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Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT

Yixiang Li¹, Jianxin Chen¹, Jing Yang²

¹School of Art and Design, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)

|June 26, 2026

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This summary is machine-generated.

This study introduces an adaptive safety signage framework for intelligent manufacturing, integrating IoT data and AI to dynamically match risks. It enhances workplace safety by providing real-time, context-aware alerts, improving risk identification and response times.

Area of Science:

Industrial Safety Engineering
Artificial Intelligence in Manufacturing
Internet of Things (IoT)

Background:

Current safety signs in advanced manufacturing struggle to adapt to dynamic risks due to fragmented perception, semantics, and decision-making processes.
Existing safety systems lack integrated control over IoT sensor data, knowledge graph reasoning, and adaptive signage.

Purpose of the Study:

To propose a novel framework for closed-loop adaptive safety signage in intelligent manufacturing environments.
To enhance the dynamic risk response capabilities by integrating perception, knowledge reasoning, and adaptive control.

Main Methods:

A framework fusing dynamic graph attention networks, hierarchical temporal knowledge graphs, and reinforcement learning optimization.
Extraction of spatiotemporal dependencies from multi-source IoT sensor streams.

Keywords:

IoT sensor industrial field innovative manufacturing environment intelligent optimization of safety labels knowledge graph

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Risk propagation path tracing on an industrial knowledge graph for adaptive signage generation.

Main Results:

Achieved 96.7% accuracy in risk identification and a 91.3% F1 score for risk propagation.
Demonstrated a 94.2% semantic matching score and a low response latency of 43.65 milliseconds.
Validated effectiveness in a real-world aerospace workshop setting.

Conclusions:

The proposed framework offers a closed-loop solution for intelligent manufacturing safety, bridging physical perception with adaptive semantic expression.
This approach significantly improves the timeliness and relevance of safety signage in dynamic industrial settings.
The study highlights the potential of integrated AI and IoT for creating safer, more responsive manufacturing environments.