Efficient　data　processing　is　crucial　for　industrial　Internet　of　Things　(IIoT)　applications,　but　the　limited　energy　and　computing　resources　in　IIoT　devices　(IIoT-Ds)　pose　constraints.　This　article　utilizes　a　unmanned　aerial　vehicle　(UAV)　as　a　computing　server　for　enhanced　IIoT　mission　execution.　Specifically,　the　energy　consumption　of　IIoT-Ds　and　the　UAV,　as　well　as　the　weighted　cost　of　the　communication　and　computing　scheduling　strategy　in　the　UAV-aided　IIoT,　are　jointly　taken　into　account.　An　optimization　problem　based　on　the　system　energy　consumption　is　built　under　the　constraints　of　UAV　motion,　computing　offloading,　and　transmitting　power　allocation.　A　problem　decoupling-based　alternating　optimization　method　is　proposed　to　solve　the　minimization　problem　by　decomposing　it　into　three　subproblems:　1)　UAV　motion　optimization;　2)　computing　offloading　configuration;　and　3)　transmitting　power　allocation.　Through　comparing　the　proposed　communication　and　computing　scheduling　strategy　with　existing　methods,　simulation　results　illustrate　its　attainment　of　quasi-optimal　performance,　thereby　validating　the　effectiveness　of　the　alternating　optimization　method.