物联网通信技术在日光温室环境温湿度预测中的应用OA

To improve the accuracy and real-time performance of temperature and humidity predictions in solar greenhouses,the paper systematically constructs an integrated'sensing-transmission-processing-prediction'application process based on IoT communication technology.By deploying multi-source sensing nodes and a narrow band Internet of Things(NB-IoT)communication network,high-frequency and low-latency data collection and transmission are achieved,while a communication data-driven preprocessing and feature modeling mechanism is introduced.Combined with the long short-term memory(LSTM)prediction model integrating communication parameters,dynamic temperature and humidity predictions are realized.Case results show that this solution reduces the average prediction error to 0.54℃,shortens prediction latency to 1.17 s,and achieves an abnormal data recovery rate of 94.6%,significantly improving data integrity,prediction response speed,and system practicality.