SafetyGPT: An autonomous agent of electrical safety risks for monitoring workers’ unsafe behaviors
Workers’ unsafe behavior is one of the major causes of accidents in electric power production. Intelligent monitoring of workers’ unsafe behaviors can effectively prevent the expansion of safety risks, thereby blocking the development process of risks to accidents. Electric power production processe...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525002236 |
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| _version_ | 1849321712177381376 |
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| author | Wei Li Fuqi Ma Zhiyuan Zuo Rong Jia Bo Wang Abdullah M Alharbi |
| author_facet | Wei Li Fuqi Ma Zhiyuan Zuo Rong Jia Bo Wang Abdullah M Alharbi |
| author_sort | Wei Li |
| collection | DOAJ |
| description | Workers’ unsafe behavior is one of the major causes of accidents in electric power production. Intelligent monitoring of workers’ unsafe behaviors can effectively prevent the expansion of safety risks, thereby blocking the development process of risks to accidents. Electric power production processes are diverse in nature and require the frequent switching of operating scenarios. This makes it difficult to identify what is “unsafe” since worker behaviors within the given electrical context also exhibit variability and diversity. Existing methods have insufficient generalization and adaptability, which makes them inadequate for the case of electric power production. Therefore, this paper proposes Safety Generative Pre-trained Transformers (SafetyGPT), an autonomous agent of safety risk based on a multi-modal large language model, which incorporates a human–machine collaborative monitoring mode for unsafe behaviors of workers. SafetyGPT loads the electric power production video, and the backend supervisors set instructions for SafetyGPT based on task requirements. The model encodes visual and textual features into corresponding tokens, realizes multi-modal feature alignment and fusion through the cross-attention mechanism, and then generates targeted responses through the large language model. Next, the proposed method is applied to real production site data to confirm the effectiveness and superiority through comparison with other methods designed to identify unsafe behaviors. Experimental results show that the accuracy of the proposed method for the identification of unsafe behaviors in complex environments is 96.5%, and that it can generate reasonable recommended plan based on the identification results, assist backend supervisors in making decisions, and effectively improve the safety level of power production. |
| format | Article |
| id | doaj-art-6a39e45e8df54656b3f8c18ee02fc283 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-6a39e45e8df54656b3f8c18ee02fc2832025-08-20T03:49:41ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-07-0116811067210.1016/j.ijepes.2025.110672SafetyGPT: An autonomous agent of electrical safety risks for monitoring workers’ unsafe behaviorsWei Li0Fuqi Ma1Zhiyuan Zuo2Rong Jia3Bo Wang4Abdullah M Alharbi5School of Electrical Engineering, Xi’an University of Technology, Xi’an, Shaanxi 710048, ChinaSchool of Electrical Engineering, Xi’an University of Technology, Xi’an, Shaanxi 710048, China; School of Electrical Engineering and Automation, Wuhan University, Wuhan, Hubei 430072, China; Corresponding author at: Xi’an University of Technology, Yanxiang Road No. 58, Xi’an, Shaanxi Province 710054, China.School of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, United KingdomSchool of Electrical Engineering, Xi’an University of Technology, Xi’an, Shaanxi 710048, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan, Hubei 430072, ChinaDepartment of Electrical Engineering, College of Engineering in Wadi Addawasir, Prince Sattam bin Abdulaziz University, Wadi Addawasir 11991, Saudi ArabiaWorkers’ unsafe behavior is one of the major causes of accidents in electric power production. Intelligent monitoring of workers’ unsafe behaviors can effectively prevent the expansion of safety risks, thereby blocking the development process of risks to accidents. Electric power production processes are diverse in nature and require the frequent switching of operating scenarios. This makes it difficult to identify what is “unsafe” since worker behaviors within the given electrical context also exhibit variability and diversity. Existing methods have insufficient generalization and adaptability, which makes them inadequate for the case of electric power production. Therefore, this paper proposes Safety Generative Pre-trained Transformers (SafetyGPT), an autonomous agent of safety risk based on a multi-modal large language model, which incorporates a human–machine collaborative monitoring mode for unsafe behaviors of workers. SafetyGPT loads the electric power production video, and the backend supervisors set instructions for SafetyGPT based on task requirements. The model encodes visual and textual features into corresponding tokens, realizes multi-modal feature alignment and fusion through the cross-attention mechanism, and then generates targeted responses through the large language model. Next, the proposed method is applied to real production site data to confirm the effectiveness and superiority through comparison with other methods designed to identify unsafe behaviors. Experimental results show that the accuracy of the proposed method for the identification of unsafe behaviors in complex environments is 96.5%, and that it can generate reasonable recommended plan based on the identification results, assist backend supervisors in making decisions, and effectively improve the safety level of power production.http://www.sciencedirect.com/science/article/pii/S0142061525002236Generative artificial intelligenceRisk identificationElectric power productionMulti-modal large language modelUnsafe behaviors |
| spellingShingle | Wei Li Fuqi Ma Zhiyuan Zuo Rong Jia Bo Wang Abdullah M Alharbi SafetyGPT: An autonomous agent of electrical safety risks for monitoring workers’ unsafe behaviors International Journal of Electrical Power & Energy Systems Generative artificial intelligence Risk identification Electric power production Multi-modal large language model Unsafe behaviors |
| title | SafetyGPT: An autonomous agent of electrical safety risks for monitoring workers’ unsafe behaviors |
| title_full | SafetyGPT: An autonomous agent of electrical safety risks for monitoring workers’ unsafe behaviors |
| title_fullStr | SafetyGPT: An autonomous agent of electrical safety risks for monitoring workers’ unsafe behaviors |
| title_full_unstemmed | SafetyGPT: An autonomous agent of electrical safety risks for monitoring workers’ unsafe behaviors |
| title_short | SafetyGPT: An autonomous agent of electrical safety risks for monitoring workers’ unsafe behaviors |
| title_sort | safetygpt an autonomous agent of electrical safety risks for monitoring workers unsafe behaviors |
| topic | Generative artificial intelligence Risk identification Electric power production Multi-modal large language model Unsafe behaviors |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525002236 |
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