Flexible manufacturing significantly enhances production adaptability but imposes higher requirements on the design efficiency of automation control systems. Mining existing project information and transforming it into reusable function blocks is an effective approach to achieving efficient development of industrial control programs. However, the precision of manually generated function blocks depends on the individual understanding of the developer, which lacks stability and can easily cause misunderstandings in later use and maintenance. Therefore, we proposed a graph-based intelligent wrapping framework for industrial control program snippets to enhance the normalization of custom function blocks. The framework utilizes service computing and LLMs to intelligently assist in generating function blocks. The whole process includes program graph representation, semantic understanding, and automatic organization. Finally, we evaluated the proposed framework through comparative experiments in three practical projects that involved up to$\mathbf{1 0 0}$function blocks.

The secure access of numerous heterogeneous devices ensures the stability of the industrial Internet of Things. Centralized authentication can be overwhelmed by an influx of authentication requests from malicious devices. Distributed device-to-device authentication is vulnerable to tampering attack evidence. Blockchain authentication eases evidence tampering, but traditional blockchains impose high-performance requirements on devices, rendering them unsuitable for resource-limited devices. Therefore, in this article, we apply IOTA to device access authentication. To the best of authors' knowledge, this is the first application. Based on this, we propose a blockchain-enabled distributed authentication mechanism for industrial device access. The authentication involves a consensus phase based on optimized IOTA and a unique identification code validation phase. We optimized the tip selection algorithm of IOTA to make it faster and more stable. The performance experiment results demonstrate that our mechanism meets the time-consuming requirements of device access. The security experiments indicate that the authentication phases effectively intercept the attacks.

The rise of smart factories is driving the complete automation of manufacturing environments, making anomaly detection a more important task than ever. With highly intelligent equipment deployed on fully-automated production lines, even a slight anomaly may seriously impact the entire manufacturing process. However, most industrial data containing slight anomalous features exhibits strong correlations that are not fully exploited by existing machine learning models. In general, the anomaly detection model needs to run within programmable logic controllers (PLCs) since it is the main controller of the intelligent equipment, and PLCs have limited resources making it difficult to execute larger models effectively. To address the challenge, we propose a graph anomaly detection method in programmable logic controllers based on service computing (PCSC). The model establishes a data relationship graph through clustering and then feeds it into a neural network with a symmetric structure consisting of graph convolutional layers and LSTM units. This approach enables the analysis of correlations within industrial data and facilitates the extraction of slight abnormal features. For efficient execution of the model, we establish service computing nodes in the PLCs that support model splitting. We tested the model on several publicly available datasets and an actual dataset from an injection molding production line. The results show that the model performs well on different datasets.