The invention discloses an electric vehicle charging pile fault prediction method based on a deep learning technology, and the method comprises the following steps: step 1, charging pile pairing data storage, step 2, charging pile detection module installation, and step 3, charging pile fault prediction. By setting an integrated control module in a charging pile body, a data acquisition module in the integrated control module detects fault data in real time, and if the fault data occurs, a data processing module transmits an error report and position information of the charging pile body to a remote wireless receiving module through a wireless transmission module; a transfer module drives the maintenance personnel to carry out maintenance prompt through a repair requesting module, the integrated control module enables a voice module, a display module and an alarm module to give an alarm through a driving module, a user is warned that the charging pile body breaks down, and the good in-time fault checking capacity is achieved.

    本发明公开了一种基于深度学习技术的电动汽车充电桩故障预测方法,包括以下步骤,步骤一,充电桩配对数据存储,步骤二,充电桩检测模块安装,步骤三,充电桩故障预测,通过在充电桩体上设置有集成控制模块,集成控制模块内的数据采集模块对其故障数据进行实时检测,若是发生故障数据,数据处理模块将错误报告以及充电桩体所在位置信息通过无线传输模块传输给远程的无线接收模块上,中转模块经过报修模块驱使维修人员对其进行维修提示,且集成控制模块通过驱动模块分别使语音模块、显示模块和警报模块发出警报,警示使用人员充电桩体发生故障,具有较好的故障及时排查能力。


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    Title :

    Electric vehicle charging pile fault prediction method based on deep learning technology


    Additional title:

    一种基于深度学习技术的电动汽车充电桩故障预测方法


    Contributors:
    WANG JUN (author) / WU DAN (author) / LEI TING (author) / XU YAN (author)

    Publication date :

    2021-08-20


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    B60L PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES , Antrieb von elektrisch angetriebenen Fahrzeugen / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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