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Aiming at the current situation that truck load monitoring mainly relies on fixed weighing, a real-time truck load monitoring system based on Internet of Things technology is designed. The system is based on embedded system and multi-sensor fusion algorithm, and consists of signal acquisition, signal processing and transmission, cloud server and other units. The main control chip STM32F105RCT6 and resistive strain gauge sensor are used to realize data acquisition and data processing; the processed information is sent to the cloud through the wireless communication module, and the cloud server uses neural network technology to fit the obtained data, realizing user Real-time monitoring and control of the truck's load capacity in a remote situation. The experimental test proves that the system can accurately and efficiently collect the load of the truck, and the accuracy of the measured load of the truck is within 2.75%, the wireless transmission effect is stable, and the load data can be uploaded to the cloud platform in real time and fed back to the user. This system comprehensively uses technologies such as smart sensors, Internet of Things and cloud computing, and has the characteristics of strong real-time, high precision, and convenient assembly.