Mechanical processing production management technology based on event scheduling and digital management system

Introduction Mechanical processing production management plays a critical role in optimizing production efficiency and ensuring product quality. Traditional management methods face challenges such as equipment failures, insufficient flexibility in resource scheduling, and low production efficiency. This study proposes a mechanical processing production management technology based on event scheduling and a digital management system to improve production efficiency and order qualification rate while reducing costs. Methods A digital management system integrating IoT and data analysis technologies was developed to enable real-time monitoring and management of the production process. A multi-objective event scheduling method incorporating the Whale Optimization Algorithm-Grey Wolf Optimizer (WOA-GWO) was adopted to optimize production scheduling. The system employs an event-driven mechanism to capture production line events in real time and dynamically adjust resource allocation and production plans. Results On the Industrial Internet of Things Simulation Dataset (IIoTSD), the recognition accuracy of the system stabilized at around 98%. On the Mechanical Processing Production Historical Dataset (MPPHD), accuracy stabilized at approximately 95%. In practical enterprise applications, the resource utilization rate remained above 90%, and the production cost stayed below CNY 100,000 by the 500th batch. The order qualification rate was maintained at around 98%, and production efficiency remained at approximately 0.95. Discussion The proposed approach effectively enhances the level of automation and intelligence in mechanical processing production lines, strengthening the market competitiveness of enterprises. The system demonstrates superior performance in event recognition, resource scheduling, and cost control, providing an intelligent solution for production management. Future work will focus on improving system resilience against external disruptions, enhancing algorithm generalizability, and developing lightweight deployment solutions for small and medium-sized enterprises.