is envisioned that industrial metaverse should be integrated into smart manufacturing to upgrade industry for more visible, intelligent and efficient production in the future. Therefore, a conceptual model, named IMverse Model, and novel characteristics of the industrial metaverse for smart manufacturing are proposed in this article. Besides, an industrial metaverse architecture, named IMverse Architecture, is proposed involving several key enabling technologies. Typical innovative applications of the industrial metaverse throughout the whole product life cycle for smart manufacturing are presented with insights. Nonetheless, in prospect of future, the industrial metaverse still faces limitations and is far from implementation. Thus, challenges and open issues of the industrial metaverse for smart manufacturing are discussed, then outlook is provided for further research and application.

With the rapid development of cloud manufacturing technology and the new generation of artificial intelligence technology, the new cloud manufacturing system (NCMS) built on the connotation of cloud manufacturing 3.0 presents a new business model of “Internet of everything, intelligent leading, data driving, shared services, cross-border integration, and universal innovation”. The network boundaries are becoming increasingly blurred, NCMS is facing security risks such as equipment unauthorized use, account theft, static and extensive access control policies, unauthorized access, supply chain attacks, sensitive data leaks, and industrial control vulnerability attacks. Traditional security architectures mainly use information security technology, which cannot meet the active security protection requirements of NCMS. In order to solve the above problems, this paper proposes an integrated cloud-edge-terminal security system architecture of NCMS. It adopts the zero trust concept and effectively integrates multiple security capabilities such as network, equipment, cloud computing environment, application, identity, and data. It adopts a new access control mode of “continuous verification + dynamic authorization”, classified access control mechanisms such as attribute-based access control, role-based access control, policy-based access control, and a new data security protection system based on blockchain, achieving “trustworthy subject identity, controllable access behavior, and effective protection of subject and object resources”. This architecture provides an active security protection method for NCMS in the digital transformation of large enterprises, and can effectively enhance network security protection capabilities and cope with increasingly severe network security situations.

随着新一代人工智能的发展,大模型(如GPT-4o等)凭借大规模训练数据、网络参数和算力涌现出强大的生成能力、泛化能力和自然交互能力,展现出改变工业世界的巨大潜力.尽管大模型已在自然语言等多个领域取得突破性进展,但其在工业应用中的探索仍处于初级阶段,当前工业大模型的系统性研究仍属空白.工业应用中特有的异质数据模态、复杂多样的专业化场景、长流程的关联性决策、以及对于可信性实时性的高要求,使得通用大模型无法直接用于解决复杂的工业问题,亟需开展全新的工业大模型基础理论和关键技术研究.本文系统地探讨了工业大模型的挑战问题、概念内涵、体系架构、构建方法、关键技术与典型应用.从5个挑战问题的分析出发,提出了工业大模型的全新定义和体系架构;同时,提出了工业大模型的四阶段构建方法,阐述了工业大模型核心关键技术;然后,基于所提出的工业大模型6种核心应用能力,探讨了面向产品全生命周期的工业大模型典型应用场景,并给出了“基石”工业大模型原型系统在生成式人工智能方面的应用实例;最后,探讨和展望了工业大模型未来的研究方向和开放性问题.本文将为工业大模型这一全新研究方向的开辟与发展,提供基础理论、关键技术和行业应用的全面指导.

Due to the emergence of new network attack technologies, cloud manufacturing platforms may be subject to various network attacks at any time. Traditional network attack prediction aims to predict the upcoming types of network attacks with monitored data characteristics, and this prediction method cannot accurately learn the network attack traffic that the cloud manufacturing platform may suffer in the future. Network attack traffic prediction means the future network attack traffic prediction by using past data. Traditional machine learning approaches cannot investigate complex nonlinear features. Deep learning methods can investigate nonlinear characteristics, yet they suffer from the problem of overfitting. In addition to this, deep learning approaches suffer from gradient disappearance and explosion. To solve them, we design a network attack traffic method called S-Informer, which integrates the filter of Savitzky–Golay, the self-attention of ProbSparse in a generative decoder and an encoder for eliminating noise, reducing the network scale and improving the speed of prediction. Real-life dataset-based experimental results show that S-Informer achieves higher prediction accuracy than several commonly-used algorithms.

