Design and Implementation of Pollution Source Automatic Monitoring System Based on Big Data

Under the background of current environmental monitoring, the traditional monitoring system has been difficult to meet the needs of real-time monitoring, data analysis and decision support of pollution sources. Therefore, this paper proposes and implements a set of monitoring system based on big data, aiming at dealing with environmental pollution problems more comprehensively and efficiently. The system design adopts the architecture of distributed data collection, storage, processing and analysis, and integrates Internet of Things, cloud computing and big data technologies. Sensor networks and satellite data sources are widely used to ensure that the system can obtain multi-dimensional and high-frequency monitoring data in real time. The experimental results show that the system based on big data has obvious advantages in real-time performance compared with the traditional system. By introducing deep learning algorithm, the system realizes intelligent early warning mechanism, which can capture potential pollution sources and abnormal patterns in real time and provide real-time and accurate early warning information for decision makers. The big data processing and analysis layer adopts the distributed computing framework, which realizes the real-time cleaning, analysis and mining of massive monitoring data. By introducing deep learning algorithm, the system successfully discovered complex pollution patterns and potential laws, which provided a scientific basis for environmental governance. The automatic monitoring system of pollution sources based on big data can provide more powerful and intelligent tools for environmental protection and promote the development of environmental monitoring technology in a more advanced and sustainable direction.