Currently, most residual film recycling machines lack an effective operational status monitoring system, making it difficult for operators to grasp the working conditions of the equipment in real time and respond promptly to sudden failures. At the same time, the calculation of the working area still relies on manual or post-event statistics, resulting in insufficient real-time capability and accuracy. These two issues significantly restrict operational efficiency and compromise the recovery process. To address this, a residual film recycling machine operational status monitoring system was designed based on "device-edge-cloud" collaboration. The system achieved real-time perception of key operational parameters at the device layer, anomaly detection and control at the edge layer, and data management and efficient computing at the cloud layer, thereby realizing real-time acquisition, anomaly detection, alarm control, and remote monitoring of operational status parameters for residual film recycling machine. Firstly, the overall design of the monitoring system was presented, including hardware and software selection, deployment, and core program development. It then elaborated on the processing methods for key data, particularly the mechanism for extracting effective operational data based on working condition information and the algorithm for calculating operational area. Field experiments showed that the monitoring relative errors for machine speed, beating shaft rotation speed, pickup rotation speed and baling rotation speed, were 0. 84%, 0.54%, 3.46%, and 1. 27%, respectively. The effectiveness of door status and pickup depth alarms reached 100% and 98%, respectively, and remote monitoring effectiveness reached 100%. Regarding operational area calculation, the real-time incremental area calculation method based on valid operational trajectories achieved an average accuracy of 92. 57%, while the closed area calculation method based on trajectory point contour reached an accuracy of 97.09%. This system enabled real-time perception of key operational parameters, intelligent decision-making at the edge, and collaborative management in the cloud. It not only enhanced operational reliability and avoided frequent manual inspections but also provided effective data support for the optimized design and intelligent operational decision-making of residual film recycling machine.