文本挖掘支持下的煤矿安全风险识别与评价
Coal Mine Safety Risk Identification and Evaluation Supported by Text Mining
许爱国1
作者信息
- 1. 山西离柳焦煤集团有限公司,山西 吕梁 033000
- 折叠
摘要
本文旨在探讨文本挖掘技术在煤矿安全风险识别与评价中的应用.通过分析828份煤矿事故报告,本研究构建了一个能够动态评估煤矿安全风险的模型,结合历史数据与实时监测数据,可以提高煤矿安全管理的效率与效果.首先利用自然语言处理技术提取事故报告中的关键风险因素,并通过Apriori算法识别这些因素之间的关联规则.进一步地,开发了一个综合风险评估模型,该模型不仅评估了当前的安全状况,还能预测潜在的风险,为煤矿提供了科学的预防策略.研究结果显示,所开发的模型能够有效地识别和评估煤矿中的安全风险,为矿区安全管理提供了有力的决策支持.然而,研究也发现该模型在数据依赖性和泛化能力方面存在局限,未来的工作将集中在提升数据处理能力和模型适应性上,以期达到更广泛的应用和更高的准确性.
Abstract
This article explores the application of text mining technology in coal mine safety risk identification and evaluation.By analyzing 828 coal mine accident reports,a dynamic model for coal mine safety risk evaluation was developed,integrating historical data with real-time monitoring data to enhance the efficiency and effectiveness of safety management.The study first utilizes natural language processing technology to extract key risk factors from accident reports,and identifies the association rules between these factors through the Apriori algorithm.Furthermore,a comprehensive risk assessment model was developed,which not only evaluates the current safety situation but also predicts potential risks,providing scientific prevention strategies for coal mines.The research results demonstrate that the proposed model can effectively identify and evaluate safety risks in coal mines,providing robust decision-making support for safety management in mining areas.However,the study highlights limitations in the model's data dependency and generalization ability.Future work will focus on improving data processing capabilities and model adaptability,to achieve wider applications and higher accuracy.
关键词
文本挖掘/煤矿安全/风险识别/风险评价Key words
Text mining/Coal mine safety/Risk identification/Risk evaluation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据