钻井工程地面风险智能诊断与预警数据集

本数据集旨在通过视频监控与智能分析技术支撑钻井现场安全管理与设备健康评估。数据主要来源于钻井平台关键设备与作业现场的实时视频监控，包含三类视频数据文件，覆盖设备监控、作业行为识别及人员行为预警三类核心场景。其中，关键设备监控数据为网络收集和整理，人员行为识别和预警数据采用头戴式（EYESO EG100）和桌面式眼动数据采集平台采集所得。在钻井作业仿真环境中采集42名司钻培训人员的眼动数据，实验模拟司钻作业场景，包括正常作业、不熟练作业、疲劳作业、动作错误、环节遗漏和顺序错误6种行为类型。空间范围涵盖钻井井口场景、操作台装置区及中控室屏幕区域。数据集潜在价值在于支持钻井作业人员不安全行为实时识别与预警，减少事故发生率，并可用于工业安全监控、人因工程研究和机器学习模型开发，推动高风险行业智能安全管理创新。数据量为1.49 GB。

This dataset aims to support safety management and equipment health assessment at drilling sites through video surveillance and intelligent analysis technologies. The data is primarily derived from real-time video surveillance of key equipment and operation sites on drilling platforms, including three categories of video data files, covering three core scenarios: equipment monitoring, operation behavior recognition, and personnel behavior early warning. Among them, the key equipment monitoring data is collected and organized via networks, while the personnel behavior recognition and early warning data are collected using head-mounted (EYESO EG100) and desktop-based eye-tracking data acquisition platforms. Eye-tracking data of 42 driller trainees were collected in a drilling operation simulation environment, where the experimental scenarios simulate driller operations including six behavior types: normal operation, unskilled operation, fatigued operation, operational errors, missing operational steps, and sequence errors. The spatial coverage includes the drilling wellhead scene, operating console device area, and central control room screen area. The potential value of this dataset lies in supporting real-time identification and early warning of unsafe behaviors of drilling operators to reduce accident incidence rates, as well as being applicable to industrial safety monitoring, human factors engineering research, and machine learning model development, promoting intelligent safety management innovation in high-risk industries. The total data volume is 1.49 GB.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于钻井工程地面风险智能诊断与预警的视频数据集，主要包含设备监控、作业行为识别和人员行为预警三类场景的视频数据。数据来源于钻井平台实时监控和眼动采集，模拟了42名司钻人员的多种作业行为，旨在支持不安全行为实时识别与预警，减少事故发生率，并可用于工业安全、人因工程和机器学习研究。