Gas management units have a large number of gates to be supervised. This paper introduces an intelligent gas well management system. By installing all kinds of intelligent sensing terminals in the gas well, it can monitor the massive gas well in real time, and form the gas well internet of things with the help of NB-IOT technology, and gather the data to the data analysis cloud platform. Finally, the perception data is processed and analyzed in the data analysis cloud platform, so as to conduct safety supervision and accuracy of the massive gas wells. At the same time, it uses the historical monitoring big data collected by the system for a long time to build the risk prediction model of the gas well, and carries out the safety risk level assessment and fault prediction and early warning for the key gas well, so as to comprehensively realize the intelligent management of the gas well.

For most construction projects, the complex engineering environment, the backward data collection technology, and the unreasonable monitoring network have resulted in many problems in monitoring data such as lots of noise and missing data items, therefore, it is of great significance to study the safety monitoring system of construction projects based on wireless sensor network (WSN). For this reason, this paper proposed a construction safety monitoring and evaluation (CSME) model based on multi-sensor fusion. First, the system structure and data flow model of the construction safety monitoring system were constructed; then, combining with a multi-sensor deep fusion system which was built on physical and information systems, this paper designed a spectrum sensing algorithm for sensor signals within the construction area. After that, tempo-spatial correlation analysis was conducted on the monitoring data, and a multi-sensor monitoring network joint sparse (MSMN-JS) model was constructed, which realized reconstruction of missing data items. At last, this paper used experimental results to prove the application value of the algorithm model to the safety monitoring and evaluation of construction projects.