Construction Method for a Domain-Specific LLM for Substation Safety Hazard Diagnosis Based on a Bilateral Dual-Branch Dual-LoRA Architecture

Substation safety inspection requires models that recognize visual hazards and organize image-linked diagnostic text. This paper specifies a bilateral Dual-LoRA adaptation framework for Qwen2.5-VL-7B-Instruct, with the pretrained backbone and native patch merger frozen, and reevaluates two archived adapter-output runs. The authors report a collection of 1173 records across five hazard categories. The two output archives contain the same 121 image identifiers and reference labels. A deterministic parser assigns categories from the risk-type field, enabling full confusion matrices and per-class precision, recall, and F1. Archived Run A correctly classifies 115 images (95.04%; Macro-F1 0.9282) and Run B 116 (95.87%; Macro-F1 0.9358). The one-image difference is not significant under the exact paired McNemar test (p = 1.000). Dangling-object recall is 7/10 for both runs. Reproducible text-overlap and structural-field audits characterize the stored diagnostic responses. Analytical inventories compare adapter placement and rank separately from measured outputs. The evaluation is limited to an internal cohort; overfitting risk remains, and unseen-site generalization and independent field effectiveness have not been established. The bilateral specification and proposed inspection workflow are distinguished from what the retained checkpoints and output records independently verify.