Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings
In the power industry, secondary operation risk assessment is a critical step in ensuring operational safety. However, traditional assessment methods often rely on expert judgment, making it difficult to efficiently address the challenges posed by unstructured textual data and complex equipment rela...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1934 |
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| author | Pengyu Zhu Youwei Li Peidong Xu Ping Li Zhenbing Zhao Gang Li |
| author_facet | Pengyu Zhu Youwei Li Peidong Xu Ping Li Zhenbing Zhao Gang Li |
| author_sort | Pengyu Zhu |
| collection | DOAJ |
| description | In the power industry, secondary operation risk assessment is a critical step in ensuring operational safety. However, traditional assessment methods often rely on expert judgment, making it difficult to efficiently address the challenges posed by unstructured textual data and complex equipment relationships. To address this issue, this paper proposes a hybrid model that integrates graph convolutional networks (GCNs) with semantic embedding techniques. The model consists of two main components: the first constructs a domain-specific knowledge graph for the power industry and uses a GCN to extract structural information, while the second fine-tunes the RoBERTa pre-trained model to generate semantic embeddings for textual data. Finally, the model employs a hybrid similarity measurement mechanism that comprehensively considers both semantic and structural features, combining K-means clustering similarity search with a multi-node weighted evaluation method to achieve efficient and accurate risk assessment. The experimental results demonstrate that the proposed model significantly outperforms the traditional methods in key metrics, such as accuracy, recall, and F1 score, fully validating its practical application value in secondary operation scenarios within the power industry. |
| format | Article |
| id | doaj-art-6ad8a69a998f465fba84bda6b1ab3352 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-6ad8a69a998f465fba84bda6b1ab33522025-08-20T01:48:58ZengMDPI AGSensors1424-82202025-03-01256193410.3390/s25061934Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic EmbeddingsPengyu Zhu0Youwei Li1Peidong Xu2Ping Li3Zhenbing Zhao4Gang Li5State Grid Jiangsu Electric Power Co., Ltd., Huaian Power Supply Branch, Huaian 223000, ChinaState Grid Jiangsu Electric Power Co., Ltd., Huaian Power Supply Branch, Huaian 223000, ChinaState Grid Jiangsu Electric Power Co., Ltd., Wuxi Power Supply Branch, Wuxi 214200, ChinaState Grid Jiangsu Electric Power Co., Ltd., Huaian Power Supply Branch, Huaian 223000, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Computer, North China Electric Power University, Baoding 071003, ChinaIn the power industry, secondary operation risk assessment is a critical step in ensuring operational safety. However, traditional assessment methods often rely on expert judgment, making it difficult to efficiently address the challenges posed by unstructured textual data and complex equipment relationships. To address this issue, this paper proposes a hybrid model that integrates graph convolutional networks (GCNs) with semantic embedding techniques. The model consists of two main components: the first constructs a domain-specific knowledge graph for the power industry and uses a GCN to extract structural information, while the second fine-tunes the RoBERTa pre-trained model to generate semantic embeddings for textual data. Finally, the model employs a hybrid similarity measurement mechanism that comprehensively considers both semantic and structural features, combining K-means clustering similarity search with a multi-node weighted evaluation method to achieve efficient and accurate risk assessment. The experimental results demonstrate that the proposed model significantly outperforms the traditional methods in key metrics, such as accuracy, recall, and F1 score, fully validating its practical application value in secondary operation scenarios within the power industry.https://www.mdpi.com/1424-8220/25/6/1934graph convolutional networkknowledge graphsemantic searchrisk assessment |
| spellingShingle | Pengyu Zhu Youwei Li Peidong Xu Ping Li Zhenbing Zhao Gang Li Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings Sensors graph convolutional network knowledge graph semantic search risk assessment |
| title | Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings |
| title_full | Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings |
| title_fullStr | Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings |
| title_full_unstemmed | Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings |
| title_short | Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings |
| title_sort | secondary operation risk assessment method integrating graph convolutional networks and semantic embeddings |
| topic | graph convolutional network knowledge graph semantic search risk assessment |
| url | https://www.mdpi.com/1424-8220/25/6/1934 |
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