在煤矿井下长时间作业的矿工易处于疲劳状态，进而可能引发安全事故。研判矿工疲劳状态、掌握矿工不利于安全生产的疲劳动态，进而预防煤矿安全生产事故发生，是亟待解决的问题。本研究旨在采集、优选和融合矿工主、客观疲劳状态特征指标，提出特征信息融合视域下矿工疲劳状态识别方法，用于识别矿工疲劳状态等级。以期为矿工疲劳检测、识别和管控提供新方法，助力主动煤矿事故预防，助力提升煤矿等高危领域高可靠性安全保障。

    （1）提出了矿工疲劳状态等级阈。通过矿工作业实地调研，界定了矿工疲劳状态，包括矿工疲劳状态的定义、致因和度量方法；通过疲劳等级量表修正和专家评价方法，将矿工疲劳状态划分为非疲劳状态（0级）、轻度疲劳状态（1级）、中度疲劳状态（2级）和重度疲劳状态（3级）4个等级；运用相似性融合计算方法，确定了矿工疲劳状态等级阈，分别是0级[0，0.39)、1级[0.39，0.63)、2级[0.63，0.83)和3级[0.83，1]；基于模糊综合评价法，提出了矿工疲劳状态等级识别标准，实现了对矿工疲劳状态等级的量化界定。

    （2）开展了矿工疲劳状态识别试验，分析了矿工疲劳状态特征指标，优选了矿工主、客观疲劳状态特征指标。采用Biopac MP160多导生理信号采集仪，进行了封闭环境的矿工手工搬运油桶作业试验，获取了作业过程中矿工主、客观疲劳指标；基于时间量化规律分析，运用Friedman检验和预测模型分析方法，分析了矿工疲劳状态特征指标；基于灰色关联分析，优选了MEAN、Min HR、……和TINN共28项矿工主、客观疲劳状态特征指标。研究发现：引入矿工瞬时心率和体重指标，构建的矿工最大耐受时间（T_ME）修正模型，能准确预测矿工T_ME。

    （3）提出了特征信息融合视域下矿工疲劳状态识别方法。以优选后的矿工主、客观疲劳状态特征指标为基础，运用主成分分析法，提取了RMS、MF、……和E₂共22项矿工深度融合疲劳特征指标，并将其作为基于海洋捕食者算法最小二乘支持向量机（MPA-LSSVM）的输入参数，研究矿工多特征信息融合指标和等级阈值间的映射结果与矿工疲劳状态间的量化关系。融合矿工主、客观深度疲劳特征信息，提出了特征信息融合视域下矿工疲劳状态识别方法。研究发现：运用MPA算法优化LSSVM分类模型，建立了基于MPA-LSSVM的矿工疲劳状态识别模型，实现了矿工疲劳状态等级的快速识别。

    （4）应用MPA-LSSVM算例，验证了特征信息融合视域下矿工疲劳状态识别方法的有效性。将矿工深度融合疲劳特征指标进行归一化处理后，以此作为LSSVM模型和MPA-LSSVM模型的输入参数，运行Matlab程序，得到矿工疲劳状态等级识别结果；通过平均绝对误差、均方根误差、精确率、召回率和F1-Score评价指标验证该识别方法的有效性；基于矿工疲劳状态致因和识别，提出矿工疲劳状态管控对策。研究分析发现：与基于单一生理信号、肌肉或主观疲劳特征指标的疲劳状态识别方法相比，特征信息融合视域下矿工疲劳状态识别方法可准确识别矿工疲劳状态等级；相比基于粒子群优化算法（POS）和基于灰狼优化算法（GWO）的LSSVM分类模型，本研究构建的基于MPA-LSSVM的矿工疲劳状态识别模型，具有较高的识别准确率、回归精度和敏感度。

      开展矿工疲劳状态识别方法研究，减少或杜绝矿工疲劳作业，助力主动事故预防机制建设。本研究可为煤矿安全管理提供新视野、新方法，亦可为其他相关研究提供借鉴。

      Safety accidents could be caused by fatigue of underground miners after long time working. It is an urgent task that “study and judge the fatigue state of miners and mastering the fatigue dynamics of miners, to prevent the occurrence of coal mine safety production accidents”. This paper aims to collect, optimize and integrate the subjective and objective physiological fatigue characteristic indexes of miners, construct a feature information fusion recognition method for the fatigue state of miners, identify the level of the fatigue state of miners. In order to provide a new method for miner fatigue detection, identification and control, and assist in proactive coal mine accident prevention and help to improve high-reliability safety guarantees in high-risk areas such as coal mines.

