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Supporting Operator Complexity Management in Supervisory Control Systems

支持监控系统中的操作员复杂性管理

基本信息

批准号：
386566-2012
负责人：
Histon, Jonathan
金额：
$ 1.38万
依托单位：
University of Waterloo
依托单位国家：
加拿大
项目类别：
Discovery Grants Program - Individual
财政年份：
2015
资助国家：
加拿大
起止时间：
2015-01-01 至 2016-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572985
关键词：
Supporting Operator Complexity Management Supervisory

项目摘要

Human operators in the aviation, process control and nuclear power industries play key roles in maintaining the safe operation of these enterprises with significant contributions to world trade and commerce. However, when mistakes are made, the consequences can be costly and often tragic. A key source of mistakes can be excessive complexity. Experienced operators develop powerful strategies and techniques for managing and reducing complexity. However, the introduction of new technologies can undermine or disrupt those strategies. The primary objective of this research is to understand how to effectively support operator complexity management strategies while introducing flexibility and increased efficiency to system operations. The proposed research program will investigate complexity management within the Air Traffic Control domain and extrapolate findings to other domains. Observations of current system operations and the results of part-task experiments examining specific changes to the operator task environment will be used to generate information and task design requirements and develop and evaluate prototype decision support tools preserving safe and more efficient operations. Additional case studies within other supervisory control domains will be used to further understand the key research issues.

航空、过程控制和核电行业的人类操作员在维护这些企业的安全运营方面发挥着关键作用，对世界贸易和商业做出了重大贡献。然而，一旦犯了错误，后果可能是昂贵的，而且往往是悲惨的。错误的一个关键来源可能是过度复杂。经验丰富的操作员开发强大的策略和技术来管理和降低复杂性。然而，新技术的引入可能会破坏或破坏这些战略。本研究的主要目标是了解如何有效地支持运营商的复杂性管理策略，同时引入灵活性和提高系统运行效率。拟议的研究计划将调查复杂性管理在空中交通管制领域和推断的结果到其他领域。对当前系统操作的观察和检查操作员任务环境具体变化的部分任务实验结果将用于生成信息和任务设计要求，并开发和评估原型决策支持工具，以维护安全和更有效的操作。其他监督控制领域的其他案例研究将用于进一步了解关键的研究问题。