Progressive time delay bandwidth speed measurement analysis method and system based on deep learning

Abstract

The invention discloses a progressive time delay bandwidth speed measurement analysis method based on deep learning and a system thereof. A deep neural network is used to solve a learning process of feature expression. In order to improve the training effect of the deep neural network, people adjust the connection method and activation function of neurons, establish and simulate the neural networkfor analysis and learning of the human brain, and simulate the mechanism of the human brain to explain data. According to the invention, an intelligent design algorithm of deep learning is adopted, aCS architecture is combined, enough servers and demand clients are used for algorithm speed measurement, then user data is counted, and when the user data volume is enough, the required data can be obtained more quickly and accurately through deep learning.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a progressive delay bandwidth speed measurement analysis method and a system thereof based on deep learning. Background technique [0002] In the modern era of network popularization, the network speed is increasing day by day. On the eve of the 5G era, people have more and more real-time requirements for the availability of network bandwidth. People need a faster and more accurate bandwidth speed measurement method to realize their own bandwidth. Thus, the progressive delay bandwidth speed measurement analysis method and system based on deep learning were born. As the name implies, this method is based on the intelligent method of Deep Learning (DL), uses a progressive speed measurement analysis method, occupies the user's bandwidth as little as possible, and obtains the current user's bandwidth at the fastest speed. The advantage of this technology is that the more user ...

AI Technical Summary

Problems solved by technology

The advantage of this technology is that the more user data there is, the more it can reduce the bandwidth occupied by users during use, and the faster results can be obtained. At the same time, the disadvantage is that deep learning requires a large amount of user data.

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