Retraction Note to: The characteristics of rainfall in coastal areas and the intelligent library book push system oriented to the Internet of Things

Abstract

Driven by economic globalization and the development of a low-carbon economy, the Internet of Things has brought new hope to countries around the world. In the context of climate change and rapid urbanization, the health of rivers in coastal areas of China is facing huge risks. The water cycle in coastal areas is complex and highly dependent on human activities. At the same time, the relative lack of long-term measured hydrological data makes it difficult to model the flow of coastal rivers, and also difficult to predict and analyze water quality. Therefore, this paper establishes a method to evaluate the latent heat structure based on the percentage of precipitation or the occurrence frequency of various systems in order to quantify the time, space distribution, and climatic conditions of the latent heat structure of the deep convective system under different regions and weather conditions. In addition, this research first analyzes the rainfall distribution rules and rainfall characteristics in coastal areas, then compares the physical mechanism-based SOBEK hydrological model with the neural network model, and conducts modeling and model characteristics analysis of the coastal areas. In response to these challenges, this article first analyzes and summarizes the temporal and spatial distribution of coastal rainfall and typical rainfall characteristics. This article focuses on the intelligent data mining push service system used by academic libraries at home and abroad. After careful research, academic libraries based on data mining will promote the development of intelligent push services. With the help of SQLServer2019 database management system, Android technology, and PHP component architecture, combined with collaborative filtering algorithms, intelligent book recommendation is realized. The recommendation principle is based on user characteristics and effectively promotes interest classification to create recommendation models and service systems.