In this study, the performance of a current sensor based on giant magnetostrictive materials (GMM) and fiber Bragg grating (FBG) has been improved by optimizing the spectral characteristics of gratings. By analyzing the influence of FBG on the current sensor characteristics, three key parameters (gate region length, refractive index modulation depth, and toe cutting system) are selected for optimization. The optimal grating parameters are determined to improve the linearity and sensitivity of sensor output. Experimental tests reveal that after grating optimization, the current sensor shows excellent performance parameters, including a linearity of 0.9942, sensitivity of 249.75 mV/A, and good stability in the temperature range of 0–60 °C. This research can provide a reference for improving the grating design and performance of existing GMM-FBG current sensors.

Real-time estimation of the safety distance between power operation workers and energized equipment is essential for preventing electric shock, arc flash injury, and equipment accidents during live or near-energized operations. However, large-scale deployment remains difficult because conventional binocular ranging systems require accurate offline calibration, while monocular vision suffers from metric-scale ambiguity. This paper proposes a weakly calibrated multi-camera vision framework for safety distance estimation in power operation scenes. First, a cross-camera tracking strategy combining mask-guided ReID features, Kalman filtering, spatio-temporal constraints, and Hungarian matching is developed to maintain globally consistent worker identities. Second, a region-wise self-supervised depth estimation model is introduced: photometric consistency is imposed within worker regions, while cross-view feature reprojection is imposed within rigid equipment regions. Intrinsic parameter regularization and scene-inherent dimensional priors are then used to recover metric scale without placing additional calibration targets. Third, the YOLOv8-seg backbone is enhanced with a CNN-Transformer C2f_CT module to improve pixel-level segmentation of workers, energized equipment, and structural components under cluttered backgrounds. Experiments on three representative power operation scenarios show that the proposed method achieves 92.3% MOTA, 94.2% Rank-1 accuracy, 82.0% mAP, 8.7% relative distance error, and 22 FPS end-to-end speed. When using five-frame smoothing and uncertainty threshold, the proposed method can achieve 98.7% warning precision, 0.9% missed alarm rate and 1.3% false alarm rate. The results indicate that the proposed framework provides a practical balance between deployment cost, geometric accuracy, and real-time warning capability.

Automated power-worksite monitoring requires accurate event recognition and auditable explanations. This paper presents PowerXGraph++, a regulation-constrained spatiotemporal evidence-graph framework for missing PPE, boundary crossing, unsafe proximity, falls, and fire/smoke. Typed evidence tokens are encoded by a transformer; full-context prediction is separated from explanation selection, and explanations are tested for sufficiency, necessity, sparsity, and regulation consistency. Distillation, event-boundary counterfactuals, background-invariance training, and non-inferiority checkpoint selection preserve predictive performance. On annotation-derived construction-PPE graphs, three-seed Macro-F1, violation recall, and rationale F1 were 0.989, 0.965, and 0.901. Under the same frozen predictor, the supervised, grammar-aware selector attained rationale F1 of 0.983 and regulation consistency of 1.000, although the post hoc baselines lacked equivalent supervision. A single YOLO reproduction run using seed 17 on 197 matched person graphs yielded Macro-F1 of 0.844, missing-PPE recall of 0.591, and rationale F1 of 0.660. Expert evaluation produced Fleiss’ kappa of 0.740 and method-to-majority rationale F1 of 0.870. Held-out-camera reasoning and a two-camera fence-crossing pilot provide additional evidence under supplied-evidence conditions; the results do not establish automatic generation of every semantic predicate or unrestricted raw video performance.