End-cloud cooperative processing method and device

Abstract

The embodiment of the invention provides an end-cloud cooperative processing method and device. In the embodiment of the invention, a task deployment component, a data stream processing component supporting end-side deployment and a computing container supporting end-cloud consistent deployment are mutually matched to realize the end-cloud collaboration method, and the method specifically comprises the following steps: the task deployment component provides a deployment service between end clouds for a machine learning task, and the data stream processing component is deployed on an end side; and the computing container is used for providing data service and trigger service for the machine learning task, providing a multi-end consistent and end-cloud consistent operation environment and a computing engine for the machine learning task, and supporting execution of the machine learning task. According to the embodiment of the invention, the characteristics of the end side and the cloud side are considered, end-cloud advantage complementation is realized, a more real-time, more reasonable and safer end-cloud seamless connection collaborative service is provided for the mobile equipment, and a machine learning task is enabled to be seamlessly operated between the cloud side and the end side.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to a device-cloud collaborative processing method and device. Background technique [0002] In recent years, the new generation of artificial intelligence (AI) technology represented by deep learning has developed rapidly, and has continuously made breakthroughs in the fields of image, voice, and natural language processing, and has gradually entered the stage of application implementation. Cloud computing technology can provide massive computing, storage and other services for AI applications that rely on machine learning tasks, and has been widely used in language translation, speech recognition, big data, etc. Deployed on cloud-side devices. [0003] Mobile devices, as end-side devices with the widest coverage of users, the highest frequency of users, and the richest application scenarios, are important carriers for the implementation of AI applicat...

AI Technical Summary

Problems solved by technology

[0004] The existing method of centralized execution of machine learning tasks through cloud-side devices has problems such as poor real-time response and low utilization of device-side capabilities, especially when the computing power and storage capabilities of mobile devices have been significantly improved. The huge potential contained in it has not been fully utilized

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