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In view of the current prosperity of China’s fresh market transactions, fresh e-commerce enterprises in the end of the construction of the logistics network of the pain point problem, this study of fresh e-commerce in the “last mile” distribution of site planning issues. This paper focuses on a fresh food ecommerce enterprise’s end distribution network and explores the key influencing factors in fresh food product distribution from three dimensions: timeliness, economy and service quality. And then establishes a mathematical model for selecting community stores for end distribution of fresh food e-commerce enterprises with the objectives of minimizing input costs and maximizing customer satisfaction. The model takes into account the costs of building and operating community stores and the transportation costs of distribution. In the model introduces three types of penalty costs due to site selection variability and transforms the model from a dual-objective to a single-objective model. The study takes the example of store location selection in 35 neighborhoods in region B by enterprise A and designs an immune optimization algorithm to solve the model. Based on the results of the algorithm, we verify the rationality and validity of the model, and obtain the optimal solution that ensures customer satisfaction and minimizes all costs.

The COVID-19 outbreak in early 2020 not only had a significant impact on China at the macro level, but also triggered changes in public psychology and irrational behavior at the individual level, one of the typical features of which was the panic buying behavior exhibited by residents during the outbreak. Based on Perceived Risk theory, emotion infection theory and information processing theory, this paper investigates the influence of herd mentality on panic buying behavior and the mediating role of Perceived Risk and information overload, and validates the model by collecting data from 326 residents through a questionnaire. The results show that herding mentality, Perceived Risk, and information overload all have positive effects on panic buying; Perceived Risk and information overload partially mediate the effect between herding mentality and panic buying, and information overload carries more mediating effects. These results suggest that higher levels of herding, Perceived Risk, and information overload can intensify individuals’ panic buying and cause further irrational buying behavior. In response to the findings, this paper also proposes countermeasures to deal with panic buying from three aspects: individuals, media, and government.

With deep great breakthroughs of deep learning in the field of computer vision, the field of audio recognition has gradually introduced deep learning methods and achieved excellent results. These results are mainly for speech and music recognition research, and there is very little research on environmental sound classification. In recent years, people have begun to expand the research object of deep learning to the environmental sound, and achieved certain results. In this paper, we use ESC-50 as our test set, based on the SoundNet network and EnvNet network to propose a feature fusion method[1]. After the features extracted by SoundNet and EnvNet were merged, they were classified using fusion features. Experimental results show that this method has better classification accuracy for the recognition of environmental sounds than using either of the two networks separately for classification.