This invention relates to the field of
coal mine gas
geological disaster prevention and intelligent
identification technology, and discloses a method and
system for intelligent identification and graded early warning of underground gas geological anomalies in
coal mines. The method includes: S1, collecting multi-source heterogeneous
monitoring data in the target area of the
coal mine and performing
standardization processing and spatiotemporal benchmark unification; S2, constructing a multi-dimensional feature
fingerprint database based on historical or accumulated standardized datasets; S3, acquiring real-time multi-source
monitoring data for the current tunneling or exploration process and extracting its features; based on the multi-dimensional feature
fingerprint database, using evidence theory to fuse and calculate multiple extracted real-time feature evidences to obtain a comprehensive confidence assessment result for the existence of a specific type of gas geological anomaly in the undiscovered area ahead; S4, generating graded early warning information based on the comprehensive confidence assessment result and a preset
risk level threshold. This invention achieves advanced identification,
risk assessment, and targeted early warning of geological disasters such as underground gas outbursts.