The use of the Dataflow model integrates the batch processing and stream processing of big data computing. Nevertheless, the existing cluster resource scheduling frameworks for big data computing are oriented either to stream processing or to batch processing, which are not suitable for batch processing and stream processing jobs to share cluster resources. In addition, when GPUs are used for big data analysis and calculations, resource usage efficiency is reduced due to the lack of effective CPU-GPU resource decoupling methods. Based on the analysis of existing cluster scheduling frameworks, a hybrid resource scheduling framework called HRM is designed and implemented that can perceive batch/stream processing applications. Based on a shared state architecture, HRM uses a combination of optimistic blocking protocols and pessimistic blocking protocols to ensure different resource requirements for stream processing jobs and batch processing jobs. On computing nodes, it provides flexible binding of CPU-GPU resources, and adopts queue stacking technology, which not only meets the real-time requirements of stream processing jobs, but also reduces feedback delays and realizes the sharing of GPU resources. By simulating the scheduling of large-scale jobs, the scheduling delay of HRM is only about 75% of the centralized scheduling framework; by using actual load testing, the CPU resource utilization is increased by more than 25% when batch processing and stream processing share clusters; by using the fine-grained job scheduling method, not only the GPU utilization rate is increased by more than 2 times, the job completion time can also be reduced by about 50%.