In order to solve problems that heterogeneous big data processing has low real-time response capability in Internet Of Things (IOT), data processinging and persistence schemes based on Hadoop were analyzed. A model of Hadoop big data processing system model based on "Context" named as HDS (Hadoop big Data processing System) was proposed. This model used Hadoop framework to complete data parallel process and persistence. Heterogeneous data were abstracted as "Context" which are the unified objects processed in HDS. Definitions of "Context Distance" and "Context Neighborhood System (CNS)" were proposed based on the "temporal-spatial" characteristics of "Context". "Context Queue (CQ)" was designed as an assistance storage so as to overcome defect of low real-time data processing response capability in Hadoop framework. Especially, based on temporal and spatial characteristics of context, optimization of task reorganizing in client requests CQ was introduced in detail. Finally, taken problem of vehicle scheduling in petroleum products distribution as an example, performance of data processing and real-time response capability were tested by MapReduce distributed parallel computing experiments. The experimental results show that compared with ordinary computing system SDS (Single Data processing System), HDS is not only of obviously excellence in big data processing capability but also can effectively overcome defect of low real-time data processing response of Hadoop. In 10-server experimental environment, the difference of data processinging capability between HDS and SDS is more than 200 times; the difference between HDS with and without assistance of CQ for real-time data processing response capability is more than 270 times.