Through the application of statistical models, the active mission success estimation (AMSE) introduced in this paper can be performed during a rapidly developing unanticipated failure scenario to support decision making. AMSE allows for system operators to make informed management and control decisions by performing analyses on a nested system of functional models that requires low time and computational cost. Existing methods for analyses of mission success such as probabilistic risk assessment or worst case analysis have been applied in the analysis and planning of space missions since the mid-twentieth century. While these methods are effective in analyzing anticipated failure scenarios, they are built on computational models, logical structures, and statistical models that often are difficult and time-intensive to modify, and are computationally inefficient leading to very long calculation times and making their ability to respond to unanticipated or rapidly developing scenarios limited. To demonstrate AMSE, we present a case study of a generalized crewed Martian surface station mission. A crew of four astronauts must perform activities to achieve scientific objectives while surviving for 1070 Martian sols before returning to Earth. A second crew arrives at the same site to add to the settlement midway through the mission. AMSE uses functional models to represent all of the major environments, infrastructure, equipment, consumables, and critical systems of interest (astronauts in the case study presented) in a nested super system framework that is capable of providing rapidly reconfigurable and calculable analysis. This allows for AMSE to be used to make informed mission control decisions when facing rapidly developing or unanticipated scenarios. Additionally, AMSE provides a framework for the inclusion of humans into functional analysis through a systems approach. Application of AMSE is expected to produce informed decision making benefits in a variety of situations where humans and machines work together toward mission goals in uncertain and unpredictable conditions.

中文翻译：

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通过功能建模估计主动任务成功率

通过应用统计模型，本文介绍的主动任务成功估计 (AMSE) 可以在快速发展的意外故障情况下执行，以支持决策。AMSE 允许系统操作员通过对需要低时间和计算成本的功能模型的嵌套系统进行分析来做出明智的管理和控制决策。自 20 世纪中叶以来，现有的任务成功分析方法，如概率风险评估或最坏情况分析，已被应用于空间任务的分析和规划。虽然这些方法在分析预期的故障场景方面是有效的，但它们建立在计算模型、逻辑结构和统计模型上，这些模型通常难以修改且需要大量时间进行修改，并且计算效率低下，导致计算时间非常长，并使得他们对意外或快速发展的场景做出反应的能力受到限制。为了演示 AMSE，我们提供了一个广义载人火星地面站任务的案例研究。由四名宇航员组成的机组人员必须进行活动以实现科学目标，同时在返回地球之前生存 1070 火星溶胶。第二个工作人员到达同一地点，在任务中途增加定居点。AMSE 使用功能模型在嵌套的超级系统框架中表示所有主要环境、基础设施、设备、消耗品和感兴趣的关键系统（案例研究中的宇航员），该框架能够提供快速可重新配置和可计算的分析。这使得 AMSE 可用于在面临快速发展或意外情况时做出明智的任务控制决策。此外，AMSE 提供了一个框架，通过系统方法将人类纳入功能分析。AMSE 的应用有望在人类和机器在不确定和不可预测的条件下共同努力实现任务目标的各种情况下产生明智的决策优势。