The application discloses a
coal mine underground gas prevention and treatment method, device, equipment and medium. Multi-source
sensing data of a target area in a
coal mine is acquired, spatio-temporal alignment and standardized fusion are performed to generate a spatio-temporal
data matrix, a pre-trained GNN-LSTM
hybrid model is input, and gas emission prediction data of each monitoring point within a preset time window is output. The prediction data and the running state of the ventilation
system are jointly input into a pre-trained deep
reinforcement learning decision model, the optimal
ventilation control instruction is determined from a preset action set based on multi-objective optimization, and the instruction is sent to a
ventilation control equipment to adjust the frequency of a fan or the opening degree of a
damper. Through the fusion of multi-source information, dynamic and accurate prediction and
intelligent decision, advanced warning and
automatic control of gas prevention and treatment are realized, the frequency and duration of gas over-limit are effectively reduced, gas accumulation is prevented from the source, and the safety level of
coal mine production is significantly improved.