Skilled human resource becomes an essential resource for implementing intelligent manufacturing in the new era, prompting high demands on Intelligent Manufacturing Training (IMT). Empowering the effective IMT, the new mode of IMT based on Industrial Metaverse is proposed as well as detailed comparison with traditional training modes. The layered technical architecture is discussed as a guidance for training system construction, as well as specific solutions for the six key technologies based on primary research, including rapid modeling, natural interaction, real-time communication, industrial avatar/agent, industrial tools access, industrial AIGC, etc. Verifying the effectiveness of Industrial Metaverse based IMT, a prototype system “TrAiN” for industrial internet skill training is built, constructing a private Industrial Metaverse based on specific industrial equipment and fields in certain factory, facilitating the virtual training. Future research hotspots on Industrial Metaverse based IMT are prospected at the end based on the primary research and application.

The different types of experimentation and reasons why to use simulation experiments in the various application domains are the topic of this chapter of the SCS M&S Body of Knowledge. It addresses the types of simulation techniques, introduces the simulation of discrete systems using DEVS in detail, and also comprises a section on continuous systems. It concludes with current views on hybrid M&S, real time simulation, and how to cope with comprehensive systems.

The SCS M&S Body of Knowledge is a living concept, and core research areas are among those that will drive its progress. In this chapter, conceptual modeling constitutes the first topic, followed by the quest for model reuse. As stand-alone applications become increasingly rare, embedded simulation is of particular interest. In the era of big data, data-driven M&S gains more interest as well. Applying the M&S Framework (MSF) to enable neuromorphic architectures exemplifies the ability of simulation to meaningfully contribute to other fields as well. The chapter closes with sections on model behavior generation and the growth of simulation-based disciplines.

The SCS M&S Body of Knowledge is closed by a chapter on trends, desirable features, and challenges. Where are we going with simulation? How are other supporting disciplines evolving? What are current simulation technology trends? The following section on desirable features elaborates on the ideal characteristics set to make modeling and simulation technology rapidly develop into a generic and strategic technology. The chapter concludes with technical and conceptual challenges that will have to be addressed soon, hopefully contributing to the next iteration of the BoK.

新一代人工智能的迅猛发展,正在深刻影响全球新一轮工业革命.数据要素作为数字经济时代的核心要素,在智能制造应用需求和新一代人工智能的融合推动下正释放巨大价值.数据驱动的工业智能,尤其是以深度学习为代表的工业智能研究前沿,成为学术界和产业界的关注焦点.鉴于此,从工业数据全生命周期中数据预处理、数据建模、数据分析应用等各个关键环节出发,从各维度分析了数据驱动的工业智能,尤其是基于深度学习的代表性新理论与新技术.同时,深入探讨了面向智能制造的典型应用.最后,指出了数据驱动的工业智能研究领域面临的挑战和未来发展方向,这将为基于新一代人工智能的工业智能这一新兴交叉研究领域的发展,提供重要的理论与技术支撑.

The dynamical evolution of electrical discharge machining (EDM) has drawn immense research interest. Previous research on mechanism analysis has discussed the deterministic nonlinearity of gap states at pulse-on discharging duration, while describing the pulse-off deionization process separately as a stochastic evolutionary process. In this case, the precise model describing a complete machining process, as well as the optimum performance parameters of EDM, can hardly be determined. The main purpose of this paper is to clarify whether the EDM system can maintain consistency in dynamic characteristics within a discharge interval. A nonlinear self-maintained equivalent model is first established, and two threshold conditions are obtained by the Shilnikov theory. The theoretical results prove that the EDM system could lead to chaos without external excitation. The time series of the deionization process recorded in the EDM experiments are then analyzed to further validate this theoretical conclusion. Qualitative chaotic analyses verify that the autonomous EDM process has chaotic characteristics. Quantitative methods are used to estimate the chaotic feature of the autonomous EDM process. By comparing the quantitative results of the autonomous EDM process with the non-autonomous EDM process, a deduction is further made that the EDM system will evolve towards steady chaos under an autonomous state.

Metaverse expands the cyberspace with more emphasis on human-in-loop interaction, value definition of digital assets and real-virtual reflection, which facilitates the organic fusion of man, machine and material in both physical industry and digital factory. The concept of Industrial Metaverse is proposed as a new man-in-loop digital twin system of the real industrial economy which is capable of man-machine natural interaction, industrial process simulation and industrial value transaction. With the comparison with Metaverse and Digital Twin, the key features of Industrial Metaverse are summarized, which are man-in-loop, real-virtual interaction, process asserts and social network. Key technologies of Industrial Metaverse are surveyed including natural interaction, industrial process simulation, industrial value transaction and large-scale information processing and transmission technologies, etc. Potential application modes of Industrial Metaverse are given at the end as well as the challenges from technology, industry and application.

