Industrial　Internet　of　Things　(IIoT)　is　a　new　stage　for　traditional　industry　to　achieve　intelligent　development.　However,　the　problems　of　limited　resource,　communication　congestion,　and　capacity-constrained　batteries　have　emerged　due　to　massive　wireless　sensing　devices　(WSDs).　With　the　recent　advent　of　the　wireless　power　transfer　(WPT)　technique　and　the　development　of　edge　computing　technology,　the　IIoT　pays　much　attention　to　the　interconnected,　instant,　and　high-intelligent　system.　In　this　article,　we　first　consider　a　three-layer　architecture　to　describe　the　IIoT　environment　and　we　propose　a　resource　allocation　strategy　aiming　at　maximizing　the　residual　energy　of　WSDs.　The　optimization　of　residual　energy　is　formulated　as　a　mixed-integer　nonconvex　programming　NP　hard　problem,　by　constraining　the　offloading　decision,　power　allocation,　computing　resource　allocation,　and　time　allocation.　In　addition,　an　improved　hybrid　whale　optimization　algorithm　(IHWOA)　is　proposed　to　search　for　the　approximate　optimal　solution.　Rapid　convergent　and　stable　efficient　solution　is　obtained　through　simulation　experiments.　Finally,　numerical　results　prove　that　the　proposed　solution　achieves　good　performance.