🤖 AI Summary
To address the challenge of diagnosing Time-Sensitive Networking (TSN) switch status in offline industrial environments lacking network connectivity, this paper proposes an offline LED state recognition and assessment method based on executable QR codes (sQRy). The approach encodes a lightweight embedded diagnostic program into an sQRy, enabling end-to-end closed-loop execution—including program generation, cross-platform transmission, and local execution on mobile terminals. This work represents the first application of sQRy technology to industrial network device health monitoring, permitting offline parsing of TSN switch LED patterns, runtime state classification, and operational compliance evaluation without network access. Experimental validation across multiple TSN switch models confirms the method’s effectiveness, significantly enhancing field maintenance in terms of real-time responsiveness, portability, and robustness. The proposed framework establishes a novel paradigm for intelligent, resource-efficient diagnostics of industrial equipment in bandwidth- or connectivity-constrained scenarios.
📝 Abstract
Executable QR codes, also known as sQRy, are a technology aimed at inserting executable programs in a QR code. Through a concrete example, in this paper, we demonstrate their usage in the context of industrial networks in order to assess the operation of a TSN switch by analyzing its status LEDs even in the absence of an internet connection. The entire generation chain that is used to create the sQRy, as well as the corresponding execution chain that, starting from the sQRy, runs it on a mobile device, has been detailed through examples.