The Internet of Things (IoT) is an important component of the new digital infrastructure and is deeply integrated with the fifth-generation mobile communication (5G), big data, cloud computing, artificial intelligence (AI), blockchain, and digital twin. It is profoundly changing the technology system and promoting the digital economy, ushering in a new stage of smart IoT system in which everything is connected. This paper reviews the development status of IoT in China, proposes the concept of smart IoT system (IoT 2.0), and expounds on the implications, architecture, technical pedigree, and key enabling technologies. Practical cases of smart IoT system are explored considering the application scenarios of intelligent manufacturing, smart agriculture, smart grid, smart healthcare, intelligent transportation, and intelligent environmental protection, demonstrating the application values of the smart IoT system. Furthermore, we suggest that a technology integration innovation project that integrates IoT, AI, 5G, and new application field technologies should be implemented; focus should be placed on the research, development, and industrialization of intelligent products such as smart IoT systems / cloud native platforms / low-code (no-code) application development environments and toolsets, high-end sensors for smart IoT systems, and IoT chips / special components; and application demonstration of cloud-edge-end collaborative, autonomous controllable, safe, and credible smart IoT systems should be conducted.

近年来,元宇宙(Metaverse)作为一种新理念和新技术,正成为科技界对技术发展竞相探讨和产业界对新产业方向思考与实践的热点.工业元宇宙(Industrial Metaverse)正是元宇宙在工业领域的落实与拓展.我们认为,工业发展新阶段即发展万物智联的智慧工业互联网系统的新需求,正是工业元宇宙发展的重要背景及推动力,它将催生工业元宇宙在技术、模式、业态等方面得到进一步的发展.

针对云制造系统不同安全域之间信任关系孤立导致的用户跨域访问重复进行身份认证和云服务跨域协同被拒绝的问题,设计了 一种面向云制造系统的域间互信过程模型,提出了基于域间互信的用户认证和服务跨域协同高效可信安全优化技术,实现了用户可信身份跨域传递和云制造服务跨域协同,并在企业进行了应用验证,给出了所提方法与传统方式的对比分析.分析结果表明,提出的高效可信安全技术能够在提升云制造系统认证和服务跨域协同效率的同时不降低现有安全机制的防护强度.

Aiming at the problem that the traditional anomaly detection method based on threshold cannot effectively detect sensor numerical anomalies in cloud manufacturing system, this work proposes a new method to detect some sensor numerical anomalies form the industrial control system. It is the central part of a cloud manufacturing system. Firstly, this work constructs a Savitzky-Golay (S-G) filter to reduce data noises. Furthermore, an extreme learning machine based on genetic algorithm (GA-ELM) model is proposed to detect sensor numerical anomalies form the industrial control system. The genetic algorithm (GA) is used to reduce feature dimensions from 51 to 10 and the extreme learning machine algorithm (ELM) is used for classification to achieve the purpose of anomaly detection. Finally, using the public dataset called Secure Water Treatment (SWaT), the classification accuracy is 98.96%. It shows a better performance of the proposed method.

The new intelligent manufacturing system integrates information and communication technologies (ICTs) with industrial technologies and supports the rapidly unfolding new round of industrial revolution. The rapid evolution of ICTs gives intelligent manufacturing the potential for accelerated development. This paper proposes a technical system for the new intelligent manufacturing system and elaborates the connotation and characteristics of a technical subsystem regarding ICTs from the perspective of industrial Internet system technologies and four basic technologies, namely, industrial big data, artificial intelligence (AI), fifth-generation mobile communication (5G), and modeling simulation/digital twin. Subsequently, we present the vertical, horizontal, and end-to-end application scenarios of intelligent manufacturing enabled by ICTs, and propose several suggestions for promoting the new intelligent manufacturing system through ICTs. First, special science and technology projects should be established focusing on advanced networks, collaborative computing, and industrial knowledge reasoning. Second, Industrial development should focus on the R&D and industrialization of the following technologies: 5G application, network collaboration, intelligent and intelligently connected products based on new-generation AI technology, and domestication of modeling simulation/digital twin tool sets and systems. Third, Application demonstration should be conducted regarding industrial design based on 5G Plus industrial virtual reality, industrial platforms for AI Internet of Things, and intelligent design of industrial products based on modeling simulation/digital twin technology. Meanwhile, it is necessary to improve the efficient and collaborative working mechanism for promoting new intelligent manufacturing policies, accelerate the construction of interconnection standard groups, promote the industrial chain and supply chain through industry–university–research collaboration, and enhance the deep integration of industry and education in intelligent manufacturing.