Treffer: 基于Python 的矿山企业安全大数据管理系统的 探索与应用.

In order to promote the informationization construction of mining enterprises and enhance the safety informationization management capabilities, we explore the application of Python programming language and PyCharm editor to establish a basic database and big data analysis model for safety inspections in a certain mining enterprise. We construct a multi-dimensional analysis and evaluation system for historical safety inspection issues, new safety inspection issues, and other data, including time, space, problem categories, and causes. We explore the laws and development trends of intrinsic safety and safety inspection problems, and take targeted solutions accurately. The establishment of databases and analysis model can also be used for comprehensive evaluation of security inspection is-sues, visually displaying important information such as the time, place, category, and cause of the problem, providing objective data support for timely detection of repetitive problems and hidden danger investigation, avoiding the long-term existence of security problems that cannot be eradicated, and effectively reducing security risks. At present, the security big data model is mainly applied in security inspection work, and there is still room for exploration in the fields of security management system construction, "dual control" system operation, security education and training, accident prediction and warning in the future. [ABSTRACT FROM AUTHOR]

针对某露天矿山安全管理中存在的问题,采用Python编程语言及 PyCharm 编辑器,建立安全数据库和大数据分析模型,分析安全问题。基于安全检查历史数据库、新增安全检查问题等数据,构建时间、空间、致因、问题类别等多维度评价体系,探求本质安全和安全检查发现问题的规律及发展趋势,并采取针对性的解决措施。数据库和分析模型的建立还可辅助开展安全检查问题综合性评价,直观展示问题产生的时间、场所、原因、类别等重要信息,为及时发现重复性问题隐患提供客观数据支撑,避免重复性问题隐患风险长期存在,有效降低安全风险。安全大数据模型现阶段主要应用于安全检查工作,在安全管理制度体系建设、“双控” 体系运行、安全教育培训、事故预测警示等领域的应用仍有探索空间。 [ABSTRACT FROM AUTHOR]

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