Scholarlink

<p>The increasing use of refrigeration monitoring platforms in the food retail sector is crucial for enhancing operational efficiency and mitigating food waste. These systems are essential for ensuring food safety and quality, as they alert personnel to abnormal temperature readings, which are often caused by equipment malfunctions or operational issues. However, the resulting high volume of alarms poses a significant challenge, requiring tailored management strategies and continuous supervision by expert staff. This article presents an intelligent alarm labeling system based on neural networks that evaluates various alarm dimensions. The proposed system effectively detects recurrent patterns and determines a device’s status based on its temperature measurements. The system was validated using a massive monitoring dataset from different supermarkets in Catalonia, Spain. The results show that the system can detect recurrent alarms with 98% accuracy and correctly identify device states with 93% accuracy, enabling more effective temperature alarm management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A neural network-based system for automated refrigeration alarm management in food retail

  • 期刊论文

  • 发表日期：

    2026年3月1日

查看全文
  • Pau Ferrer-Cid,
  • Xavier Albets-Chico,
  • Jose M. Barcelo-Ordinas,
  • Jorge Garcia-Vidal

摘要

The increasing use of refrigeration monitoring platforms in the food retail sector is crucial for enhancing operational efficiency and mitigating food waste. These systems are essential for ensuring food safety and quality, as they alert personnel to abnormal temperature readings, which are often caused by equipment malfunctions or operational issues. However, the resulting high volume of alarms poses a significant challenge, requiring tailored management strategies and continuous supervision by expert staff. This article presents an intelligent alarm labeling system based on neural networks that evaluates various alarm dimensions. The proposed system effectively detects recurrent patterns and determines a device’s status based on its temperature measurements. The system was validated using a massive monitoring dataset from different supermarkets in Catalonia, Spain. The results show that the system can detect recurrent alarms with 98% accuracy and correctly identify device states with 93% accuracy, enabling more effective temperature alarm management.