Digital twin virtual-real mapping method for the S-bend of a scraper in a fully mechanized mining face considering pullback error

To address the challenge of accurately detecting the S-bend shape of the scraper conveyor in a fully mechanized mining face caused by pullback errors, this paper proposes a virtual-real mapping method for the S-bend based on deep learning and machine vision. First, the overall framework is established. Then, the collaborative sliding process of the hydraulic support and scraper conveyor is analyzed, and a mechanism model for the pullback error is constructed. Next, an improved BiLSTM-LSTM-ReLU model is designed to repair abnormal data from the hydraulic support displacement sensor, yielding ideal data. Subsequently, using the YOLOv8n algorithm and an image coordinate transformation model, the actual position and attitude of the scraper conveyor are detected. The pullback error is calculated, and the ideal data is compensated and corrected. Finally, an experimental platform is constructed, and a digital twin system is developed to complete the closed-loop verification process, encompassing data acquisition, anomaly repair, target recognition, and error feedback. The experimental results demonstrate that, under complex coal seam conditions, repairing abnormal sensor data using the deep learning model improves data smoothness by 72.3% and trend consistency by 88.7%. Furthermore, with the integration of visual feedback, the system reduces the final pullback error from the traditional ±3 mm to ±1.44 mm-a reduction of approximately 52%.