Analysis of IoT Big Data Storage and Management Technologies

With the rapid development of the Internet of Things (IoT), the IT environment has become increasingly complex, and the demand for data storage and management has risen accordingly. To enhance the efficiency of information data transmission in the IoT context, it is essential to rationally apply IoT big data storage and management technologies, promote information sharing, and highlight the practical value of information data. Therefore, this article, based on the fundamental concepts of IoT big data, analyzes the challenges faced in IoT big data storage and management. At the same time, it clarifies the application scenarios and key technical aspects of IoT big data storage and management technologies, aiming to improve the quality of IoT data management and meet the storage requirements of IoT data in the new era.

Accuracy Improvement and Error Analysis of Smart Water Meters

In recent years, traditional water management has gradually undergone a digital transformation, and the concept of smart water management has been widely proposed. As a key component of smart water management systems, the measurement accuracy of smart water meters directly affects water resource management and optimization. This paper analyzes both internal and external factors that contribute to errors in smart water meters. Internal factors include the meter’s own quality, insufficient sensor accuracy, and defects in signal-processing algorithms; external factors encompass external environmental interference, improper installation methods, and a lack of regular maintenance and calibration. In response to these factors, this paper proposes precision-enhancing strategies from four perspectives: mechanical optimization design, optimization of information-processing algorithms, multi-sensor fusion, and optimization of installation and maintenance, aiming to provide valuable reference for further improving the measurement performance of smart water meters.

Design and Implementation of an Intelligent Water Meter Remote Monitoring System

With the development and widespread adoption of IoT technology, in order to address the numerous challenges inherent in traditional water meter measurement and management, this paper explores the design and implementation of an intelligent remote monitoring system for water meters based on IoT technology. The system enables real-time monitoring of water meter data and remote meter reading, effectively enhancing the efficiency and accuracy of water meter management. Leveraging advanced sensor and communication technologies, the system can collect, transmit, and analyze water meter data in real time, providing water utilities with accurate information on water consumption as well as timely alerts for anomalies. This paper provides a detailed account of the system’s design and implementation process, demonstrating the feasibility and effectiveness of the intelligent remote monitoring system in improving the management level of water metering. The system holds significant practical value and broad potential for wider adoption.

IoT Water Meter Reading Application Service System Based on MongoDB and RabbitMQ

Currently, the Internet of Things (IoT) is being widely adopted and promoted across various industries. It connects physical objects in the real world to the internet via information-sensing devices, enabling them to exchange data—thus achieving "object-to-object communication"—and facilitating intelligent identification and management. This paper, based on a unified meter-reading platform system for smart water meters, addresses the performance bottlenecks encountered by the application service platform within the IoT platform architecture when handling large-scale device connections. To overcome these challenges, we propose a solution that leverages MongoDB as the data storage backend and RabbitMQ as the message middleware. Specifically, we utilize MongoDB’s native sharding and replica cluster architecture to store both uplink and downlink data from devices. We also establish a document-based storage model for device data in the database, which significantly enhances the concurrent performance and data persistence efficiency of the application service system. Furthermore, by employing RabbitMQ message queues, we decouple functions such as protocol access, protocol parsing, and data push after protocol parsing, thereby improving the system’s real-time data processing capabilities.