This paper presents a three-stage approach for transforming industrial requirement documents into executable control models using large language models (LLMs). The method consists of component extraction, system behavior structuring, and execution flow construction, each guided by prompt-based LLM reasoning. Instead of generating code directly, the large language model produces a structured representation with components, operations, and assumption–guarantee pairs that define logical dependencies. This intermediate model supports human-in-the-loop correction and is translated into control code using rule-based mapping. A case study on a signal-driven production line shows the approach achieves over 90% accuracy across all modeling stages with minimal manual intervention, demonstrating its potential for safe and efficient industrial automation modeling.

Industry 4.0 demands intelligent and customized production, driving the upgrade of traditional systems. However, many industrial devices lack source code or documentation, making control program migration highly challenging. This issue is critical for PLC replacement, system modernization, and integration of heterogeneous devices. Recently, large language models (LLMs) have demonstrated strong capabilities in code generation and logical reasoning. When combined with the IEC 61499 standard—known for its modular and event-driven design—LLMs offer new possibilities for automatic control software generation. This paper proposes a method that uses LLMs to transform industrial control logic into IEC 61499-compliant programs. It extracts finite state machines representing software logic, converts them into formal requirements, and generates the IEC 61499 function block with the assistance of LLMs. The method enables efficient, data-driven migration of control software.

To address the scalability limitations and maintenance challenges of traditional PLC-based systems, modern industrial deployments are increasingly adopting multi-core edge servers and virtualization technologies such as containers for resource isolation and dynamic deployment. In this context, high-concurrency local communication between containers must meet stringent real-time requirements, including low latency and predictability. This paper proposes a deterministic Software Bus tailored for virtualized industrial edge systems. The design integrates shared memory pooling, centralized event scheduling, and dynamic routing to enable zero-copy data exchange and event-driven communication across different industrial edge applications in containers. Experimental results demonstrate that the Software Bus incurs minimal overhead during connection initialization, supporting rapid deployment and dynamic module integration. In one-way message transmission, it significantly reduces average latency compared to traditional TCP and UDS mechanisms, while maintaining stable maximum latency under varying payloads.

The core requirement of industrial automation systems is continuous and stable running. System design for the next-generation Industrial Internet must meet the high reliability requirements of industrial automation systems. This paper presents a design method for a dual-mode redundancy mechanism in virtualized runtime systems for the Industrial Internet. Through reliability analysis and comparison of multiple redundancy architectures, the applicability of the dual-mode redundancy mechanism in industrial scenarios is established. Compared with traditional hardware redundancy solutions, this method features lightweight deployment and fast iterative updates, significantly reducing system resource consumption and labor costs. Experimental data show that as the number of connected devices increases, the complexity growth curve of the virtualized runtime system is significantly lower than traditional hardware redundancy mechanisms, and performance indicators meet the stability and reliability requirements of next-generation industrial scenarios.

In the realm of industrial edge computing, higher-configured terminal processors are gradually replacing inefficient ones. Edge computing devices encounter challenges such as replicating rapid software development and software upgrade and maintenance. In response to these issues, an edge-cloud two-tier architecture has been proposed. In the previous article, while conducting research on the edge system architecture, we contemplated on how to effectively utilize the CPU virtualization capabilities of edge devices to achieve elastic expansion of device resources. This paper will commence from the perspective of efficient utilization of multi-core CPU resources. By conducting analyses of relevant research technology cases and identifying the deficiencies in the research, our edge computing multi-core control system architecture is re-engineered. Through comparative experiments of multi-core task testing, it is concluded that the multicore processor scheduling system designed in this paper can significantly enhance the task execution efficiency of edge computing systems.

With the rapid evolution of industrial architecture, industrial edge computing is one of the most popular architectures at present. However, backup methods designed for traditional industrial architectures are not satisfactory in terms of device cost and applicability when they are applied in this new architecture. Against this background, this work presents a new backup method for industrial edge applications with better performance on resource consumption and high stability. The Function Block of IEC 61499 is used as the standard industrial edge application in this work. By carrying out experiments and employing multiple backup approaches, the resource occupancy rate and recovery time can be reduced simultaneously. The innovative methods and resource allocation models tackle the limitation of traditional backup methods effectively.

