Equipment measurement data processing method and system based on deep neural network, and terminal

Abstract

The invention discloses an equipment measurement data processing method and system based on a deep neural network, and a terminal, and relates to the technical field of power station equipment data processing, and the key points of the technical scheme are as follows: carrying out entity recognition on target equipment measurement data through a recognition model established based on a bidirectional long-short-term memory neural network and a conditional random field; obtaining a short text sequence which is labeled by a label and jointly expressed by a character vector and a word vector; expanding the short text sequence, inputting the expanded short text sequence into a convolutional neural network, obtaining short text deep semantics by learning deep features in the short text, and performing clustering processing according to the short text deep semantics to obtain clustering equipment measurement data; obtaining a mapping relation between historical equipment measurement data and a standard code through training of a pre-constructed training model, and after clustering equipment measurement data are input into the training model, obtaining a new measurement data prediction code label in combination with the mapping relation. According to the method, unified and standardized automatic coding processing can be carried out on different devices.

Description

technical field [0001] The present invention relates to the technical field of power station equipment data processing, more specifically, it relates to a deep neural network-based equipment measurement data processing method, system and terminal. Background technique [0002] The process of power station safety monitoring involves many different types of sensor equipment and operating equipment, and there are certain differences in the management of each power station, which makes data sharing difficult. At present, the definition of equipment measurement data in the core basic platforms of each station, such as monitoring systems and condition monitoring systems, only considers the implementation of their respective systems. At that time, there was no unified definition standard for equipment measurement data. The staff of each station are highly subjective to the equipment measurement data, resulting in simple text language expression, less vocabulary, and various descrip...

AI Technical Summary

Problems solved by technology

The staff of each station are highly subjective to the equipment measurement data, resulting in simple text language expression, less vocabulary, and various descriptions. Compared with PPIS data rules, the words are relatively irregular, resulting in different implementation standards and efforts. It is difficult to integrate the measurement data of equipment at each station

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