A text classification method of power grid assets based on improved FastText

Abstract

In today’s era, information technology is constantly updated, and the degree of social information is getting higher and higher. Power grid enterprises have a large amount of asset data, and There are many types of assets, such as tools, information system and vehicles. These assets need to be managed. Although there are unified codes for the types of assets at present, manual input is still inaccurate, so there will be some inaccurate descriptions, which makes it very difficult to promote the management of power grid assets. In this paper, an improved FastText text classification method is proposed to identify the information of various power grid assets. The purpose is to ensure that the technical object type coding of power grid assets matches the type of power grid assets, and to realize the efficient quality management of power grid asset object type coding data Concrete by using n-gram model information, read between the word and the word order in hidden layer of network by traditional method and the average improvement for the average sum of squares, and the network output layer for the softmax improved hierarchical softmax and negative sampling method, improved rapid extraction and classification of information, and can better meet the automatic identification data grid assets. The experimental study in this paper proves that the classification accuracy of the improved FastText model on the grid asset data set is significantly improved, and the intelligent classification of grid assets can be realized.