Application of data mining technology in coal mine hidden hazard management
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摘要: 针对目前煤矿隐患管理缺乏对隐患数据深入分析的问题，介绍了适合隐患关联规则发现的数据挖掘算法，提出用支持度-置信度-Kulczynski度量模式表达隐患因素间的关联关系。对隐患数据预处理、转换后构建隐患数据仓库，并在隐患责任部门、隐患种类、隐患等级和隐患发生地点4个维度上进行挖掘分析，发现多维度间存在的较强关联规则，给出针对性的辅助决策。现场实际应用表明，通过使用数据挖掘算法，减少了隐患的发生次数，为煤矿隐患治理提供了可靠支持。Abstract: For lack of deep analysis of hidden hazard data in current coal mine hidden hazard management, data mining algorithms which were suitable for discovering association rule of hidden hazard were introduced, and support-confidence-Kulczynski model was proposed to indicate association relationship among hidden hazard factors. Data warehouse is built after preprocessing and conversion of hidden hazard data, and mining analysis is conducted on four dimensions such as department, category, level and address of hidden hazard, so as to provide corresponding assistant decision-making according to strong association rule founded among dimensions. The actual application results show that occurrence of hidden hazard is reduced and reliable support is provided for coal mine hidden hazard management by use of the data mining algorithm.
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Keywords:
- coal mine hidden hazard /
- data mining /
- support /
- confidence /
- Kulczynski measurement /
- association rule
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