为准确评估和预测机务维修人员工作负荷,结合机务人员的受力、姿势、能耗等工效学因素,首先以任务所需时间资源与可用时间资源之比为主评估量,以能量消耗率及工效学负荷为修正因子,建立机务维修工作负荷评估模型;其次应用工效学仿真软件...为准确评估和预测机务维修人员工作负荷,结合机务人员的受力、姿势、能耗等工效学因素,首先以任务所需时间资源与可用时间资源之比为主评估量,以能量消耗率及工效学负荷为修正因子,建立机务维修工作负荷评估模型;其次应用工效学仿真软件Simens JACK 8. 4(简称JACK)仿真模拟得到模型中的工作所需时间和修正因子,并提出基于机务维修工作负荷评估模型和JACK仿真软件的机务人员工作负荷评估方法;最后用该方法仿真评估A320主轮拆卸作业工作负荷。研究表明:所提方法能够弥补现有民航机务人员工作负荷评估方法中存在的实验室化严重、操作性差、主观性强、预测困难等不足,可方便有效地评估及预测维修人员工作负荷。展开更多
The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on expe...The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.展开更多
文摘为准确评估和预测机务维修人员工作负荷,结合机务人员的受力、姿势、能耗等工效学因素,首先以任务所需时间资源与可用时间资源之比为主评估量,以能量消耗率及工效学负荷为修正因子,建立机务维修工作负荷评估模型;其次应用工效学仿真软件Simens JACK 8. 4(简称JACK)仿真模拟得到模型中的工作所需时间和修正因子,并提出基于机务维修工作负荷评估模型和JACK仿真软件的机务人员工作负荷评估方法;最后用该方法仿真评估A320主轮拆卸作业工作负荷。研究表明:所提方法能够弥补现有民航机务人员工作负荷评估方法中存在的实验室化严重、操作性差、主观性强、预测困难等不足,可方便有效地评估及预测维修人员工作负荷。
基金This work was supported by the Scientific Research Projects of Tianjin Educational Committee(No.2020KJ029)。
文摘The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.