      (1) An level threshold of miners' fatigue state was proposed. Through on-the-spot investigation of mining operations, the fatigue state of miners was defined, including the definition, causes and measurement methods of miners' fatigue state; Based on the revision of the fatigue rating scale and the expert evaluation method, the fatigue status of miners was divided into 4 levels, namely the non-fatigue state (level 0), the mild fatigue state (level 1), the moderate fatigue state (level 2) and the severe fatigue state (level 3). Through similarity fusion calculation, the level thresholds of miners' fatigue state are determined, which are level 0 [0, 0.39), level 1 [0.39, 0.63), level 2 [0.63, 0.83) and level 3 [0.83, 1]; Based on the fuzzy comprehensive evaluation method, the identification standard of the degree of fatigue state of miners was proposed, which realized the quantitative de2finition of the level of miners' fatigue state.

      (2) A series of fatigue state identification tests were implemented, the change of miners' fatigue state characteristic index was analyzed, and the main and objective fatigue state characteristic indexes of miners were optimized. Biopac MP160 multi-channel physiological signal acquisition instrument was been used, the operation test of the miners' manual handling of oil drums in a closed environmental space was carried out, and the subjective and objective fatigue indicators of the miners during the operation were obtained; Based on time quantitative law analysis, Friedman test and prediction model analysis methods were used to analyze the change rule of miners' fatigue state characteristic index. The grey relational analysis was used to optimize the miners' subjective and objective fatigue state characteristic indicators, and 28 subjective and objective fatigue state characteristic indicators of miners, such as MEAN, Min HR, and TINN, et al, were selected.The study found that based on the miner's instantaneous heart rate and body weight, a miner's T_ME correction model was constructed, which could accurately predict the miner's T_ME.

      (3) An identification method for the fatigue state of miners based on feature information fusion was proposed. Based on the optimized subjective and objective fatigue characteristics of miners, the principal component analysis method is used to extract 22 miners' deep fusion fatigue characteristic indicators such as RMS, MF, ……, E₂. Deep fusion fatigue characteristic index, the quantitative relationship between the mapping results of multi-source fatigue fusion characteristic index and grade threshold and the physiological fatigue state of miners is studied. Miners' subjective and objective deep fatigue characteristic information was fused and the identification method of physiological fatigue state of miners with effectively recognition of multi-source information fusion was proposed. The study found that the MPA algorithm was used to optimize the LSSVM classification model, and the MPA-LSSVM-based miner fatigue state recognition model was established to realize the rapid recognition of miner fatigue state levels.

      （4）The MPA-LSSVM example was used to verify the effectiveness of the recognition method for fatigue state of coal miners in feature information fusion. After normalizing the miner's deep fusion fatigue characteristic index, which was used as the input variable of the LSSVM calculation example and the MPA-LSSVM calculation example, and the Matlab program was run to obtain the identification result of the degree of the miner's fatigue state; The effective feature of the recognition method was verified by evaluation indicators such as mean absolute error, root mean square error, precision rate, recall rate and F1-Score; Based on the causes of miners' fatigue state, the management and control countermeasures of miners' fatigue state were proposed and identified. The analysis shows that compareding with fatigue state identification methods based on single physiological signal, muscle or subjective fatigue characteristic index, the identification method of miners' fatigue state in feature information fusion has the advantage of accurately identifying the level of miners' fatigue state; Compared with the LSSVM classification model of particle swarm optimization （POS）and gray wolf optimization（GWO）, the classification model based on MPA-LSSVM proposed in the paper had high recognition accuracy, regression accuracy and sensitivity.

      Carrying out the identification of the physiological fatigue state of the miners; reducing or eliminating the fatigue of the miners, and assisting the active pre-control accident prevention mechanism. This study can provide new horizonsand methods for coal mine safety management, and also provide reference for other related researches.

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