Personalized deep learning method for fog computing environment

A deep learning and fog computing technology, applied in biological neural network models, electrical components, transmission systems, etc., can solve problems such as the inability of general models to meet industry individualization, and achieve continuous optimization of industry-specific deep learning computing capabilities and efficient recognition. The effect of knowing computing power and ensuring security

Active Publication Date: 2018-04-03
山东浪潮创新创业科技有限公司
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AI Technical Summary

Problems solved by technology

[0004] With the development of deep learning technology, cloud training and learning will produce general data models such as object detection. However, in specific application scenarios, general models cannot meet the individual needs of the indust

Method used

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  • Personalized deep learning method for fog computing environment
  • Personalized deep learning method for fog computing environment

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Embodiment 1

[0037] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but not as a limitation of the present invention.

[0038] Such as figure 1 As shown in , the cloud gathers a large number of computing resources, trains the general model through massive training data, distributes the trained general model to each fog computing node, and then utilizes the computing and storage capabilities of the fog computing node to train Deep learning model; by collecting data from intelligent sensing devices, real-time reasoning is performed on fog computing nodes, and real-time output results; errors in reasoning are identified, continuous training optimizes the model, and the industry's personalized model can be selectively imported The cloud, while receiving the general model of the cloud and continuously improving and optimizing it. in,

[0039] The cloud node is responsible for continuous training and optimization of ...

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Abstract

The invention discloses a personalized deep learning method for fog computing environment. A cloud node trains a general model through mass training data, the general model obtained by training is distributed to each fog computing node, the computation and storage ability of the fog computing node is used for training a deep leaning model which meets edge side industry requirements; data is collected from intelligent sensing equipment, and real-time reasoning is carried out on the fog computing node to output a result in real time; and errors which appear in reasoning are identified, the modelcan be continuously trained and optimized, the industrial personalized model can be selectively transmitted into the cloud, and meanwhile, the general model of the cloud is received and continuouslyimproved and optimized. Compared with the prior art, the method disclosed by the invention improves real-time business execution efficiency, meanwhile, the personalized deep learning method is storedin the fog computing node to guarantee the safety of the model, in addition, the model can be shared to the cloud according to the permission of the user, and therefore, the model exhibits openness.

Description

technical field [0001] The invention relates to cloud computing, Internet of Things, artificial intelligence and deep learning technologies, in particular to a personalized deep learning method for a fog computing environment. Background technique [0002] In recent years, artificial intelligence technology has developed rapidly, and its commercialization speed has exceeded expectations. Artificial intelligence will bring subversive changes to the entire society, and it has become an important development strategy for various countries in the future. In particular, the algorithm evolution with deep learning as the core, its super evolutionary ability, with the support of big data, through training and construction of a large-scale convolutional neural network similar to the structure of the human brain, can already solve various problems. [0003] Deep learning requires a large amount of data and computing resources for training. Cloud services can meet the requirements to a...

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Application Information

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IPC IPC(8): G06N3/063H04L29/08
CPCG06N3/063H04L67/10H04L67/12
Inventor 孙善宝于治楼谭强
Owner 山东浪潮创新创业科技有限公司
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