Online　gathering　large-scale　heterogeneous　tasks　and　multi-skilled　participant　can　make　the　tasks　and　participants　to　be　shared　in　real　time.　However,　their　online　gathering　will　bring　many　intractable　objective　requirements,　which　makes　task-participant　matching　become　extremely　complex.　To　cope　well　with　the　gathering,　we　design　a　hierarchy　tree　and　time-series　queue　to　organize　tasks　and　participants.　The　data　structures　we　designed　can　effectively　meet　all　requirements　that　are　brought　due　to　tasks　and　participants　gathering　online.　In　addition,　based　on　the　designed　data　structures,　we　study　online　large-scale　heterogeneous　task　allocation　problem　from　three　aspects:　the　computing　pattern,　the　tree　creation　method,　and　the　extension　of　matching　strategy.　Our　best　method　(TsPY)　is　based　on　parallel　computing　in　the　computing　pattern,　adopts　time　first　and　then　space　in　the　tree　creation　method,　and　increases　the　short-distance　first　strategy　in　the　matching　strategy.　Finally,　we　conducted　detailed　experiments　under　the　conditions　of　different　participant　geographical　distributions　(i.e.,　uniform　distribution,　Gaussian　distribution,　and　check-in　empirical　distribution),　different　sensing　methods　(i.e.,　participatory　sensing　and　opportunistic　sensing),　and　different　recommendation　methods　(i.e.,　point　recommendation　and　trajectory　recommendation).　The　experimental　results　show　that　TsPY　has　a　good　performance　in　multiple　indicators　such　as　algorithm　running　time,　task-participant　matching　rate,　participant　travel　distance,　and　redundant　tasks　removed.　Compared　with　serial　computing,　parallel　computing　can　reduce　the　algorithm　running　time　by　more　than　66%　on　average　in　our　experimental　environment.　Compared　with　space　first　and　then　time,　creating　a　tree　based　on　time　first　and　then　space　can　increase　task-participant　matching　rate　by　more　than　13%　on　average.　Increasing　the　short-distance　first　strategy　can　reduce　the　participant　travel　distance　by　more　than　4%　on　average.