用AI预测能源工地危险事件，让专家参与改进系统
HARNESS: Human-Agent Risk Navigation and Event Safety System for Proactive Hazard Forecasting in High-Risk DOE Environments
- 融合大模型与历史数据，自动识别潜在风险
- 专家参与修正后，预测准确率持续提升
- 适合高危场所安全预警，尤其适合能源行业
在任务关键型工作场所，操作安全至关重要。本文提出人类-代理风险导航与事件安全系统（HARNESS），一个模块化AI框架，用于预测美国能源部（DOE）环境中的危险事件并分析运营风险。HARNESS整合大型语言模型（LLMs）、结构化工作数据、历史事件检索和风险分析，主动识别潜在危害。通过人机协同机制，领域专家（SMEs）可修正预测结果，形成自适应学习闭环，持续提升系统性能。结合专家协作与迭代式智能推理，显著增强预测安全系统的可靠性与效率。初步部署显示良好效果，未来工作将聚焦于准确性、专家一致性及决策延迟的量化评估。
原文摘要 · Abstract (English)
Operational safety at mission-critical work sites is a top priority given the complex and hazardous nature of daily tasks. This paper presents the Human-Agent Risk Navigation and Event Safety System (HARNESS), a modular AI framework designed to forecast hazardous events and analyze operational risks in U.S. Department of Energy (DOE) environments. HARNESS integrates Large Language Models (LLMs) with structured work data, historical event retrieval, and risk analysis to proactively identify potential hazards. A human-in-the-loop mechanism allows subject matter experts (SMEs) to refine predictions, creating an adaptive learning loop that enhances performance over time. By combining SME collaboration with iterative agentic reasoning, HARNESS improves the reliability and efficiency of predictive safety systems. Preliminary deployment shows promising results, with future work focusing on quantitative evaluation of accuracy, SME agreement, and decision latency reduction.
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