Intelligent Laboratory Management System Based on Loongson Chip
1. Hangzhou Ruijin Electronics Co., Ltd, Hangzhou 310020, China
2. College of Artificial Intelligence, Zhejiang Vocational and Technical College of Industry and Trade, Wenzhou 325000, China
| Abstract: | Traditional manual laboratory management is time-consuming and labor-intensive, while intelligent laboratory management systems can greatly improve the efficiency of laboratory management and are also an important condition for scientific and standardized management of school laboratories. The intelligent laboratory management system is an advanced network system with laboratory management and experimental management as its core. It can provide users with laboratory information, view laboratory personnel, laboratory management and other functions, and can recognize faces when wearing masks. Laboratories are important places for scientific research and teaching. This article takes Loongson 2k1000la as the core, STM32 as the lower computer development board, connects multiple sensors, uploads data to Alibaba Cloud Ubantu16 server, and can be controlled and synchronized through a mobile app. It can also be visualized in the cloud to build an intelligent laboratory management system. The system will mainly include the following three functions: temperature and humidity detection, fire prevention, RFID based device usage management, facial recognition attendance, and personnel information management. Through this system, management personnel can real-time monitor the situation of the laboratory, achieve intelligent management of the laboratory, and ensure the prevention of safety hazards. The combination of hardware platform and cloud server expands the functional scope of the system, while also reducing the computational burden of MCU, which conforms to the design principles of the Internet of Things and provides possibilities for realizing facial recognition, environmental comprehensive analysis, and data visualization services. |
| Keywords: | Loongson; Intelligent Laboratory Management System; Environmental Monitoring; Face Recognition |
| DOI: | 10.57237/j.cst.2025.01.003 |
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Received: 10 January 2025
Accepted: 25 February 2025
Published Online: 24 March 2025
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