Research on the Construction of Multimodal Large Models and Self-supervised Learning for Panoramic Perception of Power Equipment.
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| Title: | Research on the Construction of Multimodal Large Models and Self-supervised Learning for Panoramic Perception of Power Equipment. |
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| Authors: | ZHENG, Guoshun1 zhengguoshun_fj@163.com |
| Source: | Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p1175-1184. 10p. |
| Subjects: | Digital twin, Data augmentation, Electric power equipment, Probabilistic generative models, Machine learning, Recommender systems, Visual perception |
| Abstract: | The global information perception of power equipment is the key to supporting the efficient and stable operation of the new power system. This paper adopts the digital twin technology and constructs a new framework for panoramic perception of transformer vibration status. To address the difficulties in obtaining power defect samples, the dominance of normal samples, and the reliance on large-scale data of multi-modal large models, a power defect data enhancement method based on diffusion models is proposed. Under the premise of ensuring the rationality of the generated image structure, this method utilizes the trained multi-modal large model Qwen-VL-Max to extract the high-order semantic information of real power scene images and combines the prompt engineering technology to generate synthetic images with power defect features and high quality. Moreover, for the common problem of data sparsity in multi-modal recommendation systems, a multi-modal fusion recommendation algorithm based on collaborative self-supervised learning is proposed. This algorithm effectively enhances the representation ability of multi-modal data through the joint learning of the deep features of the data, thereby alleviating the problem of performance decline in recommendations caused by data sparsity. Experimental results show that, compared with the current mainstream multi-modal recommendation algorithms, this algorithm has significant improvements in multiple recommendation evaluation indicators. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195131805 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Research on the Construction of Multimodal Large Models and Self-supervised Learning for Panoramic Perception of Power Equipment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22ZHENG%2C+Guoshun%22">ZHENG, Guoshun</searchLink><relatesTo>1</relatesTo><i> zhengguoshun_fj@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2026, Vol. 33 Issue 3, p1175-1184. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+equipment%22">Electric power equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilistic+generative+models%22">Probabilistic generative models</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+perception%22">Visual perception</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The global information perception of power equipment is the key to supporting the efficient and stable operation of the new power system. This paper adopts the digital twin technology and constructs a new framework for panoramic perception of transformer vibration status. To address the difficulties in obtaining power defect samples, the dominance of normal samples, and the reliance on large-scale data of multi-modal large models, a power defect data enhancement method based on diffusion models is proposed. Under the premise of ensuring the rationality of the generated image structure, this method utilizes the trained multi-modal large model Qwen-VL-Max to extract the high-order semantic information of real power scene images and combines the prompt engineering technology to generate synthetic images with power defect features and high quality. Moreover, for the common problem of data sparsity in multi-modal recommendation systems, a multi-modal fusion recommendation algorithm based on collaborative self-supervised learning is proposed. This algorithm effectively enhances the representation ability of multi-modal data through the joint learning of the deep features of the data, thereby alleviating the problem of performance decline in recommendations caused by data sparsity. Experimental results show that, compared with the current mainstream multi-modal recommendation algorithms, this algorithm has significant improvements in multiple recommendation evaluation indicators. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20260203003363 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1175 Subjects: – SubjectFull: Digital twin Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Electric power equipment Type: general – SubjectFull: Probabilistic generative models Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Recommender systems Type: general – SubjectFull: Visual perception Type: general Titles: – TitleFull: Research on the Construction of Multimodal Large Models and Self-supervised Learning for Panoramic Perception of Power Equipment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: ZHENG, Guoshun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 33 – Type: issue Value: 3 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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