In recent years, with the progress and development of social science and technology, remote monitoring of Internet of Things systems has attracted more and more attention. The remote monitoring system is mainly a network system with convenient layout, simple maintenance, and high security performance built on the basis of the wireless network, which can realize real-time monitoring, and collects transmit information. It is mostly used in remote monitoring of room temperature, remote monitoring of intelligent furniture, engineering construction, and teaching. Communication technology dominates the operation of remote monitoring systems. With the introduction of 5G technology, mobile Internet technology has been pushed to the top of technology again. Using 5G mobile communication technology in the monitoring system, people can observe or operate the monitored things at any time. The sensor is an indispensable component of the remote monitoring system, and a new type of optical fiber is added to the sensor to make the system function more complete. However, the related technology is not very mature, and the research on remote monitoring system is relatively backward in China. Relevant studies have found that when the remaining energy of the information node in the remote monitoring system reaches 10%, the information node will die and the speed of information transmission will decrease. Therefore, adding new technologies is conducive to improving the performance of remote monitoring systems.

The application degree and application scope of 5G Internet of Things technology and big data analysis technology are becoming wider and wider, bringing opportunities for the development of traditional enterprises and providing technological innovation support for the development of new enterprises. Based on 5G Internet of Things technology and big data technology, this paper designs and studies an intelligent agricultural monitoring platform. We collect crop growth data and monitor crop growth status through this platform to study the 5G-oriented IoT big data analysis method system. This paper studies the data collection and storage issues involved in the huge agricultural IoT data environment. This article analyzes the specific sources of agricultural big data, the specific methods of data collection, and the methods of various database storage technologies. Combining wireless sensor network technology, large-source data processing technology, and distributed data storage technology, a method is proposed to solve the problem of rural Internet data collection and storage in the big data environment. This paper proposes a spatiotemporal block processing TSBPS to store the first detection data. The method uses spatiotemporal preblocking, data compression, and caching to significantly improve the recording speed of near real-time storage and microdetection data. In the experimental part of this article, experiments are carried out on the key parts of the IOT-HSQM system model that may limit storage or query performance. Experimental results show that this article compares TSBPS and direct writing methods. The maximum write speed increased by 79%, and the average write speed increased by 42%. The IOT-HSQM system model can meet the requirements of compiling and query performance and statistical analysis.

In the era of rapid development of informatization, the Internet of Things (IoT) and cloud computing have also been born and developed, quietly changing our way of life. The application of sensors has also brought great convenience to our lives, but there are still shortcomings that need to be studied in depth. This article aims to study the application and realization of Internet and cloud computing in sensor information storage. Aiming at the problems of sensors with multiple sources of information, large amount of data concurrency and low efficiency, the single‐point data agent is clustered and clustered to achieve high availability of sensor information storage. After a series of experimental results analysis, the information storage system constructed in this article can input 12 700 pieces of information per second and output 134 pieces of information per second. Therefore, sensor information storage based on the IoT and cloud computing has higher work efficiency, safety performance and availability than other sensor information storage. The architecture of information storage based on the IoT and sensors made in this article has far‐reaching significance for the development of smart devices in the future, and has great research value.