利用IEEE体验API (xAPI)标准的扩展现实环境中的标准化风险缓解度量

Jennifer Rogers
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摘要

最近的报告表明,作为更大的自动化和数字化转型计划的一部分,能源行业在运营风险管理培训和实施以及扩展现实硬件和软件方面的组织需求和支出都在增加。此外,沉浸式技术的最新进展,以及COVID造成的更分散、异步的工作条件,导致可扩展的沉浸式模拟越来越接近现实世界的环境。虽然最近的标准已经定义了适合在通用学习环境(IEEE P9274.1)中跟踪和测量人类行为数据的JSON语法,并且以更接近工作场所中人类行为的方式(通常在操作风险管理系统中跟踪),但尚未定义基于风险的本体,以便更紧密地将模拟环境系统中的数据与操作环境中的数据交叉并关联起来。因此,不能充分衡量基于现实的扩展风险缓解培训的真正功效。在这项工作中,根据xAPI标准语法和允许的扩展构建了基于风险的本体和矩阵,并用于转换来自能源行业基于操作风险的模拟场景的历史数据子集。从这个初始子集转换的数据非常接近操作风险报告数据,并提供了对模拟环境中人类行为数据的见解,这些数据可以轻松地与现有的卓越运营和风险缓解kpi进行比较和关联。在更大、更复杂的数据集(如眼动追踪和生物识别)中,对模拟环境中其他高级数据的映射的影响也进行了考虑和探索。
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Standardized Risk Mitigation Measurement in Extended Reality Environments Utilizing the IEEE Experience API (xAPI) Standard
Recent reports indicate increased organizational appetite and spend in the energy industry in both the areas of operational risk management training and enablement and in extended reality hardware and software, as part of larger automation and digital transformation initiatives. Furthermore, recent advances in immersive technology, along with more dispersed, asynchronous working conditions due to COVID, have resulted in scalable, immersive simulations that more and more closely resemble real world environments. While recent standards have defined JSON syntax appropriate for tracking and measuring human behavior data in generic learning environments (IEEE P9274.1) and in a manner that more closely approximates human behavior in the workplace, as typically tracked in operational risk management systems, no risk-based ontology has yet been defined that more closely crosswalks and correlates data from simulated environment systems to those in operational environments. Thus, the true efficacy of extended reality-based risk mitigation training cannot be fully measured. In this effort, a risk-based ontology and matrix was constructed in accordance with the xAPI standard syntax and allowable extensions and was utilized to transform a subset of historical data from simulated operational risk-based scenarios from the energy industry. Transformed data from this initial subset closely approximated operational risk reporting data and provided insights into human behavior data in simulated environments that can be easily compared and correlated to existing operational excellence and risk mitigation KPIs. Implications for mapping of additional advanced data from simulated environments in larger, more complex datasets, such as eye tracking and biometrics, were also considered and explored.
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