GTM 기반 특허 분석을 통한 콜드체인 운송 기술의 발전 예측 및 기술 공백 식별 연구

This study conducts an in-depth analysis of patent information in the field of cold chain transportation to identify critical technologies and forecast their future development trajectories. As an essential component in ensuring the quality of perishable goods, cold chain logistics currently faces major challenges—including inadequate infrastructure, uneven technological standards, high operational costs, and low levels of digitalization. To address these issues, we propose a predictive framework that integrates deep learning-based semantic modeling with advanced patent analytics. Through extensive mining of patent texts, the model not only pinpoints core technologies within the domain but also anticipates their evolutionary pathways. Moreover, by leveraging the Generative Topographic Mapping (GTM) technique, the study reveals latent technological gaps. The findings offer both theoretical insights and empirical evidence to support strategic innovation among cold chain enterprises and inform industrial policy development by regulatory authorities.