Logistics Service Design Of Fresh Products Based On Kansei Engineering

With the rapid development of the Internet and mobile technology more mature,more and more users are more inclined to choose online shopping,and fresh products as a daily life in the high frequency of just needed products,consumers online shopping habits in the gradual formation.In addition to the problem of fresh cold chaining foundation,the core of fresh market is mainly the flow synergy and supply chain capacity improvement.However,the key to affect the user experience and satisfaction is whether the user's perception of fresh product logistics services is satisfied,reflected in the timely delivery,transport status can be tracked,offline delivery satisfaction and so on.Therefore,how to deeply explore the logistics service factors that consumers are more interested in when the main research direction of this paper.Kansei engineering as a human engineering technology,more and more have to be applied to the design of services,there are already scholars will be emotional engineering methods for logistics services research,so this paper will be fresh product logistics services as a new design domain,explore property space and semantic space.Summarize the research in this article as follows:(1)Choose the design domain for fresh product logistics services,on the basis of Kansei engineering,with user online comments as the material,fresh product logistics services research.In addition,the method of selecting attribute property space and semantic space is optimized.This paper builds the property space of fresh logistics service based on local community discovery algorithm.The local community discovery algorithm is based on the seed node to build the space without knowing the global node,and considers the weighted influence of the neighbor node on the space construction,which makes the property space construction more accurate.At the same time,this paper also according to the dependent synth to carry out the construction of semantic space,dependent synth from the point of view of word and dependent relationship can be more accurate positioning of semantic space.(2)Considering the particularity of fresh comments,this paper puts forward the expansion method of reverse property space for fresh comments.In addition to the process of finding percessive words according to attribute words,fresh flu words directly based on fresh product names will be extracted as semantic space seed words,and property words will be expanded in reverse according to local community discovery and dependent synth relationship,making the final property space and semantic space more complete.(3)In terms of the connection between property space and semantic space,compared with the traditional questionnaire method and window co-existing method,this paper also uses dependent synths to associate good attribute words and sensual words,and stores them in the form of contact triples,and then builds correlation tables and association trees based on the connection between property space and semantic space.(4)It is mainly compared and verified from two aspects: firstly,it measures the results of property space and the results of semantic space extracted in this paper,and secondly,it measures the connection between property space and semantic space.To verify the extraction results,this paper adopts and directly extracts the method to compare,and from the diversity,effectiveness,concentration of the three indicators of specific comparison,the results show that the DP-based method in this paper than the traditional DE-based method from the property space point of view,in terms of effectiveness increased by 18.776 %,in the concentration increased by 3.659 %.From the perspective of perceptive spatiality,the total score of DP-based methods was 1.75 times higher than that of DE-based methods by 13.482 %,the effectiveness increased by 11.449 %,and the concentration was increased by 3.083 %.To verify the correlation relationship,this paper takes the comparison with the window co-existing method,and verifies the correlation of the five dimensions of reliability,assurance,responsiveness,empathy and information on the same data set,and the results show that the correlation accuracy based on dependent synth is higher.In general,this paper introduces the method of Kansei engineering into the design of fresh product logistics service,optimizes the scheme of finding property space and semantic space based on local community discovery and dependent synth,proves that the method based on dependency correlation can establish the connection between property space and semantic space more accurately,and finally realizes the more in-depth exploration of raw product logistics service elements.