A data stream classification method and device based on temporal feature learning
A technology of feature learning and data flow, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as difficult big data classification requirements for servers
Active Publication Date: 2020-07-17
上海飞旗网络技术股份有限公司
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However, with the expansion of data scale and the increase of data types, it is difficult for servers to efficiently cope with the data stream classification requirements of big data
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[0065] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0066] In order to better understand the above technical solutions, the technical solutions of the present invention will be described in detail below through the accompanying drawings and specific examples. It should be understood that the embodiments of the present invention and the specific features in the examples are detailed descriptions of the technical solutions of the present invention, and It is not a limitation to the tec...
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The invention provides a data stream classification method and device based on time series feature learning. When the number of data streams to be classified in the cache is relatively small, the timing features of the data streams to be classified in the cache are extracted by using the first setting method of starting some feature extraction threads. In this way, more time slice resources can be allocated to the recognition thread corresponding to the LSTM neural network, thereby reducing the time-consuming determination of the recognition label, and then classifying the data stream to be detected in a timely manner. When the number of data streams to be classified in the cache is large, the time series features of the data streams to be classified in the cache are extracted by using the second setting mode of opening all feature extraction threads. By enabling all feature extraction threads, it is possible to ensure that time series feature extraction is performed on each data stream to be classified in the cache, and to avoid missing time series feature extraction on some data streams to be classified in the cache. In this way, it can be ensured that all acquired data streams to be classified are classified through the LSTM neural network.
Description
technical field [0001] The present invention relates to the technical field of big data data classification, in particular to a data flow classification method and device based on time series feature learning. Background technique [0002] With the continuous development of big data technology, various types of servers are faced with an increasing demand for data processing, which poses new challenges to the data processing capabilities of servers. When the server processes massive data, data classification is a common step. By classifying massive data, the data processing efficiency of the server can be improved and time cost can be saved. However, with the expansion of data scale and the increase of data types, it is difficult for servers to efficiently cope with the data flow classification requirements of big data. Contents of the invention [0003] In order to improve the above problems, the present invention provides a data stream classification method and device b...
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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/044G06N3/045G06F18/241
Inventor 杨贻宏
Owner 上海飞旗网络技术股份有限公司