Developed during a joint initiative with the Technical University of Munich’s Digital Product School and the Güntner Group, FrostSense is an intelligent defrosting framework integrating real-time sensor data, machine learning algorithms, and user-focused interaction design. Through more than 30 stakeholder interviews, the team identified critical pain points in traditional, schedule-based defrost cycles—leading to the creation of a responsive system that detects frost buildup precisely and automates defrosting when needed. A cross-cultural team of 15 managed over 200 tasks using agile methods, refining both hardware and software components to achieve up to 20% energy savings. This project not only enhances operational efficiency but also ensures traceability for meeting stringent industrial and regulatory standards.

在慕尼黑工业大学 Digital Product School 与 Güntner 集团的校企合作项目中，我们开发了 FrostSense——一套融合实时传感器数据、机器学习算法和用户体验设计的智能除霜框架。通过 30 多次利益相关者访谈，项目团队深度剖析了传统定时除霜模式的弊端，进而打造出一套可自动侦测霜层并在需要时触发除霜的高效系统。我们带领 15 人的跨文化团队，采用敏捷方法管理超过 200 项任务，对软硬件进行反复迭代和优化，成功实现了最高可达 20% 的能耗节省。不仅如此，FrostSense 也在工业监管和质量追溯方面表现卓越，充分满足严格的生产和合规需求。