Storm Streaming Technology Applied to Intelligent Analysis of Meteorological Data in the Era of Big Data

With the continuous improvement and rapid growth of meteorological service informatization, it is very necessary to apply real-time meteorological data (MD) processing to meteorological information business. In the era of big data, it is a subject of great significance to obtain meteorological observation data in real time to provide meteorological researchers with knowledge decision-making based on MD and predict future meteorological changes. Based on this, this paper applies the modern storm streaming technology (MT) to the intelligent analysis of MD, and proposes a BP Neural network algorithm (NNA); The advantages of storm flow technology in rainfall prediction model and the pretreatment process of meteorological observation data are analyzed; The application of storm flow technology in intelligent analysis of MD is discussed; Through the learning results of neural networks with different numbers of hidden neurons and the formal prediction and verification analysis of MD, the results show that the application of storm MT to the intelligent analysis of MD has good performance in accuracy and stability. Compared with other model prediction data, the BPF model prediction data generated in the learning process also has good stability, and its error has been effectively reduced. In this paper, storm flow technology is applied to the intelligent analysis of MD, which greatly improves the accuracy of MD analysis.