In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.