Human action recognition algorithms have garnered significant research interest due to their vast potential for applications. Existing human behavior recognition algorithms primarily focus on recognizing general behaviors using a large number of datasets. However, in industrial applications, there are typically constraints such as limited sample sizes and high accuracy requirements, necessitating algorithmic improvements. This article proposes a graph convolution neural network model that combines prior knowledge supervision and attention mechanisms, designed to fulfill the specific action recognition requirements for workers installing solar panels. The model extracts prior knowledge from training data, improving the training effectiveness of action recognition models and enhancing the recognition reliability of special actions. The experimental results demonstrate that the method proposed in this paper surpasses traditional models in terms of recognizing solar panel installation actions accurately. The proposed method satisfies the need for highly accurate recognition of designated person behavior in industrial applications, showing promising application prospects.

Streaming feed-forward 3D reconstruction produces depth predictions at successive frames, but cross-view geometric consistency and local depth accuracy may vary. We present a training-free, output-level approach that combines Multi-Reference Geometric Risk assessment with global-referenced conservative sparse depth refinement, leaving the pretrained backbone, its predicted poses, and its streaming state unchanged. Risk derived from predicted depth, intrinsics, and poses achieves a median Spearman correlation of 0.635 with scale-aligned depth error across 11 public scenes and a correlation of 0.556 on the real-robot Sequence E. With matched 2% sparse depth anchors, local refinement reduces Sequence E held-out AbsRel by 40.69% relative to Sparse Global. Spatial refinement also improves mean held-out depth error in all 55 public scene–policy comparisons and all 200 predefined fixed-mask scene–policy–seed comparisons. Retrospective risk-based frame ranking provides diagnostic benefits under reference-scale normalization, but the observed allocation gains are protocol-dependent and do not establish consistent improvements under historical sparse-scale propagation or deployable online scheduling. In an independent Bonn Leica comparison using shared ground-truth camera poses, Spatial increases recall at a 5 cm threshold but has lower precision and F-score than Sparse Global. These findings establish the value of conservative sparse depth refinement and multi-reference risk as complementary geometric-quality evidence, while distinguishing depth accuracy from downstream 3D geometric performance.