Research On User Requirements Discovery And Design Strategies For Intelligent Home Appliance Products

With the development of technology and diversification of market products,the competitive space of enterprises has become saturated.In order to start and maintain profitable growth,enterprises should attach importance to the usability and functional design of new products,and provide personalized products that can meet different customer needs.At present,the demand of target customers in the smart home industry is showing a hidden trend of diversification and complexity.Due to limitations in manufacturing technology,budget,and other aspects,various requirements conflict on the same product,making it difficult to achieve them simultaneously.Therefore,in the process of designing and manufacturing products with complex functions,objective conditions should also be fully considered to achieve coordination and unification of the parameters of various functional modules.In view of this,this study aims at product design,uses behavioral analysis theory to study user use processes,uses text mining and natural language processing methods to analyze online feedback text acquisition functional requirements of e-commerce platform users,uses a deep neural network model to establish the relationship between design elements and functional requirements,and determines the final product design strategy through computer assistance.The main research content and main conclusions of this article are as follows:First,determine the basic functions of the research object.Through market research,it is determined that the research object is a floor sweeping robot in the field of smart home products.Through behavioral observation,user interviews,and voice thinking methods,it records its behavioral processes,patterns,and usage when performing usage activities,and collects various action information when users interact with the product,thereby capturing potential needs for product attributes and functions that are easily overlooked by users during the use of the product,It can provide user feedback and demand analysis to build an excellent complementary mechanism.Secondly,research a large amount of online feedback data closely related to product design to obtain more accurate functional requirements.Apply a topic model to analyze the usage feedback information of relevant shopping platforms,extract product topics,summarize the extracted topic keywords based on behavioral research results,and build a product functional feature set.Extracting feature pairs from online reviews through dependency syntax analysis,quantifying them based on an emotional dictionary,calculating the emotional intensity of product functional features,and then converting them into demand values for product features.Thirdly,select product samples and parameterize design elements.Obtain the design attribute information of product samples through the Internet,use the method of manually extracting parameters in combination with the product information released by the manufacturer,classify and number the product design elements of the samples,and establish a product design element space.Finally,by constructing a product mapping model,a detailed improvement strategy for the sweeping robot product is obtained.Consider user functional requirements and product design element work as a categorical choice between the needs of different customers and the configurable attributes of the product,that is,based on the habits of various consumers,configurable attributes that can effectively address the needs,and consider this as an output,combining the results and objective conditions of customer needs,to arrive at a final product design improvement strategy.The common needs extraction method based on user usage behavior and the differentiated needs extraction method based on user feedback and comments in the context of Internet big data proposed in this article effectively solve the problem of lag in demand sources in traditional user needs identification methods,and also provide a theoretical basis for the summary and summarization of fine grained demand feature terms;Building a mapping model of multi-layer neural networks based on one hop coding can better explore the corresponding relationship between user functional requirements and product design elements,summarize multiple attribute complex product design improvement strategies,and help designers,enterprise researchers,and decision makers design excellent products with stronger competitiveness with the goal of lower cost,more efficiency,and higher satisfaction,It is conducive to better product development in the smart home industry.