A non-invasive household electrical load identification and safety warning method and system

By employing a collaborative architecture of high-precision feature extraction at the edge and federated learning in the cloud, combined with a user feedback optimization mechanism, the problems of blind spots in the identification of new devices and device aging in non-intrusive load identification have been solved, realizing the application of intelligent identification and safety early warning technology for household electricity use.

CN122418979APending Publication Date: 2026-07-17XIHUA UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XIHUA UNIV
Filing Date
2026-04-17
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing non-intrusive load identification technologies struggle to identify new devices in complex household electrical environments, and their accuracy declines due to device aging. Furthermore, the lack of user interaction and feedback loops leads to the accumulation of identification errors and a poor user experience.

Method used

It adopts a collaborative architecture of high-precision feature extraction at the edge and incremental update through cloud-based federated learning, combined with a user feedback-guided recognition optimization mechanism. It extracts multi-dimensional load features through a sliding window, detects load switching events, and performs incremental learning and user feedback correction in the cloud to dynamically update the model.

Benefits of technology

It enables effective identification of new equipment and adaptation to equipment aging, improves identification accuracy and user participation, prevents error accumulation, and ensures the system's identification stability and safety early warning capabilities during long-term operation.

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Abstract

The application provides a non-intrusive household electrical load identification and safety warning method and system, belonging to the technical field of non-intrusive load monitoring; the method comprises: collecting total road electric signals on the edge side, extracting multi-dimensional load characteristics and detecting load switching events; when an unmatched event is detected, triggering a cloud collaborative federated learning incremental update mechanism to realize global model optimization under the premise of protecting privacy; when the identification result is uncertain, a similar device candidate list is pushed to the user, and the model parameters and feature weights are updated according to the user correction feedback; the system comprises: an edge side device, a cloud platform and a user terminal, which jointly constitute an end-edge-cloud-user collaborative closed loop architecture; the application realizes adaptive identification of new devices, stable maintenance of long-term identification accuracy and positive optimization cycle of user participation, effectively improving the intelligent level of non-intrusive load identification and the safety of household electricity.
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