Abstract: Comprehensive perception and real-time connectivity are fundamental functional elements of intelligent coal mines. Currently, coal mining faces suffer from insufficient overall environmental perception capabilities. Limitations include the small number of monitoring points for perception devices, inadequate terminal wireless network coverage, and a lack of high-precision positioning services. These shortcomings result in inadequate data sample sizes required for comprehensive perception of mines and coal mining faces, low information transparency, and reduced accuracy in hazard identification and safety warnings. To address these issues, this study investigated coal mining faces as the application scenario and CH₄ as the monitoring target, exploring key technologies and monitoring models for large-scale data perception of coal mine environmental parameters. By investigating low-power wireless CH₄ sensing and self-calibration technologies, the study enabled the deployment of numerous CH₄ sensors in coal mining faces for comprehensive perception, resolving technical challenges associated with calibration-free maintenance. The study also addressed the difficulties of identifying the identities and locations of numerous sensors by developing device encoding and positioning technologies for sensing devices. Additionally, the study proposed high-speed wireless data transmission technologies suitable for the linear space of mines, along with autonomous routing discovery, network fault detection, timely isolation of fault nodes, and self-recovery for wireless backbone link nodes. These advancements solved the real-time data transmission and maintenance challenges arising from the deployment of large numbers of CH₄ sensors and the mobility of coal mining faces. Furthermore, a continuous monitoring model for large-scale data based on edge computing was developed. This model processed the collected CH₄ sensor data using spatial digital cloud mapping technology to achieve continuous monitoring and comprehensive perception of CH₄ across the entire coal mining face, as well as hierarchical data processing at operational sites. The key technologies and monitoring model for large-scale data perception of CH₄ in coal mining faces accumulate foundational technical knowledge for comprehensive perception studies of other mine environmental parameters.