Heterogeneous data pattern matching method based on attention mechanism

Abstract

The invention discloses a heterogeneous data pattern matching method based on an attention mechanism, and the method comprises the following steps: inputting source data and to-be-matched data, wherein the to-be-matched data is heterogeneous data; classifying the source data and the to-be-matched data, wherein the source data and the to-be-matched data comprise numeric data, character data and rare type data; performing feature extraction on the classified data according to a newly established data index system; inputting features extracted from source data into a pattern matching model based on an attention mechanism for training, inputting features of to-be-matched data into the trained model for similarity calculation of attributes among heterogeneous data, and establishing a semantic mapping relationship between elements in a source pattern and a to-be-matched pattern according to a bidirectional filtering method, namely pattern matching. According to the method, the complexity of the neural network is reduced based on the attention mechanism algorithm, interference data is further filtered, and the efficiency and accuracy of heterogeneous data pattern matching are improved.

Description

technical field [0001] The invention specifically relates to a heterogeneous data pattern matching method based on an attention mechanism. Background technique [0002] With the development of intelligent manufacturing, related product design, production equipment, sensing equipment and networked terminals, etc., will generate more demand for data processing. At the same time, producers themselves are continuously generating data, which will permeate enterprise operations, product value chains and even the entire life cycle of products. However, at present, the digital foundation of Chinese enterprises is generally relatively weak, and different types of enterprises have uneven basic digital capabilities. Therefore, there are still many heterogeneous data sources stored in different forms within the enterprise, between the upstream and downstream of the industrial chain, and between various production equipment and information systems across fields, forming a large number o...

AI Technical Summary

Problems solved by technology

Although the method based on BP neural network solves the problem that the rule-based method cannot determine the relevant weights to a certain extent, there are still problems such as too large matching space, high complexity, and low efficiency.

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