煤矿安全隐患治理能力评估与预测方法研究
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摘要
本文针对煤矿安全隐患排查治理工作的实际需要，围绕企业隐患治理能力及其排查隐患的数量等展开评估与预测方法的研究工作。论文研究工作主要分为三部分：第一部分借鉴软件能力成熟度模型，提出企业安全隐患治理能力成熟度概念与模型，并设计了煤炭行业的评估与预测指标体系，从横、纵两个方向评估，横向采用专家法和自组织特征映射神经网络对比评估企业间的能力成熟度；纵向基于信息熵衡量企业自身隐患治理能力的改进程度。第二部分针对企业所排查出隐患的治理能力，提出单变量的频分多曲线拟合方法来评估企业未来可能的隐患治理能力及其发展趋势。第三部分提出渐进遗忘式在线极速学习机算法，基于第一部分构建的指标体系，研究企业安全管理中的相关因素对隐患数量的影响规律，构建基于管理指标的隐患数量预测模型。研究主要选择煤炭行业的数据作为评估与预测对象，并验证本文所研究方法的有效性、适用性。
In this paper, the main research object is the hidden enterprise production dangers and theevaluating and forecasting methods are the key points. The researches consist of three parts. Inthe first part, the paper designs an evaluating indicator system, and proposes a capabilitymaturity model to evaluate the hidden danger governance of enterprise based on Expert GroupAssessment and the self-organize feature map (SOFM) algorithm. In addition, a self-evaluatingmodel is put forward based on the information entropy theory. In the second part, a frequencydivision curve fitting algorithm that is a multiple regression analysis method for univariate isproposed. The algorithm is used to forecast the development trend of the hidden dangergovernance. In the last part, the paper mainly study the relationship between the evaluatingindicator system and the development trend of the hidden danger, and bring forward a novelonline sequence extreme learning machine with gradual forgetting mechanisms. All researchingachievement is tested to analysis their effectiveness, and the sample data is mainly collected fromthe coal mine enterprises.
In this paper, the main research object is the hidden enterprise production dangers and theevaluating and forecasting methods are the key points. The researches consist of three parts. Inthe first part, the paper designs an evaluating indicator system, and proposes a capabilitymaturity model to evaluate the hidden danger governance of enterprise based on Expert GroupAssessment and the self-organize feature map (SOFM) algorithm. In addition, a self-evaluatingmodel is put forward based on the information entropy theory. In the second part, a frequencydivision curve fitting algorithm that is a multiple regression analysis method for univariate isproposed. The algorithm is used to forecast the development trend of the hidden dangergovernance. In the last part, the paper mainly study the relationship between the evaluatingindicator system and the development trend of the hidden danger, and bring forward a novelonline sequence extreme learning machine with gradual forgetting mechanisms. All researchingachievement is tested to analysis their effectiveness, and the sample data is mainly collected fromthe coal mine enterprises.
引文
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