论文标题
智能团体餐饮的饮食数据获取成本
Cost of Dietary Data Acquisition with Smart Group Catering
论文作者
论文摘要
饮食数据管理的需求正在增长，因为公众对食物摄入量的认识。结果，通过射频识别（RFID）或计算机视觉（CV）解决方案收集饮食数据的智能食堂的部署越来越大。由于在两种情况下都涉及人工劳动，因此人力分配对数据质量至关重要。在低估了人力要求的地方，数据质量将被损害。本文使用了基于从多个智能食堂收集的实际数据进行数值模拟，研究了饮食数据质量与投资的人力之间的关系。我们发现，在基于RFID和基于简历的系统中，饮食数据获取的长期成本都由人力主导。我们的研究提供了对饮食数据获取的成本组成的全面理解，以及对未来成本有效系统的有用见解。
The need for dietary data management is growing with public awareness of food intakes. As a result, there are increasing deployments of smart canteens where dietary data is collected through either Radio Frequency Identification (RFID) or Computer Vision(CV)-based solutions. As human labor is involved in both cases, manpower allocation is critical to data quality. Where manpower requirements are underestimated, data quality is compromised. This paper has studied the relation between the quality of dietary data and the manpower invested, using numerical simulations based on real data collected from multiple smart canteens. We found that in both RFID and CV-based systems, the long-term cost of dietary data acquisition is dominated by manpower. Our study provides a comprehensive understanding of the cost composition for dietary data acquisition and useful insights toward future cost effective systems.
