Research And Application Of Intelligent Wardrobe Design Based On Dual Data Source Decision

In the context of the people's increasing demand for a better life,the product design schemes of household products relying on traditional manufacturing can no longer match the gradual enrichment of life needs,and traditional furniture design models have encountered great problems and challenges.First of all,designers cannot accurately capture diversified user needs in the process of designing output,and it is difficult to dig out the design elements required for product design innovation.Secondly,there are more and more design elements involved in the life cycle of smart home products,which puts forward higher requirements for designers' personal overall perspective and knowledge reserves.In response to such problems and challenges,by studying the relationship between product design decision-making methods based on e-commerce data analysis and supplemented by life cycle analysis elements and smart home product design patterns,explore the relationship between dual data source decision-making and smart home products.A new model of combined design.After re-examining the product design under the life cycle and the user value in the e-commerce platform,data crawling,data analysis,data visualization,life cycle hierarchical analysis and other concrete methods are applied to analyze the elements applied in product design.The first step is to analyze the user's perceptual evaluation data for the product through a mining mechanism based on text data,from which to mine the user's age,gender,preference distribution,and various aspects of the product feedback,so as to capture the user's demand for the product in real time,And then transformed into design elements applied in the product design process.The second step is to organize cross-scoring by experts in different fields according to the life cycle level analysis model to enrich the design elements derived from the decision-making method based on e-commerce data.The final output is a smart home product design plan that meets the diverse needs of users' consumer psychology,market development trends,corporate profitability,and environmental protection.Take the smart wardrobe design plan as an example.Through the analysis of ecommerce data,we can accurately explore the current development trend of smart wardrobes,redefine the user's psychological feedback on the use of smart wardrobe products,and combine the life cycle level analysis on product manufacturing,operation,and recycling links.The basic elements are supplemented,so that the smart wardrobe design plan breaks the limitations brought by the personal cognitive level,grasps the overall coordination of the design plan,and produces the design plan from a global perspective,so as to prove the combination of the two types of decision-making methods.Complementary effectiveness.The combination of the two decision-making methods provides a new idea for the output of the design scheme.