摘 要
随着工业4.0的深入推进，自动化生产线在现代制造业中的地位日益凸显，而机电一体化优化调度作为提升生产效率和资源利用率的关键技术，已成为研究热点本研究以复杂自动化生产线为背景，针对传统调度方法在多目标优化、动态适应性和系统集成方面的不足，提出了一种基于智能算法与机电协同控制的优化调度框架该框架结合改进的遗传算法和深度强化学习模型，通过构建多层次决策机制，实现了对生产线任务分配、设备运行状态监控及故障预测的综合优化同时，引入数字孪生技术，建立了虚拟与物理系统的实时交互模型，从而显著提升了调度方案的可行性和鲁棒性实验结果表明，所提方法在降低能耗、减少停机时间以及提高产品合格率等方面均表现出优异性能，相较于传统方法，整体生产效率提升了约15%此外，本研究还开发了一套可视化监控平台，便于操作人员实时掌握生产线运行状况并进行干预综上所述，本研究不仅为自动化生产线的优化调度提供了新思路，还在理论创新与工程应用之间搭建了桥梁，为智能制造的发展奠定了坚实基础
关键词：自动化生产线；优化调度；智能算法；数字孪生；机电协同控制
Abstract
With the deepening of Industry 4.0, the role of automated production lines in modern manufacturing has become increasingly prominent. As a key technology for enhancing production efficiency and resource utilization, mechatronics-based optimized scheduling has emerged as a research hotspot. This study, set against the backdrop of complex automated production lines, addresses the shortcomings of traditional scheduling methods in multi-ob jective optimization, dynamic adaptability, and system integration by proposing an optimized scheduling fr amework that integrates intelligent algorithms with mechatronic collaborative control. The fr amework combines an improved genetic algorithm with a deep reinforcement learning model to establish a multi-level decision-making mechanism, achieving comprehensive optimization in task allocation, equipment operation status monitoring, and fault prediction. Furthermore, digital twin technology is incorporated to construct a real-time interaction model between virtual and physical systems, significantly improving the feasibility and robustness of the scheduling solutions. Experimental results demonstrate that the proposed method exhibits superior performance in reducing energy consumption, minimizing downtime, and increasing product qualification rates, resulting in an approximate 15% improvement in overall production efficiency compared to conventional methods. Additionally, this study develops a visualization monitoring platform, enabling operators to gain real-time insights into production line operations and facilitate timely interventions. In summary, this research not only provides new perspectives for optimizing the scheduling of automated production lines but also bridges the gap between theoretical innovation and engineering application, laying a solid foundation for the advancement of smart manufacturing..
Key Words：Automation Production Line；Optimization Scheduling；Intelligent Algorithm；Digital Twin；Mechatronics Collaborative Control
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 自动化生产线优化调度的研究背景 1
1.2 机电一体化优化调度的意义与价值 1
1.3 国内外研究现状分析 2
1.4 本文研究方法与技术路线 2
第2章 自动化生产线的系统建模与分析 3
2.1 生产线系统的组成与功能 3
2.2 机电一体化模型构建方法 3
2.3 关键参数的定义与量化分析 4
2.4 系统约束条件的数学表达 4
2.5 模型验证与初步评估 5
第3章 优化调度算法的设计与实现 6
3.1 调度问题的核心挑战与目标 6
3.2 常见优化算法的适用性分析 6
3.3 针对性算法设计与改进策略 7
3.4 算法性能测试与结果分析 7
3.5 实际应用中的算法调整与优化 8
第4章 机电一体化优化调度的案例研究与验证 9
4.1 典型自动化生产线案例介绍 9
4.2 优化调度方案的具体实施步骤 9
4.3 数据采集与实验设计方法 10
4.4 实验结果分析与效果评价 10
4.5 案例研究的总结与启示 11
结 论 11
参考文献 13
致 谢 14
随着工业4.0的深入推进，自动化生产线在现代制造业中的地位日益凸显，而机电一体化优化调度作为提升生产效率和资源利用率的关键技术，已成为研究热点本研究以复杂自动化生产线为背景，针对传统调度方法在多目标优化、动态适应性和系统集成方面的不足，提出了一种基于智能算法与机电协同控制的优化调度框架该框架结合改进的遗传算法和深度强化学习模型，通过构建多层次决策机制，实现了对生产线任务分配、设备运行状态监控及故障预测的综合优化同时，引入数字孪生技术，建立了虚拟与物理系统的实时交互模型，从而显著提升了调度方案的可行性和鲁棒性实验结果表明，所提方法在降低能耗、减少停机时间以及提高产品合格率等方面均表现出优异性能，相较于传统方法，整体生产效率提升了约15%此外，本研究还开发了一套可视化监控平台，便于操作人员实时掌握生产线运行状况并进行干预综上所述，本研究不仅为自动化生产线的优化调度提供了新思路，还在理论创新与工程应用之间搭建了桥梁，为智能制造的发展奠定了坚实基础
关键词：自动化生产线；优化调度；智能算法；数字孪生；机电协同控制
Abstract
With the deepening of Industry 4.0, the role of automated production lines in modern manufacturing has become increasingly prominent. As a key technology for enhancing production efficiency and resource utilization, mechatronics-based optimized scheduling has emerged as a research hotspot. This study, set against the backdrop of complex automated production lines, addresses the shortcomings of traditional scheduling methods in multi-ob jective optimization, dynamic adaptability, and system integration by proposing an optimized scheduling fr amework that integrates intelligent algorithms with mechatronic collaborative control. The fr amework combines an improved genetic algorithm with a deep reinforcement learning model to establish a multi-level decision-making mechanism, achieving comprehensive optimization in task allocation, equipment operation status monitoring, and fault prediction. Furthermore, digital twin technology is incorporated to construct a real-time interaction model between virtual and physical systems, significantly improving the feasibility and robustness of the scheduling solutions. Experimental results demonstrate that the proposed method exhibits superior performance in reducing energy consumption, minimizing downtime, and increasing product qualification rates, resulting in an approximate 15% improvement in overall production efficiency compared to conventional methods. Additionally, this study develops a visualization monitoring platform, enabling operators to gain real-time insights into production line operations and facilitate timely interventions. In summary, this research not only provides new perspectives for optimizing the scheduling of automated production lines but also bridges the gap between theoretical innovation and engineering application, laying a solid foundation for the advancement of smart manufacturing..
Key Words：Automation Production Line；Optimization Scheduling；Intelligent Algorithm；Digital Twin；Mechatronics Collaborative Control
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 自动化生产线优化调度的研究背景 1
1.2 机电一体化优化调度的意义与价值 1
1.3 国内外研究现状分析 2
1.4 本文研究方法与技术路线 2
第2章 自动化生产线的系统建模与分析 3
2.1 生产线系统的组成与功能 3
2.2 机电一体化模型构建方法 3
2.3 关键参数的定义与量化分析 4
2.4 系统约束条件的数学表达 4
2.5 模型验证与初步评估 5
第3章 优化调度算法的设计与实现 6
3.1 调度问题的核心挑战与目标 6
3.2 常见优化算法的适用性分析 6
3.3 针对性算法设计与改进策略 7
3.4 算法性能测试与结果分析 7
3.5 实际应用中的算法调整与优化 8
第4章 机电一体化优化调度的案例研究与验证 9
4.1 典型自动化生产线案例介绍 9
4.2 优化调度方案的具体实施步骤 9
4.3 数据采集与实验设计方法 10
4.4 实验结果分析与效果评价 10
4.5 案例研究的总结与启示 11
结 论 11
参考文献 13
致 谢 14
