Knowledge graph-enhanced framework for electric power engineering report generation

Abstract

Due to the complex structural hierarchy, with deeply nested associative relations between entities such as equipment, specifications, and business processes, intelligent power grid engineering is challenging. Meanwhile, limited by the fragmented data and loss of contextual information, the generated reports are prone to the problems such as content redundancy and omission of critical information, failing to meet the demands of efficient decision-making and accurate management in modern power systems. To address these issues, this paper proposes a knowledge graph (KG)-enhanced framework to automatically generate electric power engineering reports. In the KG construction phase, a feature-fused entity recognition model named BERT-BiLSTM-CRF is adopted to improve the accuracy of entity recognition in scenarios involving power engineering professional terminology, thereby solving the problem of ambiguous entity boundaries in traditional models; then a BERT-attention relation extraction model is proposed to enhance the completeness of extracting complex hierarchical and implicit relations in power grid data. In the report generation phase, an improved Transformer architecture is adopted to accurately transform structured knowledge into natural language reports that comply with engineering specifications, addressing the issue of semantic inconsistency caused by the loss of structural information in existing models. By validating with real-world projects, the results show that the proposed framework significantly outperforms existing baseline models in entity recognition, confirming its superiority and applicability in practical engineering.

Keywords

Entity recognition / Improved transformer model / Knowledge graph enhancement / Power grid engineering report generation / Relation extraction

CRediT authorship contribution statement

Chen Qian: Writing―original draft, Resources, Methodology, Investigation, Formal analysis, Data curation, Funding acquisition, Conceptualization. Yu-Yan Chen: Writing―review & editing, Validation, Resources, Methodology. Jia-Ying Yang: Writing―review & editing, Validation, Investigation, Conceptualization. Xiao-Wen Le: Investigation, Validation, Data curation. Xiao-Yang Shen: Validation, Investigation, Project administration. Yi-Heng Zeng: Writing―review & editing, Supervision, Investigation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work was supported by State Grid Shanghai Economic Research Institute under Grant No. SGTYHT/23-JS-004.

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