Research On Key Technologies Of Chemical Environment Remote Monitoring Based On NB-IoT

In the process of chemical production,some harmful gases may be generated due to factors such as exposure of raw materials,improper operation of equipment,etc.,resulting in significant safety hazards to chemical production.Therefore,real-time monitoring of the chemical environment is of great significance for ensuring safe production.Existing chemical environmental monitoring systems tend to be large-scale and wired,and do not fully utilize emerging Internet of Things technologies.Due to the important position of coal chemical industry in the field of chemical industry,this paper studies key technologies related to remote monitoring of coal chemical environment based on Narrowband Internet of Things（NB-IoT）technology.Based on the analysis of the architecture of the coal chemical environmental monitoring system based on NB-IoT,three key technologies have been identified,namely,sensor node development in the perception layer,data visualization in the application layer,and data fusion algorithms.Firstly,based on the analysis of the causes of chemical accidents and the hazards of coal chemical raw materials in recent decades,and according to the relevant standards for chemical environmental monitoring,suitable sensor components were selected for the main parameters in the coal chemical environment.Sensor elements with models SHT30,MQ-7,MQ-137,and MQ-136 are used to collect the data of air temperature and humidity,CO gas concentration,NH₃ gas concentration,and H₂S gas concentration in the coal chemical industry environment.The microcontroller with the model of STM32F03RCT6 is selected as the core controller,and a sensor node in the IoT perception layer is designed.The reading of environmental monitoring data is realized through IIC interface,asynchronous serial port,ADC interface,etc.Secondly,in terms of data visualization,the method of data exchange between the web terminal and the mobile phone interface is adopted.On the website,the One NET IoT Cloud Platform is used to visualize the data and display and view the historical data in a line chart.On the mobile phone,provide the IP,product ID,and device ID through the link URL to obtain the uploaded data information.Finally,in terms of multi-sensor data fusion,a two-level fusion model is used to process the environmental parameter data collected by the sensor nodes,and the safety evaluation coefficient is generated.The first level of fusion is the preprocessing of data.First-order Kalman filtering improves data accuracy,and three improved principles are used to eliminate data outliers.The second level of fusion is artificial neural networks.The BP neural network is used to realize the first-level fusion data’s re-fusion and comprehensive evaluation of the air quality in coal chemical enterprises.After experimental analysis and system test results,it is found that the wireless sensing node of the system can accurately obtain chemical and environmental parameter data.Data preprocessing can effectively reduce invalid data,improve sensor accuracy,and secondary data fusion can be well evaluated in ecological assessment.