{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T06:01:44Z","timestamp":1781157704290,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for Central Universities","award":["2025JCCXJD02"],"award-info":[{"award-number":["2025JCCXJD02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Multi-source heterogeneous data in substation secondary systems are typically characterized by high entropy and disorder, which pose significant challenges for cross-modal information integration and efficient retrieval. Therefore, a method for multimodal information extraction and knowledge graph construction is proposed, enabling structured processing of heterogeneous data from multiple sources. For the image modality, positional and semantic information is extracted using YOLOv8n and Optical Character Recognition (OCR) techniques. To mitigate the effects of uncertain connection topology and noise interference, a Heuristic Circular Stepping Search Algorithm (HCSA) is designed to achieve deterministic path tracing of information flows. For the text modality, a RoFormer-BiLSTM-CRF model enhanced with Rotary Position Embedding (RoPE) is developed to alleviate information degradation in long-sequence texts, thereby enabling high-accuracy extraction of entities and relationships. Furthermore, by combining the domain ontology mapping rules and string similarity, the extracted device entities from the two modalities are aligned, thereby converting scattered data into a structured knowledge graph. Experiments conducted on the secondary-side data of a substation in China demonstrate that the proposed method effectively extracts multimodal information from substation secondary systems, providing valuable support for information management and decision-making assistance in complex industrial systems.<\/jats:p>","DOI":"10.3390\/e28060655","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:50:58Z","timestamp":1781157058000},"page":"655","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Method for Multimodal Information Extraction and Knowledge Graph Construction in Substation Secondary System"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2052-240X","authenticated-orcid":false,"given":"Wenting","family":"Zha","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dengrui","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Su","sequence":"additional","affiliation":[{"name":"State Grid Anhui Electric Power Co., Ltd., Wuhu Power Supply Company, Wuhu 241000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yi, L., Yinbin, Y., Yang, Z., Qinran, H., and Xing, D. (2021). Construction method of equipment knowledge graph in power grid monitoring field based on multi-source data fusion. Proceedings of the 2021 IEEE Sustainable Power and Energy Conference (iSPEC), IEEE.","DOI":"10.1109\/iSPEC53008.2021.9735819"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1093\/ijlct\/ctaf143","article-title":"Knowledge graph construction and fault identification for new energy power equipment","volume":"20","author":"Zhang","year":"2025","journal-title":"Int. J. Low-Carbon Technol."},{"key":"ref_3","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational linguistics: Human Language Technologies, Association for Computational Linguistics. Volume 1 (Long and Short Papers)."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, R., Fu, R., Xu, K., Shi, X., and Ren, X. (2023). A review of knowledge graph-based reasoning technology in the operation of power systems. Appl. Sci., 13.","DOI":"10.3390\/app13074357"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Qu, H., Zhang, Y., Liang, K., Li, S., and Huo, X. (2023). A knowledge-graph-driven method for intelligent decision making on power communication equipment faults. Electronics, 12.","DOI":"10.3390\/electronics12183939"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tang, Y., Liu, T., Liu, G., Li, J., Dai, R., and Yuan, C. (2019). Enhancement of power equipment management using knowledge graph. arXiv.","DOI":"10.1109\/ISGT-Asia.2019.8881348"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, B., Chen, X., Ouyang, Y., Gan, Y., Lyu, B., Liu, H., and Li, C. (2022). A construction method of knowledge graph for electric power field. Proceedings of the 2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), IEEE.","DOI":"10.1109\/AEMCSE55572.2022.00094"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, X., Li, P., Wang, Y., Ren, X., Zhao, Z., and Li, G. (2025). Knowledge Graph-Augmented ERNIE-CNN Method for Risk Assessment in Secondary Power System Operations. Energies, 18.","DOI":"10.3390\/en18082104"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cai, W., and Ma, W. (2025). Learnable Convolutional Attention Network for Unsupervised Knowledge Graph Entity Alignment. Entropy, 27.","DOI":"10.3390\/e27090924"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yu, Z., Zhang, A., Feng, H., Du, H., Wei, S., and Zhao, Y. (2022). Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks. Entropy, 24.","DOI":"10.3390\/e24070964"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huang, H., Chen, Y., Lou, B., Hongzhou, Z., Wu, J., and Yan, K. (2019). Constructing knowledge graph from big data of smart grids. Proceedings of the 2019 10th International Conference on Information Technology in Medicine and Education (ITME), IEEE.","DOI":"10.1109\/ITME.2019.00147"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hu, R., Liu, S., Wang, J., Mi, X., Liu, Y., Hao, P., and Shi, X. (2023). Contrastive Learning-Based Multi-Modal Entity Alignment and Feature Fusion Method for Electric Power Knowledge Graph. Proceedings of the 2023 4th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), IEEE.","DOI":"10.1109\/ICHCI58871.2023.10277762"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"47","DOI":"10.54097\/dcc7ba37","article-title":"Information extraction and knowledge map construction based on natural language processing","volume":"7","author":"Wang","year":"2024","journal-title":"Front. Comput. Intell. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1109\/TKDE.2022.3224228","article-title":"Multi-modal knowledge graph construction and application: A survey","volume":"36","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhu, W., Mao, J., Xia, F., and Dai, Z. (2025). Power Switchgear State Perception via Enhanced YOLOv11 and Knowledge Graph Fusion. Proceedings of the 2025 10th International Conference on Automation, Control and Robotics Engineering (CACRE), IEEE.","DOI":"10.1109\/CACRE66141.2025.11119565"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s42452-024-06443-7","article-title":"Intelligent recognition algorithm of connection relation of substation secondary wiring drawing based on D-LLE algorithm","volume":"7","author":"Liu","year":"2024","journal-title":"Discov. Appl. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, S., Ma, C., Wang, J., Dai, H., Lin, J., Feng, S., and Luo, F. (2023). A Construction Method for the Knowledge Graph of Power Equipment Based on Ontology Modeling. Proceedings of the 2023 8th Asia Conference on Power and Electrical Engineering (ACPEE), IEEE.","DOI":"10.1109\/ACPEE56931.2023.10135669"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yan, S., Li, J., Wang, J., Liu, G., Ai, A., and Liu, R. (2023). A novel strategy for extracting richer semantic information based on fault detection in power transmission lines. Entropy, 25.","DOI":"10.3390\/e25091333"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kor, Y., Tan, L., Reformat, M.Z., and Musilek, P. (2020). Gridkg: Knowledge graph representation of distribution grid data. Proceedings of the 2020 IEEE Electric Power and Energy Conference (EPEC), IEEE.","DOI":"10.1109\/EPEC48502.2020.9320066"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"You, Z., Yi, Q., Yiquan, Z., Yue, H., Ting, Y., and Peng, L. (2024). Research on the Design of Multimodal Knowledge Graph for Power Grid Infrastructure Construction. Proceedings of the 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM), IEEE.","DOI":"10.1109\/ISPCEM64498.2024.00138"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, W., Dang, Y., and Zhang, X. (2025). A Multimodal Semantic-Enhanced Attention Network for Fake News Detection. Entropy, 27.","DOI":"10.3390\/e27070746"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, X., Li, G., Wang, T., Dong, Z., Qin, Z., and Chu, F. (2025). Knowledge extraction and retrieval-augmented generation for intelligent maintenance of wind power equipment based on graph attention networks. Chin. J. Mech. Eng., 100141.","DOI":"10.1016\/j.cjme.2025.100141"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, X., Shi, J., Dong, L., Zhang, Y., Pan, J., and Huang, H. (2024). Construction of a Multimodal Knowledge Graph for Power Grid Construction Safety Based on Large Language Models. Proceedings of the 2024 International Conference on New Power System and Power Electronics (NPSPE), IEEE.","DOI":"10.1109\/NPSPE62515.2024.00013"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, X., Zhou, H., Su, Z., Wang, Y., Wang, J., Sun, H., Chen, Y., and Lu, L. (2024). Research on Substation Alarm Intelligent Handling Strategy Based on Image Recognition and NLP. Proceedings of the 2024 3rd Asia Power and Electrical Technology Conference (APET), IEEE.","DOI":"10.1109\/APET63768.2024.10882828"},{"key":"ref_25","first-page":"252","article-title":"Construction and query of power equipment knowledge map based on graph database","volume":"Volume 12779","author":"Xuan","year":"2023","journal-title":"Proceedings of the Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1109\/TPDS.2014.2330597","article-title":"BFS-4K: An efficient implementation of BFS for kepler GPU architectures","volume":"26","author":"Busato","year":"2014","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"127063","DOI":"10.1016\/j.neucom.2023.127063","article-title":"Roformer: Enhanced transformer with rotary position embedding","volume":"568","author":"Su","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ye, Z., Qi, D., Liu, H., Yan, Y., Chen, Q., and Liu, X. (2024). RoUIE: A Method for Constructing Knowledge Graph of Power Equipment Based on Improved Universal Information Extraction. Energies, 17.","DOI":"10.3390\/en17102249"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/6\/655\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:53:58Z","timestamp":1781157238000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/6\/655"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,9]]},"references-count":28,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["e28060655"],"URL":"https:\/\/doi.org\/10.3390\/e28060655","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,9]]}}}