In　addressing　the　optimal　scheduling　challenges　of　an　Integrated　Energy　System　(IES)　affected　by　multiple　uncertainties,　this　paper　introduces　an　Affine　Rolling　Optimization　approach　for　IES　within　a　park.　The　objective　is　to　mitigate　the　impact　of　uncertainty　on　scheduling　and　monitor　how　uncertainty　evolves　within　the　scheduling　scheme.　The　method　utilizes　affine　arithmetic　to　create　an　uncertain　IES　model　for　the　park.　Building　upon　the　concept　of　rolling　optimization　within　the　Model　Predictive　Control　framework,　it　proposes　an　intraday　rolling　optimization　scheduling　method　based　on　affine　arithmetic.　To　validate　the　effectiveness　of　this　approach,　the　study　analyzes　cases　with　numerical　examples,　using　a　northern　industrial　park　as　a　real-world　illustration.