With the development of Internet of Things technology, more and more devices are connected to the Internet, including not only traditional computers, mobile phones and other smart terminal devices, but also various sensor devices. These sensor devices can collect a variety of environmental information and physical quantities, such as temperature, humidity, air pressure, light intensity, vibration, etc. These data have the characteristics of real-time, scale and diversity, and need to be processed and analyzed by appropriate algorithms. On the basis of previous studies, this project summarized the application of various machine learning algorithms in device state detection, compared the differences of various machine learning algorithms in sensor device detection and made comparative analysis, calculated the evaluation parameters of MSE, RMSE, MAE, MAPE, R² and other aspects of the machine learning regression model. Compare the effects of various regression models for better monitoring and prediction of equipment status. Through the analysis of a large number of historical data, different equipment state models can be established, and these models can be used to monitor and predict the current equipment state. This can effectively avoid production line downtime or other losses caused by equipment failures or abnormalities. At the same time, through the in-depth analysis of historical data, we can find some potential problems and take corresponding measures to prevent them. This project aims to summarize the application of various machine learning algorithms in device status detection, compare and contrast the differences of various machine learning algorithms in sensor device detection, realize efficient processing and analysis of sensor data, calculate MSE, RMSE, MAE, MAPE, R² and other evaluation parameters, and evaluate and compare each model. To provide more accurate, reliable and efficient equipment condition monitoring and forecasting services for enterprises and individuals.

中文翻译：

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各种机器学习算法在物联网设备异常状态监测中的准确性对比分析

随着物联网技术的发展，越来越多的设备连接到互联网，不仅包括传统的电脑、手机等智能终端设备，还包括各种传感器设备。这些传感器设备可以采集多种环境信息和物理量，如温度、湿度、气压、光强、振动等。这些数据具有实时性、规模性和多样性的特点，需要经过处理和处理通过适当的算法进行分析。本项目在前人研究的基础上，总结了各种机器学习算法在设备状态检测中的应用，比较了各种机器学习算法在传感器设备检测中的差异并进行了对比分析，计算了MSE、RMSE、MAE的评价参数， MAPE、R² 和机器学习回归模型的其他方面。比较各种回归模型的效果，以便更好地监测和预测设备状态。通过对大量历史数据的分析，可以建立不同的设备状态模型，并利用这些模型来监测和预测当前的设备状态。这样可以有效避免因设备故障或异常造成的生产线停机或其他损失。同时，通过对历史数据的深入分析，可以发现一些潜在的问题，并采取相应的措施加以预防。本项目旨在总结各种机器学习算法在设备状态检测中的应用，对比对比各种机器学习算法在传感器设备检测中的差异，实现传感器数据的高效处理和分析，计算MSE、RMSE、MAE、MAPE、 R²等评价参数，对各模型进行评价比较。为企业和个人提供更加准确、可靠、高效的设备状态监测和预测服务。