摘 要
随着工业自动化水平的提升，机械伤害事故的预防成为保障生产安全和人员健康的重要课题。本研究旨在通过机器学习技术构建机械伤害事故预测模型，以实现对潜在风险的早期识别与精准评估。研究基于大规模工业事故数据集，采用多种特征工程方法提取关键变量，并结合支持向量机、随机森林及深度学习等算法进行建模与优化。实验结果表明，所提出的集成学习模型在预测精度、召回率及F1分数等方面均显著优于传统统计方法，特别是在处理复杂非线性关系时表现出更强的适应性。此外，研究创新性地引入了时间序列分析与因果推断技术，进一步提升了模型对动态环境变化的响应能力。该研究的主要贡献在于将机器学习技术与工业安全领域深度融合，为机械伤害事故的预防提供了科学依据和技术支持，同时为未来智能化安全管理系统的开发奠定了理论基础。研究成果可广泛应用于制造业、建筑业及其他高风险行业，具有重要的实际应用价值和推广前景。
关键词：机械伤害事故预测；机器学习；集成学习模型；时间序列分析；因果推断技术
ABSTRACT
With the advancement of industrial automation, the prevention of mechanical injury accidents has become a critical issue in ensuring production safety and personnel health. This study aims to construct a predictive model for mechanical injury accidents using machine learning techniques to achieve early identification and precise evaluation of potential risks. Based on a large-scale industrial accident dataset, multiple feature engineering methods were employed to extract key variables, which were then integrated with algorithms such as support vector machines, random forests, and deep learning for modeling and optimization. The experimental results demonstrate that the proposed ensemble learning model significantly outperforms traditional statistical methods in terms of prediction accuracy, recall rate, and F1 score, particularly when handling complex nonlinear relationships. Additionally, this study innovatively incorporates time-series analysis and causal inference techniques, further enhancing the model's responsiveness to dynamic environmental changes. The primary contribution of this research lies in the deep integration of machine learning technology with the field of industrial safety, providing a scientific basis and technical support for the prevention of mechanical injury accidents, while laying a theoretical foundation for the development of future intelligent safety management systems. The findings can be widely applied in manufacturing, construction, and other high-risk industries, showcasing significant practical application value and prospects for promotion.
Keywords: Mechanical Injury Accident Prediction; Machine Learning; Ensemble Learning Model; Time Series Analysis; Causal Inference Technology
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 机械伤害事故预测的研究背景与意义 1
1.2 基于机器学习的事故预测研究现状 1
1.3 本文研究方法与技术路线 2
第2章 数据采集与特征工程 3
2.1 机械伤害事故数据来源与处理 3
2.2 特征选择与降维方法分析 3
2.3 数据预处理对模型性能的影响 4
第3章 机器学习模型构建与优化 5
3.1 典型机器学习算法的选择与比较 5
3.2 模型参数调优与性能评估 5
3.3 集成学习在事故预测中的应用 6
第4章 实验验证与结果分析 7
4.1 实验设计与数据集划分 7
4.2 预测模型的准确性与稳定性分析 7
4.3 结果对比与改进方向探讨 8
结论 10
参考文献 11
致 谢 12
