In　order　to　reduce　the　impact　of　continuous　data　noise　and　loss,　a　dynamic　fusion　local　outlier　factor　(DFLOF)　method　is　proposed　for　data-cleaning　of　the　municipal　wastewater　treatment　process　(WWTP).　First,　a　data　dynamic　segmentation　method　based　on　sliding　window　is　designed　to　obtain　the　abnormal　attribute　of　each　segment,　including　mean　value,　maximum　value　and　peak　interval.　Then,　a　data　reliability　evaluation　model　based　on　the　DFLOF　is　established　to　evaluate　each　data　segment　by　using　the　dynamic　fusion　local　outlier　factor　algorithm,　which　improves　the　accuracy　of　abnormal　data　detection　and　elimination.　Finally,　a　data　compensation　method　based　on　radial　basis　function　neural　network　is　proposed　to　compensate　the　missing　data　and　further　realize　the　data-cleaning　of　the　WWTP.　The　proposed　cleaning　method　is　applied　to　a　real　WWTP,　the　experimental　results　show　that　the　data-cleaning　method　based　on　the　dynamic　fusion　local　outlier　factor　is　able　to　clear　abnormal　data　and　improve　the　data　quality.　Copyright　©2022　Control　and　Decision.