Developing a three-dimensional function evaluation model integrating scenario, function, and user requirements for intelligent child safety seat design

The rapid development of intelligent child safety seats (CSSs) has introduced advanced sensing, adaptive, and interactive functions that extend beyond conventional safety standards. However, existing evaluation methods remain largely focused on physical protection, with limited attention to the performance of intelligent functions across diverse real-world scenarios and their alignment with the needs of children and caregivers. This limitation may result in a mismatch between designed functions and actual usage requirements, particularly when scenario context, functional performance, and user needs are not systematically integrated. This study proposes a Scenario–Function–User (S-F-U) evaluation model that integrates scenario analysis, functional classification, and user-centered evaluation into a unified framework. Representative scenarios were modeled to quantify the impact of contextual factors on functional requirements, and user requirements were collected through questionnaires, interviews, and user testing to construct a multidimensional evaluation indicator system. A cross-scenario functional optimization scheme was generated and validated using Conjoint Analysis with orthogonal experimental design. The validation results, as indicated by Pearson’s r = 0.734 (p < 0.001) and Kendall’s τ = 0.628 (p < 0.001), revealed a statistically significant association, but not complete alignment, between user preference rankings derived from Conjoint Analysis and functional priority rankings generated by the S-F-U model. This suggests that consumer preference rankings may not fully correspond to safety-driven functional priorities. Whereas Conjoint Analysis reflects perceived utility, the proposed S-F-U model emphasizes functional indispensability and system-level safety dependencies. The findings demonstrate that the S-F-U framework provides a rigorous and user-centric approach for identifying design deficiencies and optimizing intelligent functions, offering effective guidance for the development of next-generation intelligent CSS.