Study Reveals Innovations in Earthquake Experience Platform Design
A recent study conducted to gather user requirements for an earthquake experience platform has unveiled critical approaches to optimize design efficiency. Through a comprehensive methodology involving over 270 participants, the research incorporated interviews and questionnaires to assess user needs.
In the initial data acquisition phase, 92 questionnaires were collected from individuals aged 18-45, ensuring diverse gender representation (53.3% male and 46.7% female). Following this, a requirement classification phase garnered 100 valid responses in a subsequent survey to refine user specifications using the Kano model. This model categorizes user needs into basic, expected, and excitement factors, aiding designers in prioritizing features that enhance user satisfaction.
The study employed a novel digital representation method to facilitate the identification of functional features in product design. By leveraging Natural Language Processing (NLP) and machine learning, researchers classified affective requirements and matched them to existing technical patents. This innovative approach significantly enhances design efficiency and reduces workload through improved retrieval systems, driven by similarity algorithms.
Furthermore, the methodology involved constructing the Grey Relational Analysis (GRA) matrix and a House of Quality (HOQ) framework to systematically translate user demands into functional features. The encoded data was complemented by advanced vectorization techniques that transformed functional features into digital formats, enabling intelligent matching with patent technologies.
Findings indicate that this structured approach not only streamlines the product development process but also promotes innovative design by integrating cross-domain technologies. The results reinforce the importance of aligning user preferences with technical capabilities, laying the groundwork for future enhancements in product design frameworks.
This research was ethically approved and highlights a significant leap forward in designing technology that meets real-world challenges related to natural disasters.
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