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The advancement of 5G technology has brought the prosperous development of Internet of Vehicles (IoV). IoV services are not only computational intensive but also extremely sensitive to the delay. As a promising computing paradigm, mobile edge computing (MEC) can be applied to IoV scenarios. However, due to the limited resources of a single MEC server, it is difficult to cope with the suddenly increased computation loads caused by emergencies, or the intensive resource requests from busy regions. Therefore, we propose a novel regional intelligent management vehicular system with dual MEC planes, in which MEC servers in the same region cooperate with each other to achieve resource sharing. We classify computing tasks into different types according to their delay tolerances and focus on the optimization problem of resource allocation for different type tasks. And then, we design a resource allocation algorithm based on deep reinforcement learning, which can adapt to the changeable MEC environment to process high-dimensional data. Simulation results confirm that our proposed scheme is feasible and effective.