Needs Analysis And Design Through Data Fusion

User needs serve as the central driving force in product design.A deep understanding of user requirements is crucial for designing products that meet their expectations.Traditional small data methods exhibit drawbacks such as delays,limited data volume,and low efficiency when analyzing needs,while Large data methods suffer from fragmentation and superficial analysis.To overcome the limitations of both approaches,this study compares user needs data obtained through each method,explores a framework for integrating Large and small data in user needs analysis,and establishes a product design process based on the Analytic Hierarchy Process(AHP)and Quality Function Deployment(QFD)theory.Using intelligent drinking water products as a case study,the research first collects user review data as a source of Large data and clusters these reviews using Latent Dirichlet Allocation(LDA)topic modeling.This process outputs user needs dimensions and indicators based on the Large data approach.Subsequently,the Dynamic Topic Model(DTM)is employed to analyze the dynamic trends of user needs over years and seasons.For small data,the study primarily uses questionnaires and user interviews to obtain user needs and applies SPSS data analysis software to conduct one-way ANOVA and factor analysis,exploring differences in user needs.By comparing the results of data obtained from both methods,the study proposes a path for integrating dimensions and indicators,dynamic and static aspects,as well as commonalities and differences,ultimately completing the acquisition and analysis of user needs through the integration of Large and small data.This process transforms user needs into product features,culminating in the design of intelligent drinking water products.The main research findings include:(1)Besides the five common needs dimensions(function,quality,aesthetics,price,and service),Large data reveals a product operation needs dimension that users focus on.Based on DTM time-series analysis,it is discovered that user attention to service needs increases over time,while attention to product functionality and quality needs declines.Seasonally,users pay the most attention to product functionality during winter.(2)Small data unveils user needs concerning health care,and one-way ANOVA and user interviews are used to study differences in drinking habits among various age groups.(3)By comparing user needs data analysis results from both methods,the study achieves integration in dimensions and indicators,dynamics and statics,and commonalities and differences.Integrating Large and small data provides a more comprehensive and in-depth analysis of user needs than either method alone.(4)The AHP method determines the weight of user needs,mitigating the subjectivity in confirming the importance of needs in QFD.This process translates user needs into specific design features,culminating in a user needs-driven design for an intelligent instant hot water dispenser.This study offers a valuable theoretical reference for user needs analysis and provides a design strategy and data support for the development of future intelligent drinking water products.