Sparse　crowdsensing　collects　data　from　a　subset　of　the　sensing　area　and　infers　data　for　unsensed　areas,　reducing　data　collection　costs.　Previous　works　have　primarily　focused　on　independently　collecting　and　inferring　single　types　of　data.　However,　real-world　scenarios　often　involve　multiple　types　of　data　that　can　complement　each　other　by　providing　missing　spatiotemporal　distribution　information.　In　this　paper,　we　fully　consider　both　intra-data　correlations　among　data　of　the　same　type　and　inter-data　correlations　among　data　of　different　types,　enabling　collaborative　execution　of　various　tasks.　In　addition,　we　enhance　the　adaptability　in　practical　application　scenarios　by　utilizing　real-time　collected　sparse　data　to　guide　task　execution.　For　this　purpose,　we　propose　a　multi-task　adaptive　budgeting　framework　for　online　sparse　crowdsensing,　called　MTAB-SC.　This　framework　consists　of　three　parts:　training　data　updating,　data　inference,　and　data　collection.　First,　we　propose　a　multi-task　data　updating　method　to　keep　models　up-to-date.　Second,　we　design　a　data　inference　network　for　multi-task　data　joint　inference.　Finally,　to　allocate　suitable　budgets　for　each　task　and　facilitate　collaborative　data　collection　across　multiple　tasks,　we　propose　an　Adaptive　Budgeting　for　Collaborative　Data　Collection　model　(AB-CoDC).　The　effectiveness　of　our　proposals　is　demonstrated　through　extensive　experiments　on　two　real-world　datasets.