Appliance Anomaly Detection Using Environment and State Learning Model
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Solution Overview
Problem
Existing home appliance remote monitoring systems fail to consider the actual environment and appliance state within a residence, leading to ineffective anomaly detection.
Innovation Solution
An anomaly detection method using a learning model generated through machine learning, which takes into account first and second environment and state information to detect anomalies in appliances based on actual residence conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a learning model is used to detect anomalies by considering environment and state information, then anomaly detection accuracy is improved, but device complexity increases
Solution Approach 1:
The anomaly detection system is segmented into distinct functional modules: a learning model acquisition unit that obtains pre-trained models, an information acquisition unit that collects environment and state data, and a detection processing unit that executes anomaly detection. This segmentation allows the complex anomaly detection function to be implemented through coordinated simple modules, improving detection accuracy while managing system complexity through modular architecture.
2Reliability
If environment information and state information are collected and processed, then anomaly detection reliability is improved, but loss of time increases
Solution Approach 1:
The learning model is acquired and prepared in advance before actual anomaly detection is needed. The model undergoes preliminary training and validation phases where it learns from historical environment and state information. This preliminary action ensures the model is ready for immediate deployment, improving detection reliability while minimizing real-time processing delays when actual anomalies need to be detected.
Data Source
AI summary
An anomaly detection method includes: receiving a notification that indicates that a state of an appliance provided in a residence has undergone a change and acquiring second environment information that includes at least second person presence information that indicates presence or absence of a person in the residence at a second time point at which the notification was received and second time point information that indicates the second time point; and executing detection processing for detecting an appliance anomaly by inputting, into a learning model, second state information that indicates the state of the appliance after the change indicated by the notification and the second environment information acquired.