Abstract: Due to the complexity of liquid and gas pipelines in coal mines，a single monitoring system for ventilation，drainage，and compressed air cannot meet the challenge of real-time monitoring of the entire underground pipeline. Research on leakage detection algorithms for pipeline networks based on fiber optic sensing technology and online monitoring devices and software for underground pipeline networks，aiming to locate faults such as pipeline breakage caused by aging or mechanical collisions，flow overload caused by personnel misoperation or equipment abnormalities，pipeline damage，and pipeline leakage problems caused by personnel installation. Propose a detection scheme for underground pipeline leaks in coal mines using fiber optic sensing technology. Through distributed fiber optic vibration technology，long-distance high-precision leakage detection of underground wind and water pipelines in coal mines is achieved. Research is conducted on the pressure，flow rate，vibration and other parameters collected by the trial production of an online monitoring device for the pipeline network. Through the analysis and judgment of multi parameter information and the control function of nearby pipeline electric gate valves，a comprehensive and reliable intelligent control strategy for underground pipeline networks in mines is achieved; And design pipeline monitoring software with leakage function. The monitoring system management software mainly includes module functions such as data collection，leakage fault diagnosis and processing，database management，user management，and WEB publishing. By storing and analyzing a large amount of data collected from various sensors，the threshold for the healthy operation of each pipeline is obtained. When the real-time collected data and the set values for normal operation of each pipeline exceed the error range，the software platform will issue a fault warning and automatically close the nearby electric gate valve to cut off the faulty pipeline，ensuring that the mine achieves the goal of reducing manpower and increasing efficiency in intelligent mining.