Design and Implementation of Internet of Things Search System Based on Artificial Intelligence Control

With the rapid development and popularization of Internet of Things (IoT) technology, traditional search engines are no longer able to effectively process the massive information provided by IoT terminals, bringing new challenges to their retrieval. In response to the above issues, this article intends to study an intelligent retrieval system based on artificial intelligence (AI), using methods such as deep learning and natural language processing to intelligently optimize the retrieval results, in order to improve the correlation and accuracy of the retrieval results. The main idea is to design and develop a data acquisition module for different types of IoT devices; deep neural networks are used to process and analyze the collected data, in order to identify and classify important information; by utilizing natural language processing methods to optimize user queries, the system can more accurately understand user needs and retrieval intentions. As the number of concurrent queries increases, the throughput of the system significantly improves. From 10.2 queries per second for 10 concurrent queries to 700.0 queries per second for 1000 concurrent queries, it indicates a significant improvement in the system’s processing power and good scalability. The implementation of this article provides an efficient intelligent solution for information search in the Internet of Things, which is of great value in promoting the widespread application and development of the Internet of Things.