@Article{cmc.2026.082804,
AUTHOR = {Jianfeng Liu, Yongjiao Yang, Kangyi Yang, Changhua Hu, Zijia Xu, Qingguo Shi, Yi Su},
TITLE = {Enhancing Power Enterprise Inspection and Supervision: A LoRA-Based Lightweight LLM Framework Integrating Retrieval-Augmented Generation and Prompt Engineering},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27092},
ISSN = {1546-2226},
ABSTRACT = {Power enterprise inspection and supervision require greater intelligence, efficiency, and standardization; however, existing approaches are limited by inefficient knowledge retrieval, inaccurate issue identification, and insufficient support for standardized reporting and rectification tracking. This study proposes a lightweight, domain-adaptive large language model (LLM) framework based on Low-Rank Adaptation (LoRA), integrating Retrieval-Augmented Generation (RAG) and structured prompt engineering to enable evidence-grounded inspection tasks. The framework achieves parameter-efficient adaptation through low-rank decomposition and constructs a domain-specific multimodal knowledge base, enhancing output traceability, consistency, and task generalization. A key contribution is the introduction of a Sensitive Information Control Gate, which enforces role-based access control and automated redaction, ensuring secure and compliant generation in regulated environments while preserving traceability. Experimental results demonstrate that the proposed method achieves improved performance over the base model and demonstrates competitive effectiveness under the evaluated conditions, supported by statistical analysis (paired <i>t</i>-test, <i>p</i> &lt; 0.01, bootstrap 95% confidence intervals), while maintaining high parameter efficiency with only 0.4%–0.5% trainable parameters.},
DOI = {10.32604/cmc.2026.082804}
}