{"title":"针对工人安全的多模式集合人工智能模型管理架构","authors":"","doi":"10.1016/j.shaw.2024.04.008","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>Following the Republic of Korea electric power industry site-specific safety management system, this paper proposes a novel safety autonomous platform (SAP) architecture that can automatically and precisely manage on-site safety through ensemble artificial intelligence (AI) models. The ensemble AI model was generated from video information and worker's biometric information as learning data and the estimation results of this model are based on standard operating procedures of the workplace and safety rules.</p></div><div><h3>Methods</h3><p>The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana).</p></div><div><h3>Results</h3><p>The functional evaluation shows that the main function of this SAP architecture was operated successfully.</p></div><div><h3>Discussion</h3><p>The proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.</p></div>","PeriodicalId":56149,"journal":{"name":"Safety and Health at Work","volume":"15 3","pages":"Pages 373-378"},"PeriodicalIF":3.5000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2093791124000325/pdfft?md5=85a28d31668f052e3b844c7c2aaa717c&pid=1-s2.0-S2093791124000325-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Management Architecture With Multi-modal Ensemble AI Models for Worker Safety\",\"authors\":\"\",\"doi\":\"10.1016/j.shaw.2024.04.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><p>Following the Republic of Korea electric power industry site-specific safety management system, this paper proposes a novel safety autonomous platform (SAP) architecture that can automatically and precisely manage on-site safety through ensemble artificial intelligence (AI) models. The ensemble AI model was generated from video information and worker's biometric information as learning data and the estimation results of this model are based on standard operating procedures of the workplace and safety rules.</p></div><div><h3>Methods</h3><p>The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana).</p></div><div><h3>Results</h3><p>The functional evaluation shows that the main function of this SAP architecture was operated successfully.</p></div><div><h3>Discussion</h3><p>The proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.</p></div>\",\"PeriodicalId\":56149,\"journal\":{\"name\":\"Safety and Health at Work\",\"volume\":\"15 3\",\"pages\":\"Pages 373-378\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2093791124000325/pdfft?md5=85a28d31668f052e3b844c7c2aaa717c&pid=1-s2.0-S2093791124000325-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Safety and Health at Work\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2093791124000325\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Safety and Health at Work","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2093791124000325","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
摘要
引言 根据大韩民国电力行业现场特定安全管理系统，本文提出了一种新型安全自主平台（SAP）架构，该架构可通过集合人工智能（AI）模型自动、精确地管理现场安全。方法通过 Hadoop 生态系统中的 Kafka/NiFi、Spark/Hive、HUE 和 ELK（Elasticsearch、Logstash、Kibana）设计并实现了集合人工智能模型。结果功能评估表明，该 SAP 架构的主要功能运行成功。
Management Architecture With Multi-modal Ensemble AI Models for Worker Safety
Introduction
Following the Republic of Korea electric power industry site-specific safety management system, this paper proposes a novel safety autonomous platform (SAP) architecture that can automatically and precisely manage on-site safety through ensemble artificial intelligence (AI) models. The ensemble AI model was generated from video information and worker's biometric information as learning data and the estimation results of this model are based on standard operating procedures of the workplace and safety rules.
Methods
The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana).
Results
The functional evaluation shows that the main function of this SAP architecture was operated successfully.
Discussion
The proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.
期刊介绍：
Safety and Health at Work (SH@W) is an international, peer-reviewed, interdisciplinary journal published quarterly in English beginning in 2010. The journal is aimed at providing grounds for the exchange of ideas and data developed through research experience in the broad field of occupational health and safety. Articles may deal with scientific research to improve workers'' health and safety by eliminating occupational accidents and diseases, pursuing a better working life, and creating a safe and comfortable working environment. The journal focuses primarily on original articles across the whole scope of occupational health and safety, but also welcomes up-to-date review papers and short communications and commentaries on urgent issues and case studies on unique epidemiological survey, methods of accident investigation, and analysis. High priority will be given to articles on occupational epidemiology, medicine, hygiene, toxicology, nursing and health services, work safety, ergonomics, work organization, engineering of safety (mechanical, electrical, chemical, and construction), safety management and policy, and studies related to economic evaluation and its social policy and organizational aspects. Its abbreviated title is Saf Health Work.
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