An electric bicycle charging identification method and system for a smart meter

By collecting the voltage and current at the meter's inlet, extracting key features, and performing hierarchical identification and secondary judgment, the problem of high false alarm rate and inaccurate identification of electric bicycle charging at home has been solved, and high-confidence electric bicycle charging event identification has been achieved.

CN122185950BActive Publication Date: 2026-07-21WASION GROUP HLDG
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
WASION GROUP HLDG
Filing Date
2026-05-18
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing methods for identifying electric bicycles charging at home suffer from high false alarm rates and inaccurate identification.

Method used

By collecting the voltage and current at the main electricity meter inlet in real time, the key features of electric bicycle charging at home are extracted, different electricity use scenarios are divided, the validity of features within the scenario is determined, the spatiotemporal information of pulse events is correlated and high-confidence scenarios are identified hierarchically, low-confidence fuzzy events are screened, and a second determination is made through a dynamic correction mechanism of posterior distortion waveform density analysis.

Benefits of technology

It significantly reduced the overall false alarm rate and improved the accuracy of identifying electric bicycles charging at home.

✦ Generated by Eureka AI based on patent content.

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    Figure CN122185950B_ABST
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Abstract

The application discloses a kind of electric bicycle charging identification method for intelligent electric meter, comprising the following steps: real-time acquisition total electric meter inlet voltage and current;Based on the voltage and current of the total electric meter inlet collected, extract the key features of electric bicycle charging identification;According to the key features extracted, different power consumption scenarios are divided, the effectiveness of the features in the scene is determined, the spatiotemporal information of pulse events is associated and high confidence scenarios are identified in layers, electric bicycle charging events are judged, and low confidence fuzzy events are screened out;For the fuzzy events marked as low confidence, a secondary determination is made based on the dynamic correction mechanism of posteriori distortion waveform density analysis to determine whether the fuzzy events are electric bicycle charging events.The application also discloses an electric bicycle charging identification system for intelligent electric meter.The application solves the technical problems of high false alarm rate and inaccurate identification of existing electric bicycle charging identification.
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