基于领域知识增强的电力安全违章视觉情报即时检索与分析

doi:10.3976/j.issn.1002-4026.2025165

Abstract

Abstract: To bridge the semantic gap between unstructured video data and structured safety rules, and to address incomplete temporal knowledge in real-time monitoring, this framework takes the Electrical Safety Work Regulations as its core. First, it constructs a power operation intelligence knowledge base and an associated knowledge graph using a six-tuple representation. Through an explicit knowledge mapping and semantic normalization module, users’ colloquial queries are transformed into machine-computable, standardized explicit rule knowledge that serves as prior constraints. Building on this foundation, a knowledge-driven visual feature selection mechanism leverages domain knowledge to focus on key violation subjects and behaviors in video streams, thereby effectively suppressing highly redundant background information and enabling data cleaning. Furthermore, a temporal collaborative reasoning framework transforms temporal evolution patterns learned in offline environments into tacit knowledge via knowledge transfer strategies, mitigating retrieval latency caused by the absence of future information in real-time monitoring. Experiments conducted on the ActivityNet Captions dataset and a self-constructed power visual violation behavior dataset demonstrate that the proposed framework significantly improves both the accuracy and efficiency of violation intelligence retrieval. Overall, the framework enables the transformation of unstructured video data into high-value structured intelligence and provides a more timely and interpretable solution for power safety early warning services.

Key words: domain knowledge enhancement, visual intelligence retrieval, knowledge graphs, real-time early warning, power safety, knowledge transfer

CLC Number:

TP311.13

LONG Chunfu, Zhao Zilong, ZHAO Hai, XU Wenbin, CHEN Jiajia, TANG Haoyu. Domain knowledge–driven real-time visual intelligence retrieval and analysis for power safety violations[J].Shandong Science, 0, (): 1-.

References

[1] 张森达, 崔昕, 曲延刚, 等. 电力领域知识图谱的构建设计[J]. 智能城市, 2025, 11(10): 66-69. DOI:10.19301/j.cnki.zncs.2025.10.015.

[2] 任晓龙, 陈曦, 司恒斌, 等. 基于Neo4j图数据库的电力系统与综合能源系统知识图谱研究[J]. 电力科学与技术学报, 2025, 40(3): 211-221. DOI:10.19781/j.issn.1673-9140.2025.03.023.

[3] Xu Mingze, Gao Mingfei, Chen Yiting, et al. Temporal recurrent networks for online action detection[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea. IEEE, 2019: 5531-5540. DOI:10.1109/ICCV.2019.00563.

[4] Wang Shaokun, Gan Tian, Liu Yuan, et al. Discover micro-influencers for brandsviabetter understanding[J]. IEEE Transactions on Multimedia, 2022, 24: 2595-2605. DOI:10.1109/TMM.2021.3087038.

[5] Gao Jiyang, Sun Chen, Yang Zhenheng, et al. TALL: Temporal activity localizationvialanguage query[C]//2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy. IEEE, 2017: 5277-5285. DOI:10.1109/ICCV.2017.563.

[6] Yuan Yitian, Mei Tao, Zhu Wenwu. To find where you talk: Temporal sentence localization in video with attention based location regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 9159-9166. DOI:10.1609/aaai.v33i01.33019159.

[7] Meng Lingwen, Wang Yulin, Ban Guobang, et al. A multi-source embedding-based named entity recognition model for knowledge graph and its application to on-site operation violations in power grid systems[J]. Electronics, 2025, 14(13): 2511. DOI:10.3390/electronics14132511.

[8] Xiao Na, Peng Bai, Li Xin, et al. Research on the construction and implementation of power grid fault handling knowledge graphs[J]. Energy Reports, 2023, 9: 182-189. DOI:10.1016/j.egyr.2023.02.073.

[9] Liu Meng, Qu Leigang, Nie Liqiang, et al. Iterative local-global collaboration learning towards one-shot video person re-identification[J]. IEEE Transactions on Image Processing, 2020, 29: 9360-9372. DOI:10.1109/TIP.2020.3026625.

[10] Zhao Yang, Lin Xuan, Xu Wenqiang, et al. AntPivot: Livestream highlight detectionviahierarchical attention mechanism[PP/OL]. arXiv (2022-06-10)[2025-11-01]. https://doi.org/10.48550/arXiv.2206.04888.

[11] Xu Mingze, Xiong Yuanjun, Chen Hao, et al. Long short-term transformer for online action detection[PP/OL]. V3. arXiv (2021-12-22)[2025-11-01]. https://doi.org/10.48550/arXiv.2107.03377.

[12] Wang Xiang, Zhang Shiwei, Qing Zhiwu, et al. OadTR: Online action detection with transformers[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada. IEEE, 2021: 7545-7555. DOI:10.1109/ICCV48922.2021.00747.

[13] Wu Jianlong, Sun Wei, Gan Tian, et al. Neighbor-guided consistent and contrastive learning for semi-supervised action recognition[J]. IEEE Transactions on Image Processing, 2023, 32: 2215-2227. DOI:10.1109/TIP.2023.3265261.

[14] Cai Jianghui, Zhang Min, Yang Haifeng, et al. A novel graph-attention based multimodal fusion network for joint classification of hyperspectral image and LiDAR data[J]. Expert Systems with Applications, 2024, 249: 123587. DOI:10.1016/j.eswa.2024.123587.

[15] Krishna R, Hata K, Ren F, et al. Dense-captioning events in videos[C]//2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy. IEEE, 2017: 706-715. DOI:10.1109/ICCV.2017.83.

[16] Chen Junwen, Mittal G, Yu Ye, et al. GateHUB: Gated history unit with background suppression for online action detection[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, LA, USA. IEEE, 2022: 19893-19902. DOI:10.1109/CVPR52688.2022.01930.

[17] CAO Y, WANG L, ZHOU J, et al. Iterative refinement of pseudo labels for weakly supervised temporal action localization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 18427-18437.

[18] Gan Tian, Wang Xiao, Sun Yan, et al. Temporal sentence grounding in streaming videos[C]//Proceedings of the 31st ACM International Conference on Multimedia. Ottawa ON Canada. ACM, 2023: 4637-4646. DOI:10.1145/3581783.3612120.

Metrics

Comments

Recommended 0

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits third parties to freely share (i.e., copy and redistribute the material in any medium or format) and adapt (i.e., remix, transform, or build upon the material) the articles published in this journal, provided that appropriate credit is given, a link to the license is provided, and any changes made are indicated. The material may not be used for commercial purposes. For details of the CC BY-NC 4.0 license, please visit: https://creativecommons.org/licenses/by-nc/4.0

Domain knowledge–driven real-time visual intelligence retrieval and analysis for power safety violations

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 3

Metrics

Comments

Recommended 0

[1]	ZHU Xi, WU Hao. Design of cloud identity management trust model based on blockchain technology [J]. Shandong Science, 2020, 33(3): 100-108.
[2]	WANG Geng, SONG Chuan-Chao, SHENG Yu-Xiao, WANG Tong-Tong, LI Sheng-En. Label propagation based detecting algorithm of stable overlapping communities [J]. J4, 2013, 26(5): 61-68.
[3]	WANG Shuai, LIU Lei, SUN Ming-Shan, LIU Xiang-Yang. Real time database of a vehicleborne embedded remote surveillance system [J]. J4, 2012, 25(1): 81-86.