Remote supervision and management method for coal mine gas extraction drilling site based on AI video analysis - HU Jincheng; ZHANG Libin; JIANG Ze; YAO Chaoxiu; JIANG Zhilong; WANG Zhengyi (Gong-kuang zidonghua, 2023)
The traditional video monitoring system for coal mine gas extraction drilling site only has monitoring and storage functions during drilling construction and drill pipe withdrawal. Important process parameters or information can only be viewed by monitoring personnel through video recordings, which poses problems such as construction information being prone to errors and difficulty for drilling site management personnel to continuously monitor on-site videos. It order to solve the above problems, A remote supervision and management method for coal mine gas extraction drilling sites based on AI video analysis has been proposed. This method includes three algorithms: information board detection, OCR recognition, and drill pipe withdrawal analysis. Information board detection is used to detect the current construction phase. PaddleOCR recognition is used to recognize the drilling process and construction information on the information board. The drill pipe withdrawal analysis is used to analyze the number of drill pipes withdrawn during the closing drilling phase, thereby achieving the full process analysis and control of drilling operations. After receiving and starting drilling tasks, the method uses information board detection and PaddleOCR recognition services, and automatically saves construction information based on the identified drilling, closing, and sealing processes and construction parameters. When identifying the start of hole closing, the method enables the drill pipe withdrawal analysis service. When identifying the end of hole closing, the method stops the pipe withdrawal analysis service. The experimental results show that the recognition accuracy of the information board detection algorithm is 96%. The average time of PaddleOCR recognition algorithm is 17.51 ms, which is 25.25 ms lower than EasyOCR and 4.34 ms lower than Chinese OCR recognition algorithms, respectively; The accuracy of the PaddleOCR recognition algorithm has been improved by 5.75% and 2.29% compared to the other two recognition algorithms, respectively. The recall rate of the PaddleOCR recognition algorithm has been improved by 9.77% and 2.36% compared to the other two recognition algorithms, respectively. The pipe withdrawal analysis algorithm can effectively identify the number of pipes withdrawn on site, with an accuracy rate of approximately 95%.