联想智能工厂生产线测试及异构数据图模型数据集
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随着工业4.0和智能制造的迅猛发展，状态检测技术在现代生产系统中发挥着至关重要的作用。通过实时监测和分析生产线各环节的运行状态，能够提升生产效率，预防设备故障，优化工艺流程，保障产品质量。为支持人工智能技术在先进制造领域的应用，本研究构建了“联想智能工厂生产线测试及异构数据图模型数据集”。该数据集源自联想智能工厂的实际生产线，涵盖了16台高精度生产设备的多源异构数据，包括电机转速、温度、压力等实时运行参数，通过工业物联网平台实现高频率、低延迟的数据传输。数据集集成了环境监控系统的温湿度信息等多类结果。数据预处理阶段，采用缺失值插补及数据标准化等方法，确保数据的完整性与一致性。标签数据涵盖正常运行及多种异常状态，有益于监督学习模型的训练与评估。该数据集的丰富性和多样性为设备故障预测、状态检测等研究任务提供了资源，推动了智能制造领域的创新与发展。
With the rapid development of Industry 4.0 and intelligent manufacturing, condition monitoring technology plays a crucial role in modern production systems. By real-time monitoring and analysis of the operational status of each segment of the production line, production efficiency can be enhanced, equipment failures prevented, production processes optimized, and product quality ensured. To support the application of artificial intelligence technologies in the field of advanced manufacturing, this study constructs the "Lenovo Smart Factory Production Line Testing and Heterogeneous Data Graph Model Dataset". This dataset is sourced from the actual production lines of Lenovo Smart Factory, covering multi-source heterogeneous data from 16 high-precision production equipment, including real-time operating parameters such as motor speed, temperature, and pressure. The data is transmitted with high frequency and low latency via the Industrial Internet of Things (IIoT) platform. The dataset also integrates multiple types of data such as temperature and humidity information from the environmental monitoring system. During the data preprocessing stage, methods such as missing value imputation and data standardization are adopted to ensure the integrity and consistency of the data. The labeled data covers normal operating conditions and various abnormal states, which facilitates the training and evaluation of supervised learning models. The richness and diversity of this dataset provide valuable resources for research tasks including equipment fault prediction and condition monitoring, promoting innovation and development in the field of intelligent manufacturing.
