State-of-the-art　smart　cities　have　been　calling　for　economic　but　efficient　energy　management　over　a　large-scale　network,　especially　for　the　electric　power　system.　It　is　a　critical　issue　to　monitor,　analyze,　and　control　electric　loads　of　all　users　in　the　system.　In　this　study,　a　non-intrusive　load　monitoring　method　was　designed　for　smart　power　management　using　computer　vision　techniques　popular　in　artificial　intelligence.　First　of　all,　one-dimensional　current　signals　are　mapped　onto　two-dimensional　color　feature　images　using　signal　transforms　(including　the　wavelet　transform　and　discrete　Fourier　transform)　and　Gramian　Angular　Field　(GAF)　methods.　Second,　a　deep　neural　network　with　multi-scale　feature　extraction　and　attention　mechanism　is　proposed　to　recognize　all　electrical　loads　from　the　color　feature　images.　Third,　a　cloud-based　approach　was　designed　for　the　non-intrusive　monitoring　of　all　users,　thereby　saving　energy　costs　during　power　system　control.　Experimental　results　on　both　public　and　private　datasets　demonstrate　that　the　method　achieves　superior　performances　compared　to　its　peers,　and　thus　supports　efficient　energy　management　over　a　large-scale　Internet　of　Things　network.