关键词: 物流工程, 语义风险, 主题模型, 快递货品, 法院判决书, 模糊聚类

Abstract: To improve the risk monitoring of express transportation and reduce the possibility of urban security incidents caused by risky express goods, this paper transforms the description of express delivery goods into the quantitative representation of risks based on the semantic mining method. The study provides a quantifiable and objective index basis for risk evaluation of express transportation. Based on the court verdict data provided by the Web big data resources, this study correlates the goods descriptions with the verdicts and explores the goods risk topics through the hidden Dirichlet distribution model. The fuzzy C-means clustering method is also incorporated to realize the quantitative representation and flexible division of the semantic risk of express delivery goods. Different from the traditional method which relies on the subjective judgment of the inspector after checking the fixed contraband list, the proposed method fully excavates the transferable knowledge from the Web text data and applies it to different types of express delivery goods, which can effectively avoid missing inspections and errors caused by manual evaluation. The results show high accuracy rate and low false alarm rate, and the obtained risk evaluation value is no longer“0”or“1” representing the judgment of“yes”or“no”. The proposed method helps to make diversified and focused early warning and response measures to avoid risks in express deliveries.

Key words: logistics engineering, semantic risk, topic model, delivery goods, court verdict, fuzzy clustering