With the increasing maturity of Internet of Things technology, temperature sensors based on Zigbee, LoRa and other transmission modes have successively appeared. However, due to factors such as outdoor environment and transmission distance, the temperature measurement data under different transmission methods may be inaccurate and the update of background data may be delayed. To master the temperature measurement system parameters in different transmission modes in detail, Zigbee and LoRa transmission modes are used to design temperature sensors respectively. At the same time, the prediction accuracy and running time of the line temperature under several existing neural networks are analyzed and compared, then based on the LSTM network with the highest accuracy, a temperature prediction method based on Long Short Time Memory and Extreme Learning Machine(LSTM-ELM) network is proposed to realize the rapid prediction of line temperature. Simulation results show that the network prediction accuracy based on LSTM-ELM reaches 93.32%, and the prediction time is greatly reduced to 875.75 s, which is nearly 2000% higher than the traditional LSTM network, and can provide a reliable basis for background management in engineering practice.

随着物联网技术的日趋成熟，基于Zigbee，LoRa和其他传输模式的温度传感器已经相继出现。但是，由于诸如室外环境和传输距离之类的因素，在不同传输方法下的温度测量数据可能不准确，并且背景数据的更新可能会延迟。为了详细掌握不同传输模式下的温度测量系统参数，分别采用Zigbee和LoRa传输模式来设计温度传感器。同时，分析和比较了几种现有神经网络在生产线温度下的预测精度和运行时间，然后基于精度最高的LSTM网络，提出了一种基于长短时记忆与极限学习机（LSTM-ELM）网络的温度预测方法，以实现线温度的快速预测。仿真结果表明，基于LSTM-ELM的网络预测精度达到93.32％，预测时间大大减少至875.75 s，比传统LSTM网络高出近2000％，可以为网络后台管理提供可靠的依据。工程实践。