The invention relates to an elevator abnormal running identification method and system. The method comprises the following steps of a, acquiring acceleration data in an elevator running process; b, calibrating normal samples and abnormal samples in the acceleration data; c, performing data cleaning on the acceleration data; d, performing deep learning identification and machine learning identification on the acceleration data being subjected to the data cleaning; and e, fusing results obtained through identification in the step (d), and judging whether an elevator runs abnormally or not according to the fused results. According to the elevator abnormal running identification method and system, the acquired data are subjected to the deep learning identification and the machine learning identification respectively, and the identification results are fused finally in order to judge whether the data are abnormal or not according to the fused results, so that the detection accuracy and themodel robustness can be improved.

    本发明涉及一种电梯异常运行识别方法及系统,方法包括以下步骤:a、采集电梯运行过程中的加速度数据;b、标定所述加速度数据中的正常样例和异常样例;c、对所述加速度数据进行数据清洗;d、对数据清洗后的加速度数据分别进行深度学习识别和机器学习识别;e、将所述步骤(d)中识别得到的结果进行融合并根据融合后的结果判断电梯是否异常运行。本发明对采集到的数据分别进行深度学习识别以及机器学习识别,并最终将识别结果进行融合,从而根据融合后的结果判断数据是否异常,如此可提高检测的精度和模型的稳健性。


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    Titel :

    Elevator abnormal running identification method and system


    Weitere Titelangaben:

    电梯异常运行识别方法及系统


    Beteiligte:
    ZHANG XINGFENG (Autor:in) / WAN MIN (Autor:in) / CAI WEIWEI (Autor:in) / JIN XUZHE (Autor:in)

    Erscheinungsdatum :

    2020-09-11


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    B66B Aufzüge , ELEVATORS / G01P MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION OR SHOCK , Messen der Linear- oder Winkelgeschwindigkeit, der Beschleunigung, der Verzögerung oder des Stoßes / G06K Erkennen von Daten , RECOGNITION OF DATA / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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