新能源汽车产业飞速发展，其特有的电气火灾危险逐渐凸显。为此，本文依托多源数据融合技术，搭建了一种新能源汽车电气火灾风险监测平台。该平台通过集成电池状态、充电参数、环境指标等多方面信息，结合人工智能分析手段，实现对电气火灾风险的提前识别和智能报警。平台采用分层架构，包含感知层、网络层、数据处理层和应用层，支持从数据收集开始的风险防控全闭环。在北京市某新能源汽车专用充电站进行的为期 3 个月的试点应用显示，该平台将火灾预警准确率从传统单一参数监测的 72.3% 提升至 98.5%，平均响应时间从 15s 缩短至 2s，误报率控制在 1% 以下，显著提升了火灾预知准确度与应急响应速度，为新能源汽车安全运行提供了技术保障。

With the rapid development of the new energy vehicle industry, its unique electrical fire hazards have become increasingly prominent. To address this issue, this paper constructs a new energy vehicle electrical fire risk monitoring platform based on multi-source data fusion technology. By integrating information from various aspects such as battery status, charging parameters, and environmental indicators, and combining artificial intelligence analysis methods, the platform achieves early identification and intelligent alarm of electrical fire risks. The platform adopts a hierarchical architecture, including the perception layer, network layer, data processing layer, and application layer, supporting a full closed-loop risk prevention and control process starting from data collection. A 3-month pilot application at a dedicated
new energy vehicle charging station in Beijing showed that the platform increased the fire early warning accuracy from 72.3% (with traditional single-parameter monitoring) to 98.5%, shortened the average response time from 15 seconds to 2 seconds, and controlled the false alarm rate below 1%. It significantly improved the accuracy of fire prediction and response speed, providing technical support for the safe operation of new energy vehicles.