A heterogeneous computing-based task processing method, applied to a work node including a plurality of heterogeneous units, which comprises steps of: breaking down an artificial intelligent analysis task into one stage or multiple stages of sub-tasks, wherein different stages of sub-tasks have different types, and one type of sub-tasks corresponds to one analysis function unit service; and completing , by one or more analysis function unit services corresponding to the one stage or multiple stages of sub-tasks, the artificial intelligent analysis task by means of a hierarchical data flow, wherein each analysis function unit service uniformly schedules a plurality of heterogeneous units to execute a corresponding sub-task; wherein the work node is provided with a heterogeneous computation management framework configured to support heterogeneous units based on a heterogeneous daemon, the heterogeneous computation management framework comprising at least: a heterogeneous daemon client and a heterogeneous daemon server; the heterogeneous daemon client is configured to maintain and manage an accelerated computation task queue of an artificial intelligent analysis task and manage a client side task context, and provide a standard read/write interface for input data and output data of the artificial intelligent analysis task at a client side; and the heterogeneous daemon server comprises an application layer task management module and a kernel layer acceleration module block type driving management module, wherein the application layer task management module is configured to provide a client service interface, maintain and manage an accelerated computation task queue of the artificial intelligent analysis task at a server side, and manage a server side task context; the kernel layer acceleration module block type driving management module is configured to provide a read/write interface for block driving data, and manage and schedule channels and queues of the acceleration module. breaking down an artificial intelligent analysis task into one stage or multiple stages of sub-tasks, wherein different stages of sub-tasks have different types, and one type of sub-tasks corresponds to one analysis function unit service; and completing , by one or more analysis function unit services corresponding to the one stage or multiple stages of sub-tasks, the artificial intelligent analysis task by means of a hierarchical data flow, wherein each analysis function unit service uniformly schedules a plurality of heterogeneous units to execute a corresponding sub-task; wherein the work node is provided with a heterogeneous computation management framework configured to support heterogeneous units based on a heterogeneous daemon, the heterogeneous computation management framework comprising at least: a heterogeneous daemon client and a heterogeneous daemon server; the heterogeneous daemon client is configured to maintain and manage an accelerated computation task queue of an artificial intelligent analysis task and manage a client side task context, and provide a standard read/write interface for input data and output data of the artificial intelligent analysis task at a client side; and the heterogeneous daemon server comprises an application layer task management module and a kernel layer acceleration module block type driving management module, wherein the application layer task management module is configured to provide a client service interface, maintain and manage an accelerated computation task queue of the artificial intelligent analysis task at a server side, and manage a server side task context; the kernel layer acceleration module block type driving management module is configured to provide a read/write interface for block driving data, and manage and schedule channels and queues of the acceleration module. <EOS>