Implementing　nonintrusive　load　monitoring　(NILM)　for　industrial　consumers　plays　a　vital　role　in　managing　power　demand　and　enhancing　energy　utilization　efficiency.　Focusing　on　the　scarcity　of　sampling　data　and　continuous　variation　of　loads　in　industrial　settings,　this　article　proposes　a　nonintrusive　industrial　load　decomposition　(LD)　method　that　considers　the　load　power　consumption　characteristics　and　time　series　correlation.　According　to　the　time-varying　characteristics　of　the　power　consumption,　the　load　is　divided　into　three　types　including　switching　load,　multi　state　load,　and　continuously　varying　load.　On　this　basis,　the　active　and　reactive　power　characteristics　are　jointly　considered,　and　integer　programming　is　used　to　build　the　load　decomposition　model.　Particularly,　the　matrix　factorization　(MF)　method　is　used　to　describe　the　continuously　varying　load.　In　addition,　the　timing　correlation　constraints　under　the　base　vector　grouping　constraint　and　production　process　constraints　are　proposed　and　integrated　into　the　load　decomposition　model.　Finally,　the　proposed　method　is　validated　on　a　public　dataset　using　a　PC　platform　and　a　Raspberry　Pi　5,　respectively.　The　results　of　the　tests　on　the　PC　platform　show　that　the　proposed　method　has　higher　accuracy　than　the　existing　methods.