随着煤矿智能化的不断发展，提升机房监控操作系统人机交互的多样化、处理和操作的集中化、信息显示的密集化等增加了提升机司机作业时的认知负荷，使提升机司机工作总体上更加以知识为基础，需要根据数据分析和细致洞察做出复杂的决策，人机互动成为“新型劳动力”。于是提升机司机的认知行为与人因失误也发生了明显的变化，人的可靠性问题日益凸显。因此，本文旨在探究智能化煤矿提升机房人机交互作业的人因失误机理，并对提升机司机的人因可靠性进行分析。

首先，本文通过对智能化煤矿提升机房的作业场景进行分析，找出智能化煤矿提升机房的特点；并对提升机司机的工作内容进行梳理，找出提升机司机在工作过程中的认知行为及认知功能，建立智能化煤矿提升机司机认知行为模型，划分人因失误发生的阶段，并对人因失误类别加以区分，确定人因失误模式。其次，通过文献研究法找出对人的行为具有影响作用的行为形成因子，根据已识别出的提升机司机认知功能，对安全检查人员和提升机司机进行访谈和问卷调查，识别出智能化煤矿提升机房人机交互作业行为形成因子；进而通过识别的行为形成因子建立提升机司机人因失误前因类别表、人因失误模式基本前因表、人因失误后果-前因追溯表等，对提升机司机人因失误事件进行追溯分析，找出人因失误事件的根原因。最后，通过对已有人因可靠性分析方法的分析，确定了用于提升机司机人因可靠性定量分析的行为形成因子，建立适用于智能化煤矿提升机房人机交互作业的贝叶斯网络人因可靠性分析模型，推算提升机司机人因失误事件发生概率，分析影响提升机司机人因可靠性的重要影响因素，提出管控措施。同时，还通过案例分析证明了所构建的认知行为模型、人因失误分类、行为形成因子以及人因可靠性分析方法的合理性与适用性。

该研究识别出了智能化煤矿提升机司机可能发生的人因失误模式及行为形成因子，构建了能够对提升机司机可靠性进行定量分析的贝叶斯网络模型，为人因失误的根原因分析提供了方法论指导，有助于识别情景环境中可能存在的缺陷或易诱发失误的弱点。

With the continuous development of intelligent coal mines, the diversification and centralization of the human-machine interface of the hoisting machine room monitoring and operating system, and the intensification of information display have increased the cognitive tasks of the hoist driver, so that the work of the hoist driver is generally more knowledge-based, and it is necessary to make complex decisions based on data and insights, and human-computer interaction has become a "new labor force". With the continuous improvement of equipment reliability, the problem of human reliability has become increasingly prominent, and the cognitive behavior and human errors of improving aircraft drivers have also undergone great changes. Therefore, this paper aims to explore the human error mechanism of human-computer interaction operation in the intelligent coal mine hoisting machine room, and to analyze the human reliability of the hoist driver.

First of all, through the analysis of the operation scenarios of the intelligent coal mine hoisting machine room, this paper finds out the characteristics of the intelligent coal mine hoist room; sorts out the work content of the hoist driver, finds out the cognitive behavior and cognitive function of the hoist driver in the working process, establishes the cognitive behavior model of the intelligent coal mine hoist driver, divides the stage of human due error, and distinguishes the category of human cause error to determine the human cause error mode. Secondly, through the literature research method, the behavior formation factors that have an influencing effect on human behavior are found, and according to the cognitive function of the elevator driver that has been identified, the safety inspectors and the elevator driver are interviewed and questionnaireed, and the formation factors of the human-computer interaction operation behavior of the intelligent coal mine lifting machine room are identified; and then the hoist driver is established through the identified behavior formation factors. Retrospective analysis of the accidents of the hoist drivers and people to find out the root causes of the human error events. Finally, through the analysis of the existing human factor reliability analysis methods, the behavior formation factors for the quantitative analysis of the human factors reliability of the hoist driver are determined, the Bayesian network human factor reliability analysis model suitable for the human-computer interaction operation of the intelligent coal mine hoisting machine room is established, the probability of the hoist driver's human error event is calculated, the important influencing factors affecting the human factor reliability of the hoist driver are analyzed, and the control measures are proposed. At the same time, the rationality and applicability of the constructed cognitive behavior model, human factor classification, behavior formation factor and human factor reliability analysis method are proved through case analysis.

The study identifies the human error patterns and behavior formation factors that may occur in intelligent coal mine hoist drivers, and constructs a Bayesian network model that can quantitatively analyze the reliability of hoist drivers, which provides methodological guidance for the root cause analysis of human causes of human faults, and helps to identify possible defects or weaknesses that are prone to induced errors in the scenario environment.

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