毕方哨兵动力电池充电线路故障在线检测数据集

依托跨区域部署的充电安全云端监测平台，汇聚并分析充电桩/充电器在实时充电作业中产生的充电数据。通过融合毫秒级电气特征解析与实时异常检测算法，系统捕捉充电过程中充电器输入/输出端电压电流波形、功率因数、转换效率、等关键安全参数（如：输出电压跌落/过冲、电流谐波畸变率超标、效率突降、漏电流超限等特征），构建充电线路健康度与故障风险实时诊断模型。过程中运用流式数据处理与模式匹配引擎对海量实时充电数据进行瞬时特征提取与安全阈值比对，从而实现对充电器内部线路老化（如接触点氧化、电容劣化）、连接器故障（如虚接、烧蚀）或外部线路隐患（如绝缘破损、短路风险）的精准识别、定位与告警，并联动执行充电中止或功率限制等主动保护指令。

Leveraging a cross-regionally deployed charging safety cloud monitoring platform, this system aggregates and analyzes charging data generated by charging piles and chargers during real-time charging operations. By integrating millisecond-level electrical feature analysis and real-time anomaly detection algorithms, it captures voltage and current waveforms at the input and output terminals of chargers, power factor, conversion efficiency and other key safety parameters — including output voltage drop/overshoot, excessive current harmonic distortion, sudden efficiency drop, excessive leakage current and other characteristic anomalies — and constructs a real-time diagnostic model for charging line health status and fault risk. During operation, stream data processing pipelines and pattern matching engines are employed to conduct instantaneous feature extraction and safety threshold comparison on massive real-time charging data, enabling accurate identification, localization and early warning of issues such as internal charger line aging (e.g., "contact oxidation", "capacitor degradation"), connector faults (e.g., "poor connection", "ablation"), or external line hidden dangers (e.g., "insulation damage", "short circuit risk"). Additionally, the system triggers and executes active protection commands including charging suspension or power restriction in an interlocked manner.

搜集汇总

背景与挑战

背景概述

该数据集专注于动力电池充电线路故障的在线检测，通过实时采集充电过程中的电压、电流、功率等电气参数，构建健康状态监测模型以诊断线路老化、接触故障等隐患。数据集采用Excel格式，应用于电动自行车充电安全场景，实现精准故障识别和主动安全防护。