基于大数据的化工安全风险预警模型

Big Data-Based Chemical Safety Risk Early Warning Model

摘要

随着化工行业的快速发展，安全风险防控问题日益严峻。传统的安全管理手段已难以应对复杂多变的化工生产环境，尤其是在事故预防和应急响应中存在诸多挑战。基于大数据的化工安全风险预警模型通过对化工生产过程中产生的大量数据进行实时监控、分析与处理，能够提前识别潜在风险，提供有效的预警信息。该模型结合了大数据技术、机器学习和数据挖掘等方法，通过分析事故发生的历史数据、实时监测数据及外部环境数据，实现对风险的精准评估与预测，从而为企业提供决策支持，提高事故预防的效率和准确性。本文探讨了大数据在化工安全风险管理中的应用，分析了风险预警模型的构建方法，并通过实际案例展示了该模型的应用效果。

Abstract

With the rapid development of the chemical industry, the issue of safety risk prevention and control has become increasingly critical. Traditional safety management methods are no longer sufficient to address the complex and dynamic environment of chemical production, particularly in terms of accident prevention and emergency response. The big data-based early warning model for chemical safety risks enables the real-time monitoring, analysis, and processing of vast amounts of data generated during chemical production, allowing for the early identification of potential risks and the provision of effective warnings. This model integrates big data technology, machine learning, and data mining methods to achieve accurate risk assessment and prediction by analyzing historical accident data, real-time monitoring data, and external environmental data. As a result, it provides enterprises with decision-making support, improving the efficiency and accuracy of accident prevention. This paper explores the application of big data in chemical safety risk management, analyzes the construction methods of the risk early warning model, and demonstrates its effectiveness through practical case studies.

关键词

大数据 / 化工安全 / 风险预警 / 机器学习 / 数据分析

Key words

big data / chemical safety / risk early warning / machine learning / data analysis

参考文献

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