In　order　to　effectively　control　the　pressure　and　energy　consumption　of　multiple　air　compressors　within　an　acceptable　range,　an　intelligent　pressure　switching　control　method　for　air　compressor　group　control　based　on　multi-Agent　RL　is　studied.　This　method　uses　sensors　in　the　air　compressor　field　control　cabinet　to　collect　data　such　as　header　pressure,　air　storage　tank　pressure,　and　air　storage　tank　temperature　and　sends　them　to　the　edge　data　collector　for　integration.　After　integration,　the　main　control　cabinet　sends　them　to　the　upper　computer.　Combined　with　the　on-site　collected　data,　a　multi-Agent-based　air　compressor　group　control　model　is　designed　to　convert　multiple　air　compressors　in　the　air　compressor　group　control　problem　into　a　multi-Agent　mode,　facilitating　unified　switching　control　of　the　air　compressor　group.　Then,　using　the　intelligent　pressure　switching　control　method　based　on　deep　Q-learning,　driven　by　a　neural　network　controller,　the　frequency　of　the　frequency　converter　is　adjusted　to　control　the　pressure　at　the　outlet　of　the　air　compressor　terminal　header　within　the　set　value　range,　completing　the　pressure　intelligent　switching　control.　After　testing,　this　method　has　good　application　results　in　pressure　control,　energy　saving,　and　other　aspects　after　being　used　for　intelligent　pressure　switching　control　of　air　compressor　group　control.　©　2024-IOS　Press.　All　rights　reserved.