Abstract

Each wagon is equipped with sensors that continuously capture and transmit weight data to the central server via Node MCUs, enabling real-time analysis through the Blynk app. By monitoring the load conditions, the system ensures wagons remain within safe weight limits, preventing overloading, which can cause damage and safety hazards, or under-loading, which reduces operational efficiency. A key feature of the system is its use of machine learning algorithms to detect patterns and anomalies related to load distribution. When potential risks are identified, the system generates automated alerts, prompting interventions such as load adjustments, wagon rerouting, or even halting operations when necessary.