The invention discloses a
coal mine ventilation optimization and air quality regulation and control method based on
adaptive learning, and relates to the technical field of
coal mine ventilation optimization, and the method comprises the following steps: arranging a plurality of sensors in a mine, carrying out the real-time collection and monitoring of key environment parameters, and uploading the collected data to a central
control system; and comparing the collected real-
time data with historical data, and dividing the collected real-
time data into conventional working condition data and abnormal working condition data according to environment states. Through real-time monitoring of various sensors and a
deep learning algorithm, accurate
ventilation control is achieved, a problem area is rapidly positioned in an emergency,
airflow is dynamically adjusted, and safety of miners is guaranteed. According to the
system, an LSTM model is adopted to predict an event trend in advance, preventive regulation and control measures are taken in time, real-time adjustment is supported, and the strain capacity is improved. Through a multi-stage
ventilation mode, the
system optimizes
energy consumption and equipment operation efficiency, reduces unnecessary energy waste and maintenance cost, and realizes balance between safety and economy.