Earticle

이 연구는 제4차 산업혁명 시대에서 빅 데이터를 활용한 재난관리정책의 방향을 제시하는 데 목적이 있다. 이는 제4차 산업혁명 시대의 첨단 기술을 활용하여 재난안전서비스를 확대하기 위해서는 인공지능을 적용한 사물인터넷, 클라우드 컴퓨 팅, 빅 데이터 분석, 블록체인, 모바일, 드론, 로봇 등 디지털 지능형 융·복합 기술을 재난관리 과정에 적용하는 것이 요구된 다. 빅 데이터를 활용한 재난관리정책의 방향은 다음과 같다. 첫째, 재난관리에 필요한 정확한 데이터를 확보하고 분석하는 것이 원활해야 한다. 둘째, 재난관리정책과 다양한 빅 데이터 분석 및 학습 시스템이 연계되어야 한다. 셋째, 초연결 제4차 산업혁명 시대에서 공공데이터 활용과 시민의 사회문제 해결에의 참여가 확대되어야 한다. 넷째, 재난관리 동원 물자 및 장비 수송에 드론, 무인자동차, 운반로봇 등을 활용하는 것이 가능해야 한다. 다섯째, 빅 데이터 분석과 관련된 전문 인력의 양성, 활용, 배출이 지속적으로 이루어져야 한다. 여섯째, 빅 데이터 분석과 핵심 기술에 대응하기 위한 사이버 보안기술의 발전이 필요하다.

This study aims to suggest the direction of disaster management policy utilizing big data in the era of the 4th industrial revolution. It is necessary to apply digital intelligent convergence technologies such as Internet of Things (IoT), cloud computing, big data analysis, block-chain, mobile, drones, and robots with artificial intelligence to the disaster management process in order to expand disaster safety services by utilizing advanced technologies of the 4th industrial revolution. The directions of disaster management policy utilizing big data are as follows. First, it should be smooth to secure and analyze accurate data required for disaster management. Second, disaster management policies and various big data analysis and learning systems should be linked. Third, in the era of the hyper-connected 4th industrial revolution, the use of public data and the participation of citizens in solving social problems should be expanded. Fourth, it should be possible to utilize drones, unmanned vehicles, and transport robots to transport disaster management mobilization materials and equipment. Fifth, the cultivation, utilization, and discharge of specialized human resources related to big data analysis should be continued. Sixth, it is necessary to develop cyber security technology to respond to big data analysis and core technologies.

생활안전사고는 일상생활에서 발생하는 안전사고를 통칭하여 사용된다. 안전에 대한 국민적 관심 증가에도 불구하고, 생활안전사고와 관련한 학술적 논의는 다소 미흡한 것으로 확인되었다. 본 연구는 생활안전사고에 대한 실태를 파악하는데 중점을 두고 있으며, 그 일환으로 발생원인을 유형화하고자 한다. 이를 위해 소방안전본부로부터 수집한 구조구급 데이터베 이스를 이용하였으며, 텍스트마이닝을 통해 원인 관련 주제어를 추출하여 군집분석과 의미연결망 분석을 수행하였다. 먼저 사고 관련 주제어를 사고 유형에 따라 군집화하였으며, 군집분석 결과는 생활안전사고가 일상생활의 경미한 안전사고뿐만 아니라 재난, 범죄 등을 복합적 사고에 기인하고 있음을 보여주었다. 다음은 의미연결망 분석을 통해 사고 소견서 문장 내 동시출현빈도가 높은 단어유형을 확인하였다. 이를 통해 의미연결망 분석이 안전사고의 종류를 파악할 뿐 아니라 사고의 원인, 환자의 상태 및 특징 등의 유형화에도 활용될 수 있음을 알 수 있었다. 본 연구의 성과는 생활안전사고의 발생원인을 과학적으로 파악함으로써 안전정책의 실효성 제고에 기여할 것으로 기대한다.

Life safety accidents are commonly used as safety accidents that occur in everyday life. Despite the increase of interests for safety, academic discussions on life safety accidents were confirmed to be insufficient. This study focuses on understanding the actual situation of life safety accidents, and as part of that, this study categorizes the causes of occurrence. To this end, a rescue first aid database collected from the Fire and Safety Headquarters was used, and through text mining, keywords related to the cause were extracted and cluster analysis and semantic network analysis were performed. First, accident-related keywords were clustered according to the type of accident, and the cluster analysis results showed that life safety accidents are caused by complex accidents such as disasters and crimes as well as minor safety accidents in daily life. Next, through semantic network analysis, the types of words with a high frequency of simultaneous appearance in the sentence of the accident opinion were identified. Through this, it was found that semantic network analysis can be used not only to identify the type of safety accident, but also to categorize the cause of the accident, the patient's condition, and characteristics. The results of this study are expected to contribute to enhancing the effectiveness of safety policies by identifying the causes of life safety accidents.

Disasters in modern society are unpredictable, complex and normalized. Although human society has naturally transitioned to the era of intelligence and resource integration with the dividends of the fourth industrial revolution, to deal with extremely uncertain future disasters, the country needs to use and Establishing a reliable disaster response system and system will not only help its own people, but also provide effective disaster early warning, disaster response, and disaster recovery through national collaboration mechanisms. This study will conduct an in-depth analysis of the basic core theory that constitutes the emergency evacuation system, that is, disaster information science, conduct a comparative study of the national disaster management systems and platforms of China and South Korea, analyze China's lack of development in this field, and provide improvement suggestions.

With the rapid development of artificial intelligence technology, it is urgent to promote the modern governance level of diversified big data of people's livelihood and improve the governance capacity of government departments. Compared with the traditional form of government decision-making, AI-assisted government decision-making is a public choice behavior based on complex big data, covering the decision-making subject, organizational system and process, which has important strategic significance. In the face of powerful artificial intelligence, service leadership focus on employees, not because employee efficiency is inferior to AI and consider the possibility of alternative employees, service leaders pay attention to the growth and development of employees, and actively create conditions for it, to motivate employees to learn knowledge of artificial intelligence, using AI to improve their skills. Therefore, in the era of artificial intelligence, service-oriented leadership has an extremely important position.