Streaming data processing method and system adaptive to cloud edge collaborative multi-data center scene

Abstract

The invention provides a streaming data processing method and system adaptive to a cloud edge collaborative multi-data center scene. According to an allocated sub-task request, computing resources, memory resources, network resources and storage resources required for processing the sub-task request are allocated to a virtual machine, adn the virtual machine processes streaming data to be processed to obtain a current processing result; the current processing result is sent to a resource position where the next sub-task request is located according to the serial-parallel cooperative processing flow of each sub-task request and the processing priority of each sub-task request; and the demand of multi-data center resource cooperative processing is met.

Description

technical field [0001] The invention belongs to the technical field of electric power system information, and relates to a stream data processing method and system adapted to cloud-edge collaborative multi-data center scenarios. Background technique [0002] In the electric power industry, with the advent of the Internet of Everything era, more and more multi-station fusion data centers have emerged. "Multi-station fusion" data centers refer to multi-level data centers including edge data centers, which are used to The data generated in business, urban governance, Internet, industrial manufacturing and other fields are stored, processed and calculated on the edge side, which is the gathering base of data information and the main load center at the same time. Different from traditional data centers, "multi-site integration" has a large number of cloud and edge data centers, which can fully leverage the synergistic advantages of cloud, edge, and terminal resources. [0003] T...

AI Technical Summary

Problems solved by technology

[0003] There are a large number of flow analysis services in the power grid. When the traffic of camera sensor equipment and video data continues to increase, the centralized processing architecture of cloud computing will not be well expanded, and will encounter huge delays and network bandwidth. Challenges, first of all, applications require very low latency when processing streaming analysis services such as video, because the output of streaming video analysis is often used to interact with users (such as AR scenes) or start other systems

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