Power material demand prediction method based on text information extraction

Abstract

The invention discloses a power material demand prediction method based on text information extraction. The power material demand prediction method includes a two-step algorithm of power material demand prediction, wherein the first step is used for processing a preliminary design document based on the text information extraction technology, and extracting the engineering attribute information which has important value for predicting the demand quantity of main equipment to realize the structural expression of the preliminary design document, and then realizing the requirement prediction of the main equipment by utilizing an SVM regression algorithm. In the second step, the dense vector expression of a primary design document is learned through a convolutional neural network by utilizing atext classification technology, the demand information of the main equipment is fused with the demand information of the main equipment, and the demand of non-main equipment is predicted through a multi-layer neural network. Compared with the existing calculation, the method can be used for predicting various types of materials. The prediction data tend to be actual, the attributes have more expression, and the method has good practicability. The material demand prediction method conforms to actual application requirements, and can be used for predicting the material requirements after the initial design is completed.

Description

technical field [0001] The invention relates to a method for predicting demand for electric power materials based on text information extraction, and belongs to the technical field of demand prediction for electric power materials. Background technique [0002] At present, with the rapid development of my country's social economy, the demand for electric energy has put forward higher requirements both in terms of quantity and quality. On the one hand, these requirements have promoted the prosperity of the power grid engineering (substation and distribution network engineering) market, and on the other hand, they have also posed greater challenges to related companies. Relevant enterprises can only adapt to the new market situation and calmly deal with these new and greater challenges only by using high-tech, especially artificial intelligence technology, to optimize enterprise management and various resource allocation, and improve resource utilization and engineering design...

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