An intelligent monitoring method for kitchen appliance hazards combined with edge computing

Abstract

The invention discloses an intelligent monitoring method for kitchen appliance danger combined with edge computing, which comprises the following steps: deploying different types of sensors in different equipment and different positions in the kitchen, forming the above sensors into a kitchen appliance sensor network, and collecting relevant information in real time. All kinds of data; the kitchen appliance sensor network transmits the collected data to the edge computing nodes deployed nearby, and the edge computing nodes include data preprocessing modules, anomaly detection modules and fault diagnosis modules; edge computing nodes also store deep learning network models and The knowledge base required for knowledge-based fault reasoning; the kitchen appliance sensor network used in the present invention is compatible with a variety of sensors, and can collect various data in real time for a variety of hidden dangers of kitchen appliances, and fully obtain a full range of potential danger sources; The processing of kitchen appliance data by edge computing nodes deployed close to the data source is conducive to reducing system delay and improving response speed.

Description

Technical field [0001] The present invention relates to edge computation and artificial intelligence technology, and in particular relates to a method of monitoring hazardous intelligent electric kitchen one binding edge computing. Background technique [0002] Abnormality detection and fault diagnosis is important to protect the safety of electric kitchen equipment, there are some prior art monitoring methods based on the conventional means: e.g. based STM32 microcontroller kitchen hazardous gas detection system core element, which is disposed through the CO , a CH3 other gas sensors, the gas concentration is detected, and whether there is abnormality is determined according to the sensitivity and the threshold value set in advance, the last remote alarm by sending an alert message and push-button telephone. However, using the traditional method of kitchen anomaly detection and fault diagnosis relies on human experience, accuracy and low intelligence, when the cause of kitchen e...

AI Technical Summary

Problems solved by technology

However, at present, the abnormal detection and fault diagnosis of kitchen appliances using traditional methods mainly rely on manual experience, with low accuracy and intelligence. The safety of life and property requires more intelligent means

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