以视频感知为基础，以网络、信息技术为媒介，以人工智能、大数据为技术支撑；构建了全矿井人工智能（AI）监管平台，介绍了平台的总体架构和核心场景；基于整个平台的设计，论述了低样本数据集增强技术、模型训练技术、数据推理与决策、微服务后台开发技术等关键技术。现场应用表明：全矿井人工智能（AI）监管平台的深度学习算法在矿山图像分类方面的准确率达到了90%以上，目标检测方面的准确率达到了80%以上。

Based on video perception, using networks and information technology as media, and supported by artificial intelligence and big data technology, a full mine artificial intelligence (AI) supervision platform is constructed, and the overall architecture and core scenarios of the platform is introduced; based on the design of the entire platform, key technologies such as low sample dataset enhancement technology, model training technology, data reasoning and decision-making, and microservice backend development technology were discussed. The on-site application shows that the deep learning algorithm of the entire mine artificial intelligence (AI) supervision platform has an accuracy rate of over 90% in mining image classification, and an accuracy rate of over 80% in object detection.