The industrial edge computing is a multidimensional architecture that integrates computing, communication, control and physical environments. The development of industrial edge applications requires flexibility and meets its characteristics. Therefore, this paper proposes a process orchestration and deployment method to develop industrial edge applications based on IEC 61499 and MTP. Considering the procedure control model and the physical model of the ISA 88, a mapping relationship is established between applications and physical devices. And based on two models, a design architecture based on modules and a deployment method are proposed. In addition, some special SIFBs are proposed to establish communication between control levels and communication between applications and devices. Finally, this method is verified with a small discrete manufacturing system.

Industry 4.0 brings new features to the manufacturing industry, including informatization, intelligence, and higher integration. Complex interrelationships among components within the industrial Cyber-Physical System (iCPS) further increase the automation system design and development difficulty. Therefore, integrated design models for contemporary industrial systems should ensure flexibility and interoperability to accommodate highly integrated systems and rapidly changing requirements. This paper proposes a generic process modeling method based on Process-Oriented Models (POM) for automatic code generation. Process-Oriented Models can be regarded as a semantic set of operations encapsulating the process and corresponding attributes in the manufacturing process. Each operation is executable. The execution results can be used for model optimization to achieve the optimization process. These models with parameters can be further converted into modular code automatically according to pre-defined mapping rules. A process manufacturing case study proves the proposed method can achieve complete and correct process modeling of automation systems. Industrial software development based on Process-Oriented Models can significantly increase the efficiency and accuracy of software development of industrial Cyber-Physical Systems.

With the increasing availability of computing, storage, and network resources, there is a growing interest in efficiently utilizing these resources. Virtualization technology plays a crucial role in cloud computing as it provides flexibility and reliability in managing resources. In this paper, we conducted experiments with several virtualization technologies in the context of industrial edge computing. These technologies aim to reduce overall hardware costs. In addition to explaining the software architecture and describing various open-source solutions, we have also designed an architecture that incorporates embedded virtualization technology. This architecture simplifies and reconstructs the traditional industrial system architecture, enabling efficient utilization and maximizing the allocation of resources for edge-end devices. Finally, we implemented ACRN virtualization technologies in industrial edge-end devices and conducted experiments to validate their effectiveness.

Manufacturing is entering a new era with Industrial Internet and edge computing. The Industrial Internet cloud platform provides massive computing power and storage spaces for field devices. With more powerful chips available, field devices are also capable of handling multiple complex computational tasks simultaneously. How to collaborate resources from both cloud platforms and edge devices become an important topic for manufacturers. In this paper, a cloud-edge collaborated batch control system is proposed based on the IEC 61499 standard and the ISA-88 standard. The ISA-88 models are implemented as an independent IEC 61499 resource to support low-code development for batch control systems. Also, cloud resources are introduced in the IEC 61499 deployment to enable cloud-edge collaboration. Finally, the design process is verified with a liquid food processing line.

Industrial automation is entering a new era of the Industrial Internet with enhanced computing, communication, and storage capabilities provided by cloud computing and field devices. The paradigm of automation systems is shifting from the ISA-95 pyramid to the two-layers architecture: industrial cloud and edge computing. Industrial software is also evolving under the new architecture in ways for which dedicated software applications are no longer suited. Service-based industrial cloud and edge applications provide maximum flexibility, interoperability, and efficiency by combining the IEC 61499 standard, microservice architecture, and container technology. This article provides orchestration methods and deployment procedures for the OT-IT hybrid industrial edge applications. The feasibility of the proposed approach is demonstrated by an industrial case study with accompanying performance analysis.

The Industry 4.0 and Industrial Internet provides vertical and horizontal integration between edge devices and industrial cloud platforms. A flexible and interoperable information model is crucial for enabling device-level intelligence as well as cloud-edge collaboration. Deep integration between the control and information model is necessary for supporting closed-loop optimization between the cloud and edge devices. This article proposes an automatic information model generation method based on the IEC 61499 and OPC unified architecture standards. Model transformation rules between two models from design time to runtime are presented. A discrete manufacturing case study is used to prove that the proposed method can significantly reduce the development time for distributed information models. The integrated control and information models at runtime can provide strong support for dynamic reconfiguration to address changing requirements.

