As　business　rapidly　evolves,　the　types　of　business　services　become　increasingly　complex.　The　conventional　approach　of　manually　configuring　rules　for　maintenance　and　quality　assurance　systems　is　gradually　proving　inadequate,　and　there　is　a　growing　demand　for　intelligent　operations　(AIOps)　and　quality　assurance.　This　paper　proposes　an　anomaly　detection　algorithm　by　ensemble　various　algorithms.　Originating　from　operation　and　maintenance　scenarios,　this　algorithm　is　gradually　applied　to　business　scenarios.　The　process　involves　streaming　data　collection,　data　preprocessing,　anomaly　detection　models,　and　monitoring　alerts,　thereby　realizing　multidimensional　anomaly　detection　in　both　operational　and　business　scenarios.　In　comparison　to　traditional　monitoring　platforms,　the　proposed　algorithm　saves　manpower　and　exhibits　higher　accuracy.　Furthermore,　when　compared　to　traditional　machine　learning　algorithms,　it　saves　resources　and　provides　faster　response　times.　©　2024　IEEE.