A Cloud-Edge-End Collaborative Remote Monitoring and Scheduling System for Textile Equipment

Textile equipment monitoring and scheduling are constrained by device heterogeneity, stringent real-time requirements, and complex dynamic resource scheduling. To address these challenges, this study proposes a cloud-edge-end collaborative remote monitoring and scheduling system for textile equipment. The proposed system aims to overcome the limitations of traditional solutions in compatibility, real-time performance, and resource utilization. This work is positioned as an applied systems study, in which the scheduling modules are used as monitoring-driven service extensions rather than as standalone algorithmic contributions. We develop (i) an adaptive multi-protocol parsing mechanism, (ii) a collaborative hierarchical alerting framework, and (iii) monitoring-driven computing-resource and production-scheduling services. The system is implemented across the terminal device layer, edge computing layer, and central cloud layer. Embedded acquisition terminals were designed to support multiple industrial protocols, including Modbus RTU, OPC UA, and EtherCAT. Dynamic protocol adaptation was used to identify, parse, and map heterogeneous protocol frames into a unified information model at runtime. In the workshop deployment reported in this study, field validation was conducted on 120 air-jet looms connected through RS485-based Modbus RTU. Other interfaces were evaluated as prototype-supported communication options rather than as quantitatively validated workshop interfaces. A cloud-edge-end collaborative alerting framework is designed by combining an improved OPTICS algorithm with a graph neural network (GNN) model. It improves the redundant-alarm filtering rate by 42.1%, achieves 96.8% root-cause diagnosis accuracy, and keeps the end-to-end alert latency at or below 200 ms at the 99th percentile. A cross-layer resource scheduling strategy incorporating a fuzzy PID controller is proposed, accompanied by a weighted multi-criteria resource-optimization model. This strategy increases the average CPU utilization of edge nodes to 84.3 ± 3.6% and reduces burst-task response latency to 236 ± 48 ms. In addition, an adaptive particle-swarm optimization module based on a scalarized composite scheduling objective reduces the equipment idle rate to 6.5% and shortens the average order completion time by 28.4%. Overall, the proposed framework demonstrates the feasibility of cloud-edge-end collaborative monitoring and scheduling in the validated RS485/Modbus-RTU-based weaving-workshop scenario, while its application to other textile processes, machine types, and communication configurations requires further protocol-specific adaptation and field validation.