The rapid transition toward unattended operation and maintenance in large-scale clean energy infrastructures urgently requires autonomous robots capable of performing active physical interventions. However

executing tool acquisition in these hazard-critical environments poses severe multifaceted risks

including the risk of direct current-arcing and thermal exposure. Traditional robotic grasping methods typically prioritize geometric stability but lack the semantic awareness required to identify and avoid hazardous functional ends

leading to a high risk of unsafe interactions and compromised downstream usability. To overcome this critical bottleneck

we propose Knowledge-Driven Safety Grasping (KDSG)

a novel framework designed to improve hazard-aware operational safety during autonomous tool acquisition. KDSG integrates two core modules: Safety-Aware Grounding and Enrichment (SAGE) and Physically Constrained Generative Grasping (PCGG). SAGE leverages a domain-specific hazard knowledge graph coupled with vision–language models to semantically truncate hazardous parts from the perceptual input

explicitly isolating safe interaction regions. Building upon this safe geometry

PCGG utilizes a distance-based generative representation to predict geometrically conformal grasps

followed by a strict physical safety filter that enforces deterministic collision avoidance. We evaluate KDSG in a high-fidelity physics simulator using a multifingered dexterous hand. Extensive comparative and ablation experiments demonstrate that our framework reduces the Safety Violation Rate to 0.0% under our simulator-defined metric

while maintaining a robust Operational Safety Success Rate of 70.3%. This work provides a promising framework for the deployment of intelligent robotic systems in hazard-critical clean energy infrastructures.