특허정보 빅데이터 분석을 통한 라스트마일 물류 유망기술 도출
Forecast of Promising Technology on Last Mile Logistics by Analyzing the Patent Information
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As the Covid-19 virus is continued, non-face-to-face is becoming commonplace and the importance of last-mile logistics delivery is growing. The purpose of this study is to derive promising technologies in the field of last mile logistics through patent analysis and to develop technology strategies based on them. From 1974 to 2020, a total of 9,894 patent information was extracted from patents registered in Korea, China, the United States, and Europe, and the final 5,609 patent data were analyzed through the valid patent screening process. In the analysis process, the latent dirichlet allocation (LDA) was applied and the time series analysis selected groups with high potential for future development. Afterwards, GTM (Generative Topographic Mapping) was applied to identify promising technologies through blank technologies. Last mile related technologies were “Automated operation technologies using robots”, “Supports related to classification and delivery”, “Low cost classification and transport optimization technologies”, “Monitoring technologies for safe delivery and management” and “Smart classification and storage operation technologies”. The technology fields presented in this study can be used to establish technology strategies to preempt companies’ market competitiveness and contribute to policy-making to foster and support promising areas of the government.
Abstract
As the Covid-19 virus is continued, non-face-to-face is becoming commonplace and the importance of last-mile logistics delivery is growing. The purpose of this study is to derive promising technologies in the field of last mile logistics through patent analysis and to develop technology strategies based on them. From 1974 to 2020, a total of 9,894 patent information was extracted from patents registered in Korea, China, the United States, and Europe, and the final 5,609 patent data were analyzed through the valid patent screening process. In the analysis process, the latent dirichlet allocation (LDA) was applied and the time series analysis selected groups with high potential for future development. Afterwards, GTM (Generative Topographic Mapping) was applied to identify promising technologies through blank technologies. Last mile related technologies were “Automated operation technologies using robots”, “Supports related to classification and delivery”, “Low cost classification and transport optimization technologies”, “Monitoring technologies for safe delivery and management” and “Smart classification and storage operation technologies”. The technology fields presented in this study can be used to establish technology strategies to preempt companies’ market competitiveness and contribute to policy-making to foster and support promising areas of the government.
참고문헌 (56)
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