Construction of Green Environmental Protection Intelligent Control and Monitoring System Based on Neural Network Algorithm

Environmental automatic monitoring system has been widely used in iron and steel enterprises, and has achieved certain results, Traditional techniques' imprecise monitoring of environmental contamination and resource use are caused by characteristics such sensor performance, data collecting frequency, and number of monitoring stations, which restrict their accuracy. The accuracy of environmental monitoring data may be increased to more precisely represent environmental conditions by incorporating neural network algorithms and continually learning and optimizing. To lower noise in the data, a significant amount of environmental monitoring data was gathered and preprocessed. While ambient spatiotemporal and temporal data were being recorded, the Inception-Long Short-Term Memory (LSTM) model was built. The outputs of the LSTM and Inception modules were combined via the fusion layer. According to the test set findings, the Inception-LSTM model had an average accuracy of 97.9% in predicting environmental conditions. Therefore, the application of the Inception-LSTM model can accurately predict environmental conditions, It can not only be applied to the environmental monitoring of the existing factory, but also to the monitoring of a certain area.