In the new round of industrial innovation, the Asset Administration Shell is proposed and gradually applied as the core element of the underlying device model. It is a digital model oriented to Industrial Cyber-Physical Systems. However, the current Administration Shell meta-model only involves the information management level of entities, which lacks associations with control. In this paper, an integrated model based on IEC 61499 is proposed to combine information and control to obtain a unified expression model. It takes advantage of the distributed and modular characteristics of Administration Shell and 61499 Function Blocks, which makes up for the lack of flexibility and dynamic reconfiguration in the industry. This integrated model is tested with an AGV and robot arm in a simulated welding product line to validate its feasibility.

As an important part of Industry 4.0, industrial software development must be highly flexible and reconfigurable to deal with a large number of requirements and random failures during the manufacturing process. Requirements understanding errors that occur in the early stages of software development often cannot be discovered prior to the testing phase. Therefore, the analysis and understanding of requirements are particularly crucial in the software development process. In this paper, a knowledge-driven functional-oriented requirements elicitation and analysis method are proposed to extract both functional and non-functional requirements from design documents. Nonfunctional requirements which are implicit in the documents will be elicited according to the domain knowledge graphs and predefined rules. A domain glossary is also constructed for the elicitation process, especially from unstructured sources. Domain knowledge graphs are modified periodically according to the feedback information to form a closed loop from requirements to code. The feasibility of the proposed method is demonstrated under an automobile welding line.

Edge computing is playing a more and more important role to bridge the gap between industrial clouds and field devices for Industrial Internet-of-Things. How to collect data effectively from various device types becomes one of the major challenges for edge computing. In industrial edge computing, data-related challenges are gradually exposed including compatibility between edge computing nodes, the process specification of data acquisition, and the efficiency of data storage. To solve the above problems, the IEEE P2805.2 Standard is proposed, which provides generic data acquisition, filtering and buffering protocols between cloud and edge computing. In this paper, the data acquisition principle and process of the IEEE P2805.2 Standard are explained in detail. A case study is presented for proof-of-concept of the proposed protocol.

The development and evolution of modern information and communication technologies is leading us to the fourth industrial revolution, in which the Industrial Internet of Things (IIoT) is assumed to be one of the key aspects to realize Industry 4.0. Federated learning facilitates the implementation of secure platform with consideration on data privacy to support IIoT. Many researchers and practitioners have expressed their interest in this area with the expectation of profound effect in the context of Industry 4.0. However, the topic is quite new and has not been investigated under its different profiles until now. There is a lack of literature from both a theoretical and an empirical point of view. Therefore, this special sector is dedicated to provide cutting-edge technologies and novel studies, which can realize and elevate the effectiveness and advantages of federated learning for advancing industrial IoT. Eleven articles have been accepted by this Special Section based on review, and revision processing.

The IEC 61499 standard provides a component-based system-level modeling language for future-proof automation systems. Automatic generation and migration of function block finite-state transition models encounter problems of the original black-box system and state explosion, especially for the FB network containing multiple types of input/output events and data in interfaces. This paper proposes a recovery method that automatically generates finite-state transition models of the FB network from unlabeled data. Type identification, input-output pairing, and correlation analysis are applied before state transition model generation so as to identify system structure and reduce state space. The experiment is implemented in a simulation system of traffic lights control.

Flexible production and intelligent manufacturing prompt a variety of production and computing requirements of industrial edge applications. Industrial edge applications need to adopt more flexible deployment methods to meet the new requirements from the new industrial cloud and edge paradigm. However, most legacy industrial applications are still based on a fixed order deployment method. In this paper, a task-oriented automatic edge-cloud collaborative microservice method is proposed to optimize the deployment process of industrial edge applications and improve the flexibility and expandability. The proposed method was verified by a case study of the thrust ball bearing producing.

Industrial wireless sensor networks (IWSNs) are the fundamental components in the next-generation factories. Due to massive heterogeneous data generated from large-scale IWSNs, it is still challenging to achieve predictable, deterministic, and real-time transmission scheduling. In this article, a framework of priority-aware packet transmission scheduling (PPTS) in cluster-based IWSNs is proposed, where the PPTS strategy, the optimization theory, and the implementation design are systematically considered. In particular, the proposed PPTS strategy not only minimizes the transmission delay of high priority packets but also greatly improves the transmission delay of low priority packets. The optimization theory for end-to-end priority-aware scheduling in cluster-based IWSNs is formalized, which contributes to the optimal solution for multidimensional network resources allocation and achieves the minimum of average transmission delay. Finally, the advantages of the PPTS framework over some existing solutions are demonstrated by a case study.