基于结构模型的调度员工作负荷优化研究

高铁调度指挥中心承担着对铁路上运行车次的运行状态监督和综合调度工作,同时还需负责控制铁路上的车流,确保各项平衡,在保障行车安全方面发挥着不可替代的作用。在高铁铁路非正常行车的应急场景下,调度人员需要迅速、正确地针对突发事件进行应急处置的操作,对列车的安全运营起着决定性的作用。本文以高铁调度员在应急处置时认知决策过程为研究对象,利用实验室仿真实验平台,面向非正常行车的应急场景,设计了认知决策机理模型的结构及对应测量模型的数据采集方法。基于信息处理模型,结合知觉负载理论和情景意识概念,增添脑力负荷元素,建立面向高铁调度员的认知决策机理模型。针对脑力负荷的评估,设计并采用了一种特征选择和支持向量回归(Support Vector Machine, SVM)参数选择联合优化的粒子群算法,对基于多源生理信号的支持向量回归模型的训练过程进行优化,综合运用信号处理理论及数学方法,针对脑电、心电和眼动三类生理信号提取各自的数据特征。对结构方程模型进行拟合,研究脑力负荷与注意力和工作记忆之间的关系,对后期调度员工作负荷及适岗能力优化更具实用价值。

Research on Dispatcher Workload Optimization based onStructural Model

The high speed rail dispatching command center is responsible for the operation status supervision and comprehensive dispatching of trains running on the railway. At the same time, it is also responsible for the control of the train flow on the railway to keep the balance of all aspects, and plays an irreplaceable role in ensuring the safety of train operation. In the emergency scenario of abnormal high speed railway operation, dispatchers need to quickly and correctly handle the emergency response to the emergencies, which plays a decisive role in the safe operation of trains. This paper takes the cognitive decision-making process of high speed railway dispatchers as the research object, using the laboratory simulation experiment platform and facing the emergency scenario of abnormal driving, the structure of the cognitive decision-making mechanism model and the data collection method corresponding to the measurement model are designed. Based on the information processing model, combined with the concept of perceptual load theory and situational awareness, adding elements of mental load, a cognitive decision-making mechanism model for high speed railway dispatchers is established. Aiming at the evaluation of mental load, a particle swarm optimization algorithm for joint optimization with feature selection and support vector machine parameter selection is designed and adopted to optimize the training process based on multi-source physiological signals , comprehensively using signal processing theory and mathematical methods to extract the respective data characteristics for the three types of physiological signals of EEG, ECG and eye movement. Fitting the structural equation model and studying the relationship between mental load and attention and working memory are of more practical value for the later optimization of dispatcher"s workload and job adaptability.